How to Increase Sales by Looking at Your Customer Data
Using customer data to drive sales — from basic analytics to AI-powered revenue intelligence.
title: "How to Increase Sales by Looking at Your Customer Data" slug: "how-increase-sales-looking-your-customer-data" description: "Using customer data to drive sales — from basic analytics to AI-powered revenue intelligence." datePublished: "2014-01-29" dateModified: "2026-03-15" category: "Data Strategy" tags: ["sales", "customer data", "analytics", "revenue"] tier: 3 originalUrl: "http://www.applieddatalabs.com/content/how-increase-sales-looking-your-customer-data" waybackUrl: "https://web.archive.org/web/20140129085632/http://www.applieddatalabs.com:80/content/how-increase-sales-looking-your-customer-data"
How to Increase Sales by Looking at Your Customer Data
I wrote this piece in 2014 because I kept seeing the same problem: companies sitting on treasure troves of customer data and doing almost nothing with it. Customer service interactions, transaction records, CRM notes. All full of signal, mostly ignored. I described how a CPG company discovered the gluten-free trend early by mining customer service calls, and how another company found that older customers struggled with soup can lids and created EZ-open packaging.
These were great stories. They were also slow. Each insight took months of manual analysis. In 2026, AI finds these patterns in days, sometimes hours.
The Original Three Data Sources
My 2014 article broke customer data into three categories. Here's how each has evolved:
Customer Service Interactions were always the richest source. I argued that customer service data was the most valuable in the organization because that's where customers tell you what they want. That's still true. But the tools for extracting value from it are unrecognizable.
Gong records and transcribes every sales call, then uses AI to analyze what top performers say differently than everyone else. CallMiner processes millions of customer service interactions to detect sentiment, compliance issues, and emerging trends. Qualtrics XM uses natural language processing to analyze open-ended survey responses at scale. The gluten-free trend that took months to spot in 2014? An AI system would flag it within a week of it appearing in call transcripts.
Transaction Data got supercharged by AI. The Target pregnancy prediction story I referenced, where Target figured out customers were pregnant from their purchase patterns, was revolutionary in 2012. Now every major retailer runs similar models. Shopify gives even small merchants AI-powered product recommendations. Amazon's recommendation engine generates 35% of its total revenue, roughly $200 billion per year. Transaction-based intelligence is no longer a competitive advantage. It's table stakes.
CRM Data went from static records to living intelligence. Salesforce Einstein analyzes your CRM data and predicts which deals will close, which customers are at risk of churning, and what actions a rep should take next. HubSpot's AI scores leads automatically and writes follow-up emails. Clari aggregates signals from email, calendar, and CRM to forecast revenue with accuracy that embarrasses the old spreadsheet-based methods.
In 2014, I described CRM data as a "goldmine" that most companies ignored. By 2026, AI turned that goldmine into an automated processing facility that runs 24/7.
The New Category: Conversation Intelligence
There's a category of sales data that barely existed in 2014: conversation intelligence. Tools like Gong, Chorus (now ZoomInfo), and Avoma record every sales interaction, analyze the content, and produce insights that were impossible before.
Which competitors get mentioned in lost deals? What questions do buyers ask right before purchasing? How much does your rep talk vs. listen? (The best reps talk less.) This data used to come from anecdotal ride-alongs. Now it's available for every call, analyzed automatically.
From Analytics to AI Sales Development
The newest frontier is AI that doesn't just analyze sales data but actively participates in the sales process. AI SDR (Sales Development Representative) tools like 11x, Artisan, and Regie.ai generate personalized outreach at scale. They research prospects, write custom emails referencing the prospect's company news and role, handle initial responses, and schedule meetings. The quality varies, and I've seen plenty of terrible AI-generated sales emails in my inbox. But the best implementations are producing results that match or beat human SDRs on volume while freeing human reps to focus on relationships and complex deals.
The Strategic Gap Remains
For all the new tools, the strategic question from my 2014 article still applies: are you looking at your data with the right questions in mind? I see companies deploy expensive AI sales platforms and then measure the same metrics they tracked in 2014. Pipeline volume. Close rate. Average deal size. These matter, but they're lagging indicators.
The companies getting the most from their customer data are the ones asking forward-looking questions. Which customer behaviors predict long-term value, not just initial purchase? What does the data say about market shifts that haven't fully materialized yet? Where are the operational gaps between what your AI tools can see and what your organization acts on?
The data is there. The tools are there. The gap is still in the humans who need to ask better questions and build organizations that actually respond to the answers.