Weak or manual product recommendations
The same smart recommendation, whether it's on your site or in an email
For ecommerce brands, RetailHQ pairs a marketing personalization platform with a dedicated forward-deployed marketer so product picks stay consistent across web, email, and SMS instead of breaking channel to channel.
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
AI on autopilot, with business rules whenever you want the wheel
Recommendations can run entirely on RetailHQ's AI engine, picking products from your inventory automatically. Or your team can fine-tune by color, price, category, or collection with no-code business rules, so merchandising expertise and machine learning work side by side instead of competing.
27%
reduction in marketing stack costs
7.4%
of total revenue generated through a personalized cross-sell flow
+26%
total sales after consolidating the marketing stack
How it works
After go-live, the work does not stop
Weekly review
What shipped, what moved, and what is next gets looked at every week rather than every quarter.
Continuous testing
Subject lines, offers, timing, and layouts are A/B tested on an ongoing basis, not once at launch.
New programs each month
The roadmap keeps producing flows and experiments long after the migration is finished.
The platform
Ten modules, one real-time engine underneath
RetailHQ's real-time CDP sits at the center of ten connected modules, all reading from a commerce-specific data model built for products and orders, not generic web events. The engine processes 2M requests per minute at under 300 milliseconds.
Your forward-deployed marketer
Why response time is the metric that matters most
A 72-hour reply is standard at most platforms. RetailHQ's forward-deployed marketers respond in 5 minutes, because each one manages under 15 accounts instead of the 60-plus norm, so your question is never sitting in a queue behind fifty others.
5-minute response time versus 72 hours industry-wide
Fewer than 15 accounts per marketer
Shared Slack channel included
Replace your stack
The agency-plus-tools model, retired
Paying an agency retainer to operate a pile of disconnected tools is a common pattern, and a costly one to maintain. RetailHQ replaces both halves: the point solutions and the outside execution team, with one platform and a dedicated marketer built in.
Replaces
Klaviyo
Attentive
Nosto
Yotpo
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.