Marketing & Customer AI

Test the Campaign Before You Run It

Who your customers are, how they feel, which of them are leaving, and which touchpoint actually earned the sale.

Customer segmentation: a chart split into three regions by branching boundaries, each region labeled Cluster 1, Cluster 2, and Cluster 3 and occupied by a different group of people. — Customer segmentation

Sentiment analysis: a block of writing with its words picked out in red, yellow, and blue, branching down into three speech-bubble faces: an angry red one, a smiling yellow one, and a flat blue one. — Sentiment analysis

Customer churn: a group of figures standing still on the left while three others run to the right, briefcases in hand, towards an open door marked Exit. — Churn prediction

A person at a dashboard of three panels, each holding a rising trend line above a grid of status markers, with a donut chart and a bar chart beneath them. — Campaign simulation

Four inbound channels (a desktop screen, a phone, an email, and a search box) converging on a group of customers, with arrows fanning out from them to the four places a purchase can happen: in store, online, mobile, and a call center. — Attribution modeling

A robot working at a laptop with several extra arms, each holding something it is turning out at once: a clipboard, a lightbulb, a window of code, and a stack of documents. — Content generation agents

What it is

The capability.

Marketing AI is a set of models, each answering a different question about the same customer. Who are they, how do they feel, who is about to leave, and what actually earned the sale?

Customer segmentation groups people by what they are likely to do, and regroups them as behavior changes. Sentiment analysis reads what customers write in reviews, tickets, and replies, and tracks how the mood shifts.

Churn prediction flags the accounts likely to leave while there is still time to act. Attribution modeling works out which spend caused an outcome, not just which channel came last.

Campaign simulation shows a campaign to a simulated audience before it runs. Content generation agents then write the copy that goes out.

How we build it

Our approach.

All of it is built from your own data. One behavioral record per customer feeds segmentation, sentiment, churn, and simulation, so the four describe one picture.

Each model is judged on its own failure: a churn model on the accounts it misses, an attribution model on the spend it misplaces. Every score comes with its reason, so a team can act on it.

Simulation runs on the persona technology behind our social digital twins: pools of personas built from real behavior, each answering as its segment would. A campaign is tested on them before the budget is committed.

Where it applies

In practice.

Two parts already run in production. Content agents write the catalog in our e-commerce case study, and our sentiment and topic agents serve a public-opinion research system. BizScout, which we are now productizing, applies the same reading to companies and markets.

It fits marketplaces and retailers with more products than copywriters, and subscription businesses where keeping a customer pays more than finding one. It fits any team spending across many channels, too.

Attribution and churn show a number within a quarter, which makes them a good first step. Simulation pays off once the spend is large enough to be worth testing first.

Beyond AI hype. Into execution.

Ready to build your AI capability?

We accept a limited number of research partnerships per quarter to ensure high-fidelity delivery.

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