E-commerce & Digital

Send-time and channel optimisation

Predict the best channel and time to contact each customer, and suppress contacts unlikely to be opened, improving engagement while reducing total message volume.

AutomationPattern-matchingRevenueCustomer experience

10–25%

increase in engagement per message sent

Opportunity assessment

Business Impact
2

Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.

Feasibility
4

Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.

Data Readiness
4

Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.

Risk Exposure
4

Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.

Change Complexity
5

Very easy to absorb. Runs quietly in the background or gives people a helpful new input. Makes everyday work easier with almost no friction.

Tooling required

Specialist AI tool

Things to consider

  • Suppression is usually worth more than timing. Reducing contact to disengaged customers protects deliverability and list health, which affects every future campaign.

  • Optimising for opens will train the model to send more to people who always open. Optimise on revenue per recipient, not on engagement rate.

  • Marketing consent and preference rules override any optimisation. The model chooses within what the customer has agreed to, never around it.

  • Experiment starter: Run optimised send timing and suppression against a holdout on the same campaign. Compare revenue per thousand recipients and unsubscribe rate, and watch deliverability metrics over the following month.

AI Transformation Playbook

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