InMoment Advanced AI: Supercharging CX

Close up of businessman using a laptop with graphs and charts on a laptop computer.

Data is gold. Data is truth… but data is useless if you can’t rely on it. 

Understanding customer and employee sentiment is more than just a competitive edge—it’s essential, with companies in every industry and sector focusing resources on comprehending it. 

We have a revolutionary tool that we’d like to share, one that has helped businesses large and small navigate this space. InMoment Advanced AI turns diverse data streams into valuable insights companies can use for their strategy. It’s been the change clients in various fields have relied on. So for starters…

What is InMoment Advanced AI??

InMoment Advanced AI is a comprehensive data analytics tool that integrates and analyzes structured and unstructured data using advanced Natural Language Processing (NLP) and AI. It offers a deep understanding of customer and employee feedback, transforming complex data into clear and actionable insights. 

Central to InMoment Advanced AI’s functionality are predictive analytics and customizable dashboards, which enable businesses to understand current data trends and anticipate future customer patterns and behaviors across these data sets. 

InMoment Advanced AI’s power lies in its ability to analyze both historical customer experience data and real-time data sources like social media and reviews. This dual capability offers businesses an advantage over competitors who may excel in historical data analysis or current data interpretation, but struggle to integrate both into timely insights. InMoment Advanced AI’s integrated approach provides a comprehensive view, turning past and present data into powerful, actionable insights for immediate strategic impact.

InMoment Advanced AI enables businesses to process virtually any type of content, enrich and understand that content, and visualize it through a powerful set of dashboarding tools. The engine that enables this enrichment uses AI and NLP to understand the content and derive valuable metadata, including: intent prediction, effort signals, and emotion detection. 

Let’s go over what these are and their broader implications.

Intent Prediction

Intent prediction is a crucial component of data analysis, focusing on deciphering the underlying intentions behind customer interactions. This technology uses deep learning models to predict a customer’s future actions or needs. 

For example, in customer service interactions, intent prediction can determine whether a customer is likely to purchase, seek support, or churn. By understanding these intentions, businesses can proactively address customer needs, enhancing the overall customer experience and increasing sales and customer satisfaction.

Effort Signals

Effort signals involve analyzing customer interactions to gauge the degree of effort a customer exerts in their journey. This metric is key in understanding customer satisfaction and loyalty, as higher effort levels correlate with negative customer experiences. 

By analyzing data such as the length and complexity of customer service interactions, businesses can identify areas where customers face difficulties. Addressing these high-effort points can significantly improve the customer experience, increasing satisfaction and loyalty.

Emotion Detection

Emotion detection is identifying and analyzing emotional states in customer interactions. This aspect of sentiment analysis uses a BERT deep learning model to assign an emotion to the speaker or subject of a sentence or thought. 

This technology can distinguish between emotions like happiness, frustration, or disappointment. Emotion detection helps businesses tailor their responses and strategies to align with customer emotions, enhancing personalized customer experiences and building stronger emotional connections with the brand.

Types of Data

Structured: The Backbone of Predictability

Structured data is the cornerstone of conventional data analysis, representing the world of quantifiable and measurable information. Characterized by its specific, organized format, structured data neatly aligns in rows and columns, reminiscent of spreadsheets or relational databases. This meticulous arrangement makes it well-suited for quantitative analysis, offering clear, objective, and mathematical insights into various aspects of business and customer behavior.

It is the language of logic and mathematics, offering a clear, structured view of the world that is easily interpreted by computers. Its strength lies in its straightforward aggregation and manipulation, allowing businesses to accurately quantify and measure trends, performance metrics, and other key indicators.

This data type is the foundation of data-driven decision-making, enabling enterprises to translate complex phenomena into understandable metrics. While it might lack the nuanced storytelling of unstructured data (we’ll get there in a second), structured data offers the definitive “what” in the story of customer and business interactions—the concrete, quantifiable facts that are essential for informed strategy and planning.

Unstructured: The Streaming Thoughts of Your Everyday Life

Unstructured data, the most raw and unrefined form, is abundant and profoundly human by nature. Emerging from sources rich in personal expression like open-ended survey questions, reviews, social media, and SMS messages, this data type offers a window into the authentic human experience. 

According to IDC, The Digital Source, 85% of customer data is unstructured and it’s growing at 55% per year, highlighting the vast and rapidly expanding landscape of human communication that structured data cannot capture. Tools like InMoment’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

What sets unstructured data apart is its embodiment of language. It directly reflects our unfiltered and unstructured thoughts in their most natural state. While structured data can be seen as the mathematics of human behavior, unstructured data is pure, unadulterated human communication.

This richness, however, presents a challenge: unstructured data is the hardest for computers to decipher, as it requires understanding nuances, context, and the subtleties of human language. Despite this complexity, our deepest and most meaningful insights lie in these unstructured narratives. Tools like InMoment’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

Bringing Them Together: The Full Story

Integrating structured and unstructured data is a key aspect of InMoment Advanced AI and, arguably, its strongest feature. Structured data provides precise, quantifiable insights, such as the exact factors contributing to customer churn

While structured data gives you the numbers, unstructured data provides the “why” behind these figures. It’s found in customer verbatims and feedback, revealing the customers’ personal stories, opinions, and suggestions. It’s the narrative that puts context and meaning behind the numbers. But on its own, unstructured data can be overwhelming and hard to navigate to find the most impactful insights.

Combining structured and unstructured data tells the full story. This integration allows businesses to quantify aspects of the customer experience and understand the underlying reasons behind these metrics. With InMoment Advanced AI, companies can sift through the rich, detailed narratives in unstructured data, guided by clear, actionable insights from structured data. This holistic approach enables a deeper understanding of customer needs and preferences, leading to more informed and effective business decisions.

InMoment Advanced AI bridges the gap. 

Spotlight Addresses Key Business Challenges

Understanding and Predicting Customer Behavior

We mentioned this earlier, but we’d like to go more in-depth—this one’s important. One of the paramount challenges businesses face today is their inability to predict future customer behaviors. InMoment Advanced AI  excels in this area using AI-powered, advanced analytics and machine learning algorithms. 

According to Gartner, by 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%, underscoring the efficiency gains possible with advanced AI solutions. This capability enables businesses to move beyond surface-level insights, delving into predictive analysis that anticipates future customer actions and preferences.

By understanding these predictive patterns, companies can tailor their strategies proactively, ensuring they are always one step ahead in meeting customer needs and expectations. This forward-looking approach is vital for maintaining competitive advantage and fostering customer loyalty.

Data Unification and Analyzation: A Single Source of Truth

Data silos are a significant barrier to effective decision-making in many organizations. 

Tyler Saxey, Director of CX at Foot Locker, states, “InMoment now ticks all of the boxes. InMoment AI solves for any previous text analytics issues. Analyzing call transcripts and getting to the root cause brings a big ROI.” InMoment Advanced AI addresses this issue head-on by offering data unification capabilities, consolidating data from various sources and providing a comprehensive and unified view of customer information. This holistic approach is vital for creating consistent and effective customer experiences across all touchpoints.

By breaking down these silos, InMoment Advanced AI ensures that all decisions involve a complete and accurate picture of customer data—no decisions are made in isolation. This unified view is invaluable for creating consistent and effective customer experiences across all touchpoints.

Regulatory Compliance: Ensuring Communication Standards

We live in a time with increased scrutiny of companies’ regulatory compliance. InMoment Advanced AI is essential in ensuring that customer communications meet the necessary standards. This aspect is crucial for highly-regulated businesses in industries like finance, healthcare, and telecommunications. 

InMoment Advanced AI can help monitor and analyze customer communications, ensuring they adhere to industry regulations and standards. This compliance monitoring not only helps avoid potential legal issues but instills trust among customers, who are increasingly concerned about how their data is handled and used. With nearly 65% of the world’s population expected to have its personal data covered under modern privacy regulations by 2023, up from 10% today, according to Gartner, the importance of incorporating advanced AI for regulatory compliance cannot be overstated.

Why Spotlight is Essential for All Businesses 

Enhancing Experiences: Tailoring Strategies for Satisfaction and Loyalty

InMoment Advanced AI significantly enhances customer and employee experiences. 

Tony Darden, COO of Jack in the Box, shares, “The use of the InMoment AI solution will allow us to easily analyze feedback in all its forms to receive more detailed and immediate insight from a wider variety of guest experiences. Our team is focused on using the additional insight to make business decisions without delay—having a faster time to guest improvement that will positively influence their experience with our brand leading to increased loyalty.” 

By leveraging advanced analytics to understand sentiment and feedback, businesses can tailor their strategies and offerings to better meet their customers’ and employees’ needs and expectations.

Reducing Churn: Anticipating and Addressing Customer Needs

Customer and employee churn is a major challenge for businesses, resulting in lost revenue and increased recruitment and training costs. InMoment Advanced AI’s predictive analytics capabilities play a vital role in identifying the early signs of dissatisfaction or disengagement. By anticipating these factors, businesses can proactively address issues before they lead to churn. This proactive approach helps retain customers and ensures that employees feel valued and engaged, reducing the likelihood of them seeking opportunities elsewhere.

Strategic Decision-Making: Prioritizing Initiatives for Maximum Impact

Data-driven decision-making is at the heart of modern business strategies. InMoment Advanced AI provides comprehensive insights that help businesses prioritize their initiatives, focusing on areas yielding the greatest cost savings or revenue increases. These insights guide businesses in allocating resources effectively, whether it’s refining marketing strategies, optimizing operational processes, or enhancing customer service. By basing decisions on solid data, businesses can maximize their ROI and align their strategies with their overall goals.

The Takeaway: A Holistic Approach for a Winning Strategy

InMoment Advanced AI’s ability to integrate data across multiple channels is a game-changer, providing a unified view of information from various sources. This cross-platform integration is crucial for strategic planning and executive decision-making. It allows businesses to make informed decisions based on a comprehensive understanding of their operations, market trends, and customer behaviors. 

By breaking down data silos, InMoment Advanced AI ensures that a complete and accurate picture of the business landscape backs every decision. A study by McKinsey & Company found that companies that utilize customer analytics comprehensively are 23 times more likely to outperform competitors in terms of new-customer acquisition and nine times more likely to surpass them in customer loyalty.

InMoment Advanced AI’s ability to transform this unified data into actionable strategies makes it indispensable. Its benefits are wide-ranging and impactful, from enhancing experiences and reducing churn to aiding in strategic decision-making and facilitating cross-platform data integration. Adopting InMoment Advanced AI is not just a step towards better data analysis, but a leap towards a more informed, customer-centric, and efficient business model.

For businesses considering Spotlight:

  • How are you currently gathering and interpreting customer and employee feedback?
  • What tools are in use for understanding customer and employee experience?
  • How is this data being used to drive experience initiatives?

A Final Word

InMoment’s InMoment Advanced AI stands out in the realm of customer experience management. Its ability to harness structured and unstructured data, combined with advanced analytics, positions it as an indispensable tool for businesses aiming to enhance customer engagement and make data-driven decisions. 

Adopting InMoment Advanced AI translates into not just collecting feedback but transforming it into a strategic roadmap for business success. Stay ahead of the pack and contact us to learn more about how InMoment Advanced AI can directly impact your business.

The Customer Success Analyst has evolved to be the go-to person for all the data – or as Marketo put it in their Linkedin job ad, “the primary deliverable of the Customer Success Decision Analyst is to convert our Customer Success operation at Marketo into a highly data-driven business where we can measure, analyze and optimize every aspect of our engagement with our customers.”

This includes data like:

  • Feature usage patterns
  • Maturity scores
  • NPS results
  • Voice of customer qualitative feedback
  • Customer journey mapping
  • Customer experience metrics
  • Capacity models

Among all of the hats that CSM’s wear, the number-crunching, data-heavy, quantitative analyst hat is one of the most time-consuming. But because of the data-savviness this role demands, CS analysts also hold the keys to unlocking incredible potential when your business is scaling up.

The CS analyst role isn’t *just* about collecting data for dashboards and reports (and basing recommendations on that data) though. It complements the Success Operations role, which builds new tools and processes to scale CSM’s everyday activities. As the person navigating multiple platforms for data on a day-to-day business, CS Analysts know how information flows and who needs what information.

For one of Wootric’s customers, Chorus.ai, CS Analysts also take ownership of the technical onboarding process for new or upgrading customers, ensuring “a smooth implementation, including initial and ongoing training for customers.”

It’s a prime position from which to watch for opportunities to make big impacts on the success of customers – and the success of the company. That’s the subtextual expectation: By being in charge of the data, the CS Analyst knows how to use it to find untapped value.

What does a CS Analyst need to know?

Experience working with large amounts of data (SQL, Python or R) and with survey and analysis tools (Wootric, Tableau, etc.) are must-haves, but the most important qualification is having taken that data and used it to produce actionable insights.

Analysts are data story-tellers. They work with the numbers and provide context for them, creating reports to recommend strategic options and solutions. A listing for a CS Analyst position at Salesforce described one of the responsibilities as “assist in developing and delivering presentations for senior executives”, which requires strong public speaking and presentation skills.

If a company struggles with data silos, CS Analysts must bridge the gap between teams. Not only must analysts overcome the technical issues of compatibility, but they need to possess strong internal communication skills to overcome any organizational walls that may be contributing to the data isolation.

While CS Analysts own the quantitative facet of Success, a customer-centric mindset and empathy for the humans behind the numbers distinguish a great analyst from a good one. These soft skills help analysts frame their analysis to produce long-term, customer-centric solutions that support CSMs to retain customers.

What does this role look like in real life?

For some companies, the CS Analyst position can be a foot-in-the-door to Customer Success.

Anthony Enrico, VP of Customer Success at Emailage, created the Analyst position because his “CSMs were being asked to spend enormous amounts of time compiling reports and the opportunity cost of spending time deepening relationships and loyalty with customers was too great.” As a leader within the organization, Anthony was also doing a lot of work with these reports, when his time was clearly better spent working on strategy, escalations with his CSMs, and focusing on new business opportunities with the company.

So Anthony hired Bryan Mehrmann, now a CSM at Emailage. Bryan was originally brought on as the first CS Analyst to support the CSM team. Bryan compiled and sent out daily reports on customer usage trends to identify and correct anomalies as early as possible. He took detailed revenue projection reports and distilled them for the C-suite for their weekly use. Bryan took on more responsibilities as time went on.

Working together, Anthony and Bryan shaped the role as it is today. As for how the position fits into the CS team, Analysts can be promoted to full CSMs after they’ve achieved a comprehensive understanding of the product, metric drivers, and relationships with Emailage’s customers.

On the other hand, CSMs may choose to specialize in VoC data analysis like Customer Success Analyst Tim Dressel at Qualer. For him, there’s the usual collection and analysis of customer data in spreadsheets, but also a lot of room for innovation and collaboration. If he sees a red flag in the metrics, he leads investigations into those customer issues, working with his cross-functional team (and collaborating, at times, with Qualer’s Head of Technology) to make sure customers’ needs make it into the software they develop.

How do you know if you need a Customer Success Analyst on your team?

Customer Success Managers are often being pulled in five different directions at once, and when that happens, they sacrifice time on one task for another. Not only does Customer Success provides data and insight crucial to their own day-to-day, but they are the go-to team for reports for the C-suite.

Customer Success needs data. Data is at its core. So if your Customer Success team doesn’t have time to live and breathe data, you may be at the tipping point to bring in an analyst who can parse the numbers for you. This is especially important for scaling processes when anecdotal experiences have to give way to metrics.

For some companies, bringing on an Analyst to Customer Success may happen by incorporating a company-wide business analyst into the team and transitioning them into a full-time Success Analyst. Depending on the company, Customer Success may not need an Analyst until their team is four or five CSMs.

The most common theme among companies looking to hire a CS Analyst is major growth.

For example, a Wootric customer that recently started trading publicly, DocuSign, decided to add the Customer Success Analyst position as they accelerated their growth.

Analysts (& their data) are a CSM’s best friend

For Customer Success, the best way to prove value, whether it’s to senior management or to a customer, is with numbers and context. Having a role dedicated to creating robust reports to highlight value and propose inventive, data-backed solutions is an excellent way to help your current CSMs be the best that they can be at scale.

Automatically send customer feedback to Salesforce, Gainsight and Slack for quick action. Learn about InMoment’s integrations.

Shot of a group of young business people having a brainstorming session in a modern office

“Our conclusion: superior CX drives superior revenue growth.”
Harley Manning, Forrester

“Customers who had the best past experiences spend 140% more compared to those who had the poorest past experiences”
Peter Kriss, Harvard Business Review

There is a lot of chatter happening in business circles about customer experience (CX) as a growth engine. It’s almost intuitive – you and I both understand how having a great experience affects us as customers. We all have businesses we love, products we’ll follow to the ends of the earth (in hopes they’ll finally go on sale), and websites we follow with almost religious fervor.

As CMO, VP of Success, or Head of Customer Support, you are constantly advocating for customer experience within your company. After all, from the very first moment the second blacksmith’s shop appeared in the village, creating competition for the first blacksmith’s shop, customer experience has been a deciding vote for who gets the business – just as much as price and quality. But as a business owner, or a professional marketer, you can’t afford to go with your gut. To win resources you need data to back up your argument that CX is the future (you know it is).

There is a correlation between CX and revenue growth, and we’ve compiled the research to back it up.

Why the effects of CX have been tricky to track

Customer experience has been treated as a ‘soft’ discipline, and I have a theory as to why. 

We’ve grown up with it. Whether watching Santa send Macy’s store shoppers to competitors in Miracle on 34th Street, or walking into Nordstrom’s shoe department to be followed around by suited young men carrying piles of boxes to the nearest padded chair. We recognize great CX when we experience it ourselves.

However, it’s inherently subjective. Subjective issues – anything based on opinion or emotion – tend to be hard to track. One person’s “helpful” is another person’s “pushy.” Your “attentive,” might be my “stalker.”

Modern tools now quantify CX

But online buyers’ journeys are different than the sales experiences most of us grew up with. With modern tracking and customer surveys, you can tell (often in real-time) whether your efforts are coming off as too much, or too little. You can identify problems and preferences, which allows you to fine tune the end experience for your target customer.

Most importantly, for the first time in human history, we have the tools to track the actual, absolute effect that positive customer experience has on a business’s bottom line. This is transforming the discipline of customer service into the science of CX.

The science of CX starts with measurement. Read the article, A Primer on the 3 Most Important CX Metrics – NPS, CSAT and CES, and start measuring CX today.

It’s no longer just “the right thing to do,” it’s an engine for measurable growth.

“CX is no longer just a discipline; it is the basic ingredient for growth”
Winning on the Battleground of CX, Forrester

Data that ties CX to Revenue

Transaction-based v. Subscription-based CX

“What we found: not only is it possible to quantify the impact of customer experience – but the effects are huge.” – “The Value of Customer Experience, Quantified,” Harvard Business Review

Harvard Business Review looked at the revenue data from two global $1B+ businesses – one was a transaction-based business, the other was a relationship-based subscription business.

We looked at two companies with different revenue models — one transactional, the other subscription-based — using two common elements that are relevant to all industries: customer feedback, and future spending by individual customers. To see the effect of experience on future spending, we looked at experience data from individual customers at a point in time, and then looked at those individual customers’ spending behaviors over the subsequent year.”

Transactional business models rely on frequency of customer return and how much they spend per visit. Modcloth would be a good example – they want you to come back every day and buy (or at least Save to Wishlist), and come up with ingenious ways to incentivize that behavior.

Subscription-based businesses include Software-as-a-Service (SaaS), or even those recipe kits from Blue Apron. No matter what they’re selling, the model is the same. It relies on retention, cross-sells and upsells.

The results?

After controlling for other factors that drive repeat purchases…

  • Transaction-based: Customers with the best past experiences spend140% more than those with the poorest past experiences.
  • Subscription-based: Customers with the best past experiences have a 74% chance of remaining a member for at least another year; customers with the worst experiences have a 43% chance of being a member one year later. In fact, those who gave the highest CX scores were likely to remain members for another six years.

CX Effects Across Multiple Industries

On Harley Manning’s Blog at Forrester, Manning (Forrester VP and research director) discusses two studies, conducted one year apart, that compared five pairs of publicly traded companies “where one company in each of the pairs had a significantly higher score than the other in Forrester’s Customer Experience Index during the period 2010 to 2015.”

The Customer Experience Index measures each brand on a scale from “Very Poor” to “Excellent” in these six categories:

  • Effectiveness
  • Ease of use
  • Emotion
  • Retention
  • Enrichment
  • Advocacy

Then, Forrester looked at the businesses’ revenue data and built models to calculate the compound annual growth rates for each of the ten companies over those five years.

The results:

The publicly traded companies studied ran the gamut of industry types, from cable to retail to airlines. But in terms of the CX effect, industry didn’t seem to matter as much as the reported CX scores each company received.

In two industries, cable and retail, leaders outperformed laggards by 24 percentage and 26 percentage points, respectively. Even in the industry with the smallest spread, airlines, the CX leader enjoyed a healthy 5 percentage point advantage in global revenue. And when we compared the total growth rate of all CX leaders to that of all CX laggards we saw that the leaders collectively had a 14 percentage point advantage.” – Harley Manning, Forrester

Unlike the Harvard Business Review’s study, Forrester did not control for outside influences that could have driven revenue growth. But, they did conclusively determine that “customers who have a better experience with a company say they’re less likely to stop doing business with the company and more likely to recommend it.” They also observed that companies with superior CX saw increased growth in customers.

And, as Harley Manning points out, “Both of those factors should drive increased growth in customers and, in turn, increased growth of customer revenue.”

Essentially, as CX rises, so does revenue growth.

But there’s another interesting correlation that Forrester’s Customer Experience Index research uncovered. The top performing brands, including USAA, Barnes & Noble, Etsy, QVC and Zappos.com, “achieved a 17% compound average growth between 2010 and 2015 – which is no small feat with many of them already in the top revenue percentiles in their respective industries.” (Salemove.com)

Compared with the brands at the bottom, who only saw a compound average growth of 3%, that is a very wide gap.

To put a possible dollar amount on this, consider: “a one-point score improvement in the CX Index can lead to an increase of $65 million in revenue in the upscale hotel industry,” according to Forrester’s Harley Manning.  

CX spending is on the rise

You may think companies still seem to feel more comfortable spending money on things that do not have a direct impact on customer experience, or that Support and Customer Success teams can still be the last area to receive investment. Think again. Per Forrester research, 71% of business and technology decision-makers reported that improving CX will be a high priority for spending in the next year.

Ready to join the CX revolution?

Now with modern survey platforms, companies of all sizes can measure and improve customer experience at scale.  Forrester’s CX Index measured six attributes of experience and probably took months to collect, analyze and report. However, a lightweight approach to CX improvement using metrics such as Net Promoter Score (NPS) can get you 90% of the way there and not break the bank. 

The key is to start small. Determine your “north star” metric. Get customer feedback, take action, repeat.  Consistently repeat this process. As your company’s customer experience improves, so will your bottomline. 

Start measuring Net Promoter Score for free with InMoment

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