We鈥檝e been taking to the blog to highlight the key use cases of our most recent updates. Today we鈥檙e exploring Total Experience Profiles (TX Profiles), which unifies customer feedback with operational data to provide a single, 360-degree view of a customer鈥檚 complete history with a brand. 

We鈥檙e going to share the feature in action as our fictional home improvement retailer, GoodHome, uses Total Experience Profiles to see what鈥檚 happening for a VIP customer whose overall satisfaction score (OSAT) has dropped from a 7.6 to a 3. 

The Character and the Conflict

Meet Gabby, the contact center manager at GoodHome. Gabby鈥檚 team has their plates full, each handling dozens of customer service calls, emails, live chats, social media interactions, and texts on a daily basis. It鈥檚 up to her to make sure that issues that need extra attention, and high-value customers at risk of churning, don鈥檛 slip through the cracks.

In the past, keeping up with everything was an almost impossible task. That鈥檚 because GoodHome lacked a central platform where everyone in the contact center could easily see everything they needed to know about their customers鈥攁nd what was happening for them across their interactions with the brand鈥攊n one single place. 

Social interactions lived in one tool. Survey data lived in another. Call transcripts were stored somewhere else altogether. These kinds of silos created hassles and headaches for everyone, and led to costly customer turnover. You know the struggle.

Finally, when GoodHome started using 糖心原创鈥檚 Total Experience Profiles, things got a lot less complicated for Gabby and her colleagues. 

The Moment of Truth

Total Experience Profiles create single, unified customer profiles, bringing together customer data from across sources (operations, transactions, surveys, CRM, and app, website, social media, and customer service interactions). Employees can use TX Profiles to zoom in on a single customer journey and see everything they鈥檝e experienced over their entire history with the brand.

Recently, Gabby used TX Profiles to check in on what was going on for a VIP customer, Jamie, when Gabby received an automated alert from 糖心原创 letting her know that this loyalty program member needed extra attention. 

Jamie鈥檚 customer satisfaction score had just dropped to a troubling 3 out of 10. What had happened?

Gabby was able to get answers quickly using Total Experience Profiles.

The Solution: Total Experience Profiles

In just one quick scroll of Jamie鈥檚 TX Profile, Gabby was able to determine the reason why her satisfaction had declined so dramatically: GoodHome鈥檚 app was acting up. 

Jamie couldn鈥檛 check on the status of her order, leaving her no choice but to call the contact center for support. But that鈥檚 the exact opposite of what she was hoping to happen when using the app鈥攕he wanted to save time and shop all on her own. 

Not only could Gabby find out the reason Jamie鈥檚 score took a nosedive, she could dig deeper into Jamie鈥檚 customer profile and see over the last year that Jamie had been an active (and satisfied) customer, completing 194 transactions and averaging an OSAT of 7.6. 

Gabby could also easily find a transcript of Jamie鈥檚 call into the contact center in her TX profile that offered even more details regarding her issues when using the app.

The Resolution and Business Impact

Thanks to Total Experience Profiles, Jamie鈥檚 issue didn鈥檛 slip through the cracks. Gabby was able to follow up, apologize for the experience, and earn this VIP customer鈥檚 loyalty back. Not only that, she was able to see that Jamie鈥檚 experience wasn鈥檛 due to user error and was, in fact, the result of an underlying issue that needed to be escalated to the app team鈥攕omething Gabby did right away, connecting the dots between the in-store, contact center, and digital experience. 

All of these seemingly small interventions can add up to a world of difference for brands like GoodHome and their customers. 

These days, Gabby and her coworkers spend a lot less time trying to find information, their customers spend a lot less time repeating themselves, and the team is able to serve customers faster and better. That鈥檚 something that鈥檚 paying off. 

After all, a 糖心原创 Market Research study, The State of Brand Loyalty, finds that 80% of consumers say they feel more loyal to brands when they deliver a better experience than their competitors do. Two customer experience factors that drive up loyalty, according to the same study? When companies deliver a consistently seamless experience and go above and beyond to help their customers. And that鈥檚 precisely what Total Experience Profiles are designed to help contact center agents and managers make happen. 

See Total Experience Profiles in action

Watch our to discover how this feature can give your team the full picture of the customer experience at the individual level and power frontline actions that move the needle for each and every customer based on their unique histories with your brand.

For years, 糖心原创鈥檚 mobile app, 糖心原创 Mobile, has empowered frontline managers to move on time-sensitive customer experience (CX) opportunities to make money and reduce risk. In fact, users log in to our app hundreds of thousands of times on a weekly basis, viewing up to nearly two million different tabs filled with alerts, insights, and actions that power frontline operations. 

That鈥檚 why we鈥檙e excited about two new features we debuted in late 2025 specifically for 糖心原创 Mobile.

The first is , which instantly alerts frontline leaders and teams right as their important metrics are changing, so they can jump in, fix issues fast, and keep experiences on track. 

The second is , which uses AI to pinpoint the underlying factors driving these shifts in CX metrics, enabling frontline managers and regional leaders to determine what鈥檚 working and what needs attention鈥攐n the go. 

Here on the blog we鈥檝e been shining a light on our Frontline-Ready AI features, and today we鈥檙e going to give you a look at what these two 糖心原创 Mobile upgrades can do for you. See them in action as our fictional home improvement retailer, GoodHome, uses both features to detect an urgent dip in the in-store pickup experience and uncover and rectify the root cause faster than ever before.

The Character and the Conflict

Meet Nathan, a manager at one of the busiest locations of GoodHome. 

In the past, Nathan and his team of frontline employees didn鈥檛 have a reliable way to get a pulse on what was happening for customers in the store, in the moment. Sure, team members could gauge the sentiment of 1:1 interactions as they were happening, but they lacked a bigger-picture view of how customers collectively felt and the issues they were facing in the aggregate. Yes, the team received reports summarizing the feedback customers shared via surveys. Eventually. After the fact. When it was too late to address the challenges customers were facing.

In the absence of actionable, timely data, employees made adjustments based on gut intuition, dated customer feedback from days or weeks earlier, and the way things had simply always been done.

Nathan knew there had to be a better way. Fortunately, that better way became a reality when GoodHome rolled out 糖心原创 Mobile to the frontline.

The Moment of Truth

Now that Nathan and his team have access to 糖心原创 Mobile, they can keep tabs on their in-store customer experience KPIs and get notified the moment important metrics change, thanks to Mobile Scorecard Notifications. When fluctuations in metrics like customer satisfaction and NPS庐 occur, they can quickly figure out what鈥檚 causing these scores to go up or down with Root Cause Assist on Mobile. 

That鈥檚 exactly what happened recently when Nathan received an automated alert letting him know that the rating for his location鈥檚 in-store pick-up experience score had fallen to a troubling -3.08. 

What was going on with in-store pickup?

The Solution: Mobile Scorecard Notifications and Root Cause Assist on Mobile

In the past, the team might have only become aware鈥攁nd been able to get to the bottom鈥攐f this type of issue after calls to the store spiked or tons of customers began lining up at customer service to ask for help with the same problem.

This time, Mobile Scorecard Notifications paired with Root Cause Assist on Mobile meant Nathan was notified and able to  intervene as the issue was unfolding. 

Here鈥檚 how. 

First, Nathan used Mobile Scorecard Notifications to set the specific metrics he wanted to be notified about and how he wanted to receive updates (daily, or in real time as scores dropped by a certain percentage or number of points).

Then, as soon as there was a change in one of those metrics, he received a notification like the one mentioned earlier. 

By tapping on the alert, Nathan was taken to 糖心原创 Mobile, where he was able to drill into additional details about the declining KPI and see a GenAI-powered summary of the underlying cause powered by Root Cause Assist on Mobile. Root Cause Assist on Mobile analyzes all of a brand鈥檚 data from survey responses, call and chat transcripts, social media interactions, employee notes, and more, to reveal the 鈥渨hy鈥� behind score changes.

In this instance, customers were having a hard time trying to view and modify their pickup orders within GoodHome鈥檚 app. 

Nathan was also able to see the impact of this issue on the store鈥檚 other KPIs: The lower in-store pickup rating was linked to a drop in the store鈥檚 overall satisfaction rating, which fell by 3.2 points from a strong 8.6 rating to a worrisome 5.4 out of 10. 

This is an example of the insights GoodHome is able to uncover using Mobile Scorecard Notifications and Root Cause Assist on Mobile.

With Root Cause Assist on Mobile, Nathan discovered additional details about the error with the app and how it was affecting overall satisfaction by various customer segments, including by rewards program member status and customer age. 

Root Cause Assist on Mobile uses all recent survey responses, call and chat transcripts, social media interactions, employee notes, and more to reveal the causes of the company鈥檚 CX score changes. 

The Resolution and Business Impact

Within seconds, Nathan could easily see there was an issue with GoodHome鈥檚 app and escalate the problem to the company鈥檚 app team, who were able to implement a fix. At the same time, the rest of the frontline was aware of the bug and was better able to assist customers affected by the app error. 

Frontline teams from the world鈥檚 leading brands, including Hyatt, Panera, and Dick鈥檚 Sporting Goods, use 糖心原创鈥檚 Mobile Scorecards every day to stay on top of customer sentiment and rising issues with live data at their fingertips. With the addition of Mobile Scorecard Notifications and Root Cause Assist on Mobile, now employees can take action to address issues as they arise faster than ever, helping their organizations make more money, reduce the risk of churn, and drive loyalty.

See these features in action

Watch our demos of and to get a look at how these capabilities put the power of AI into the hands of the frontline, enabling them to drive meaningful business outcomes鈥攏o complicated prompt engineering skills required.

As part of our series highlighting our Frontline-Ready AI features, we鈥檒l explore the key use cases and benefits of . 

Building on 糖心原创鈥檚 Digital Experience Analytics (DXA), DXA for mobile analyzes every behavior and technical experience signal into intelligence to create and deliver better experiences.

Ahead, we鈥檒l show you the solution in action. You鈥檒l see how a fictional home improvement retailer, GoodHome, uses DXA for mobile to monitor their digital experience score, identify an error that鈥檚 causing user frustration to spike, and resolve the issue鈥攁ll within minutes. 

Let鈥檚 dive in. 

The Character and the Conflict

Meet Taylor, the Digital CX Manager at GoodHome.

Her retail company鈥檚 apps and websites have hundreds of millions of users annually, and she knows that every swipe, scroll, form fill, hover, and tap across the digital experience tells a story. Collectively, these digital behaviors reveal what鈥檚 fueling sales, repeat visits, and longer-term loyalty. 

The GoodHome team has been using 糖心原创 DXA for their websites, and recently enabled DXA for their mobile app for a complete view of the digital customer journey. 

Digital Experience Analytics for mobile apps offers a balanced user experience score, highlights top friction points for prioritization and associated action recommendations, visualizes and summarizes the customer journey by page or across every page to enable action, and triggers alerts and workflows to ensure that action happens.

The Moment of Truth

Things have been going well for GoodHome鈥檚 digital channels. Really well. There鈥檚 been a 30% increase in purchases made through their app this year. 

But Taylor knows that even the smallest bump in the digital experience has the potential to derail customers from successfully completing their journeys.

That鈥檚 why now that she has access to DXA for mobile apps, she鈥檚 constantly keeping tabs on her brand鈥檚 Digital Experience Score (DXS), an AI rules-based score that evaluates activity and events online across five key pillars鈥攅ngagement, technical performance, form effectiveness, frustration, and navigation鈥攖o produce a 0-10 perspective on the health of the experience.

Turns out, GoodHome鈥檚 overall DXS has recently dropped by a staggering 3.7 points, from a 7.8 out of 10 to a failing 4.1 out of 10. The main driver? The brand鈥檚 frustration score is getting worse. Much worse. Customer frustration with the app is at a high of 8.4 out of 10, and unlike every other pillar, you do not want a high score here!

The Solution: Digital Experience Analytics for Mobile Apps in Action

In the past, the GoodHome team might have remained in the dark about what was happening for mobile app customer experiences for hours or days until survey responses, negative reviews, and contact center emails, calls, and live chat sessions spiked. 

Now, anyone with permission at the retailer can see what鈥檚 happening by drilling into the details available for each DXS pillar to identify root cause experience issues and pages where improvement is needed most. 

They can also size the affected customer journeys and view an experience. And CX teams can drive the change without having to wait for busy data scientists and technologists to engage in lengthy data investigation. 

When Taylor views the details for the frustration score, she can see that the top driver of frustration is unresponsive touches, with 39K instances occurring recently. From her 糖心原创 self-service training鈥攁nd personal experience鈥攕he knows nothing is more frustrating than when something is clicked or interacted with and doesn鈥檛 do anything.

Taylor also sees that the page in the mobile application causing the most frustration is the 鈥楳y Order鈥� page available within the 鈥楢ccount鈥� section of the app.

Diving deeper to understand why this is happening, Taylor knows she has an easy path to see any and all experiences where this frustration was shown, but she isolates it to just those on the page in question. 

Each session comes with everything she could ever want to know about the device, the customer, and their journey across the mobile app. Taylor even has the ability to replay the session and walk in the digital shoes of the customer based on the depth of data captured by 糖心原创 Digital Experience Analytics. 

But actually, she sees everything she needs in the AI-generated summary about this customer鈥檚 experience.

In this case, she discovers that unresponsive touches are happening on the 鈥淢y Orders鈥� screen of the GoodHome app, where those unresponsive touches are actually due to a native app error鈥攁 technical issue鈥攁s users like this one are trying to check on their pickup orders. DXA reporting reveals that once a customer鈥檚 order status changes to 鈥減rocessing,鈥� a bug is occurring that is preventing users from reviewing, changing, canceling, or checking on the status of their orders.

And even though the overall technical pillar score for the mobile app was great鈥攕uggesting product management and development teams might not have cause for concern鈥擳aylor knows better based on just how much frustration is happening here. She has all the evidence to partner with her colleagues to return the GoodHome app back to a level of technical and experience excellence their customers expect.

The Resolution and Business Impact

If left unresolved, this type of app error can have ripple effects across the business. Impacted customers will likely end up reaching out to customer support for help or showing up in person to check on the status of their orders, an added operational burden鈥攁nd cost for the retailer. 

Increased expenses aren鈥檛 the only concern. Broken experiences can erode customer trust and loyalty leading to less repeat activity or even no future engagement at all. In fact, a 糖心原创 Market Research study, Latest Trends on the Digital Customer Experience, finds that 51% of customers say that in the past, they have stopped being a customer of a company because of encountering too many difficulties using their website or mobile app. So GoodHome doesn鈥檛 just stand to lose app users but actually potential forever customers without action.

But thanks to using DXA for mobile apps to monitor what鈥檚 happening with GoodHome鈥檚 app, Taylor is able to share her findings with the app developers, who are then able to push out a fix, and create a score-based alert program to provide proactive behavioral experience insights for future improvement and innovation opportunities. All within minutes.

See 糖心原创鈥檚 Digital Experience Analytics for Mobile Apps in Action

Watch our demo to discover how this new solution enables you to turn taps, swipes, focused attention, and errors into meaningful customer insights to deliver experiences that reduce friction, increase engagement, and generate sustainable revenue.

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.

Most companies are sitting on a goldmine of customer feedback. The problem? By the time that feedback becomes actionable insight, the moment has passed.

Data lives in disconnected systems. Analysis requires specialized skills. Root cause identification takes weeks. Meanwhile, customers are already frustrated, and revenue is walking out the door.

That鈥檚 where Frontline-Ready AI changes the game. gives teams AI tools that don鈥檛 just analyze鈥攖hey understand, explain, and act. Insights surface in minutes, not weeks. Root causes are automatically identified. And actionable recommendations reach the right people, in the moments that matter most. No data science degree required.

The result is a shorter path from feedback to action鈥攁nd fewer customers slipping through the cracks.

Let鈥檚 dive in.

Intelligent CX Everywhere: Closing the Gap from Insight to Action

While other companies just collect feedback, 糖心原创 helps you close the gap. This means empowering teams across the entire enterprise鈥攆rom the C-suite to the newest associate鈥攖o solve problems at the source, not just in the moment.

Here鈥檚 a look at the features that make this a reality:

1. Instant clarity for everyone: Intelligent Summaries for Text Analytics

If you鈥檙e a CX leader or Insights Analyst, you know the pain: mountains of unstructured data, from reviews and comments to transcripts, that require hours of manual sifting. This often leads to delayed visibility into critical issues.

Not anymore.

We鈥檙e expanding to cover all of your unstructured data, integrating it directly into our Text Analytics engine. This is generative AI at its best鈥攚orking for you.

• How it works: In a single click, 糖心原创 generates an executive-level summary of comments, giving you an immediate, holistic understanding of the specific customer experiences tagged to a topic or theme.
  
• The value: It reduces time spent sifting through comments and lets you know right away if a topic is worth a deep dive. What used to take hours now takes seconds. 

Suddenly, you鈥檙e not combing the beach for hours, hoping to find something valuable. Instead, you鈥檙e instantly pulled toward what matters most.

This is how we democratize data analysis, letting analysts focus on strategy while empowering everyone to prioritize action.

2. Stop the digital domino effect: Digital Experience Analytics (DXA) for Mobile Apps

Mobile is where your customer lives now. If you can鈥檛 connect what happens in-app to the broader customer journey, you鈥檙e flying blind.

, we showcased a powerful example with our fictional retailer, GoodHome. An increasing Frustration Score led a digital manager to see a huge spike in Unresponsive Touches on the mobile app. Within moments, using , they saw a session replay revealing the root cause: an app bug preventing customers from checking their 鈥業n-Store Pickup’ order status.

This bug, if left unaddressed, would compound into calls to the contact center, frustrated in-store customers, and ultimately, lost revenue.

DXA for Mobile Apps gives you a complete, connected view of user behavior, friction points, and performance gaps so you can resolve issues faster and deliver seamless mobile experiences that protect and grow your revenue.

3. Resolving issues with context and confidence: Total Experience (TX) Profiles

Behind every score and summary is a real customer with a story. Frontline managers need to understand that story, but traditional CRM views only tell part of it.

eliminate guesswork by unifying every customer signal鈥攆rom purchase history and survey responses to call transcripts and app behavior鈥攊nto a single, chronological timeline.

, a Contact Center manager was able to quickly review a long-time customer鈥檚 TX Profile. They immediately saw the customer鈥檚 strong loyalty and confirmed that her recent issue with the app was not user error but rather a systemic problem. This context allowed the manager to recover the relationship with a personalized, confident, and proactive follow-up, ensuring they closed the loop with confidence.

4. Frontline action, on-the-go: Mobile Scorecards Upgrade

If the insight isn’t in the hands of the person who can act, it鈥檚 just data. We鈥檙e thrilled that many of our users return to 糖心原创 Mobile regularly. Now, we鈥檙e making that access even more timely and targeted.

• : All employees, from frontline managers to the C-suite, can now set personalized push notifications based on what they care about most鈥攍ike a 5-point dip in their store’s satisfaction score. This enables users to respond to issues faster without having to click into dashboards.
• : No more switching tabs! In a single tap, Root Cause Assist digs into your scorecard metric to reveal the underlying drivers from across all your data sources (surveys, calls, chats, etc.). This ensures that even when on the go, the frontline team can move from signal to source in seconds.

5. GenAI for the global enterprise: Spanish Language Expansion

Finally, we are dramatically expanding the accessibility of our Frontline-Ready AI. We are proud to announce our language expansion to Spanish of our most impactful GenAI features: Intelligent Summaries, Smart Response, and Root Cause Assist.

This is a massive step forward, enabling our Spanish-speaking teams to:

• Get quick summaries of calls, chats, topics, or themes.
• Uncover what鈥檚 contributing to a metric change.
• Close the loop in seconds with empathetic, personalized, and on-brand responses.

Our focus on responsible, secure, scalable AI extends with this language expansion. We鈥檙e not just relying on the built-in capabilities of large language models: we鈥檙e applying a layer of strict validation to deliver reliable, supported accuracy.

Faster Insights, Faster Action with 糖心原创

Every moment we shared during the Fall 2025 release points to one core idea: giving your teams the clarity and confidence to act. This suite of actionable tools, working together in 糖心原创, truly forms the fastest path to fixing your biggest business challenges.

Welcome to the monumental shift from guessing to knowing.

Ready for a deeper dive? Check out , which brings to life how our Frontline-Ready AI features enable every team member to turn insight into action鈥攁nytime, anywhere.

Generative AI is transforming CX, but not every application is advancing the industry. Many tools can create more noise than clarity, and introduce risk rather than results. Sustainable impact comes not from chasing hype, but from enabling smarter decisions and faster action at the front line.

Today鈥檚 popular GenAI tools like ChatGPT, Microsoft Copilot, and Gemini are incredibly powerful, but they are not built for broad enterprise CX deployment. They require prompt engineering expertise, they assume technical fluency, and when used incorrectly, they can generate . For frontline users鈥攖hose in retail, service, operations, and customer care roles鈥攖his complexity creates a barrier to adoption and trust.

Frontline teams should not have to become prompt engineers on top of their day jobs.

That is exactly why we focused on building a suite of solutions that are predictable, reliable, scalable, and secure鈥攄esigned specifically for non-technical, non-analyst users. These solutions deliver intuitive, context-aware intelligence that is embedded directly into existing CX workflows to drive fast, smart decisions at scale, without the need to be an AI expert.

We call it .

Frontline-Ready AI:
Built for speed, scale, and security

The incredible power of GenAI for customer experience is exponentially multiplied when the technology helps frontline employees鈥攍ike store managers, front desk managers, and bank branch staff鈥� actually work better, smarter, and faster.聽

We鈥檝e built a suite of tools that are as brilliant as they are practical. analyze a full conversation in less than 60 seconds, a staggering 400% time savings for managers. makes discovering emerging trends effortless, putting real-time insights at our users’ fingertips. With , we’ve automated empathy and personalization at scale for faster and more relevant customer follow-up. And bypasses hours of dashboard analysis to provide immediate, actionable answers.聽

We designed our Frontline-Ready AI solutions to confidently equip your customer-facing workforce. Our technology delivers reliable and accurate insights by focusing on four key principles: It provides predictable and reliable answers by using only your company’s data, significantly reducing the risk of hallucinations. The solutions are also highly scalable and secure, integrating seamlessly across large teams with minimal rollout time and training.

Ultimately, 糖心原创 provides an enterprise-grade platform that protects your most sensitive information while providing your teams with the insights they need.

Early success stories using Frontline-Ready AI

We’re already seeing the profound impact of these solutions in action. 

For example, a national pet supply retailer using our Themes with GenAI feature was able to pinpoint a previously hidden source of customer frustration and attrition: a specific surcharge on a subset of delivery orders. This critical insight was identified and addressed in mere hours.

Another customer, leveraging our Smart Response feature, has seen an 80% reduction in average response times. This isn’t just a small improvement; it translates to an annual savings of 3.4 years of work for their team compared to their previous templated responses. The key is accuracy: the AI-generated responses have a 92% match rate with the brand’s voice and needs. This allows employees to simply review, make minor edits, and send, streamlining the entire process.

Transform frontline CX, only with 糖心原创.

There’s a massive difference between general-purpose AI and a generative AI solution tailored for frontline employees. While general models like ChatGPT can be useful for brainstorming or content generation, they can also cause confusion and provide inconsistent information when used in customer support, sales, operations, or field service. This often leads to subpar experiences for both employees and customers.

The best generative AI solutions for frontline teams, however, are designed to fit seamlessly into employees’ existing workflows. These purpose-built tools empower your team to make smarter, faster decisions, whether they are in the field or on the floor, all without requiring them to become AI experts.

糖心原创’s AI now empowers millions of employees to act on customer and employee insights directly from our platform. By democratizing AI on an unprecedented scale, we help businesses improve individual customer interactions while also identifying and resolving friction points to drive cost savings and revenue growth.

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Ready to harness the power of market research to drive growth and retention for your business? Looking to find the best market research partner and solutions for your business?聽

Whether you鈥檙e looking to answer a single question, conduct a benchmarking study about your brand鈥檚 health, or launch an ongoing study to answer recurring organizational questions, we can help. 糖心原创鈥檚 Market Research Services help you uncover meaningful insights (quantitative, qualitative, or both) from your desired participants (customers, non-customers, or both) using your preferred format (surveys, focus groups, etc.) 鈥� and offer the level of support you need (self-serve, mid-service, or full-service).聽

Which types of market research will meet your business needs?

Before we review the types of market research services and technologies 糖心原创 offers, let鈥檚 review the most common types of market research and what you鈥檙e looking for to support your use case.聽

Qualitative vs. quantitative market research

Depending on the specific business question(s) your company is looking to answer, you may be interested in conducting qualitative market research (to uncover the attributes of something), quantitative market research (to determine the quantity or amount of something or range of outcomes), or a mix of both.聽

鈥淭ypically, the most effective market research analysis results from combining different insights,鈥� says , VP of Product, Market Research at 糖心原创. 鈥淭aking a more holistic approach helps you better understand exactly what the drivers are behind a specific issue, and how to best inform your strategy,鈥� she explains.聽

Self-serve vs. white glove market research services

Depending on the complexity of your study design, you may be looking for a market research capability that your team can get up and running quickly, all on your own or with the support of a service team that takes care of the end-to-end process for you.

鈥淪elf-service tools like 糖心原创鈥檚 Agile Research are wonderful if you want a solution that you can launch quickly and get results from right away,鈥� says , VP of Global Research and Insights at 糖心原创.

鈥淥n the other end of the spectrum, where brands want more advanced methodologies to conduct a more robust study, white-glove services will take a question that you’re trying to answer and help you build out and design an entire program,鈥� she adds. 鈥淔or example, the 糖心原创 research team will execute it for you. We’ll use different platforms to program the survey. We’ll tap into different panels to execute and get the analysis and insights and take care of all of the necessary reporting.鈥�

Ad-hoc, foundational, and ongoing market research studies

One-time questions, questions that are foundational to your business and customers, and recurring questions are the three main types of questions companies tend to explore using market research. The provider you choose should be able to support your type of inquiry.聽

Types of market research study formats

While surveys are one of the most common ways to collect market research insights, advanced market research companies offer a range of formats you can use to gather robust insights. For example, if you鈥檙e interested in gathering rich qualitative insights, you may be looking to capture a mix of written responses, video feedback, or verbal insights through focus groups or one-on-one interviews.聽

Third-party panels can also offer quantitative data from non-survey sources, including real-time consumer behavior tracked via transactions and more.聽

鈥溙切脑� goes beyond common approaches of survey-based quantitative market research by also incorporating qualitative responses via video capture and incorporating consumer behavior data from a transaction panel of millions of US debit and credit users to understand not just consumer attitudes, but unblinded trends in their spend across specific brands, and entire industries and channels,鈥� says , Sr. Director, Head of Research Insights at 糖心原创.聽

Types of market research participants 鈥� customers vs. non-customer participants

While some tools can be used to survey customers only, certain market research providers like 糖心原创 are also able to connect companies with independent panels of consumers to learn more about the industry as a whole and what鈥檚 driving non-customer behavior.

Criteria for evaluating potential market research services partners

Use these questions to evaluate your potential market research service partners.聽

Will the company you鈥檙e considering鈥�?

• Offer the methodologies you鈥檙e looking for 鈥� quantitative research, qualitative research, or a mix of both.
• Help you reach your desired audience 鈥� customers, non-customers, or both.
• Ensure your participants match your preferred demographic and behavioral criteria. For instance, can they help you reach your non-customers within key segments, if desired?
• Enable you to gather a rich set of data via your preferred touchpoints 鈥� surveys, focus groups, etc.
• Tailor the level of support offered based on your needs 鈥� self-service solutions as well as white-glove services.
• Help you launch your study on your timeline 鈥� i.e. within days, not weeks or months.

Types of market research capabilities and solutions 糖心原创 offers

糖心原创鈥檚 market research team combines in-house expertise from industry-leading professionals who bring years of experience using advanced market research techniques, along with 糖心原创鈥檚 proprietary suite of advanced market research solutions that help support the world鈥檚 leading brands based in over 150 countries.聽

Quantitative market research solutions

• Capture feedback on your most pressing questions: An extension of 糖心原创鈥檚 customer experience platform, Ask Now lets brands quickly self-serve adding temporary questions to existing surveys聽 in just minutes and collect responses in real time on your businesses most pressing questions.聽

• DIY your market research efforts: Agile Research is an easy-to-use, self-service tool that offers advanced question types customizable reporting and more! Our DIY research solution聽 provides sophisticated features made for complex market research methodologies,advanced statistical analysis and access to millions of potential respondents via a third-party panel. With Agile Research, you can build your survey from scratch or leverage pre-built survey templates designed by research experts.聽

Qualitative market research solutions

While Agile Research and Ask Now both support the collection of qualitative insights, 糖心原创 also offers two specialized qualitative market research technologies that can be used for conducting in-depth interviews, focus groups, and more.

• 糖心原创 Video: This video feedback solution enables brands to gather opinions, motivations, and feelings from customers and non-customers with AI-powered analysis capabilities that instantly interpret expressions and emotions and organize responses by key themes and topics.

• Deeply understand employee needs: 糖心原创鈥檚 crowdsourcing capability lets you get group feedback, opinions, and more from employees and customers in a social environment.聽

A range of market research services, from self-service to full-service

糖心原创鈥檚 research services team has designed and executed global research efforts for some of the world鈥檚 largest brands. These projects have included both qualitative and quantitative

research methodologies across North and South America, Europe, Australia, and Asia, leveraging 糖心原创鈥檚 suite of best-in-class research technologies. We offer research strategy, design, and analysis services and work collaboratively as an extension of your team. Our research experts partner with you to adopt a strategy that suits your needs and accomplishes your goals.

鈥淲e provide a really flexible approach,鈥� explains Wilcox. 鈥淲e can be full-service, we can be mid-service, and we can be enablers of self-service.鈥�

Go from Insights to Action 鈥� Fast 鈥� with 糖心原创 Market Research

Our suite of advanced market research technologies and industry-leading, in-house experts are here to help your company stay ahead of changing dynamics and harness richer insights at scale so you can take action with confidence to improve experiences.

Artificial intelligence (AI) promises to revolutionize the world of work. Yet, many people are wary about its rising popularity. Concerns about job displacement, privacy breaches, and ethical risks are omnipresent in discussions of AI. But fears about rising automation aren鈥檛 new 鈥� whether you鈥檙e an 1800s textile designer looking at an automated loom or a modern-day backend developer looking at ChatGPT.聽

Because we understand the value and risks of AI, we at 糖心原创 have always strived towards creating responsible AI that keeps humans in the loop and improves their daily flow of work.

So, what is responsible AI? Responsible AI is an approach to artificial intelligence that ensures AI-powered features are developed, tested, and maintained with a focus on mitigating bias, discrimination, and privacy violations, while promoting accountability and transparency.

And how should you implement responsible AI? Let鈥檚 start with some principles for creating responsible AI that we believe every organization should follow.

The Main Principles of Responsible AI

Transparency

AI systems should be easily understood 鈥� meaning that all users can comprehend its processes and outcomes. This includes being extra clear about how data is collected, decisions are made, and models are trained.

Accountability

Organizations 鈥� and the teams responsible for AI development 鈥� must be accountable for the system鈥檚 behavior and outcomes. If AI has the potential to cause harm or other problems, mechanisms should be in place to address the situation so that somebody is always accountable.

Privacy & Security

Privacy and security are essential pillars of responsible AI. Sensitive information should be safeguarded, ensuring AI systems and the companies overseeing them are resilient to breaches and attacks through robust, consistent vulnerability testing.

Ethical Data Usage

To ensure that data is being used and collected ethically, and that AI isn鈥檛 trained with bias and discrimination, organizations must implement diverse data collection, develop fair machine learning models, reprocess data to remove bias, monitor and test AI models with human review and oversight, and continuously monitor and audit their AI systems. Businesses should create a set of ethical and legal guidelines that models and data usage are measured against.聽

The Importance of AI Governance for Responsible AI

Companies need to oversee a robust AI moderation and security process to solidify truly responsible AI. This must apply to all current and future technology, prohibit law violations, and adhere to other strict rules and regulations.

Your organization should define teams and roles that handle your own AI governance processes to ensure you鈥檙e using and building responsible AI, especially as AI comes to the forefront of conversations about privacy and security. Your vendors should, too!

As an example, 糖心原创鈥檚 intake process follows the below guidelines:

1. Documentation: The new AI use case is described, including the features, purpose, and training.
2. Risk Management: Any foreseeable risks are flagged, including potential misuse of the AI system.
3. Compliance: Ethical and legal use of the data and compliance with applicable obligations are ensured.
4. Safety, Moderation, Ethics: Global experts help create robust safeguards, focusing on human-centered AI to mitigate bias further.

AI Governance: How 糖心原创 Addresses Responsible AI

糖心原创鈥檚 External AI Advisory Board

Our advisory board is a diverse collective that demonstrates thought leadership in AI policies, regulations, and technologies to promote safe, secure, and fair AI solutions.聽

The AI Advisory Board is made up of ten customer and partner representatives with backgrounds in every use case we service. Members are well-seasoned experts who provide input on AI privacy, ethics, and security for 糖心原创 and the broader experience management community. The board focuses on diverse representation across markets, with an emphasis on enterprise and small-to-medium enterprises worldwide.聽

鈥�It is important for us that the AI Advisory Board includes people from diverse backgrounds,鈥� said Rahul Kamdar, SVP of Product Management at 糖心原创. 鈥淏y bringing in different viewpoints and unique contributions, we hope to understand and solve complex problems, avoid biases, and address ethical issues to build AI solutions that truly benefit all.鈥�

糖心原创鈥檚 Internal AI Moderation Council

The internal AI Moderation Council is a team of diverse professionals across the wide spectrum of AI expertise, from legal to data acquisition. The team handles internal 糖心原创 queries about our governance processes. They keep up to date on new and pressing issues in AI regulation, ethics, and anything else pertinent to building AI that is simultaneously human-centric, responsible, and able to generate value for our clients.聽

At 糖心原创, the Moderation Council is a key part of our AI feature development process, validating models against our responsible AI principles and relevant legal requirements. Together, our experts are able to have a tangible impact on the growth of our AI in a changing world. The AI Moderation Council has a tough job, but it鈥檚 one that every organization should seriously consider creating their own internal team to tackle.聽

Ensuring Responsible AI in the Modern Age

Responsible AI encompasses principles such as transparency, accountability, privacy, and security, which are vital for ensuring ethical AI development.聽

These principles matter. 糖心原创鈥檚 approach to responsible AI includes a robust governance framework that focuses on documentation, risk management, and compliance, as well as a diverse AI Advisory Board that brings varied perspectives to address biases and ethical issues. By adhering to these principles, 糖心原创 ensures all of its AI solutions are safe, secure, and equitable.

糖心原创 has deeply integrated AI into the core of our platform for over 15 years, starting with the 2008 release of our industry-leading text analytics. Our AI is a key differentiator that enables us to scale and democratize high-quality CX and EX insights across the world鈥檚 largest enterprises.

To learn more about 糖心原创鈥檚 approach to AI, check out our AI leadership page.

You hear it everywhere: there鈥檚 more and more data out there for businesses to use, but ? The common response 鈥� and we think it鈥檚 the right one 鈥� is that analytics can help you filter out the noise and get right to the actionable insights needed for any experience program, from employee and customer to digital, contact center, and market research. But this talk track often neglects the absolutely mission-critical middle part between analytics and action: reporting. 

Let鈥檚 dive into the ways that personalized, easy, and collaborative analytics reporting can help organizations seamlessly improve experiences for everyone.

1. Reports should start with personalization

Personalized reporting ensures that your teams can actually understand and make use of the analytics that drive ROI by improving experiences, increasing satisfaction, and uncovering money and time-saving opportunities.

Each user and role needs accessible, visually appealing, curated reports. They should also be customized for mobile and desktop applications 鈥� on any device. For example, executives will likely not have time to look at a detailed desktop report, instead preferring a curated mobile report of their most vital KPIs. Frontline employees in retail might also prefer to use mobile devices, but for viewing in-the-moment customer feedback on the store floor.

糖心原创鈥檚 personalized reports show the most relevant KPIs, such as NPS, first contact resolution, cost of repeat contacts, OSAT, impact score, and sentiment, in an easily digestible way that inspires action. Further, text and speech analytics provide deeper context to these KPIs by helping all users understand what drives them up or down. 

The partial employee performance report below is an example of how 糖心原创 reports on both structured and unstructured data for a full view of employees.

Of course, reporting should extend beyond individual user needs to reflect the overall needs of the business. Per , one of the hardest questions companies need to ask themselves about their customer feedback platforms is, 鈥淲hat listening activities do we need to start, stop, or continue?鈥� Excellent reporting throughout a platform should be able to give you insight into what is and is not driving value or producing actionable insights, including the sources for most of your data and KPIs associated with each signal. 

糖心原创鈥檚 Signal Health meets this need, giving experience teams insight into how KPIs differ across signals and which topics are driving those KPIs up or down, as seen below.

2. Reports should make it easy for everyone to take action

Think back to the most complicated software you鈥檝e recently had to use. How long did it take for you to really be able to make use of all its features? It鈥檚 entirely possible you still haven鈥檛 because it鈥檚 too complicated. In the words of Nobel Prize winning behavioral economist , 鈥淚f you want people to do something, make it easy.鈥� 

If it is easy to uncover where teams need to take action and to communicate any problems, they can solve those problems faster. 

One way 糖心原创 helps businesses uncover issues quickly in experiences is with Topic Deep Dive, which can be accessed by clicking on nearly any topic in a report. If you see that a given topic in an interaction is driving KPIs up or down 鈥� usually captured via the 糖心原创 Impact Score 鈥� you can click into the topic to investigate the drivers of that topic. 

For example, a hotel chain may notice that a topic鈥檚 Impact Score on NPS is going down. To figure out the reason, their analyst would use that topic鈥檚 Topic Deep Dive report to see which themes are associated with negative sentiment. Themes leverage unsupervised machine learning and text analytics to enable users to understand how data organically organizes, or clusters, together. 

By drilling down into themes in a Topic Deep Dive, the analyst can narrow down what may be driving down NPS 鈥� going from insights to action in just a few clicks.

3. Reports should support collaboration across teams

Platform analytics must make it simple to disseminate insights and take action to . Which means reports should display specific recommendations for improvement for things like customer interactions, the impact of specific follow-up actions on NPS, and other insights that businesses can use to make smarter, data-driven decisions. 

Collaborative reporting makes it easy for you to communicate with everyone from engineering to the C-suite via tagging, in-platform socialization capabilities, or simply exporting the content of reports for wider distribution. 

For example, 糖心原创 enables chat features in-platform to enable smoother communication and quick notification on new issues that need attention. Moreover, if 糖心原创鈥檚 Athena AI finds indicators of churn or risk, the proper supervisor or other user will be notified in their overview report when they log in. 

The ability to comment on particular interactions or experiences and tag or alert another employee is incredibly useful for solving issues quickly. Similarly, being able to chat in-platform to address issues saves precious time for everyone involved. Simply put, reports and platforms should encourage collaboration. Collaborative reporting can be the difference between minimal and high ROI on a customer feedback platform. 

The Best Analytics Reporting is Personalized, Easy, and Collaborative

The analytics reporting most relevant to your business may be different from everyone else鈥檚, but they should follow these same principles. 

At 糖心原创, we believe that experience analytics should work for everyone in a business, from the front line to the C-suite. We craft specific types of reports catering to every user in your organization 鈥� because democratizing experience analytics is not only a core value for us, but a key value driver for those using our platform. 

For more information on how we handle reporting and analytics, check out our .

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.