Customer & Marketing Insights

Your marketing team runs campaigns on Meta, Google, and email. Each platform reports conversions. You add up the numbers and get 40% more conversions than actually happened.

That is attribution fraud — not by the platforms, but by the math. Every channel claims credit for the same customer. Without a unified attribution model, you are optimizing budgets based on numbers that do not add up.

The three problems that keep showing up

Attribution blindness. At eBay's emerging markets across Mexico, South Africa, Poland, Ireland, and Argentina, ad budgets were allocated by channel-reported ROAS. When we connected Meta, Google Ads, feed performance, and GA attribution data into a single dashboard, the actual ROAS picture shifted dramatically. Some channels that looked strong were riding organic traffic they did not generate. Others that looked weak were driving first-touch awareness that converted later. Reallocation based on the real picture drove 10-18% ROAS improvement across all five markets.

Churn invisibility. Paid customers at eBay Classifieds were churning despite heavy acquisition spend — agents and developers who had been paying for premium listings just stopped renewing. The retention team had no early warning system. We built a churn model combining listing activity, traffic quality, marketing touchpoints, and support interactions in R and Databricks. Retrained monthly. Result: 15% retention lift, because the team could intervene 30 days before churn instead of discovering it 30 days after.

Segmentation by intuition. A regional CPG brand knew their products sold differently by route and retailer, but their segmentation was based on geography and gut feel. Basket analysis — looking at what customers actually buy together, in what quantities, through which channels — revealed growth corridors that geographic segmentation missed entirely. Routes that looked underperforming by revenue were overperforming by margin. Retailers categorized as "small" were driving disproportionate repeat purchases.

How we approach it

We integrate your marketing, sales, product, and behavioral data into a unified customer analytics layer. The specific models depend on your questions:

For retention: churn prediction models scored weekly or monthly, with feature importance that tells your team why customers are leaving — not just which ones. Is it pricing? Product experience? Onboarding gaps? The model answers the "why" alongside the "who."

For marketing ROI: cross-channel attribution dashboards that deduplicate conversions and show true incremental impact by channel, campaign, and audience segment. We build these in Tableau, Looker Studio, or your existing BI tool.

For growth: customer segmentation frameworks using transactional data, behavioral signals, and — where available — enrichment data. RFM (recency, frequency, monetary) as a baseline, with ML clustering when the data supports it.

What you receive

A production analytics pipeline — not a one-time analysis. Churn scores refresh on schedule. Attribution dashboards update daily. Segmentation recalculates as new data arrives. We hand off the models with documentation, retraining instructions, and monitoring dashboards so your team owns it going forward.

Where this does not apply

If you are pre-product-market-fit or have fewer than 1,000 customers, statistical models will not have enough signal. Start with qualitative research and basic cohort analysis. If your marketing spend is under $50K/month across all channels, a full attribution model adds more complexity than clarity — a simpler last-touch dashboard may be enough.

Questions to pressure-test your current setup

If you turned off your highest-spend marketing channel tomorrow, do you know — with data, not intuition — what would happen to conversions? Can your team identify which customers are likely to churn next month, or do you find out when they are already gone? When you look at your customer segments, are they based on behavior data or on categories someone defined in a brainstorm two years ago?