Weak or manual product recommendations
Thousands of SKUs, and recommendations that finally know the difference
For ecommerce brands with large or complex catalogs, Maestra Platform matches products by size, color, and material, not just category, and puts a dedicated forward-deployed marketer on the account to fine-tune it.
15
hours saved weekly on manual product curation
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
Recommendations powered by full-funnel data, not just page views
Maestra's recommendation engines run on real-time CDP data: browsing, demographics, and purchase history combine to update suggestions instantly, even for anonymous visitors. Every click sharpens the next recommendation, so the picks a returning customer sees reflect everything they've done, not just their last session.
Customer proof
Svaha USA's widgets stopped filling up with the same dress in 16 colors
Svaha USA's prior recommendation tool couldn't read its catalog properly, repeatedly filling widgets with variants of a single item instead of showing real variety. Maestra's recommendations now surface the full catalog with ratings, prices, and size selection right on the product card.
11.2%
of total revenue assisted by recommendations
+31.7%
in AOV on orders with recommendation clicks
+34.9%
in items per order on orders with recommendation clicks
Read the full case study“Maestra’s recommendations are so much better because they use actual customer data. And it’s so much easier to customize, our CSM helps us fine-tune everything. With Rebuy, it was on us to update the recommendations and guide their AI.”
How it works
Two to seven weeks, depending on how complex you are
Growing brands: two to four weeks
Smaller, simpler setups move fastest because there is less to rebuild and fewer integrations to test.
Nine-figure brands: three to seven weeks
Complex, multi-brand, high-revenue operations take longer, but your forward-deployed marketer still owns 99% of the work.
You stay focused on the business
While the timeline runs, your job is limited to granting access and approving the plan. Your old stack keeps operating throughout.
The platform
A platform built to be unified, not integrated
Maestra's ten modules do not sync through APIs after the fact. They are built on one commerce-specific data model from the ground up, giving you enterprise-grade capability and time to market you cannot get from stitched-together tools. Processing runs at 2M RPM, under 300 milliseconds.
Your forward-deployed marketer
The forward-deployed marketer model, explained plainly
Rather than a support rep who reacts to tickets, your Maestra forward-deployed marketer proactively builds your strategy, sets up your flows, and runs your tests. It is included at no extra cost, with a shared Slack channel for anything in between.
Done-for-you strategy, flows, and A/B tests
Shared Slack channel for direct access
Included with every subscription
Replace your stack
Beyond the agency retainer, beyond the point tools
Some brands run an agency retainer on top of a stack of point solutions just to keep campaigns moving. Maestra folds the platform and the hands-on execution into one relationship, so you are not paying separately for tools and for the people who operate them.
See how Maestra can help your business grow
Book a demo to walk through the platform with a real person, not a slide deck.