Experimentation / CRO

Experimentation and Testing Programs acknowledge that the future is uncertain. These programs focus on getting better data to product and marketing teams to make better decisions.

Research & Strategy

We believe that research is an integral part of experimentation. Our research projects aim to identify optimization opportunities by uncovering what really matters to your website users and customers.

Data and Analytics

90% of the analytics setups we’ve seen are critically flawed. Our data analytics audit services give you the confidence to make better decisions with data you can trust.

Personalization opportunity

You know they'll need it again. You still make them hunt.

Remind them when they're actually due.

Effort
Retention
E-commerce

Step 1 · Signals

What do we know about this person?

Connected

Repeat SKU purchases with a detectable interval.

Example eligibility rule

Wait for at least three completed purchases of the same replenishable item. Predict a due window only when the intervals are reasonably stable, such as within 20% of the customer's median interval. Suppress the reminder after another purchase, return, or cancellation.

Step 2 · The need

What’s really going on?

Same SKU, same cadence. Consumable, refill, replenishment. The site waits for them to remember. A reminder, a one-click reorder, or a subscribe option at the right time could remove that repeat effort.

Avoid this assumption

Subscribe-and-save shoved on the first purchase is not this. This is timing the ask to a pattern you have already observed.

Step 3 · Worth it?

Do the math before you build.

Replenishment is a clear retention benefit because it saves customers a trip they already take. The timing should come from a reliable pattern, not a guess.

Reach

How many eligible people can you reach in the chosen channel and time period?

Cost

What will content, delivery, QA and ongoing maintenance cost?

Return

What incremental outcome would cover that cost, and what is it worth?

Planning inputs, not measured results. Estimate reach × incremental outcome rate × value per outcome, then subtract cost. Use a range and a consistent time period.

Step 4 · The response

Pick how you help.

Example format: Sequence

A timely reminder with one-click add, plus an optional subscribe.

Compare lighter and deeper responses

Choose the lightest response that solves the need.

Assist: Offer useful help

A reorder button in account or email near the predicted date. No auto-ship.

Adapt: Change a meaningful part

A timely reminder with one-click add, plus an optional subscribe.

Transform: Tailor the journey

A replenishment plan they opt into, with skip/pause that actually works.

What to watch out for

  • Predicting a cycle from two purchases.
  • Charging a subscription they did not mean to start.
  • Reminding them the week after they already reordered elsewhere.

Step 5 · The moment

Did it help? A/B test it.

Control

No cadence-aware prompt.

Treatment

Timed reorder / subscribe offer.

Success measure

Repeat purchase rate and time-between-orders.

Guardrail

Cancellations and "stop emailing me about this".

Among customers who met the cadence rule, compare the timed reminder with no cadence-based reminder. Measure incremental repeat purchase and time between orders, then monitor unsubscribes and reminders sent after the need was already resolved.

Win or lose, record what worked, for whom and why. What you learn becomes the next signal.

Tools that can deliver the response

  • Customer.io Event-triggered lifecycle messaging across email, push, SMS, and in-app, built on a customer profile and behavior-data layer.
  • Braze Cross-channel messaging platform (push, email, SMS, in-app, WhatsApp, and more) that orchestrates sends off real-time customer behavior.
  • Iterable Cross-channel journeys (email, SMS, push, in-app) built from a single visual studio, triggered by real-time customer behavior.
  • Bloomreach Ecommerce Search AI-driven ecommerce search and discovery platform (Loomi AI) that personalizes ranking, recommendations, and merchandising from first-party shopper data.

Try the framework on another situation

You have their history. Next time still starts from scratch. Ask to save one useful default, when it would actually help.

The cart is missing the thing that goes with it. Offer that piece, not a random add-on.

Work through it in the hub

Score your personalization program, explore more customer situations, and compare the tools that can deliver them.