Leveraging Unstructured Data: How Business Leaders Can Harness the Power of AI

Our data scientists have combed through mountains of unstructured customer usage data to crack the code on proactively identifying accounts that are a churn risk. After analyzing thousands of signal combinations, we found that four key indicators—Budget Issues, Unhappiness, Value Issues, and Urgency—are the ultimate predictors of revenue risk.

Nearly every B2B tech and services company sees the same pattern: when these signals align, it’s time for action.

Hold on, what is unstructured usage data? It’s the raw, untamed data that tells you what customers are *really* doing and saying—not just what they’re willing to admit in a survey or conveyed by numbers of daily average logins (also critical but lacking context). Here are the harbingers of risk; when combined, they are what the team needs to act on right now. 🧯

1️⃣ Budget Issue: This signals a customer struggling to justify the cost, possibly due to tighter budgets or a perceived lack of value.

2️⃣ Unhappy: Customer dissatisfaction can stem from unmet expectations, unresolved issues, or lack of engagement.

3️⃣ Value Issue: If a customer doesn’t see the ROI, they’ll start questioning the worth of your service.

4️⃣ Urgent: An urgent flag indicates an immediate problem that requires rapid action. They are expressing a need to engage with a teammate now.

If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

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I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

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Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

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If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

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However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

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Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

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(BTW, what costs more, your AWS bill or your payroll?)

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The reason your customer-facing teams don't have usage data is because this data is "unstructured," and it is everywhere. Imagine if your engineering team needed to check 50 email inboxes, 1,000 phone recordings, a CRM, and a ticket system to get your product usage statistics. 

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That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

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