What we've actually shipped for clients.
Every case study below is a real engagement with measured outcomes. Client names are occasionally anonymised at their request, but the numbers and approach are exactly as they happened.
What a Bizeract case study actually is
Every case study on this page is a real engagement we delivered. No composite clients, no rounded-up numbers, no before-and-after screenshots from a template. The metrics, timelines, and trade-offs below are what actually happened inside each project. Where a client preferred to stay anonymous we kept them anonymous, but the industry, city, and baseline numbers are exactly as they were.
When you read each study, pay attention to three things. First, the baseline: the starting point matters as much as the outcome, because a 41% bounce-rate cut from 68% is a very different story than the same cut from 22%. Second, the attribution window: every metric is reported against a matched pre- and post-launch window so seasonality and traffic shifts don't distort the delta. Third, the approach: the "how" is often more transferable than the numbers, because the tactics that moved the needle in one context are the ones we'd bring to yours.
We publish these for two reasons. Accountability — once a result is on the public internet with a client's industry attached, we can't quietly walk it back. And pattern-matching — if you see a situation that looks like yours, you can short-circuit half the discovery call by pointing at one of these and saying "this is what I want." See our pricing page for what engagements like these typically cost, or read our blog for the public-facing version of the frameworks we use on client work.
How a Bangalore D2C skincare brand cut bounce rate 41% with a hand-coded rebuild
A skincare brand was losing mobile shoppers at the homepage because their Shopify theme was shipping 2.1 MB of JavaScript. We rebuilt the storefront on a custom stack, kept the Shopify backend, and cut time-to-interactive from 5.4 seconds to 1.9 seconds.
From ₹1.2 L CAC to ₹38K CAC: scaling a B2B SaaS on LinkedIn Ads
A founder-led SaaS was paying ₹1.2L to acquire each paying customer on LinkedIn Ads. We rebuilt their funnel, targeting, and creative, and brought CAC down to ₹38K inside one quarter — with more customers than before, not fewer.
A Delhi fintech replaced 14 spreadsheets with one warehouse and three dashboards
A founder was spending 6 hours every Monday rebuilding the same weekly business review from spreadsheets pulled out of five different systems. We set up a BigQuery warehouse, dbt models, and three dashboards. He got his Mondays back.
Case studies — FAQ
How we scope, measure, and report client outcomes.
No single number is typical — each engagement starts from a different baseline. What is consistent is the methodology: we define the metric, set a baseline window, track attribution honestly, and report what actually moved. The results you see below are real outcomes for those specific clients in their specific market conditions. Your starting point and constraints will differ; during the discovery call we outline a realistic range based on comparable situations we have seen.
A few clients operate in competitive categories — D2C skincare, B2B SaaS, agency finance — where sharing revenue figures or ad-spend ratios under their brand name would give competitors a read on their unit economics. We respect that. Every anonymised case still names the city, industry, approximate revenue band, and exact metrics. Nothing about the numbers or approach is fictionalised. When a client is open to being named, we name them.
Yes. After the initial discovery call, if we are both leaning toward an engagement, we will connect you with one or two current clients in a similar dimension or industry so you can ask them anything — including the parts that did not work. We do not stage these calls; we introduce you and step away.
Attribution depends on the dimension. For marketing, we report on last-click and a weighted multi-touch model side by side, using GA4 + platform-native data reconciled against the CRM. For websites, we compare pre- and post-launch Core Web Vitals, conversion rate, and revenue at the same traffic volume over a matched window. For finance and analytics, we tie changes to cash-flow, tax-savings, or time-saved hours backed by timesheet data. Every case study below lists the exact metric, window, and source.
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