RESOURCES/Guide
Predictive analytics versus growth decisioning
Predictive analytics estimates what is likely to happen. Growth decisioning chooses what to do about it, launches the action and measures whether it changed the outcome. A prediction is an input to a decision, not a substitute for one: a churn score tells you who is at risk, not who is worth saving, with what, at what cost.
The model shipped, and nothing changed
A churn model reaches good accuracy, goes into production, and twelve months later retention is where it was. This is the most common outcome of a predictive programme, and it is not usually a modelling failure. The score was never connected to a decision anyone was accountable for, an action anyone could launch, or a measurement anyone would accept.
- A score without an action is a report.
- An action without an eligibility and cost model treats every at-risk customer as equally worth saving.
- An action without a control cannot be told apart from what would have happened anyway.
What sits between a score and a result
Prediction is one of six steps. The other five are where most programmes stall, and none of them is a modelling problem.
01
Predict
Estimate risk, propensity or value. Necessary, and the part most organisations have already built.
02
Value the customer
Convert risk into value at risk. A high-risk customer worth very little is not a priority.
03
Choose the action
Compare candidate treatments on expected incremental value, not on which team proposed them.
04
Apply constraints
Consent, contact policy, margin floors and regulatory limits are hard filters on what may be done.
05
Execute
Deliver through the existing engagement, product or service surface, or the decision stays theoretical.
06
Measure incrementally
Read against a randomised control. Model accuracy says nothing about whether the action helped.
Prediction versus decisioning
| Question | Predictive analytics | Growth decisioning |
|---|---|---|
| What does it produce | A probability, a score, a segment. | A chosen action for a specific customer, executed. |
| Who is worth acting on | Not addressed. Risk is not the same as value at risk. | Expected value net of margin and contact cost decides. |
| What action to take | Left to a human, usually in a separate tool. | Selected from competing candidates by arbitration. |
| Execution | Hands the list to a marketer or a reverse-ETL job. | Launches inside the systems already in place. |
| Measurement | Model accuracy: AUC, lift charts, calibration. | Incremental outcome against a randomised control. |
| Failure mode | An accurate model nobody acts on. | A wrong hypothesis, retired cheaply and on purpose. |
Diagnose your own predictive programme
For each model currently in production:
- What decision does this score change, and who owns it?
- Is the action attached to the score, or is it chosen manually each time?
- Does the action account for the customer's value, or only their risk?
- Can the action be launched automatically, or does it wait for a build cycle?
- Was the last readout about model accuracy or about incremental revenue?
- If the model were switched off tomorrow, what would visibly change?
When prediction alone is the right stopping point
- Forecasting and planning use cases, where the output is a number a human uses to make a plan.
- Risk, fraud and credit decisions, where the score feeds a governed rules process by design and autonomy is not wanted.
- Early-stage analytics work where the goal is understanding a driver rather than acting on it.
- Situations where no action is available or affordable. Adding a decision layer over an empty action space changes nothing.
Markin is an autonomous growth-science team for large B2C businesses. It investigates why revenue per customer is stuck, forms its own hypotheses across marketing, product, pricing and technical health, chooses the next best action for each customer, launches it through the systems the business already runs, and proves every one against a randomised holdout.
Decisioning tools choose between the actions your team already built. Markin decides what to build.
Questions people ask
- What is the difference between predictive analytics and growth decisioning?
- Predictive analytics estimates what is likely to happen. Growth decisioning chooses what to do about it for each customer, applies eligibility and cost constraints, executes through existing systems, and measures the incremental result against a control. Prediction is an input to decisioning, and on its own it changes no outcome.
- Do I still need models if I have a decision layer?
- Yes. Scores are one of the inputs the decision uses to estimate expected value. The change is what happens after the score exists: it is attached to an action, a constraint set and a measurement, instead of being delivered as a list.
- Why does model accuracy not predict business impact?
- Because accuracy measures how well you identify who will churn, and impact depends on whether the action you took changed their behaviour. A perfectly accurate model paired with an action that does not work produces zero incremental revenue, and only a control group reveals that.
Keep reading
Churn prediction versus retention decisioning
The same distinction in the retention case specifically.
Controlled growth experiments
How the action is proven once it exists.
How Markin generates hypotheses
Where the candidate actions come from.
Decision layer reference architecture
Where models sit relative to decisions.