Conversion audit
Funnel analysis from landing to purchase, with the specific steps losing the most sessions identified and ranked by recoverable revenue.
(Services / E-Commerce)
Paid acquisition, and the work that happens after the click
Grow eCommerce Sales
Conversion audits, product feed corrections, merchandising, checkout recovery and retention work, for storefronts that already have people arriving. We price each drop-off before touching anything, so the order of work is not a matter of opinion, then change one thing at a time against a baseline. What we report on is revenue per session, because a conversion rate can rise while revenue falls.
If a store already has traffic, buying more of it is rarely the cheapest next move. A store converting at one percent that gets to one and a half has increased revenue by half without a rupee more in ad spend, and the improvement applies to every channel at once.
So we start with what happens after arrival. Where sessions die, which products get viewed and abandoned, what the search box returns when someone types the thing you actually sell, and how many steps sit between wanting the item and owning it.
The second lever is the product feed, which is invisible and quietly decides a lot. Shopping and Performance Max campaigns are only as good as the data they are given, and most feeds are missing attributes that would let them target properly.
Feed work is also unusually satisfying because the return arrives quickly and applies everywhere at once. Correcting titles and attributes improves every shopping surface simultaneously, without a single change to the site itself, which is rarely true of anything else on this list.
(Included)
Product feed work
Titles, attributes, GTINs and product types corrected so Shopping and Performance Max have something real to optimise against.
Merchandising
Category ordering, related products and search result tuning, so the things that sell are the things that get seen.
Checkout recovery
Abandoned cart flows, address and payment friction removal, and the error messages that currently just say something went wrong.
Retention
Repeat purchase rate, email and lifecycle flows, and the honest calculation of whether your category supports subscription.
Product page work
Imagery, specification depth, reviews and delivery clarity on the pages carrying the most traffic and the weakest conversion.
(How it runs)
- 01
Find where sessions die
Analytics and session review to locate the drop-offs, then a rough revenue value assigned to each so the order of work is not a guess.
- 02
Fix the feed
Usually the fastest return available, because it improves every paid shopping surface simultaneously without touching the site.
- 03
Work the biggest leak
One change at a time on the highest value step, measured against a baseline, so you know what actually caused the movement.
- 04
Then look at retention
Once acquisition converts properly, repeat purchase becomes worth the effort. Before that it is optimising a leaking bucket.
(Is this the right thing to buy)
Who it suits
Stores that already have traffic arriving and are not converting it well. There has to be enough volume to see patterns, which in practice means a few thousand sessions a month at minimum. Below that, the data says too little and you are better served by fixing obvious usability faults directly.
When it is the wrong call
If almost nobody is arriving, this is the wrong end of the problem. Optimising the conversion rate on two hundred sessions a month changes very little in absolute terms. Acquisition comes first, and we would rather point you at search or paid work than sell optimisation of a funnel with nothing in it.
What it costs
A monthly scope covering analysis, a set number of implemented changes and reporting against a baseline. Priced by store size and platform, since work on a custom build costs more than the same change on Shopify. Feed work is often quoted as a separate one-off because it is a bounded job with an unusually fast return, and some clients only need that piece.
(Questions we get asked)
What conversion rate should we expect?
It varies far more by category and price point than by quality of execution, so benchmarks quoted without that context are close to useless. A hundred rupee impulse item and a hundred thousand rupee considered purchase behave nothing alike. The only benchmark worth tracking is your own last quarter.
Do we need a redesign to improve conversion?
Usually not, and starting there is expensive. Most conversion problems are specific and local: a checkout field that fails validation silently, a delivery cost revealed too late, a search box that returns nothing for a common term. Fixing those is cheaper and the effect is measurable.
How do you measure whether it worked?
Against a baseline captured before we change anything, on revenue per session rather than conversion rate alone. Conversion rate can rise while revenue falls if the change pushed people toward cheaper items, which is why we report both.