In the last year, we introduced a suite of powerful new Frontline-Ready AI features that seamlessly integrate into existing customer experience workflows and shorten the path from insight to action to improve experiences for customers.

Here we鈥檙e shining a light on these solutions, highlighting how these tools can help teams act faster, solve problems smarter, and deliver results in the moment, without the wait, noise, or guesswork. 

In this first post in the series, we鈥檒l walk you through the key customer experience use cases and benefits of . 

Intelligent Summaries for text analytics harnesses the power of GenAI to help brands understand the top themes and sentiment of thousands of customer interactions as they鈥檙e unfolding across channels in real time鈥攚ithout anyone having to manually listen in, read a transcript, or analyze a single conversation.

Ahead, we鈥檒l show you the feature in action. See a fictional home improvement retailer GoodHome unlock immediate value with our new solution to get an update on the brand鈥檚 CX KPIs in seconds, figure out what has caused one of the retailer鈥檚 most important metrics to decline, and implement a solution to address the root cause of the problem ASAP. 

Let鈥檚 get to it.

The Character and the Conflict

Meet Sara, the CX Manager at (the fictional composite) home improvement retailer, GoodHome.

She used to spend hours manually reviewing mountains of data to make sense of what was happening for her customers. She鈥檇 have to sift through social media conversations, online reviews, and customer support calls, emails, and live chat sessions to figure out the top issues they were struggling with and what her team needed to do to intervene.

It took so long to find answers, she wasn鈥檛 able to act in the moment, when it actually mattered. 

That all changed when GoodHome invested in Intelligent Summaries for text analytics, a solution from 糖心原创 that cuts through mountains of unstructured data (including comments, reviews, and transcripts) instantly with GenAI-powered summaries.

The Moment of Truth…

By turning to Intelligent Summaries for text analytics, Sara is now able to quickly spot what鈥檚 going on for GoodHome鈥檚 customers by getting a real-time pulse on the retailer鈥檚 most important customer experience metrics.

Recently, she used it to detect a critical drop in the company鈥檚 in-store overall satisfaction (OSAT) score. The KPI had fallen from a strong 8.6 out of 10 to a just-above average 5.4. 

What was going on?

For answers, Sara was able to ditch her typical (tedious) process of manual review, and get the information she needed in just one click.

The Solution: Intelligent Summaries for Text Analytics

Intelligent Summaries for text analytics provides a GenAI-powered analysis of the most recent, relevant customer comments on this topic, giving an executive-level summary of the issue in just seconds. 

Intelligent Summaries for text analytics provides a summary of the topic in just one click. 

Without having to do any digging into the data, she was able to get this one-click generative AI-powered summary.

Intelligent Summaries for text analytics explains shifts in metrics by summarizing recent and most relevant customer interactions for that specific topic or theme.

Sara was also able to view the Intelligent Summary on the topic when she clicked into the Topic Deep Dive for a bit more analysis. The Topic Deep Dive also revealed co-occurring themes related to the topic, that indicated that the app was crashing, customers were experiencing issues with their orders being processed, and the app wasn鈥檛 stable. 

Upon seeing the impact of 鈥渞ecurring app crash,鈥� Sara was able to view a summary of this theme that helped her identify the why: A confusing app experience was preventing GoodHome customers from viewing their orders, leading to a poor pickup experience. 

When teams dig into co-occurring themes, they can quickly understand the most impactful themes with a Summary of the most recent interactions. 

All that, without ever having to manually read or review a single comment or conversation. 

The Resolution and Business Impact

By quickly identifying the problem in the moment, Sara was able to alert the company鈥檚 app developers who intervened, implemented a fix, and stopped the app issues in its tracks.

Customers were once again able to seamlessly pick up their orders without any issues and the company鈥檚 in-store OSAT score bounced back to the average 8.6 rating the brand had achieved over the previous three months.

By acting in the moment, Sara stopped the issue from spiraling out of control鈥攑reventing customers from becoming detractors and avoiding a mountain of negative reviews that might have otherwise deterred future customers from using the app or doing business with GoodHome. 

See 糖心原创鈥檚 Intelligent Summaries for Text Analytics in Action

Watch our to discover how this GenAI solution can translate your company鈥檚 unstructured data points into actionable 10-second summaries.

Crafting a request for proposal, also known as an RFP, can be a delicate balancing act between business needs and the desire for the latest-and-greatest innovations. Because聽text analytics聽solutions are constantly adding and updating new features, it can feel almost impossible to distinguish “marketing speak” from the features and use cases that will actually drive your business forward.

Text Analytics Features: Baseline Requirements

As you consider text analytics solutions, ensure that the solution under consideration supports these baseline requirements: Topic Categorization, Sentiment Analysis, Machine Learning (ML), Model Support for Multiple Languages, and Natural Language Processing (NLP).

If the text analytics solutions you鈥檙e assessing don鈥檛 have these capabilities, or if these are the only features being highlighted, be wary. Not only do these need to be a standard part of any text analytics solution, but we also recommend vetting each according to your use cases.

Once you have established that the solution being considered has these capabilities, turn your attention to capabilities that will take your business to the next level.

Text Analytics Capabilities You Should Add to Your Next RFP

Unsurprisingly, the key to writing an RFP for a differentiated, best-in-class text analytics solution is to both know and ask for differentiated capabilities. Some of these capabilities to add to your RFP for a best-in-class text analytics solution include:

• Omnichannel Data Integration聽
• AI-Driven Natural Language Understanding (NLU)
• Low Cost of Model Maintenance
• Personalized Reporting that Drives Engagement
• Support for Real-Time Use Cases
• Scalability Across Multi-Market and Multilingual Teams

Let鈥檚 dive deeper into each of these to uncover the reasons you need these capabilities and how they can generate value for your business.

1. Omnichannel Data Integration

RFPs focused on text analytics often do not mention omnichannel data integration or analytics. When they do, the focus is typically on the number of integrations offered. Feedback on your business comes from everywhere 鈥� from calls to video to social 鈥� but data connectors and integrations are just the beginning of an omnichannel data model.聽

Best-in-class omnichannel solutions should be able to natively ingest and organize disparate data sources from across your business so that unified analytics models can be easily applied. In order to take an聽omnichannel approach聽to experiences, solutions must aggregate all experiences into a comprehensive profile that represents every bit of feedback a customer or account has given to your business. This aggregation allows for meaningful patterns to emerge so that you can calculate the total impact of issues on customers and employees.

2. AI-Driven Natural Language Understanding (NLU)

RFPs may describe natural language processing (NLP) and categorization capabilities in some capacity, but increasingly there are requests for emotional analysis and effort scoring capabilities as well. Emotions and effort are often considered part of natural language understanding (NLU) or natural language interpretation (NLI). But, as you might imagine, there is a difference in having NLU models and deploying them with effectiveness and efficiency.聽

Common NLU models include effort scoring, emotion, empathy, and sentiment, but you should also consider more advanced models that drive actionable workflows 鈥� such as risk of customer churn or employee attrition. It鈥檚 important to validate how these models are built and maintained during your evaluation process to ensure that what you see in a demonstration will carry over into your live programs.聽

You should seek out NLU engines with a strong AI foundation in NLU models that will allow for faster model training, quicker deployment of new models, and reduce the number of rules-based topic models that need to be manually maintained. Ultimately, this enables your business to act more effectively on experiences without having to use full-time employee hours to maintain accuracy or models.

3. Low Cost of Model Maintenance

If a tool requires constant upkeep over the duration of a contract, expect to incur a significantly higher cost of ownership beyond the sticker price. In your text analytics RFP, you should ask questions specifically about how much time and manual effort will need to be spent by your team or by a vendor servicing team to maintain high levels of accuracy for all these models.

One important factor to consider is how many models 鈥撀燾ategorization, sentiment, advanced NLU 鈥� will be based on manual rules compared to AI. A healthy balance will ensure that you have the flexibility to build precise models while relying on no-code or low-code training to maintain the accuracy and quality of your text analytics. This healthy balance will ensure that you鈥檙e not reallocating significant employee time from improving experiences to maintaining the solution.聽

4. Personalization Features That Drive Engagement

Engagement and adoption are critical to the success of an analytics solution 鈥� a high volume of regular users results in more actions being taken and more opportunities being addressed across your business. Personalized reporting by user and role guarantees that the right people have access to the exact information they need without redundant details that may cause analysis paralysis.

Personalized reporting is more than the ability to build different dashboard views that can be shared with individuals across the organization. Painless, personalized text analytics and reporting lead to more engagement, encouraging other organization users to adopt the solution. To be effective, personalized reporting must account for your organization鈥檚 shifting structure and automatically adjust as people change roles within your organization.

You should validate how vendors manage complex organizational hierarchies as part of the evaluation process. Additionally, think about the different systems of work 鈥� such as desktops, mobile devices, and CRMs like Salesforce 鈥� that your employees already engage with. Personalization is a huge feature that you cannot afford to ignore.

5. Support for Real-Time Use Cases

Saving a few seconds makes all the difference for聽customer experience听补苍诲听contact center聽teams. Your text analytics solution should empower you to automatically alert the right people at the right time based on emerging trends and new opportunities, prioritize support tickets, automate agent scoring, and support workflows in real time.聽

Real-time capabilities are now necessary to surface insights and solicit action from thousands 鈥� if not millions 鈥� of feedback records. As a result, your text analytics solution should surface action-oriented insights that are prioritized automatically based on whether you can act on them.

Real-time capabilities proactively call attention to what matters most, saving your business time and enabling quick delivery of key actionable insights. The effect is not only for you to have a more efficient and effective experience management program, but also to ultimately save money.

6. Scalability Across Multi-Market and Multilingual Teams

The world is more interconnected now than it ever was before. Many businesses are multilingual and require capabilities and functionalities in their solutions to properly service their customers and employees. Multilingual services and solutions can be required by law 鈥� such as in Canada 鈥� or make or break business deals in multilingual countries, such as Japan or Switzerland.

True global capabilities go much further than NLP in multiple languages 鈥� vendors should be able to meet the assumption that not all people speak all languages. It鈥檚 important to assess a vendor鈥檚 ability to host data in multiple geos, handle translations of incoming comments, multiple character sets, AI-modeling across multiple languages, and translation of labels & text that shows up in end user reporting.

To best service everyone in your organization and all those who interact with it, you should undoubtedly prioritize a solution with global capabilities which enable everyone to equitably and efficiently achieve business goals.

Discover the Leading Text Analytics Solution with 糖心原创

Conversations are intricate, and 糖心原创 has worked to surface the meaning and feeling behind people鈥檚 written words for nearly a decade. Our聽native text analytics solution is built into the core of the 糖心原创 Experience Cloud platform.

We aim to provide businesses with the tools to surface actionable insights, generate ROI, and improve experiences in one convenient spot. Ensure that you鈥檙e getting a solution that fosters delightful experiences with your business by asking the right questions in your next RFP.

One of the most significant challenges a business faces is the large influx of customer and employee data throughout the company. 糖心原创 clients, for example, have on average more than 30 integrations representing data from customer experience (CX), employee experience (EX), digital experience (DX), and contact center channels.

From the front lines to the boardroom, everyone feels frustrated that there is so much data, but no way to make sense of it. If job functions are tied to understanding this data, employees feel frustrated they lack a method to understand the actions to prioritize and do their jobs effectively.

Automated Scoring, however, helps people mitigate these frustrations and more rapidly turn insights into action, driving business results that matter at a time when companies are doing more with less.

What is an Automated Score?

Automated Scores create a weighted average of metrics that are important to a certain function or organization. Scores don鈥檛 replace people or processes that exist; rather, they make it easy to prioritize action. Ultimately, a score should be one piece of a larger picture that connects disparate data 鈥� structured and unstructured 鈥� across the business to get a more holistic view of customer experience and employee experience.

Examples of Use Automated Scores to Use in Your Business

Because Automated Scores are so flexible, they鈥檙e able to be tailored for specific purposes. To help clients more quickly get the insights they need, 糖心原创 has created several out-of-the-box scores for different use cases. Every example below is an Automated Score implemented to the benefit of our clients.

Let鈥檚 take a look at each example of an Automated Score to understand why they鈥檙e effective

CX Health Score

The CX Health Score leverages text analytics and machine learning (ML) to provide a holistic view of CX across customer touchpoints in a unified metric. It solves the problem of having too many KPIs across multiple survey programs and a lack of KPIs on non-survey sources such as calls or social media.

CX Health leverages emotions, CX-based topics, and customer effort to create an aggregate and unified score providing more depth on overall CX health than a single survey KPI or sentiment score. The benefit of such a score is a high-level view of how your business is meeting customer needs and expectations based on their feedback.

Employees throughout the business benefit from using the CX Health Score by looking at the topics most affecting it. By watching for fluctuations in the score, executives can monitor the success of customer experience initiatives and advocate for their success, leading to the top-down adoption that every successful CX program needs.

An example: analyst teams can identify parts of the business that are most impactful to the CX Health Score and refer the proper teams to any issues. For frontline store managers, the score may be used to identify areas of the business ripe for investment that can be made to improve the in-store customer experience.

Quality Assurance (QA) Coaching Score

The legacy quality assurance (QA) process can be slow and provide limited information about an agent鈥檚 actual, overall performance. QA managers will often randomly sample a few service interactions to evaluate a certain agent, but that misses the larger context of an agent鈥檚 performance. A random sample of four calls per agent per month represents a fraction of a percentage of the total calls taken 鈥� this can make the QA process feel unfair to agents. The QA Coaching Score helps QA feel more empathetic and representative of actual performance by giving managers a quality score reflecting 100% of agent calls.

QA managers can also save hours of time by using the QA Coaching Score to identify agents who are (and aren鈥檛) meeting service quality standards, then using insights to find coaching opportunities. The QA Coaching Score doesn鈥檛 eliminate the work of a QA professional; instead, it empowers them to easily and effectively address performance concerns affecting contact center metrics.

Because agent QA is automatically scored across 100% of data, QA professionals can better understand how agents are doing outside of the context of randomly sampled QA evaluations and more effectively target agents for coaching opportunities.

CX Risk Score

When a business has millions of interaction data points, it鈥檚 difficult to pinpoint where revenue is at risk. 糖心原创 created the CX Risk Score to identify specific accounts or customers at risk of churn or spend reduction, and to alert CX professionals on who to follow up with for service recovery. Clients have seen as much as a 40% reduction in customer churn simply by closing the loop in a timely fashion after customers have been identified as a churn risk.

CX Risk is defined by identifying the number and severity of risk indicators 鈥� such as low NPS, escalations, complaints, and more 鈥� present for a given interaction, customer, or account. High CX Risk can indicate a high level of customer dissatisfaction and a high risk of churn. This score can also be used by data analysts and insights users to track trends and root causes of CX Risk across the enterprise and in specific lines of business.

The CX Risk Score enhances productivity by eliminating the time needed to look at each individual risk-related metric and instead providing a 鈥榦ne-stop shop鈥� for all information in one easy-to-interpret score.

Contact Center Experience Score

The Contact Center Experience Score provides context to contact center professionals about experiences beyond customer satisfaction (CSAT). CSAT is a metric that can feel very agent-focused as opposed to a measure of the entire experience with a brand or contact center, and it only applies to the 30%鈥�40% of interactions where a survey was completed. 糖心原创鈥檚 Contact Center Experience Score is automatically applied to 100% of interactions (calls, chats, emails, support tickets, etc) that come through your contact center.

Using text and speech analytics in addition to interaction metadata, we鈥檝e created a rubric to score 100% of interactions so that a contact center supervisor fully understands agent behavior and CX trends without relying on survey responses alone. The result is a more objective view of customer experience in a conversation that can quickly and easily alert on positive or negative changes across the contact center.

With this broader view, supervisors can divert time to more analytical tasks, like performing root cause analysis and identifying coaching opportunities based on changes in the Contact Center Experience Score. Better insights for the supervisor mean greater time and revenue savings for the business.

Start Analyzing Customer & Employee Behavior: Uncover an Objective View

Automated Scores enhance the workflows throughout a business 鈥� from agents on the front lines to the C-suite 鈥� by providing a holistic view of performance across different areas of the business. The guiding logic for creating these scores is to empower people to do their jobs well with minimal frustration by arming them with the information they need. The end result? Happier customers and employees, as well as a more successful, customer-centric business.

Schedule a demo with a 糖心原创 expert to see Automated Scoring in action and learn how it uncovers an objective view of customer and employee behaviors for your business to analyze mission-critical insights and make high-impact decisions.

Words. Text. Unstructured data. Whatever you want to call it 鈥� it鈥檚 powerful for a number of reasons. Text is the natural way in which we all communicate. This is the way in which customers and employees are providing feedback every day. Second, text is universal. While quantitative data may vary based on the context of its scale, the unstructured nature of text makes it possible to combine and compare data across sources, which is invaluable for both prioritization and spotting issues.

Building the first experience text analytics engine

糖心原创 recognized the huge potential of unstructured experience data well over a decade ago, but we didn鈥檛 want to build a solution that kept data siloed with only data analysts. Instead, we followed these six tenets to build the most accurate, actionable, and scalable text analytics offering in the industry 鈥� right into the core of our platform. 

Comprehensive: While we started with open-ended comments in surveys, we have since expanded our text analytics engine to capture anything your customer or employee may say or write across social, chats, support, tickets, survey comments, and more. For example, last year alone, we processed over 6.7 million hours of speech.

Connected: Unstructured text has become the universal language of experience. While metrics may vary, sentiment and topics help us combine and compare data sources across channels. This holistic, unified view is essential to quickly react, prioritize, and innovate.

Accessible to All:聽Democratizing data to anyone who needed it was an important requirement for us from the beginning. Unlike existing text analytics solutions on the market at the time, we built an engine that could scale for mass consumption across large organizations with unlimited role-based access. Personalized reports, intuitive workflows, and topic-based alerts empower every employee, from the C-suite to the front line, to use valuable insights from unstructured data to drive decision-making and improvements. Our adoption speaks for itself.聽In the past 30 days, over 200,000 糖心原创 users from thousands of customers have accessed 3.75 million text analytics reports.

Action-Oriented: With a focus on proactive engagement, our platform automatically surfaces key themes so you can quickly dive deep into what鈥檚 truly driving customer loyalty and employee satisfaction. We use machine learning algorithms to automatically detect new issues and trends as they arise, eliminating blind spots and highlighting unexpected problems. This is extremely helpful in contact centers where there is a high volume of comments. 糖心原创 Text Analytics also enables real-time coaching and quality assurance workflows directly into 糖心原创 and other systems of work.

Scalable: Because the world鈥檚 largest enterprises rely on 糖心原创 to run their businesses, our text analytics engine had to scale, transforming text from millions of surveys, social media reactions, reviews, emails, agent notes, call transcripts, and more into actionable insights widely accessible across an organization. We process, on average, 55 million survey comment responses every 30 days.

Easy to Maintain: We intentionally designed our Text Analytics engine to require little to no maintenance for users. To ensure fast time to value, we provide hundreds of pre-built AI models and pre-packaged topic sets focused on customer and employee experience needs such as predicting churn, legal risk, upsell opportunity, employee recognition, and more. Our AI models also incorporate elements of continuous and active learning, helping to maintain a high, consistent level of accuracy with minimal human intervention. Our goal with Athena has always been to minimize the time and effort it takes to drive iterative fine-tuning of rules and accuracy maintenance, and to give our customers time back to focus resources on building topics that matter to their business.

Text reveals a whole new world of feedback

Our investment in text analytics has opened a whole new world of growth for our customers. Data-driven decisions bolster employee efficiency and responsiveness, fuel innovation, increase revenue and profitability, and improve customer experience and loyalty (NPS庐). 

糖心原创 research found that companies using customer verbatims to create and test new ideas score 10 NPS points higher than their competitors. And those that integrate feedback from four or more channels have a +14 NPS compared to those using only one channel.

Here are a few ways our customers are driving growth by analyzing and acting on customer and employee feedback. 

Rent-A-Center increases customers satisfaction by gathering open-ended feedback across the customer journey.
Since launching a Voice of the Customer program with 糖心原创, Rent-A-Center increased their NPS by 54%. Rent-A-Center uses customer feedback to develop insights, prioritize improvement actions, and ensure both customers and employees have consistently outstanding experiences. By listening to customer and employee feedback and leveraging text analytics to surface root causes of issues, Rent-A-Center is creating smoother customer experiences and making it easier for their customers to do business with them. They have seen a 19% customer growth increase on a per store average, and top-performing NPS stores outperform low performers by 28% in year-over-year sales growth.

Fidelity International drives revenue growth by taking focused action from millions of customer comments.
Top global investment management company, Fidelity International, worked with 糖心原创 to launch a globally consistent approach to measuring and improving customer experience. 糖心原创鈥檚 text analytics solution was a pivotal part of the initiative, allowing a more nuanced understanding of what customers are saying through the open comments they leave in survey feedback. Now Fidelity can more holistically understand customer journeys and use that insight and analysis to connect directly with customers to better meet their needs and focus their continuous improvement efforts.

U.S. Department of Veterans Affairs uses customer feedback to support veteran鈥檚 mental health.
Starting in fall 2017, the VA began digitally collecting customer feedback via 糖心原创 from Veterans receiving VA services. In the first two years of the program, Veterans responded with more than 4.2 million surveys, including more than 1.6 million free-text comments. This feedback is accessible to VA employees across the country for action, prompting customer service efforts and influencing VA decision making. The system is heavily engaged, and 糖心原创 Text Analytics alerts led to聽early intervention for more than 1,400 Veterans聽in need to provide them assistance within minutes of the alert.聽

Looking at our next phase of analytics innovation

For more than a decade, 糖心原创鈥檚 Text Analytics has turned every word from customers & employees into actionable insights that drive business results. With the introduction of Athena Studio, 糖心原创 continues its leadership by empowering companies of all sizes to build AI custom models for text analytics, without any coding. This puts AI insights in the hands of data scientists and business users alike, to serve both of their needs, while pushing out data at scale.

 offers investment solutions and services and retirement expertise to more than 2.5 million clients in 28 countries and offers world-class investment solutions and services and retirement expertise. Driven by the needs of clients, Fidelity recognised the value of improving the client experience through a new voice of the client programme. Powered by 糖心原创, this programme would enable a culture of customer obsession from executives to the front line, deliver service excellence and clearly demonstrate the impact of client experience on strategic outcomes.

With client obsession now a core component of the organization, Fidelity has seen tangible positive commercial outcomes, ultimately improving retention rates of client assets and driving up net new sales.

I recently sat down with Stella Creasey, Global Voice of Client Director at Fidelity, to learn more about Fidelity鈥檚 award-winning voice of the client programme and how it has enabled them to position customers at the core of all strategies and activities: 

How did customer centricity play into Fidelity鈥檚 decision to develop a new voice of the client programme?

Investment management has a history of being internally focused and risk averse. We identified that a keen focus on the customer would give us a point of differentiation in a competitive landscape. But in order to shift to a truly customer obsessed culture, we knew we needed to develop new ways to make real-time client feedback available for everyone within the business to act upon.

What was the situation prior to launching the new programme?

Fidelity has over 2.5 million clients in 28 countries, and we were running 30 separate voice of the client programmes across the business. Customer feedback largely remained within these different programmes, making it difficult to compare client experience across regions and channels. We recognised there was an opportunity for greater integration, which would enable us to prioritise actions and investments 鈥� ultimately improving client experience.

What did you do to overcome these challenges?

We formed a dedicated global voice of the client team to work with 糖心原创 on a new globally consistent approach. The new programme incorporates both relationship and touchpoint NPS, using surveys and touchpoint monitors. We trained over 1,000 Fidelity employees, which means every colleague can access, understand and act on client feedback as part of their everyday work.

Can you give an example of how Fidelity promoted culture change in the business?

Sure. To highlight senior leadership鈥檚 commitment to customer obsession, our Executive Level Close Loop programme invites senior leaders to make outbound calls to 鈥榗lose the loop鈥� with customers and address issues that have been raised in surveys. The initiative underlines the fact that 鈥� from the top down 鈥� we鈥檙e actively creating opportunities to put the customer at the very heart of all that we do.

Thanks to global consistency, you鈥檝e gained broader insights across the business. Are there ways that the new programme also gives you deeper insights, too?

The analytics team overlays behavioural metadata onto NPS feedback, so we鈥檙e able to better diagnose where journey improvements should be targeted. This adds up to richer customer insights than we had before. We also introduced 糖心原创鈥檚 text analytics as part of the initiative, so we鈥檝e gained a more nuanced understanding of what customers are saying through the open comments they leave in survey feedback, which then informs our future planning around customer experience improvements where it matters most in the customer journey. 

How do you make sure the insights you鈥檙e developing result in action?

The new programme is closely integrated with the work of our analytics team so we can link customer feedback to business metrics and understand the impact of client experience on strategic outcomes like net sales, numbers of customers selling their investments to move away from Fidelity, share of wallet and profitability. As well as helping to inform continuous improvement activities within the business, connecting insights about our customers with financial data in this way gives us powerful predictive modelling capabilities. For example, we use analysis to foresee whether clients are at risk of churn. Our client-facing staff can then steer conversations with at-risk customers to better meet their needs and improve retention. 

Read the case study and learn more about how Fidelity International uses voice of the client to increase revenue and customer satisfaction.