E-commerce: conversion, AOV, and merchandising that scales
Traffic without profit usually means weak PDP storytelling, generic recommendations, or promotions stuck behind a deployment queue. We tighten the funnel with data-aware merchandising your growth team can run.
Trusted by D2C and e-commerce teams optimizing revenue — not just traffic
●Strong AOV lift patterns in 90-day programs (2.4× class outcomes)
●Merchandising workflows your team owns without weekly engineering tickets
●Shopify and headless stacks
Before → after
A clear picture of what changes when operations run on a system designed for your workflow — not generic SaaS defaults.
Before
Healthy traffic but carts abandon
Every bundle test waits on engineering
Generic recommendations that ignore margin
After
Segment-aware merchandising and PDP experiments
Recommendation logic tied to behavior and economics
Operator tools to launch promos without deploys
Your 3-step system
Structured thinking you can repeat — diagnose the real process, build the product spine, then optimize with automation and intelligence.
1
Diagnose
We audit funnel leaks, catalog structure, and how your team ships changes today.
2
Build
We implement storefront, service, and admin tooling for merchandising and recommendations.
3
Optimize
We iterate on ranking, segments, and UX using measured revenue impact.
Case snapshot
Case snapshot — D2C brand under margin pressure
Pattern from e-commerce work: traffic looked fine; profit did not.
~2.4× average order value improvement within ~90 days in comparable engagements
Merchandising stopped filing tickets for every test
Cross-sell became intentional, not random
Composite; category, traffic quality, and brand all affect your curve.
If this sounds familiar
Common pain signals
Traffic looks healthy but carts abandon; merchandising blames "the algorithm."
Testing bundles or promotions takes engineering every time.
Underlying issue: Low conversion, high bounce, and weak cross-sell on product pages.
Outcomes you can aim for
Numbers below align with our public case-study narratives — illustrative of strong execution in this problem class, not a guarantee for every engagement.
When merchandising aligns with behavior, teams often see higher AOV and conversion — including 2.4× AOV in 90 days class results we publish.
Typical stack & patterns
Shopify, Python, ML APIs (typical).
DirectionLow conversion, high bounce, and weak cross-sell on product pages. → tailored product and integrations.
Who this is for
D2C brands, marketplace sellers, and e-commerce teams with product-market fit but revenue leaking on-site.
Why CPS TechLabs
Objections removed early — we are not here to sell a science project.
We do not rip everything out blindly
We integrate with what already works and replace modules only when the business case is obvious.
Built for real operations
Our UX and data models assume messy humans, partial data, and peak-hour pressure — not textbook workflows.
Fast delivery, phased rollout
We ship thin vertical slices early so stakeholders see real flows before we scale complexity.
FAQ
The questions buyers ask before they book time.
Will this replace our Shopify theme?
Often we extend Shopify or go headless selectively. We do not rewrite what already works without a revenue case.
How long does a first valuable slice take?
Most teams see a first production slice in roughly 2–6 weeks depending on scope, integrations, and approvals. Larger programs run in phased milestones — rarely a single big bang.
Do you provide ongoing support?
Yes. Many clients choose a monthly partnership or SLA-backed retainer after launch so you are not stuck maintaining alone.
Where are you based — can you work remotely?
We are headquartered in Prayagraj (Allahabad), Uttar Pradesh. We serve teams across Prayagraj, Lucknow, Kanpur, Varanasi, and the rest of India. Discovery and delivery are remote-first with scheduled on-site visits to UP cities when it helps.
Get your system blueprint
Tell us how you operate today — we reply with a concrete next step, not a generic brochure.