Weak or manual product recommendations
14 recommendation engines, one catalog, zero guesswork
From bestsellers to personalized post-purchase picks, ecommerce brands run all 14 on Maestra Platform, a marketing personalization platform with a dedicated forward-deployed marketer, to match products to real customer behavior.
Brands running on Maestra
The problem
When recommendations actively work against your average order value
Some recommendation setups surface whatever is cheapest or easiest to match, quietly dragging down AOV instead of lifting it. Others require so much manual customization that teams end up doing the engine's job for it, defeating the point of automation.
What we hear from brands
an ecommerce brand with a companion mobile app sees product recommendations surfacing very low-priced items, dragging down AOV
an industrial-supplies company wants recommendations based on industry affinities and cart context, but existing solutions require heavy customization
a home goods and drinkware brand says current upsells require significant manual editing and don't effectively cross-sell between categories
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
Customer proof
Enlightened Equipment's recommendations now know a men's sleeping bag from a women's
Klaviyo lacked built-in website personalization, and third-party recommendation apps kept failing for Enlightened Equipment. Maestra's recommendations went live across the homepage, product pages, cart, 404s, and empty search results, tuned to gender, behavior, and gear update cycles.
“Maestra was exactly what we were looking for. Their ML algorithms understand our product relationships perfectly, when a customer views men’s sleeping bags, they see relevant quilts and sleeping pads. And if we need to display products according to specific business logic, we can simply set up custom business rules without any coding.”
How it works
No IT project, no downtime, no guesswork
Access, not a project plan from you
You are not asked to run a migration project. You grant access and approve what your marketer proposes.
Both systems run side by side
Your existing stack keeps working the whole time, so there is no blackout period for campaigns or customer data.
Timeline set by complexity, not guesswork
Two to four weeks covers most growing brands. Complex, nine-figure businesses run three to seven weeks.
The platform
Ten modules that were never meant to be separate tools
Site personalization, recommendations, email, SMS, and the rest of Maestra's modules were built to share one commerce-specific data model, not bolted together after the fact. That foundation is what supports 2M RPM at under 300 milliseconds across the platform.
Your forward-deployed marketer
Meet the forward-deployed marketer behind your account
Every Maestra subscription includes a dedicated forward-deployed marketer who works your account like it is the only one on their desk. That is realistic because they never carry more than 15 accounts, compared to the 60 or more common elsewhere.
Fewer than 15 accounts per marketer, versus 60+ industry-wide
5-minute response time versus 72 hours elsewhere
4 strategy meetings a month instead of 1
Replace your stack
Replace the stack, not the results
The goal of consolidation is never fewer tools for their own sake, it is fewer tools that still do the job well. Brands moving off point solutions and onto Maestra report the functions they consolidated work as well as before, if not better, because they now share data natively.
Ready to put the platform to work?
Talk to an expert about your setup, your timeline, and what a switch to Maestra would actually involve.