Advance

Advance

Decisions, not dashboards.
Spend that compounds.

You have dashboards. The exec team still argues about which channel is working. Marketing reports one revenue number, finance reports a different one, both are technically correct. Advance is the modeled answer to a specific decision — attribution that survives a CFO challenge, LTV on a real cohort, a churn score wired into the system that acts on it. Every model ships with a kill-switch and a quarterly review; if it stops earning its keep, we say so and turn it off.

Work with us →Other programs ↓
Typical engagement
Retainer or 4–8 week project
Includes
Advanced Analytics · Marketing Analytics

What we do

What gets delivered.

Five things every engagement ships with — the signed plan, the model, the wiring, the kill-switch, and the mart your specific question actually needs.

  1. 01

    A measurement plan your decision owner signs.

    One page. The decisions the model has to defend each quarter. The smallest signal that would change course. Signed by the VP or CMO who lives with the output, not by us. This is the contract; the model is the implementation. Every Advance engagement ships with one.

  2. 02

    The model itself, with every assumption in plain English.

    Code, parameters, calibration tables, all in your repo. The methodology document covers every choice we made and every alternative we tried and rejected. A new hire can read it and re-run the pipeline. No black box, no notebook nobody else can open.

  3. 03

    Operational wiring to the system that acts on the prediction.

    Predictions land as a dbt-modeled mart, then route to where the decision actually gets made — the BI tool, the Meta or Google audience API, the CRM lead-score field, a Slack alert. Refresh cadence is set against decision cadence, never against compute cost.

  4. 04

    A kill-switch and a quarterly review on the calendar.

    Written conditions for retiring the model before we start. First quarterly review booked in week one. When a model stops earning its keep at review, we say so and turn it off. Retirement is decided up front so it isn't political later.

  5. 05

    The mart or model your question actually needs.

    An attribution mart with a versioned channel-calibration dim. A cohort-economics mart at (cohort × channel × month-since-acquisition) against fully-loaded spend. A churn model that lands as a `risk_score` on the customer table. A geo-lift read. Scoped per engagement against the specific decision in the brief.

Method

How the work goes.

Five phases against one signed decision. The brief gets agreed before the code. Every prediction gets a named owner. Every quarter, the model has to re-prove it earns its keep.

  1. Phase 1

    Scope the question

    You bring a decision — which channels to cut, which customer to flag, where to set inventory. We write a 2–4 page brief covering that decision, the spend at stake, the cadence it's made at, and the smallest signal that would change course. Signed by the decision owner before any code gets written. If the answer is ‘this isn't worth a model’, we say so and refund the rest.

  2. Phase 2

    Instrument & validate

    We audit the warehouse against the brief. Every input traced from raw source to mart. Counts and sums reconciled against the ad platform, the billing system, the panel — if marketing-counted bookings differ from finance-counted revenue by 28%, you find out now, not at the board meeting. Output is a validation memo: what's clean, what isn't, what we're modeling on, what we're ignoring.

  3. Phase 3

    Model & defend

    Start with the boring baseline — linear attribution, last-touch, a 30-day moving average. The new model has to beat it in defensible terms, not just be cleverer. Smallest model that answers the question: logistic regression before XGBoost, XGBoost before deep learning. Validated against a holdout the team didn't see. Calibrated against ground truth — panel data, geo-lift, controlled experiment. The methodology document is the deliverable; the notebook is the workshop.

  4. Phase 4

    Operationalize

    Predictions land in a dbt-modeled mart in your warehouse, not a CSV someone refreshes. From there they route to where the decision happens — BI tool, Meta or Google audience API, CRM lead-score field, Slack alert. One named owner on your side. A runbook covering drift, data outage, schema change. Refresh cadence set against decision cadence, never faster than that.

  5. Phase 5

    Retain or retire

    A 60-minute working session each quarter against the measurement plan. Did the decisions get made? Did predictions hold up against ground truth? Cost of keeping vs. value of keeping — if maintenance overhead exceeds the dollar impact, we kill it. Kept, modified, or retired. The call is written down.

Capabilities

What we model.

Boring statistics before clever ML. If a regression survives holdout, a neural net is overkill. We pick the smallest model that answers the question, and we pick a library your team can read and run after handoff.

  • Multi-touch attribution
  • Bayesian MMM
  • Geo-lift testing
  • Cohort LTV
  • Survival-based LTV
  • Churn prediction
  • Demand forecasting
  • Lead scoring
  • Anomaly detection
  • Fractional analytics lead
  • Pre-board pressure-testing
  • Hiring support
  • Quarterly model review
  • Kill-switch governance
  • Holdout validation
  • Calibration to ground truth

Outcomes

What this looks like
in practice.

Five anonymized patterns drawn from prior engagements. Different industries, same shape — a modeled number that re-grounded the decision underneath the budget.

Pattern · multi-brand CPG

Cross-channel attribution that survived two CMO changes.

Three brands sharing a paid media buy. The attribution model had been rebuilt three times in 18 months because nobody could agree on the weights. We wrote the measurement plan first — seven decisions the number had to defend — then built a weighted multi-touch model with per-channel time-decay, calibrated to a Nielsen panel. Versioned channel-calibration dim. The argument moved from ‘which weights’ to ‘which decisions’.

Pattern · healthtech marketplace

Cohort economics on a definition both marketing and finance signed.

Series-B marketplace, blended CAC reporting nobody trusted. Marketing counted bookings, finance counted completed-net revenue, the gap was 28–34% per cohort. A signed one-page metric contract defined what an ‘activated patient’ was. We stitched ad-click → signup → booking → completed appointment → repeat booking into one event spine, then built a cohort-LTV mart at (cohort × channel × month-since-acquisition) against fully-loaded spend. Two channels got cut in week six against the new read.

Pattern · mid-market retail

Promotional spend instrumented for measurable lift, not gut feel.

Multi-banner shopper-marketing program running into the tens of millions on instinct. We instrumented lift at SKU × banner × week. The quarterly status deck became a weekly attributed-lift review. Promotions that didn't earn their lift got cut at the next review. Reporting cycle moved from three days to four hours.

Pattern · fractional analytics lead

A senior pair of hands before a full-time Director hire makes sense.

The shape that fits companies that have outgrown their first analyst but aren't ready for a $200k Director. Two days a week. Standing pre-board slot to pressure-test the numbers the CEO is about to defend. Typical length 12–15 months — usually closes when a Series B or new VP makes the full-time hire the right call, at which point we write the JD and run the technical screens.

Pattern · subscription churn

A churn model that earns its keep at quarterly review — or gets killed.

12+ months of cohort history, predicting 30-day churn. Logistic regression on engagement, billing, and support-ticket features first; XGBoost only if it beat a survival-curve baseline. Predictions ship as a `risk_score` on the customer table, refreshed weekly. Success-management reads the top-decile list each Monday. Retired at quarterly review if lift-on-save-rate against a control falls below the maintenance threshold.

Next

Ready to start with Advance?

Send us a note describing the decision you're trying to make and the spend at stake. If your warehouse is sound, we'll scope the model. If it isn't, we'll say so and route you into Diagnose or Build first — building Advance on unstable ground is how good consulting goes wrong.

Start the conversation→
DiagnoseOr see Diagnose →See what's broken. Know what to fix first.BuildOr see Build →Wire it right. Document it well.
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