Growth

AI for growth audiences, offers, and repeat purchases.

Explore how EkamFlow helps growth teams prioritize conversion audiences, test relevant offers, and improve product discovery with customer predictions.

Book a demoSee how it works
Decision workspaceIllustrative demo
Business signals
Customer conversion and purchase historyProduct interest and campaign responsesOffer eligibility, availability, and experiment outcomes
EkamFlow decisioningPrivate model

Customer C-4128

Conversion opportunity

Interest patternPurchase interest is increasing
Recommended decisionInclude an eligible customer in a conversion test
Continue in an existing workflowGrowth experimentation team

Illustrative workflow and sample data.

Put the decision to work

How does EkamFlow help growth teams?

EkamFlow helps growth teams choose a more informed starting point for conversion, repeat-purchase, and expansion experiments. Purchase propensity can identify relevant audiences. Offer recommendations can help a team choose an appropriate incentive, while product recommendations can support discovery and cross-sell. The growth team still defines the experiment, applies business rules, and compares results with an agreed baseline. A likely buyer is not necessarily a customer who needs a discount, so incremental outcomes and contribution margin should guide the evaluation.

Evaluation measure 01Incremental conversion
Evaluation measure 02Contribution margin
Evaluation measure 03Repeat purchase rate

Agree on a baseline before rollout. Measures shown are evaluation criteria, not promised results.

Department workflows

Practical workflows for Growth.

Connect a business challenge to a decision, an action, and an outcome your team can evaluate.

Workflow 01

Select an audience for a conversion experiment

Recent activity does not always mean a customer is ready to purchase.

Explore purchase propensity
Signals
Product interest, purchase history, campaign responses, and known conversion outcomes.
AI decision
Estimate purchase likelihood for a defined objective and window.
Team action
Choose an eligible audience and a comparison group for the experiment.
Measure
Incremental conversion and cost per conversion.

Workflow 02

Test offers without losing sight of margin

A blanket discount can reduce contribution without creating additional purchases.

Explore next best offer
Signals
Offer response history, customer activity, available offers, and eligibility.
AI decision
Recommend a relevant offer within the business's constraints.
Team action
Test the offer with margin limits and a holdout audience.
Measure
Additional purchases, contribution margin, and incentive cost.

Workflow 03

Improve product discovery

Customers may not find relevant complementary products in a broad catalog.

Explore product recommendations
Signals
Product attributes, order history, browsing interest, and availability.
AI decision
Recommend relevant products for an eligible customer.
Team action
Review product fit and availability before testing recommendations in an existing journey.
Measure
Recommendation conversion, average order value, and returns.

Illustrative example

A repeat-purchase offer with a holdout audience

  1. A growth team selects an eligible customer cohort for a repeat-purchase experiment.
  2. Purchase propensity informs audience planning and offer recommendations help choose a relevant incentive within margin limits.
  3. The team compares purchases and contribution with a holdout group before deciding whether to expand the test.

Start with one decision

What does a useful pilot need?

Choose one conversion event, one customer cohort, and one action the team can vary. Record experiment exposure and outcomes, agree on a holdout audience, and define a margin or cost threshold before launch.

Considerations for this department

Avoid equating purchase likelihood with incremental impact. Account for other campaigns and product changes during the experiment, and evaluate new customers separately when their purchase history is limited.

Review EkamFlow data handling

Common questions

Growth questions, answered.

Practical answers about use cases, data, and evaluating a pilot.

How can growth teams use purchase propensity?

Purchase propensity estimates the likelihood of a defined conversion from historical activity. Growth teams can use it to plan relevant audiences, then test an action against a comparison group to assess whether it creates additional conversions.

Can EkamFlow replace A/B testing?

Customer predictions help select an informed experiment and audience. An A/B test or agreed comparison group is still useful for measuring whether the action changes outcomes. The growth team defines and evaluates the experiment.

How can teams evaluate next best offers?

Test eligible offers against a baseline or holdout group and measure additional purchases alongside contribution margin and incentive cost. A high offer acceptance rate alone does not show that a discount was profitable.

What data is useful for a growth pilot?

Historical conversion events, purchase history, product engagement, previous offer responses, and experiment outcomes can be useful. Define the conversion window, available actions, and eligibility rules before agreeing on scope.

Work across teams

Connect this decision to the wider business.

Agree on customer definitions, action ownership, and outcome feedback across the teams involved. Then adapt the workflow to your industry's data and priorities.

Connected departments

Industry applications

Explore further

Connected customer decisions

Evaluate EkamFlow for a specific business goal.

Discuss the use case, available data, and the next step with the team.

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