Weak or manual product recommendations

A bigger basket beats a deeper discount

Maestra Platform grows order value through relevance rather than price, an all-in-one retention marketing platform for ecommerce brands, with a forward-deployed marketer running the tests.

Brands running on Maestra

Customer logoBlue Q logoCustomer logoCustomer logoCustomer logoCustomer logo

The problem

Your recommendation widget doesn't know what it's looking at

Basic "you might also like" widgets show the same popular items to everyone, regardless of what a customer is actually viewing. When the engine can't read product attributes like color, size, or material, it recommends the wrong variant, the wrong fit, or an item that's already out of stock.

What we hear from brands

an ethically sourced jewelry brand needs better website and email personalization to lift conversions and cross-sell new jewelry lines

a leather handbag brand says its current product recommendations are static and not predictive

a premium womenswear brand calls its merchandising functionality damagingly bad, relying on manual picks instead of algorithmic intelligence

The new way

When browsing isn't enough, ask the customer directly

Product-picking quizzes cure decision fatigue by asking a few short questions and surfacing matching products immediately, while capturing preferences that sharpen targeting across every channel. Pair that with value-packed bundles built from products that actually go together, and discovery stops depending on the customer getting lucky.

Outcomes brands report

15

hours saved weekly on manual product curation

From the Selkirk Sport case study

+22%

revenue growth on the same budget

From a published case study

20x

lift in new subscriber volume

From a published case study

Customer proof

Sixteen colors of one dress is not a recommendation

4.8 rating on G2
G2 Momentum Leader, Marketing Automation

The previous widget treated every variant as a separate product and filled itself with the same item. Understanding variants freed the slots for things the shopper had not seen.

Sixteen colors of one dress is not a recommendation (Maestra case study)Read the full case study

+34.9%

more items in the average basket

How it works

The switch, from the marketing team's side

01

Week one

You agree the roadmap and grant access. Nothing changes for your customers.

02

The weeks after

Your marketer migrates data, warms domains, and rebuilds the programs while your current campaigns keep going out.

03

Launch week

Sending moves over channel by channel, and the old tools stay available until you decide you are done with them.

The platform

Where the customer profile actually comes from

Store events, order data, campaign response, and site behavior write into the same record, with cleansing and error monitoring built in rather than bolted on afterwards.

Real-time CDPAnalyticsSegmentationOmnichannel journey builder
The Maestra platform interface

Your forward-deployed marketer

Deliverability is somebody's job here

Domains, sender signatures, and Postmaster are configured at migration and watched afterwards, with inbox placement treated as a metric rather than a complaint to answer.

Domains, signatures, and Postmaster configured for you

Email health monitored, not just reported

Warm-up handled during migration

Replace your stack

One vendor to hold accountable

When email, the website, and the recommendations belong to three companies, a problem that spans them belongs to nobody. One platform makes the answer somebody's responsibility.

Replaces

Bloomreach

Attentive

Nosto

Optimizely

Sendlane

Read the cases before you read a pitch

The brands on the site describe what they switched from and what changed afterwards, in their own numbers.