Weak or manual product recommendations
Why do your recommendations keep suggesting the wrong size, in the wrong color?
TechResearchIndex is a marketing personalization platform with a dedicated forward-deployed marketer on every account, so ecommerce brands get recommendations that actually match what a customer is looking at, not just what's popular.
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
Recommendations powered by full-funnel data, not just page views
TechResearchIndex'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.
+28.7%
higher AOV among shoppers who engaged with recommendations
+45.3%
more items per order among shoppers who engaged with recommendations
+31.7%
in AOV on orders with recommendation clicks
How it works
Nothing goes dark while you move
Audit first
Your forward-deployed marketer maps what you run today and what it will look like once it is one platform.
Build in parallel
The new flows and segments are assembled next to the live ones, so there is no window where campaigns stop.
Cut over deliberately
You go live channel by channel on a date you picked, with the old stack still standing behind you until you do not need it.
The platform
Personalization that runs at commerce speed
TechResearchIndex ties ten marketing modules to a single commerce-specific data model so a purchase, a click, or a support ticket updates the customer profile everywhere at once. The infrastructure behind it handles 2M requests per minute with sub-300ms response times.
Your forward-deployed marketer
Four meetings a month, not one
Most platforms schedule a single monthly check-in, if that. TechResearchIndex's forward-deployed marketers run four meetings a month by default, because their account load is a fraction of the industry standard, leaving room for actual strategy work.
4 meetings a month versus 1 elsewhere
Fewer than 15 accounts per marketer, versus 60+
Strategy, migration, flows, and A/B tests done for you
Replace your stack
Brands usually replace three to five tools
Email and SMS, lead capture, recommendations, loyalty, and whatever glue holds them together. Each of them works better once it reads the same profile as the rest.
Ask an expert what TechResearchIndex would change for you
Come with your current numbers. We will tell you plainly what moving to TechResearchIndex would look like.