Turning Twenty Years of Customer Data Into a Growth Strategy for a Medical Equipment Company

Key Results

20

YEARS OF CUSTOMER DATA ANALYZED

~$100M

COMPANY REVENUE

At a glance. Client: a medical equipment company with roughly $100M in revenue (anonymized). Data: 20 years of customer records. Methods: focus groups, cohort analysis, cluster analysis, product-affinity matrices, and conjoint analysis. Deliverables: customer segmentation, an ABC account structure, case-manager targeting, bundle and upsell pathways, pricing models, and go-to-market recommendations.

Executive Summary

A medical equipment company with roughly $100 million in revenue had two decades of customer data and no clear picture of who its most valuable customers were or what drove their purchases. Its products reached patients through case managers, which made the buying decision harder to read than a typical consumer sale. I led the customer segmentation research and the statistical work that turned that history into targeting and commercial recommendations.

The Challenge

When a product is chosen by a patient, recommended by a case manager, and paid for through a referral process, the sales data alone can't explain why customers buy or which ones are worth pursuing. The company needed to know which customer groups behaved differently, which products sold together, how buyers traded features against price, and where to focus a sales team with limited time.

Research and Diagnosis

I combined qualitative research with large-scale statistical analysis. Focus groups surfaced how patients and case managers talked about their needs and decisions. Cohort analysis showed how customer groups behaved over time, and cluster analysis identified distinct segments in the data. Product-affinity matrices revealed which products were bought together. Conjoint analysis and attribute prioritization measured which features customers valued most and what they would trade to get them.

What We Did

The analysis became a set of commercial tools: an ABC account structure so the sales team could prioritize its time, case-manager targeting, bundle and upsell pathways built from the affinity findings, pricing models, and executive recommendations on positioning, adoption, and go-to-market.

What It Means

In healthcare categories with complex buying journeys, the company that understands its customers' decision logic can price, bundle, and sell with far more precision than a competitor relying on sales history. Most mid-sized healthcare companies already have the data to do this. What they rarely have is the statistical capability to use it.

Related services: Market Research | Healthcare & Telehealth | Marketing Analytics