Can't personalize beyond basic email and SMS

Personalization apps you bolted on, and the ones that keep failing

Maestra Platform does it natively instead, an all-in-one retention marketing platform for ecommerce brands where a forward-deployed marketer replaces the bolted-on stack.

5.6x

growth in Memorial Day sales revenue

From a published case study

34x

Meta ads ROAS, up from 17x on the previous dynamic product ads

From a published case study

2.6x

growth in Meta ads ROAS versus the previous dynamic product ads

From a published case study
4.8 rating on G2

Brands running on Maestra

Customer logoCustomer logoCustomer logoCustomer logoCustomer logoCustomer logo

The problem

Recommendations that ignore what someone just did

Product recommendation modules often run on fixed logic: bestsellers, or a static "you might also like" block that never updates. A shopper who just looked at one category keeps seeing suggestions from a completely different one, which reads as inattention rather than personalization.

What we hear from brands

product recommendations are static and not predictive, according to a handbag brand

a kitchen-appliance brand frustrated that a shopper who just viewed a juicer gets shown a blender next

static product carousels ignore customer segments, according to a sustainable underwear brand

The new way

Turn the same catalog into a different asset for every channel

Maestra converts a product feed into on-brand dynamic visuals, with tags, ratings, and current pricing, that render in real time for ads, email, and the website alike. When a price changes, the image updates everywhere it appears, without a design request.

Customer proof

The mobile popup was converting at a quarter of a percent

4.8 rating on G2
G2 High Performer, Customer Data Platform

Rules written for desktop were quietly wasting the majority of the traffic. A framework of A/B tests on timing and trigger took the mobile capture rate to three times what it had been.

The mobile popup was converting at a quarter of a percent (Maestra case study)Read the full case study

0.26% → 0.8%

the pop-up's lead capture rate

How it works

Why this migration does not stall

01

One owner

A named marketer owns the move end to end instead of a ticket queue that passes it around.

02

Specialists on call

Deliverability and data specialists are pulled in by your marketer when a step needs them, without you making the introduction.

03

Momentum after launch

The improvements that are easy to postpone keep getting shipped, because someone whose job it is keeps pushing them.

The platform

Real time means the visit you are in right now

Profiles update as the shopper moves, at 2M requests per minute with responses under 300 milliseconds, so a recommendation or a banner reacts to what happened seconds ago instead of what a nightly sync knew this morning.

Including

Real-time CDPSite personalizationProduct recommendationsEmail, SMS and MMSMobile pushMaestra AI
The Maestra platform interface

Your forward-deployed marketer

Lessons from hundreds of brands, applied to yours

Your marketer has run this migration and these flows for ecommerce brands before, so the first version of a program starts from what already works elsewhere rather than from a blank canvas.

Patterns taken from hundreds of implementations

Ecommerce-specific expertise, not general martech

Faster results because the first draft is not guesswork

Replace your stack

The connectors go too

Every tool in a stack needs a connection to the store and to the other tools, and each of those is a thing that breaks quietly. Consolidating removes the maintenance along with the vendors.

ReplacesZapierKlaviyoNostoWisepopsRebuy

A question worth asking your current vendor

Ask what they would build for you next month. If the answer is a help article, the comparison is already made.