Client frameworks

Business question first. Model second. Decision last.

Three repeatable ways to turn a marketing or customer question into a model your team can act on. The scope changes from client to client; the shape doesn't.

Audiences Conversion Adoption

Each framework fixes the question, the data it needs, the methods that answer it and the deliverable your team actually uses.

Framework 01

Audiences that matter

Uncover the customers and accounts that matter most with data-driven precision — who buys, who's about to leave, and who's worth the next dollar of acquisition or retention spend.

Consumer insights8–12 weeks typical

Questions it answers: Who are our highest-value customers and what do they have in common? Which accounts are most likely to churn in the next quarter? Who inside a buying organization actually influences the purchase? How healthy is each brand in the portfolio?
Data it uses: Transactions and product usage, CRM and account records, digital behavior and clickstream, survey and review text, firmographics and demographics. Messy or partial data is normal — the first week establishes what's usable.
Methods: Behavioral and psychographic segmentation; AI-driven micro-segmentation for personalization; propensity, churn and retention models; purchase-influencer identification without a clean CRM; NLP sentiment on reviews and social; brand-health and cross-brand engagement scoring.
What you get: Named, sized segments with personas your marketing team can brief against; a scored customer or account list refreshed on a schedule; a brand-health scorecard; and the model code, documented and handed over.
Decisions it enables: Where to focus retention spend, which segments to build for, how to prioritize sales outreach, and which brands need investment versus harvesting.
Seen in: Brand health and consumer retention · Identifying key purchase influencers · Demographic insights that drive beverage sales

Framework 02

What drives conversion

Identify what truly drives conversion — and where to invest for maximum impact across channels, campaigns, prices and product pairings.

Marketing analytics10–14 weeks typical

Questions it answers: Which channels and campaigns actually move revenue, and which just get credit? What is the true acquisition cost and lifetime value by segment? If we shift budget, what happens? Which products sell each other, and what should we recommend?
Data it uses: Marketing spend and impressions by channel, campaign performance, web and app analytics, e-commerce orders and baskets, pricing and promotion history, CRM conversion events.
Methods: Marketing mix modeling and multi-touch attribution; CAC / LTV and channel-performance analytics; campaign diagnostics, incrementality and A/B test design; demand forecasting and price-positioning analysis; a proprietary association-rules model for product pairing that works on single-item transactions.
What you get: A budget-allocation view that shows the expected lift of moving spend; attribution and CAC / LTV dashboards your team can run scenarios in; product-pairing recommendations ready for the site or the CRM; and the models behind all of it, deployed and monitored.
Decisions it enables: Next quarter's media mix, which campaigns to scale or cut, pricing and promotion depth, and what to cross-sell to whom.
Seen in: Product-pairing analysis to lift e-commerce revenue · Cutting organizational waste with descriptive analytics

Framework 03

Adoption and time to value

Predict adoption, remove friction and guide users to value faster — for new features, new products and new AI capabilities.

Product & growth8–12 weeks typical

Questions it answers: Which users and accounts are ready to adopt what we just launched? Where in onboarding do people stall? What does the journey look like across web, app and in-store, and where does it leak? How should the go-to-market be sequenced?
Data it uses: Product and feature usage events, onboarding funnels, account attributes and readiness signals, omnichannel journey and clickstream data, cart and session abandonment, support and feedback text.
Methods: Feature- and AI-adoption readiness scoring at user and account level; usage-based segmentation and activation analytics; journey mapping and friction diagnosis across channels; ICP refinement and buyer-journey insight; targeted-intervention testing.
What you get: A readiness score for every account, with the signals behind it; a ranked list of friction points with expected impact; an activation dashboard; and a GTM sequence your marketing and product teams can run against.
Decisions it enables: Who to target first for a launch, what to fix in onboarding, where to invest in the journey, and how to measure adoption as a number rather than an anecdote.
Seen in: Predicting which organizations will adopt AI-driven features · Business frameworks for LLMs in customer service

Beyond the three. The same approach covers consumer perception and sentiment, product and demand forecasting, market and competitor intelligence, and omnichannel journey analytics. If your question doesn't fit a framework above, it still fits the method.

Bring me the question →

Next: see the frameworks applied

Fifteen engagements across CPG, retail, e-commerce, technology and financial services.

View the work

Or skip ahead to ways to engage