What we've actually shipped for clients.
Every case study below reflects real engagement patterns and outcomes we deliver in that dimension. Some are named clients; others are representative scenarios published while we collect permission to name the client behind the numbers.
What a Bizeract case study actually is
Every study on this page is built from an engagement we actually run — the approach, tools, and numbers reflect real client work in that dimension, not a template. Where we have permission to publish a named client's exact figures, we do; where we don't yet, the study is a representative scenario using realistic numbers for that kind of engagement until it is replaced with the named version.
Start from the baseline
The starting point matters as much as the outcome. A 41% bounce-rate cut from 68% is a very different story than the same cut from 22%, so every study states where the client began.
Check the attribution window
Every metric is reported against a matched pre- and post-launch window, so seasonality and traffic shifts do not distort the delta. The window and data source are named in each study.
Read the approach, not just the number
The "how" is more transferable than the result. The tactics that moved the needle in one context are the ones we would bring to yours — that is the part worth comparing.
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 a situation looks like yours, you can short-circuit half the discovery call by pointing at it. See our pricing page for what engagements like these typically cost, or read our blog for 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.
How an India-US SaaS company closed a 14-month books gap and stopped missing FEMA filings
A founder-led SaaS company had an India parent and a US subsidiary, two separate bookkeepers who never spoke to each other, and a missed RBI overseas-investment filing that surfaced only when a bank flagged an inbound remittance. We closed a 14-month reconciliation gap and put one team across both entities' calendars in six weeks.
How a 40-person services agency cut lead response time from 6 hours to 4 minutes
Every inbound lead landed in a shared Gmail inbox and a WhatsApp Business app that three account managers checked whenever they had a free moment. Average first response time was 6 hours, and leads that came in after 6pm often waited until the next morning. We built a routing and auto-qualification pipeline that got first response under 5 minutes without adding headcount.
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 apply: define the metric, set a baseline window, track attribution honestly, and report what actually moved. The studies below are representative scenarios, built from the patterns and numbers we see repeatedly on real client work in each dimension, published while we collect permission to name specific clients. 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, and approximate revenue band, and the approach described is exactly how we run that kind of engagement. When a client is open to being named with real figures, we name them and swap the placeholder.
Yes, for named engagements. After the initial discovery call, if we are both leaning toward working together, 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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