iOS privacy changes killed clean cross-device tracking back in 2021. Since then, cookie deprecation, retail media fragmentation, and AI search have eaten what was left. We build measurement systems that drive real decisions on incomplete data.
The Problem
Privacy-first tracking is now permanent, and the signal keeps shrinking
iOS App Tracking Transparency ended reliable cross-device attribution years ago, and B2C brands never got that visibility back. Since then, Chrome's third-party cookie phase-out and Google's Privacy Sandbox rollout have closed off more of what marketing teams used to lean on for last-click reporting. Every quarter, a channel that used to show clear ROI drifts further into 'direct' or 'unknown source.' Teams still budgeting off last-click numbers are optimizing against a shrinking, increasingly biased sample.
Platform data silos multiplied as retail media and marketplaces grew
Consumer brands now sell and market across more disconnected systems than ever – owned site, Amazon, TikTok Shop, retail media networks, email, and in-store POS all report performance differently, and none of them reconcile with each other. Each platform has its own attribution model and takes credit for the same sale. Marketing teams spend more hours reconciling dashboards than acting on what's in them, and the fragmentation gets worse every time a brand adds a channel to chase growth.
Generic analytics tools still don't fit how consumer brands actually buy
Standard analytics platforms were built for software funnels, not consumer purchase behavior – seasonal demand swings, influencer-driven discovery, customers who research on TikTok, price-check on Amazon, and buy on the brand's own site three days later. They also miss the newest blind spot: AI answer engines like ChatGPT and Google's AI Overviews now surface product recommendations with no clickable, trackable link back to the source. Without a framework built for how B2C customers actually move, teams optimize for whatever their tool happens to measure well, not what predicts revenue.
How We Help
We start with a data audit that maps what you can actually measure today against what you think you're measuring. Most B2C brands have plenty of analytics tools but no integrated system – five dashboards that disagree with each other isn't a data stack. We map every customer touchpoint, flag where attribution has quietly broken, and identify where first-party data can replace what platforms no longer report.
Strategy centers on first-party data that doesn't depend on someone else's tracking pixel. That means customer data platforms that unify order, engagement, and service history in one place; post-purchase surveys that capture the 'how did you hear about us' data attribution can no longer supply; and modeled measurement – media mix modeling, incrementality tests – that gives you directional truth instead of false precision. None of this requires waiting on a platform to fix its own tracking.
Execution means analytics infrastructure built for B2C complexity, not a generic dashboard template. We build reporting on true customer acquisition cost by channel, lifetime value by cohort and by first-purchase category, and retention curves segmented by acquisition source. Predictive models flag which new customers are trending toward repeat purchase and which are trending toward churn, early enough to act on it.
Measurement is judged on whether it changes a decision, not on how much data it captures. We set a reporting cadence tied to your actual planning calendar – weekly for paid media, monthly for retention and LTV – so the team isn't staring at a dashboard nobody acts on. Automated alerts flag the changes that matter, a channel's true CAC moving or a cohort's retention dropping, instead of burying them in a report nobody opens.
What we deliver
- Integrated customer data platforms – one system of record for orders, engagement, and service history across every sales channel
- First-party data collection – post-purchase surveys and zero-party data capture that replace what platform attribution no longer tells you
- Custom B2C dashboards – true CAC by channel, LTV by cohort, and retention curves, not vanity metrics
- Predictive and incrementality models – media mix modeling and churn-risk scoring built for consumer purchase behavior
Perfect attribution has been dead since 2021 and it is not coming back. The brands winning right now aren't chasing clean tracking – they've built decision-making systems that work without it.
Our Methodology
Our B2C analytics methodology runs a 90-day insight optimization sprint. Phase one is data audit and gap analysis – we map current analytics capabilities against what the business actually needs to decide, from channel mix to retention investment to assortment. Phase two builds the integrated data layer and first-party collection systems that don't depend on third-party tracking. Phase three stands up predictive models and automated reporting tied to your planning cadence. Unlike analytics consultants who sell a dashboard and move on, we stay accountable for whether the reporting actually changes what you spend and where.
How We Work
The first 30 days is analytics assessment and integration planning – we audit what's tracked today, where the gaps are, and which fixes matter most. Days 30-60 build the unified data layer and first-party collection systems, working directly with your marketing and product teams so the framework matches how decisions actually get made. The final 30 days stand up predictive analytics and automated reporting. Most engagements run 6-9 months to get through full optimization, with ongoing analysis support after that based on what the business needs.
- Improved decision-making accuracy
- Clearer channel-level attribution
- Reduced reporting overhead
- Earlier churn and retention signals
If your b2c company needs data, reporting & analytics leadership, we should talk.
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Frequently asked questions
How much does a data and analytics engagement cost for B2C companies?
B2C analytics engagements typically run $18-45K monthly depending on data complexity and how many systems need to be integrated. That's still well below hiring a senior analytics lead ($150K+ base) plus the data engineers needed to support them. Most brands see the investment pay for itself within 4-6 months through better marketing efficiency and fewer dollars wasted on channels with overstated ROI. Scope – number of data sources, custom modeling needs, technical complexity – is what moves the number.
How long before we see results from a data and analytics engagement?
Basic reporting fixes and a unified dashboard typically show value within 60 days, and most of that is just cleaning up data that was already being collected badly. First-party data collection and improved attribution confidence build over 90-120 days as new data accumulates. Predictive models and incrementality testing need 6+ months of data to reach meaningful accuracy. The fastest wins almost always come from fixing existing data quality issues, not building new systems from scratch.
How does the analytics team integrate with our existing staff?
We work directly with your marketing, product, and operations teams to understand what decisions the analytics need to support. Weekly data reviews keep insights tied to what's actually being decided that week. Monthly strategy sessions adjust the measurement framework as the business changes. Our team includes data scientists and B2C-specific analysts who train your staff to read and act on the reporting themselves, not stay dependent on us.
What makes Winston Francois different from traditional analytics consultants?
Most analytics consultants sell data collection and reporting; we build for decision-making. A generic consultant hands you a dashboard built from a template – we build measurement systems around the specific ways consumer behavior gets tracked and lost across your channels. Attribution has not been fully solvable since iOS privacy changes hit in 2021, and every platform change since has made it worse, so we design for that reality instead of promising to fix it.
How do you measure ROI from a data and analytics engagement?
We track analytics impact through the decisions it actually changed – reallocated media spend, retention programs launched off a churn signal, LTV-driven changes to acquisition targets. Key metrics include attribution confidence improvement, time-to-insight, and whether recommendations from the reporting correlate with real business results. We also track whether the team is actually using the dashboards, because unused analytics don't move revenue no matter how accurate they are.
What type of B2C company is the right fit for this service?
Consumer brands selling across multiple channels – owned site plus at least one marketplace or retail media presence – typically generating $5M+ in annual revenue. The best fit is a company making real marketing and product decisions but with low confidence in the numbers behind them, whether from fragmented data or years of degraded attribution. If you have more data than you know what to do with but still argue about which channel is actually working, this is built for you.
