AI Data Scientist for Marketing Teams
Marketing teams need the same four answers every quarter — which channels drive incremental revenue (attribution), which customers are worth acquiring (LTV), who's about to leave (churn), what makes a page convert (CRO). OctOpus is the AI data scientist that ships all four as production models, on your stack, in days not months.
Multi-touch attribution that learns, not assumes
First-touch and last-touch attribution are folklore. OctOpus builds data-driven attribution: a model that learns each touchpoint's incremental contribution to conversion from the actual journey data. Markov-chain attribution for shorter funnels, Shapley-value attribution for credit decomposition, uplift modelling for incremental-vs-baseline conversion. Output: revenue dollars assigned to channel × campaign × creative.
Customer lifetime value, calibrated
LTV models from transactional + behavioural data. The agent picks a regression target (90-day LTV, 1-year LTV, infinite-horizon) based on data depth and validates against a chronological holdout. Calibration matters more than raw R² — a model that nails the rank-order but is 30% off in absolute dollars still drives bad budget decisions. OctOpus reports both.
Churn with intervention targeting
Churn prediction + uplift modelling: NOT 'who's going to leave' but 'who would stay IF we intervened'. The agent splits the dataset by treatment exposure when that flag is in the data, builds a meta-learner (T-learner, X-learner), and outputs a per-customer expected uplift so the campaign budget hits the right segment.
Key capabilities
- Attribution · LTV · churn · CRO — one workflow.
- Markov / Shapley / uplift attribution depending on data shape.
- Calibrated LTV — rank AND dollars matter.
- Uplift modelling for intervention targeting, not just classification.
- Exports scored customers to your CRM / paid-ads platform.
Frequently asked questions
How does OctOpus do marketing attribution?
Data-driven attribution, not first/last touch. OctOpus picks the right method by funnel shape: Markov-chain for short funnels with clear paths, Shapley-value for credit decomposition across many touchpoints, uplift modelling when you have treated/control exposure. Output is revenue assigned to channel × campaign × creative, with confidence bounds.
Can it predict customer lifetime value?
Yes — calibrated LTV at the horizon that matches your decision cadence (90-day for paid-ads bidding, 1-year for budget planning, infinite-horizon for cohort analysis). The agent reports BOTH rank metrics (Gini) and dollar metrics (MAE on actual revenue) so you know whether to trust the absolute predictions.
What about uplift modelling for retention campaigns?
If your data has a treatment flag (who got the retention offer vs who didn't), OctOpus fits an uplift meta-learner — T-learner for small treatment groups, X-learner for skewed exposure — and outputs per-customer expected uplift. Your campaign hits the customers who'd stay BECAUSE OF the intervention, not customers who would've stayed anyway.
Does it integrate with our CRM / ads platform?
Scored customers export as CSV by default; for live integrations OctOpus hosts a prediction API that your CRM / paid-ads platform hits at runtime. Salesforce, HubSpot, Braze, Iterable webhooks are straightforward; custom destinations get a webhook connector.