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Customer Journey Analytics: What You Need to Know

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Gartner Identifies Three Steps to Effectively Execute a Customer Journey Map

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Next-Generation Strategies for B2B Sales and Marketing Leaders to Be Discussed at the Gartner Sales and Marketing Conference, October 9-11 in Las Vegas

Customer journey maps are a critical component of many organizations’ customer experience (CX) framework, however, almost one-third of organizations still face difficulty incorporating journey maps into their  CX efforts, according to Gartner, Inc. That’s because many lack governance and oversight, and that ultimately undermines even the best maps’ ability to drive action.

Gartner Says Strong Governance and Oversight are Key to Successful Customer Journey Maps

Gartner defines customer journey mapping as a collaborative process of gathering qualitative and quantitative data to understand customers’ desired journeys and identify gaps between their expectations and their perceptions of the experience delivered by a brand at steps along the journey. The main goal of journey mapping is to determine the challenges and opportunities a brand faces in improving its CX and improve satisfaction, loyalty and advocacy.

“While CX leaders have long understood the value of customer journey maps and the impact they have on an organization’s ability to exceed or meet customer expectations, many still struggle to utilize them effectively in their CX initiatives,” said  Jane-Anne Mennella , senior research director at Gartner.

Read More:   Fake Artificial Intelligence (AI) Vs. Real AI: How To Tell The Difference Between The Scammers & The Real Deal

“There are a number of reasons why this happens, from stakeholders who don’t understand their role, to using incomplete or incorrect data sources,” added Ms. Mennella. “Essentially, many fail because they don’t incorporate the governance and oversight needed to realize the journey map’s true ROI.”

Read More: Cyara Empowers Contact Centers to Deliver Personalized Customer Journeys

According to Gartner, the main objectives of a customer journey map should be to (a) identify specific CX problems and opportunities, and (b) gain alignment and consensus on how to address those problems and opportunities. To execute on this effectively, and establish the appropriate governance and oversight, CX teams should follow three key steps:

  • Master the Foundational Elements First:  The same attention paid to laying the groundwork for journey mapping initiatives should be given to the actual creation of the journey map itself. Before building a journey map, CX leaders should consider the following: affirm leadership and key stakeholder support, build a cross-functional team composed of representatives from all departments who support the CX, assess data sources and needs, and know the audience for whom you are mapping the journey.
  • Focus on the Actionable and Accurate:  Determining and building successful customer journeys requires clear communication among the team and a strong understanding of the entire journey the customers take. CX leaders should start with aligning team goals and expectations. Next, the customer journey map should include the following criteria: it should be created from the customer’s perspective and reflect the customer’s entire journey — from evaluation, purchase, use through to loyalty, satisfaction and/or advocacy. Lastly, CX teams should consider validating the data included in the journey to ensure it accurately reflects the experience, feelings, thoughts and actions of the customers.
  • Cultivate Value from Journey Maps:  When journey maps fail, research shows it typically happens following the design phase. To get maximum value from customer journey maps, CX leaders must turn the insight derived from journey maps into action and experiences, ensure those journey maps are current, and develop a communications plan to reinforce progress toward realizing the customer’s desired journey.

Additional details on how CX leaders can find true ROI from their customer journey maps is available to Gartner for Marketers clients in the report  “Create Actionable, Insight-Driven Journey Maps.”  More topics like this will be discussed at  Gartner’s Sales & Marketing Conference  in Las Vegas, NV, October 9-11, 2018.

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The Essential Guide to Customer Journey Analytics

Last Edited

December 22, 2023

Dec 22, 2023

customer journey analytics gartner

min reading

customer journey analytics gartner

Traditionally, companies focus on specific touchpoints to enhance marketing and customer experience. To gain accurate insights, it is crucial to examine the entire customer journey across channels and over time. Monitoring individual touchpoints is not enough.

According to McKinsey's study, customer satisfaction and revenue growth are strongly related to customer journeys. It represents the customer's entire experience with a brand, encompassing all interactions over time, not just isolated touchpoints.

You can identify paths that lead to a desired outcome by using customer journey analytics.

What is Customer Journey Analytics?

According to 67% of marketing leaders, creating a connected customer journey across all touchpoints and channels is key. The term customer journey analytics refers to every touchpoint in a customer's journey, across multiple channels, over time. By combining millions of customer interactions, this method provides insights from the customer's perspective. By using a data-driven methodology, it facilitates discovering, analyzing, and influencing these journeys.

According to Gartner, customer journey analytics examine how customers interact with an organization across all current and future channels.

Using this analytical tool, marketers and customer experience specialists can reach individual customers on a personal level as well as a broad scale. Real-time analysis of vast datasets allows organizations to pinpoint pivotal customer journeys. It is important to prioritize such pathways to meet overarching business objectives, such as maximizing revenue, reducing churn, or improving customer experience.

Using customer journey analytics, we can more easily answer the following questions:

How many customers take this route?

What were the steps customers took before calling?

Before calling, what steps did customers who did not purchase take?

Is there a best time to interact with a customer?

Which channel is best for interacting with customers?

What's wrong with traditional marketing analytics tools?

With conventional marketing analytics tools, marketing teams face intricate challenges when dealing with complex customer inquiries. Generally, these challenges arise because of the following six limitations:

Exponential Growth in Data

Inflexible, Static Data Model

Data Integration Hurdles

Scarcity of Skills and Resources

Lack of Real-time Analytics

Failure to Capture Multichannel Journeys

The integration of diverse customer data points is essential to overcoming these multifaceted challenges. To effectively communicate with every customer, you must create unified, compelling messages that consider their context. The traditional marketing analytics landscape, however, struggles to keep up with the increasing volume, velocity, and variability of data.

Using customer journey analytics tools can be an effective way to overcome these limitations. Marketers can leverage these sophisticated solutions by:

Understanding customer interactions across multiple touchpoints.

Real-time insights enable timely and relevant engagement strategies.

Integrating agile data and adaptive modeling, while ensuring flexibility and scalability.

Optimizing resource utilization through automation of repetitive tasks.

The evolving digital landscape requires more agile, intuitive, and comprehensive solutions in addition to traditional marketing analytics tools. Marketers can successfully navigate the complexities of modern customer engagement by using customer journey analytics.

How Does Customer Journey Analytics Differ from Journey Mapping?

Measuring the impact of customer behavior on kpis:.

With customer journey analytics, businesses can convert static journey maps into dynamic, live dashboards. As a result of this transformation, specific KPIs can be monitored, allowing clear insights into the effectiveness of strategies. An organization's approach to customer engagement and business growth can be refined by measuring tangible results against predefined performance indicators.

Analyzing and Influencing Customer Behavior in Real Time:

With customer journey analytics, you can gain insight into customer behavior in real-time using advanced machine learning algorithms. As a result of this capability, businesses can identify pivotal events in a customer's journey at a glance. Companies can personalize engagements by predicting and adapting to these behaviors instantly, ensuring timely interventions and strengthening customer relationships.

Comprehensive View of Your Customers’ Journeys:

Customer paths are typically viewed in a limited, homogeneous way in traditional journey mapping. In contrast, customer journey analytics reveal the various and unique paths customers take across channels and touchpoints. The comprehensive view enables customized strategies by recognizing that each customer, even within similar buyer personas, embarks on unique paths.

Discovering Real Journeys Your Customers Are Taking:

According to Jake Sorofman's Gartner insight, true customer journeys are discovered, not created. While traditional methods often result in inward-looking maps, analytics goes deeper. Businesses can discover authentic customer paths using data from various sources, including e-commerce platforms, call center logs, and marketing automation.

Using Data to Discover the Most Significant Opportunities:

By analyzing vast datasets, customer journey analytics can uncover significant opportunities within customer interactions. It provides a comprehensive understanding of customer behavior by integrating data sources ranging from e-commerce platforms to point-of-sale systems. As a result of this data-driven approach, businesses can identify critical pathways that can boost conversions, increase loyalty, or maximize revenue, enabling them to prioritize initiatives that drive meaningful results.

Business Benefits of Customer Journey Analytics

There are many advantages to using customer journey analytics, but we can summarize them under four headlines as:

1) Improve Customer Experience

Customer experience is king in today's competitive market. Providing a seamless and personalized customer experience is becoming increasingly important to businesses. A sole focus on individual touchpoints has proven insufficient in the past. Companies can identify pain points and areas for improvement by leveraging customer journey analytics. In this way, businesses can gain a deeper understanding of their customers' preferences, resulting in customized engagements that resonate and foster loyalty.

Leading companies such as Amazon demonstrate this by dynamically adjusting their online platforms in real-time, offering customized experiences based on individual preferences.

2) Accelerate New Customer Acquisition

Getting new customers efficiently is crucial to driving growth. An analysis of customer journeys can provide invaluable insights into high-impact purchasing pathways. Businesses can craft targeted campaigns by understanding customer preferences, behaviors, and lifestyles. It is crucial to reach potential customers at the right time, with tailored offers, to ensure a steady inflow of new patrons.

3) Reduce Customer Churn

In order for a business to grow sustainably, customer retention is paramount. You have to recognize and address attrition factors. By analyzing customer journeys, businesses can identify at-risk customers proactively.

With data-driven insights, you can identify potential friction points and take proactive measures to improve customer satisfaction, loyalty, and long-term retention.

4) Maximize Customer Lifetime Value

Analyzing customer journey provides insights into acquisition channels, purchasing patterns, and retention strategies that influence CLV. Adapting interactions to high-value customers' journeys fosters loyalty and repeat purchases by offering bespoke experiences. Through loyalty programs or tailored communications, such personalized approaches resonate deeply with customers, ensuring they feel understood and valued.

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What is Customer Journey Analytics

Published: June 28, 2023

A customer doesn’t just mindlessly purchase a product or service. They go through an entire journey, from discovering your brand, to purchasing your product or service, to sometimes recommending it to someone else. 

Customer journey analytics

To make sense of your customer’s journey, you’ll need to leverage customer journey analytics.

Get Started with HubSpot's Analytics Software for Free

Every business, startup or enterprise — in any industry — needs to understand how customers interact with their brand. Insights gathered from customer journey analytics can help, while leading to increased customer lifetime value, customer loyalty, and revenue growth. 

In this blog post, we cover the following:

What is customer journey analytics?

  • Customer Journey Stages
  • Customer Journey Analytics Benefits

Customer Journey Analytics Software

  • Customer Journey Analytics vs. Customer Journey Mapping

Customer journey analytics is a collection of data that helps you to understand how your prospects or customers behave, engage, and convert along the customer journey. 

Customer journey analytics often begins with a customer journey map , which is a visual representation of every step the customer goes through with your business. Then, it applies data on how your customer behaves throughout different phases of that map,  to help you assess the effect your customers’ journey has on your business, or what’s holding customer’s back from completing that journey and purchasing a product

Customer Journey Analytics Steps

1. outline a customer journey map..

Customer journey map template

Create your customer journey map using HubSpot’s template

The first step to customer journey analytics is creating a customer journey map. A typical customer journey map includes the following: the buying process, user actions, emotions, pain points, and solutions. The customer journey map is the foundation for further analysis.

2. Identify the right analytics tools.

To accurately conduct customer journey analysis, you'll need the right tools. 

A good customer journey analytics tool will monitor, track, and analyze data like website data, conversion data, and detail data across multiple channels.

Customer data platforms (CDPs) also play a role in supporting customer journey analytics. The platforms assign unique IDs to your website and app to build single customer views, which can include information such as location, browser, device type, operating system, historical transactions, and visitor logs.

3. Collect your data.

A robust analytics platform should enable you to collect data on customer behavior. Data can be broken down into two main buckets: user data and interaction data. 

  • User data: Provides context on a user and their traits. Data can include email, age, industry, and occupation.
  • Interaction data: Gives information about how a user interacts with your product or service.

4. Analyze data.

Data in itself is not meaningful without analysis. The purpose of customer journey analytics is to make sense of the data and extract insights that can inform your business strategy. 

For example, an e-commerce company might identify, through analysis, that requiring customers to create an account to complete a purchase leads to the customer not completing the purchase — a solution could be implementing a guest checkout option.

5. Update customer journey map.

Using the insights you’ve gained, you can now update the customer journey map accordingly. For example, you might add additional pain points uncovered through data analysis, like requiring customers to create accounts to complete a purchase.

6. Use customer journey analytics to test new strategies.

The next step is to figure out how to enhance the customer journey experience. Testing new strategies like adding a guest checkout option, making the account creation process faster with fewer steps, and sending abandoned cart emails are all examples.

Benefits of Customer Journey Analytics

By leveraging customer journey analytics, you'll be able to improve your customer’s experience with actionable insights, while unlocking benefits like:

Better Understanding Customers

By gathering and synthesizing data, you will better understand what aspects of the buyer’s journey lead them to purchase a product or service, or not. For example, an e-commerce company might learn that customers that come from a specific social media platform are more likely to buy, or discover that certain audience demographics or affinities are more likely to become leads.

Pinpointing Where You’re Losing Customers

Not all customers follow through, and unless they fill out a survey, it can be difficult to figure out why they churn. By leveraging customer journey analytics, you can pinpoint where you’re losing potential customers. 

For example, a business can lose potential customers during channel or device transitions. A prospect  might start filling out a form on a mobile device but choose to complete it on a laptop. If information entered is lost, the potential customer might not take the time to complete the signup process.

Optimizing and Solving for Prospects

With a better understanding of customers’ pain points and the reasons behind them, you'll be able to figure out how to improve and strategize around an accurate customer journey.

Improve ROI

Are your investments in customer experience worth it? By using customer journey analytics, you’ll be able to measure ROI for customer experience initiatives. From there, you can streamline, remove, or cost cut initiatives that don’t benefit your bottom line, or double down on the aspects of the buyer’s journey that do.

For instance, if you run an incredibly expensive advertising campaign that doesn’t yield the same level of new customers or purchase page visits as unpaid or more in-house content, you can aim to save money on ads and focus on the more affordable strategies that actually earn you money.

1. HubSpot Marketing Hub: Advanced Marketing Reporting

HubSpot customer journey analytics

Get started with customer journey analytics

HubSpot Marketing Hub is equipped with robust customer journey analytics capabilities and tools, which can map data around conversions, leads, deals, and website engagements around different stages of the customer journey. 

The Advanced Marketing Reporting tool also enables you to  attribute every customer interaction to revenue, analyze conversion rates and time between nurturing path steps, and provides further data to help you build informed strategies that can improve ROI and purchase rates.

2. Content Square

Content Square

Content Square captures UX, performance and product, and content data throughout the customer journey. The platform also enables you to visualize metrics so that they are easily digestible. You will be able to get insights like bounce rate and number of lost conversions, and dig deeper to pinpoint why.

3. Google Analytics

Google Analytics is a widely used website analytics software that enables you to track user behavior on different platforms, including mobile applications. Features like daily traffic reporting give you insight into what visitors are engaging with. Plus, its Analytics Amplifier can combine Google Analytics and HubSpot data .

Customer Journey Mapping vs. Customer Journey Analytics

Customer journey analytics and customer journey mapping are often confused with each other. Although they’re complementary,  customer journey mapping visually presents customer journey stages from start to finish, while customer journey analytics offers data about a  customers’ interactions in each stage.

Customer journey maps often include the following:

  • The buying process: By pulling data from places like CMS and prospecting tools, you will be able to figure out what goes into a customer’s purchasing process. 
  • User actions: This part of the customer journey map details the actions the customer takes throughout their journey.
  • Emotions: Emotions help color your understanding of how your customer is feeling and reacting as they go through their journey with your business.
  • Pain points: Adding pain points to your customer journey map gives you a comprehensive picture of the challenges your customer may experience.
  • Solutions: Figuring out solutions can help your customers experience fewer pain points. The data and insights you’ve gathered can help inform your solutions.

Customer journey analytics delves deeper. The customer journey map is the “what” and customer journey analytics is the “why” because it organizes customer or prospect data around each stage.  

Here’s an example of how customer journey analytics works in HubSpot Marketing Hub:

HubSpot’s Advanced Marketing Reporting Tool  

Customer Journey Map vs. Analytics Example: Let’s say your business offers CMS tools and your ideal customer, a graphic design firm, finds you through a targeted Instagram ad.

In the customer journey map you’ve built , your target customer considers using your CMS tools to build a new website that showcases their strengths. They schedule a demo before trying the free version and are initially excited, but become frustrated with the limited design elements offered by the free version. Their biggest pain point quickly becomes lack of versatility. They then look into purchasing the paid version or go to a cheaper competitor.

With customer journey analytics, you’ll apply real-time data to that map: From journey mapping, you’ve identified the steps your customer often takes  and their common pain points. A strong customer journey analytics tool can then collect, aggregate, synthesize, and visualize data to help you make sense of your customer’s actions and see if your mapping and journey-based strategies work. 

For example, data might show how your customer is interacting with your product. 

A good Customer Journey Analytics platform combines data like user data, survey results, and website analytics, you can gain a comprehensive view of why your customer is experiencing those pain points and consequently address their concerns.

Cultivate an Impactful Customer Journey

In order to remain competitive, it is important to understand and create strategies to enhance the customer’s journey. Customer Journey Analytics is just one component. Other key steps include creating buyer personas , mapping out the customer journey , and continuously updating strategies based on data.

To get started with improving the customer journey, learn more about HubSpot’s marketing solution Marketing Hub .

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customer journey analytics gartner

The Power of Customer Journey Metrics and Analytics: Enhancing Customer Experience Along the Journey

Customer journey map on a chalkboard.

Understanding and optimizing the customer journey has become paramount for driving growth and success. The customer journey encompasses every interaction a customer has with a brand, from initial awareness to the final purchase decision. However, unlocking the true potential of the customer journey requires more than mere observation. It necessitates a data-driven approach and the utilization of customer journey metrics and analytics .

30% of organizations have established their customer journey maps but struggle to use them effectively. Gartner.com

Customer journey metrics and analytics provide invaluable insights into customer behavior , preferences, and pain points along the entire journey. By harnessing the power of these tools, businesses can enhance the overall customer experience and drive customer satisfaction, loyalty, and brand advocacy.

Table of Contents

Enhancing Overall Customer Satisfaction Across the Customer Journey

Key customer experience metrics and their importance.

To enhance overall customer satisfaction across the customer journey, it is crucial to identify and track key customer experience metrics. Metrics such as Net Promoter Score (NPS), Customer Effort Score (CES), and Customer Satisfaction (CSAT) provide valuable insights into customer sentiment at different touchpoints. By measuring these metrics, businesses can pinpoint areas of improvement, address pain points, and optimize the customer experience.

Measuring customer satisfaction and loyalty

Measuring customer satisfaction and loyalty is essential for understanding the impact of the customer journey on overall satisfaction. Through surveys, feedback mechanisms, and sentiment analysis, businesses can gather data on customer satisfaction and loyalty levels. This data enables organizations to identify areas where satisfaction may be lacking and take proactive measures to improve the customer experience.

The impact of customer journey analytics on customer experience

Customer journey analytics plays a crucial role in enhancing overall customer satisfaction. By analyzing customer behavior, interactions, and preferences throughout the journey, businesses can gain valuable insights into customer needs and pain points . Leveraging customer journey analytics allows organizations to identify patterns, optimize touchpoints, and personalize experiences.

Mapping the Customer Journey Touchpoints for Improvement

Creating a customer journey map.

Creating a comprehensive customer journey map is essential to gaining a clear understanding of the various touchpoints and interactions that customers have with your product or service. A customer journey map visually represents the steps and stages a customer goes through, from initial awareness to post-purchase support. By mapping out the customer journey, businesses can identify key touchpoints and opportunities for improvement.

Identifying touchpoints and optimizing interactions

Once the customer journey map is in place, it becomes easier to identify specific touchpoints where customer interactions occur. These touchpoints could include website visits, customer support interactions, social media engagement, or product/service usage. By analyzing these touchpoints, businesses can gain insights into customer behavior and preferences, allowing them to optimize interactions to provide a seamless and positive customer experience .

Using analytics to identify pain points and opportunities for improvement

Analytics tools and customer journey metrics are invaluable in identifying pain points and areas of improvement along the customer journey. By analyzing data such as customer satisfaction and loyalty metrics, customer engagement levels , and conversion rates, businesses can pinpoint the areas where customers may face challenges or dissatisfaction. This data-driven approach enables organizations to make informed decisions and implement strategies to address pain points and enhance the overall customer experience.

Maximizing Business Success by Using Customer Journey Metrics

Measuring and optimizing customer lifetime value (clv).

Measuring customer lifetime value (CLV) is a crucial component of maximizing business success. CLV represents the total value a customer brings to a business throughout their relationship. By understanding CLV, businesses can allocate resources effectively, personalize experiences, and foster customer loyalty . Customer journey metrics play a significant role in measuring and optimizing CLV.

Reducing churn through insights from customer journey analytics

Churn, or customer attrition, is a significant challenge for businesses. Customer journey analytics can provide invaluable insights into the reasons behind churn. By analyzing customer journey metrics, businesses can identify pain points, bottlenecks, or areas where customers are more likely to drop off. Armed with this information, businesses can proactively address these issues, optimize touchpoints, and enhance the customer experience to reduce churn .

Improving overall customer satisfaction and brand loyalty

Customer journey metrics are instrumental in improving overall customer satisfaction and brand loyalty. By continuously measuring customer satisfaction, sentiment, and loyalty metrics at various touchpoints, businesses can gauge the effectiveness of their strategies and initiatives. Understanding the impact of different touchpoints on customer satisfaction allows businesses to identify areas of improvement and tailor their efforts accordingly. By consistently delivering exceptional experiences across the customer journey, businesses can foster strong brand loyalty, increase customer satisfaction, and drive long-term business success.

Implementing Effective Customer Experience Metrics and Net Promoter Score

Understanding cx metrics and their significance.

To gauge and improve customer experience, it is essential to understand the significance of customer experience (CX) metrics. CX metrics provide a quantitative and qualitative assessment of customer perceptions, satisfaction, and loyalty. Metrics such as Customer Satisfaction (CSAT), Customer Effort Score (CES), and Net Promoter Score (NPS) help measure different aspects of the customer experience. By understanding these metrics and their significance, businesses can gain insights into the effectiveness of their customer experience strategies, identify areas for improvement, and track progress over time.

Introduction to Net Promoter Score (NPS)

Net Promoter Score (NPS) is a widely used customer experience metric that measures customer loyalty and advocacy. It is based on a simple question: “How likely are you to recommend our company/product/service to a friend or colleague?” Respondents rate their likelihood on a scale from 0 to 10. NPS classifies customers into three categories: promoters (score 9-10), passives (score 7-8), and detractors (score 0-6). By calculating the difference between the percentage of promoters and detractors, businesses can obtain an NPS score that serves as a clear indicator of customer loyalty and brand perception.

Leveraging NPS as a customer experience metric

NPS is a powerful tool that helps businesses assess customer loyalty and understand their position in the market. It not only measures the likelihood of customer recommendations but also provides insights into customer sentiment and satisfaction. By leveraging NPS, businesses can identify promoters who act as brand advocates and focus on nurturing and retaining them. Additionally, by addressing concerns raised by detractors, businesses can improve the overall customer experience and increase customer satisfaction.

When implemented effectively, NPS can drive customer-centric strategies, influence decision-making, and prioritize initiatives aimed at enhancing the customer experience. By regularly tracking and analyzing NPS data, businesses can identify trends, make data-driven improvements, and foster a customer-centric culture throughout the organization.

By understanding CX metrics, introducing NPS, and leveraging it as a customer experience metric, businesses can gain valuable insights into customer loyalty, perception, and satisfaction. This enables them to make informed decisions, improve the customer experience, and build strong customer relationships, leading to long-term business success.

How Can Wizaly Help You Understand and Measure Customer Experience?

Harnessing the power of customer journey metrics, analytics, and effective customer experience measurement is crucial for businesses looking to drive success and achieve sustainable growth in today’s competitive landscape. By incorporating these strategies, businesses can elevate the overall customer experience, foster customer loyalty, and maximize their bottom line.

At Wizaly, we understand the importance of accurate customer journey attribution and comprehensive data analysis. Our platform empowers businesses to unlock the full potential of their marketing efforts by providing accurate customer journey metrics and actionable insights. With Wizaly, you can optimize your marketing budget, measure customer satisfaction, and make data-driven decisions

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Customer Journey Analytics

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Empower business intelligence and data science teams to stitch and analyse cross-channel data with a powerful analytics toolkit.

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Build great experiences with true customer journey insights.

Today, most businesses collect mountains of data in their quest to better understand their customers so they can create great products and experiences. But the challenge isn’t just collecting more data, it’s integrating, analysing, understanding and sharing that data across the business. It requires the right data, from all channels, working together to paint a holistic picture of the customer journey, as well as the right tools to analyse the journey and quickly activate discovered insights.

Customer journey analytics provides a toolkit to business intelligence and data science teams that help them stitch and analyse cross-channel data. Its capabilities deliver context and clarity to the complex multichannel customer journey. This context, when paired with tools like SQL and Analysis Workspace, provide actionable insight into how to remove pain points from the customer conversion process and deliver positive experiences in the moments that matter most. Plus, you can pair customer journey analytics with Adobe Experience Platform, which gives you access to any data stored there — including Adobe Analytics data.

“Customer journey analysis is the top customer analytics priority for 2019.” — “Survey Analysis: Customer Experience Maturity and Investment Priorities, 2019,” Gartner, 3 June 2019.

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The customer analytics journey: move up the maturity curve

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Partner, Analytics Practice

Phil Tedesco

VP, Growth & Strategy, Analytics Practice

Customer analytics lets you take your customer data, turn it into insights, and then use those insights to acquire, serve, and retain customers. Used correctly, customer analytics can help small companies compete effectively with larger competitors and can help industry leaders achieve their goals (experts estimate that Netflix saves $1 billion a year with its customer-centric recommendation engine).

The customer analytics process is a journey. In order to derive the maximum benefits from customer analytics, organisations need to understand their level of maturity regarding analytics, complete each step thoroughly, then build on each step to move up the maturity curve carefully and intentionally.

As you will see, companies that effectively advance their maturity recognise the importance of utilising their most relevant data, selecting the most appropriate use cases, taking action, and continuously refining their process.

The challenges facing businesses on the customer analytics journey – and how to solve them

1. dealing with data.

Data is the bedrock of customer analytics, and also the core challenge. You need easy access to high-quality data in one place – a single source of customer truth – to serve as your foundation for customer analytics.

Challenges:

Creating a 360-degree view.

Most companies struggle to take the data that they capture and synthesise it into a 360-degree view of their customers. In fact, ensuring data quality and access from a variety of sources are the top two concerns for analytics and measurement professionals (source: Forrester / Burtch Works Q3 2019 Global State of Customer Analytics Survey). On average, poor data quality costs businesses approximately $10 million to $14 million each year, according to research firm Gartner – in part because the vast majority of the data that companies generate is siloed and/or unstructured. Even once you do have the data, proper data stitching requires significant resources and expertise.

Trying to build instead of buy

Despite the fact that access to high-quality data is a top concern, too many companies try to take on this daunting challenge themselves. Unfortunately, most organisations simply do not have the in-house expertise and industry-wide perspective to perform the high-level data engineering that leads to optimum results. Companies should strongly consider whether their internal resources are truly qualified (and available) to build and maintain a sustainable process for ingesting, enriching, harmonising, and visualising data.

Start with the data you have

You don’t need the full 360-degree view to get started. Think about data on a continuum. If the data you have is mostly incomplete, you may not want to rely on it, especially for highly strategic business decisions. However, if your data is fairly complete, then use it as a starting point and plan to build on it. Consider what data you need to achieve your goals; for example, if you’re focused on increasing the lifetime value of current customers, you may not need data about prospective customers.

Bring in the experts

Customer analytics service providers with data engineering expertise typically have a deep understanding of your industry, which means they understand what analytics models will be most effective, and can focus on the most useful data right from the start. Experts can be a costly up-front investment, but also allow you to get to answers faster and more efficiently, which typically provides significant ROI (for example, the cost to build a customer record correctly up front is approximately one-tenth the cost of rebuilding an incorrect record). Some organisations choose to take a hybrid approach, using a data engineering vendor to build the data set that is then maintained by an internal team.

Align people, processes, and goals

Having clarity right from the start is key, which is why it’s important to agree on specific customer-centric objectives, and how the right data will help you achieve those goals. For example, if you want to increase customer retention, what data will you need to score every customer on their likelihood of being retained? Do you already have this data, or can it be easily obtained? Once you have the retention scores, what action can you take? Does everyone on your team agree? Spend some time thinking through the process and gaining alignment, and recognise that you can’t achieve everything at once.  

2. Developing an analytics use case

Many companies have a wealth of data, but a dearth of insight about their customers. Determining how you will use the data to build use cases is the key to discovering opportunities and building profitable customer relationships.

Differentiating the customer experience

Determining what each customer wants, how to manage the customer relationship, and how to get the most value out of them requires personalisation. Simply having clean, high-quality data doesn’t make this data useful. Segmenting your customer base is a start, but will not typically give you the detail required to maximise your ROI. Customers today have too many choices, which is why you need a personalised model to speak directly to them and get them to take action.

78% of consumers said personalised content made them more likely to repurchase

– McKinsey & Company, Next in Personalization 2021 Report , 12 November 2021

Staying focused

Many companies try to do too much at once, and lose sight of their most critical business objectives and customer objectives. It’s smart to plan ahead, but beware of the temptation to start designing too many use cases while you’re still in the data engineering phase, which can lead to missed opportunities and extra work.

Identifying the right model for your business’s maturity level

Many organisations get caught up in flashy tools like artificial intelligence, try to be too aggressive with their goals, or don’t adequately account for different levels of data literacy between units. Be honest about your capabilities, and be wary of supposed shortcuts such as pre-packaged software, which may seem like a good start, but is not designed to help you develop a sophisticated model tailored for your unique business.

Pick a use case that will deliver financial ROI

How do you decide which model to use when you have multiple choices, many of which could likely deliver results? Look for the one that is most likely to make your company a meaningful amount of money, which will validate your efforts and improve buy-in. Consider starting with up-selling, cross-selling, and retaining your existing customers rather than acquiring new ones (retention is up to 95% more profitable than acquisition, according to research from Bain & Company, in part because you already have the data).

Test and learn

Customer analytics is a process. Do controlled experiments to test and learn, then repeat the cycle. Once you determine the uplift from a specific model, you can measure the ROI and look for ways to improve it in other models. The companies that are experts at leveraging customer analytics are the ones that are never satisfied with their results. A good model is simply a starting point for a great model. An experienced customer analytics service provider can guide you through this process, with work plans and checklists to help you continuously improve your results.

3. Transforming insights into relevant business actions

Once you’ve put the effort into your data engineering and creating use cases, it’s time to extract all of that value. This is the last mile, where you determine the best course of action for maximum positive impact on your business.

Understanding that a model is not a decision or an action

Organisations often lose sight of the fact that they need to take action. They spend all of their efforts on getting approval for a customer analytics project or dissecting the data. But a customer analytics model is simply a synthesis of your data. You need the right people – and the right processes – to turn these insights into action.

Being open to new discoveries

Many companies are not ready for insights that reveal something entirely new about their customers. They’re looking for models that confirm and validate what they’re already doing. Organisations that have a mature grasp of customer analytics, however, recognise that they may need to incorporate operational changes, and modify how they interact with customers.

Getting buy-in from business stakeholders

If there is a lack of data literacy within an organisation, you’ll struggle to have people see the value of measurement in analytics and execute the plan. Gaining buy-in from senior executives and stakeholders throughout the company is key, especially since so many departments (e.g., marketing, IT, merchandising, etc.) must all work closely together.  

Collaborate from the start

Mature companies have all stakeholders aligned from the very beginning. Getting buy-in from the start of a customer analytics project means that everyone agrees upon the current situation and the end objectives. When you understand where your customers are – and you clearly define your goals – you build confidence and trust with your team, which helps you overcome any resistance to change and deliver results.

Choose a service provider with advanced execution capabilities

These days, many customer analytics models are commodities, so consider how service providers differ in terms of their execution capabilities and last-mile solutions. Customers expect greater levels of personalisation, which means you need a partner with advanced execution capabilities to improve your effectiveness. The best providers can help you scale across your enterprise when the time is right for you. Their experience in last-mile execution and transforming insights into action will allow everyone to make the most informed decisions.

Have the analytics enablers involved early on

Organisations that are highly successful at using customer analytics invite the analytics enablers to the table from the start, so they can gain a thorough understanding of the business operations and goals before they even touch the data, and determine the best place to seamlessly embed an analytical output. Including these experts also allows them to guide others through the analytics process by identifying appropriate models, explaining the outputs, and discussing the potential impact.

Close the loop, learn, and refine

Customer analytics is a never-ending journey. Mature organisations study the effectiveness of their campaigns, continuously refine them based on the data and facts that emerge, then retrain the model and keep repeating this entire process. These organisations are already performing customer analytics at a high level, but are always looking for better tools and processes to improve their results. This disciplined approach, experimental mindset, and eagerness to explore new opportunities are hallmarks of successful companies.

Need expert help?

Kantar’s customer analytics solutions help you maximise the value of each and every customer relationship by blending cutting-edge analytics and deep human understanding powered by broad technology expertise.

Please get in touch to learn how we can help you succeed in your customer analytics journey.

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A complete guide to customer journey analytics.

13 min read Customer journey analytics can help you to nail down exactly why your customers behave the way they do and tie your customer experience efforts to financial outcomes. Learn how to use customer journey analytics for improved CX with our ultimate guide.

What is customer journey analytics?

Customer Journey Analytics is the process of understanding the impact of every interaction a customer has with your business.

Often, customer journey analytics starts with a customer journey map , which is presented as a graph, flow chart, or other visual that documents each stage of the relationship between a customer and a brand.

However, instead of just charting their customer journey on a map, customer journey analytics takes a further step to analyze what effect each interaction has on your customers’ decisions.

Further information is overlaid to help analyze how each interaction drives customers toward the end goal.

Customer journey analytics can include analysis of:

  • Customer needs
  • Emotional highs and lows
  • Key metrics per step in the journey
  • Customer satisfaction scores , customer effort scores , and other survey results

Customer journey analytics can help you to direct your customers’ attention and resolve any pain points that stop them from taking desired actions. It helps you to augment your customer experience and develop a customer journey that not only gets customers to where you want them to go, but helps them connect to the journey itself.

Learn the analytics and ROI on customer journey management in our free course. 

Customer journey analytics vs. customer journey mapping

Many brands have a broad sense of their customer journey but haven’t optimized it by creating a comprehensive customer journey map or analyzing what affects their customers’ experience.

Customer journey analytics and customer journey mapping are complementary but different processes. Here are the main ways in which they are distinct, and how they work together.

What is customer journey mapping?

Customer journey mapping is the process of laying out the end-to-end journey in a clear way. Creating a map of every touchpoint your customer will experience means you can see what steps your customers take to reach the end goal of a purchase, signup, or other action.

Often, journey maps are documented at the process level. For example, an insurance provider would map the claims process, and a bank would document the new account process.

Some common components of customer journey maps include:

  • The process being evaluated
  • The stages of the journey
  • Critical customer interactions and touchpoints
  • Representative customer quotes
  • Key customer expectations
  • Metrics like satisfaction score, mention volume, NPS
  • Trends in topics related to this part of the journey

Our ultimate guide to customer journey mapping can help you to draft your first customer journey map or optimize one you have already.

How do you use customer journey analytics with customer journey mapping?

As we’ve already explained, customer journey analytics is the process of gathering as much information as you can from every part of the journey and analyzing the journey for pain points and successes.

Understanding which parts of the journey function as planned and which obstacles are in the way of your customers’ progress means you can take action to ensure they complete their journey as you intend.

Benefits of customer journey analytics

There are several benefits to completing customer journey analytics. From better understanding your customers’ behavior to a better ROI for your customer experience , customer journey analytics gives you better insights and a more informed strategy for improvement.

Your brand becomes more customer-centric

Understanding the customer journey allows your company to be more customer-centric . It allows you to closely evaluate the activities, expectations, thoughts, and feelings of your customers . You learn what they like and dislike, how to move them through your buying cycle, and how to satisfy and retain them . When journey mapping is complemented with customer journey analytics it helps you understand the priority for your customer experience initiatives.

Your business becomes more unified

In addition, with the right focus, customer journey mapping and customer journey analytics break down internal silos. They empower you to streamline services across departments. Not only that, but they help to align everyone by providing a common understanding of the customer experience. Employees get greater visibility into what happens upstream and downstream of their interactions with customers, letting everybody provide a more consistent, high-quality experience.

You can find track issues as they happen

With a sophisticated customer journey analytics platform, you can pinpoint issues in real-time. You can test new approaches and see their influence on your customer experience and bottom line with analytics that update as quickly as you need them.

You see direct and indirect feedback in one place

Explicit feedback – for example, the information you gather through surveys – is easier to pinpoint to specific interactions customers have with your brand. The customer has an experience and directly after, you request input.

Implicit feedback is more complex to understand. This type of data might include operational data such as sales numbers, or it might cover social mentions, what your customers say on the phone to your care center, third-party reviews, and more.

Understanding how your audience thinks, feels, and acts in response to customer interactions without directly asking them might seem impossible, but with tools such as conversation analytics , you’re able to link your customer journey to this type of customer data.

See how Qualtrics CustomerXM enables customer journey analytics

An example of using customer journey analytics

Customer journey analytics can be used to understand the impact of sub-journeys limited to single processes – such as opening a new account – or the entire digital customer journey .

Below is an example of how you can use customer journey analytics to chart the success of each journey.

Resolving a customer satisfaction issue for a specific sub-journey

Let’s take a printer business that provides hardware to its customers. The brand has realized that the repair sub-journey is currently leading to low Net Promoter Scores (NPS) and a higher cost to serve per customer.

The journey

First, the brand needs to chart the customer journey. It looks like the below:

  • A customer has an issue with their printing device
  • They call the customer care center to schedule a repair
  • The service agents arrive at their place of residence
  • The repair is made

However, there are other ways this journey might unfold. For example:

  • The service agents arrive at their place of residence but the customer is not present
  • The repair cannot occur, so the customer has to call again to reschedule the repair
  • The repair is made at a later date when the customer is present

The analysis

Overlaying the NPS scores on this latter journey, the company realizes that the NPS score drops when the customer has to reschedule the repair. Asking the customer to go through the same process once again to rebook their appointment is causing customers to feel less satisfied with their experience.

Using natural language processing (NLU), the team can also see that there is a more negative sentiment expressed in the open text question they have added to the NPS survey. With the additional calls to the care center, the cost to serve each customer also increases.

The resulting action  

The brand decides it’s best to provide other means to customers to book their appointments at a time to suit them. Offering customers a self-service booking system that they can access via their mobile on an app or through the website gives the customers more control over when their appointment occurs. Adding a facility to reschedule any booked appointments for a more convenient time and accentuating this with push or text notifications when the repair team is on their way can help to see if this reduces the instances of missed repairs and reduces the impact on the customer care center .

With customer journey analytics in place, the brand team can see if this improves NPS scores at the same points in the customer journey, and measure in financial terms the impact of actions taken for improved customer experience .

How to use customer journey analytics

Customer journey analytics provides the insight you need to successfully manage your customer’s journey. From lowering customer churn to helping you predict customer behavior, putting a customer journey analytics solution in place will help you to leverage your customer behavioral data for financial success.

But how do you start using customer journey analytics? Below is the outline of the actions you’ll need to take.

1. Map your customer journeys and aggregate data

First, you need to create a customer journey and aggregate the customer data that you already have. Good customer journey analytics tools will be able to do this for you, cutting down the time your team needs to spend sourcing data from third-party locations, customer service chat logs, and survey results.

Competent customer journey analytics software will also be able to track data in real-time, allowing you to build a comprehensive map that reacts to current customer behavior . It should also be able to draw data from numerous sources, helping you to break down traditional business silos and understanding customer interactions from all business angles: sales, marketing, and more.

Learn the five competencies for customer journey mapping

2. Analyze your customer behavior and data

Once you have your customer journeys mapped out and your data collected, you can link specific interactions to particular customer behavior, survey results, social media comments , and more. You’ll need a customer journey analytics solution to be able to link all of this data together in an efficient way.

3. Take action informed by data-led insights

Customer journey analytics provides you with the ability to see cause and effect, as well as providing you with concrete steps to change specific interactions or the entire customer journey. When customers react badly to specific processes or interactions, you can test how changes in your customer journeys affect their future decisions.

Not only that, but you can coordinate your teams across your business to work on customer satisfaction with their experience, based on the data you’ve analyzed. For example, if customers are led to purchase through your marketing but aren’t happy with their purchase, they will deal with your marketing , sales, and customer care teams. Understanding what specifically caused a problem for them means you can inform each team of actions they can take to improve.

How customer journey analytics can improve your customer experience

Brands often hit a wall when trying to measure customer experience . Charting your customers’ often nebulous sentiment and which actions have an impact on customer experience can be difficult without the right tools to hand.

Understanding the return on investment for specific actions taken for customer experience is difficult for a number of reasons:

  • Data is siloed or overwhelming
  • Business departments work separately with a lack of oversight
  • Actions aren’t based on data
  • There isn’t a way to track the impact of actions on customer experience

Qualtrics CustomerXM allows you to see the value of customer journeys with rich data analysis, provided through conversational analytics . With natural language understanding, Qualtrics is able to provide you unrivaled insights into customer emotions, sentiment, and more to paint a complete picture of friction points and their rationale. Powered by feedback from multiple areas of your business, you are able to create a plan of action with a tangible effect on your customer experience and business outcomes.

With a deeper understanding of customer behavior, your brand is able to not only understand the return on investment of your actions but develop a customer experience that delivers results. Extending your customer lifetime value , increasing customer satisfaction, and reducing customer churn becomes easier when you understand the triggers for the behavior.

Learn how to take action on customer journey management with our free online course

Related resources

Customer Journey

How to Create a Customer Journey Map 22 min read

B2b customer journey 13 min read, digital customer journey 13 min read, consumer decision journey 14 min read, customer journey orchestration 12 min read, customer journey management 14 min read, customer journey stages 12 min read, request demo.

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March 19, 2024

Medallia Named a Leader in the 2024 Gartner® Magic Quadrant™ for Voice of the Customer Platforms report

Gartner ranks Medallia highest in all four Use Cases in the 2024 Critical Capabilities for Voice of the Customer Platforms

PLEASANTON, Calif. – March 19, 2024– Medallia, Inc. , the global leader in customer and employee experience, today announced that it has been named a Leader for the third consecutive year in the Gartner Magic Quadrant™ for Voice of the Customer (VoC). Medallia was named a leader for its Ability To Execute’ and Completeness Of Vision.

“Medallia is so proud to be named a leader in the Gartner Magic Quadrant™ for Voice of Customer,” said Joe Tyrrell, CEO at Medallia. “Medallia has invested over $750 million in the past two years to strengthen core voice of the customer product offerings as well as expanded generative AI capabilities to help brands shift from simply looking at data and analytics to taking action, in real-time, to deliver personalized experiences at scale. With this our customers are able to more easily understand the difference between data that is interesting and the data that is truly important to improving experiences.”

Medallia also received the highest ranking for the four use cases (Overall CX, Brand Perception & Digital Journey, Drive Revenue Growth, and Customer Service & Retention) evaluated in the 2024 Critical Capabilities for Voice of the Customer Platforms report.

Critical Capabilities research complements a Gartner Magic Quadrant by allowing deeper insight into the providers’ product or service offerings by identifying which ones best fit various use cases. Magic Quadrants position vendors in a market, while Critical Capabilities provides a deeper dive into the providers’ product and service offerings. Magic Quadrants contain a broader analysis of the vendors in a market, while the companion Critical Capabilities directly focuses on the product/service offering.**

 We believe that “the competition for customer loyalty and retention has never been higher. Medallia’s unified, enterprise-grade experience platform has a proven ability to measure and deliver a positive ROI from customer programs in multiple uses for large and complex organizations,” said Simonetta Turek, Chief Product Officer at Medallia. ”Our 20-year commitment to breaking down silos across the organization to empower teams across the end-to-end customer journey to impact customer satisfaction and revenue growth is a clear differentiator across the CX industry.”

To learn more about Medallia’s industry leadership, visit our site at: https://www.medallia.com/why-medallia/

About Gartner Gartner, Magic Quadrant™ for Voice of the Customer Platforms, 31 January 2024, Michael Maziarka, et. Al.

**Gartner Research Methodologies, " Gartner Critical Capabilities", "March 15, 2024", https://www.gartner.com/en/research/methodologies/research-methodologies-gartner-critical-capabilities

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This Gartner, Magic Quadrant for Voice of the Customer Platforms report was not published in 2022 and 2023

GARTNER is a registered trademark and service mark, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

About Medallia 

Medallia is the pioneer and market leader in customer, employee, citizen, and patient experience. As the leading enterprise experience platform, Medallia Experience Cloud is the mission-critical system of record that makes all other applications customer and employee aware. The platform captures billions of experience signals across interactions including all voice, video, digital, IoT, social media, and corporate-messaging tools. Medallia uses proprietary artificial intelligence and machine learning technology to automatically reveal predictive insights that drive powerful business actions and outcomes. Medallia customers reduce churn, turn detractors into promoters and buyers, create in-the-moment cross-sell and up-sell opportunities, and drive revenue-impacting business decisions, providing clear and potent returns on investment. For more information visit www.medallia.com .

© 2024 Medallia, Inc. All rights reserved. Medallia®, the Medallia logo, and the names and marks associated with Medallia’s products are trademarks of Medallia. All other trademarks are the property of their respective owners.

PR Contact:

Jenny Zehentner [email protected]

customer journey analytics gartner

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Medallia Named a Leader in the 2024 Gartner® Magic Quadrant™ for Voice of the Customer Platforms Report

Medallia Named a Leader in the 2024 Gartner ® Magic Quadrant™ for Voice of the Customer Platforms Report

Gartner ranks Medallia highest in all four Use Cases in the 2024 Critical Capabilities for Voice of the Customer Platforms

Medallia, Inc. , the global leader in customer and employee experience, today announced that it has been named a Leader for the third consecutive year in the Gartner Magic Quadrant™ for Voice of the Customer (VoC). Medallia was named a leader for its Ability To Execute’ and Completeness Of Vision.

“Medallia is so proud to be named a leader in the Gartner Magic Quadrant™ for Voice of Customer,” said Joe Tyrrell, CEO at Medallia. “Medallia has invested over $750 million in the past two years to strengthen core voice of the customer product offerings as well as expanded generative AI capabilities to help brands shift from simply looking at data and analytics to taking action, in real time, to deliver personalized experiences at scale. With this our customers are able to more easily understand the difference between data that is interesting and the data that is truly important to improving experiences.”

Medallia also received the highest ranking for the four use cases (Overall CX, Brand Perception & Digital Journey, Drive Revenue Growth, and Customer Service & Retention) evaluated in the 2024 Critical Capabilities for Voice of the Customer Platforms report.

Critical Capabilities research complements a Gartner Magic Quadrant by allowing deeper insight into the providers’ product or service offerings by identifying which ones best fit various use cases. Magic Quadrants position vendors in a market, while Critical Capabilities provides a deeper dive into the providers’ product and service offerings. Magic Quadrants contain a broader analysis of the vendors in a market, while the companion Critical Capabilities directly focuses on the product/service offering.**

We believe that “the competition for customer loyalty and retention has never been higher. Medallia’s unified, enterprise-grade experience platform has a proven ability to measure and deliver a positive ROI from customer programs in multiple uses for large and complex organizations,” said Simonetta Turek, Chief Product Officer at Medallia. ”Our 20-year commitment to breaking down silos across the organization to empower teams across the end-to-end customer journey to impact customer satisfaction and revenue growth is a clear differentiator across the CX industry.”

To learn more about Medallia’s industry leadership, visit our site at: https://www.medallia.com/why-medallia/

About Gartner

Gartner, Magic Quadrant™ for Voice of the Customer Platforms, 31 January 2024, Michael Maziarka, et. Al.

**Gartner Research Methodologies, "Gartner Critical Capabilities", "March 15, 2024", https://www.gartner.com/en/research/methodologies/research-methodologies-gartner-critical-capabilities

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This Gartner, Magic Quadrant for Voice of the Customer Platforms report was not published in 2022 and 2023

GARTNER is a registered trademark and service mark, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

About Medallia

Medallia is the pioneer and market leader in customer, employee, citizen, and patient experience. As the leading enterprise experience platform, Medallia Experience Cloud is the mission-critical system of record that makes all other applications customer and employee aware. The platform captures billions of experience signals across interactions including all voice, video, digital, IoT, social media, and corporate-messaging tools. Medallia uses proprietary artificial intelligence and machine learning technology to automatically reveal predictive insights that drive powerful business actions and outcomes. Medallia customers reduce churn, turn detractors into promoters and buyers, create in-the-moment cross-sell and up-sell opportunities, and drive revenue-impacting business decisions, providing clear and potent returns on investment. For more information visit www.medallia.com .

© 2024 Medallia, Inc. All rights reserved. Medallia®, the Medallia logo, and the names and marks associated with Medallia’s products are trademarks of Medallia. All other trademarks are the property of their respective owners.

customer journey analytics gartner

PR Contact: Jenny Zehentner [email protected]

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Medallia Named a Leader in the 2024 Gartner® Magic Quadrant™ for Voice of the Customer Platforms Report

Gartner ranks Medallia highest in all four Use Cases in the 2024 Critical Capabilities for Voice of the Customer Platforms

Medallia, Inc. , the global leader in customer and employee experience, today announced that it has been named a Leader for the third consecutive year in the Gartner Magic Quadrant™ for Voice of the Customer (VoC). Medallia was named a leader for its Ability To Execute’ and Completeness Of Vision.

“Medallia is so proud to be named a leader in the Gartner Magic Quadrant™ for Voice of Customer,” said Joe Tyrrell, CEO at Medallia. “Medallia has invested over $750 million in the past two years to strengthen core voice of the customer product offerings as well as expanded generative AI capabilities to help brands shift from simply looking at data and analytics to taking action, in real time, to deliver personalized experiences at scale. With this our customers are able to more easily understand the difference between data that is interesting and the data that is truly important to improving experiences.”

Medallia also received the highest ranking for the four use cases (Overall CX, Brand Perception & Digital Journey, Drive Revenue Growth, and Customer Service & Retention) evaluated in the 2024 Critical Capabilities for Voice of the Customer Platforms report.

Critical Capabilities research complements a Gartner Magic Quadrant by allowing deeper insight into the providers’ product or service offerings by identifying which ones best fit various use cases. Magic Quadrants position vendors in a market, while Critical Capabilities provides a deeper dive into the providers’ product and service offerings. Magic Quadrants contain a broader analysis of the vendors in a market, while the companion Critical Capabilities directly focuses on the product/service offering.**

We believe that “the competition for customer loyalty and retention has never been higher. Medallia’s unified, enterprise-grade experience platform has a proven ability to measure and deliver a positive ROI from customer programs in multiple uses for large and complex organizations,” said Simonetta Turek, Chief Product Officer at Medallia. ”Our 20-year commitment to breaking down silos across the organization to empower teams across the end-to-end customer journey to impact customer satisfaction and revenue growth is a clear differentiator across the CX industry.”

To learn more about Medallia’s industry leadership, visit our site at: https://www.medallia.com/why-medallia/

About Gartner

Gartner, Magic Quadrant™ for Voice of the Customer Platforms, 31 January 2024, Michael Maziarka, et. Al.

**Gartner Research Methodologies, "Gartner Critical Capabilities", "March 15, 2024", https://www.gartner.com/en/research/methodologies/research-methodologies-gartner-critical-capabilities

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This Gartner, Magic Quadrant for Voice of the Customer Platforms report was not published in 2022 and 2023

GARTNER is a registered trademark and service mark, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

About Medallia

Medallia is the pioneer and market leader in customer, employee, citizen, and patient experience. As the leading enterprise experience platform, Medallia Experience Cloud is the mission-critical system of record that makes all other applications customer and employee aware. The platform captures billions of experience signals across interactions including all voice, video, digital, IoT, social media, and corporate-messaging tools. Medallia uses proprietary artificial intelligence and machine learning technology to automatically reveal predictive insights that drive powerful business actions and outcomes. Medallia customers reduce churn, turn detractors into promoters and buyers, create in-the-moment cross-sell and up-sell opportunities, and drive revenue-impacting business decisions, providing clear and potent returns on investment. For more information visit www.medallia.com .

© 2024 Medallia, Inc. All rights reserved. Medallia®, the Medallia logo, and the names and marks associated with Medallia’s products are trademarks of Medallia. All other trademarks are the property of their respective owners.

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    To create value-driven customer journey maps that are aligned with the enterprise-wide CX initiatives, marketing leaders must master foundational elements such as securing buy-in from key stakeholders, assessing the need and availability of data sources, and an in-depth understanding of buyer personas.

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    Customer Journey maps are an essential component of the customer experience framework that organizations need to deliver and prioritize effective, innovative customer experiences. Our guide is designed to offer CMOs and customer experience leaders a guide to build a customer journey map that delivers value at all customer journey stages.

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  9. PDF Journey Analytics: Transforming customer journeys

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  17. Gartner '19: Guide for Customer Journey Analytics

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  25. Medallia Named a Leader in the 2024 Gartner® Magic Quadrant

    PLEASANTON, Calif. - March 19, 2024- Medallia, Inc., the global leader in customer and employee experience, today announced that it has been named a Leader for the third consecutive year in the Gartner Magic Quadrant™ for Voice of the Customer (VoC).Medallia was named a leader for its Ability To Execute' and Completeness Of Vision. ...

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  28. A generative AI reset: Rewiring to turn potential into value in 2024

    It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...

  29. Five9 Hires Leading Gartner CX Analyst Steve Blood

    Blood was most recently a Vice President and Analyst in Gartner's Customer Service and Support team, part of the Sales & Customer Service practice. ... contact center applications, digital customer service and customer service analytics. During his tenure at Gartner, Steve served as an advisor of customer service technology to end users and ...

  30. Medallia Named a Leader in the 2024 Gartner® Magic Quadrant™ for Voice

    Gartner ranks Medallia highest in all four Use Cases in the 2024 Critical Capabilities for Voice of the Customer Platforms. Medallia, Inc., the global leader in customer and employee experience, today announced that it has been named a Leader for the third consecutive year in the Gartner Magic Quadrant™ for Voice of the Customer (VoC).Medallia was named a leader for its Ability To Execute ...