Summary

When India’s largest NBFC set out to build a digital marketplace enabling dealers to sell consumer durables and lifestyle products on No Cost EMI, it took an unusual approach: financing options came first, with other purchase options expanding around them later, the reverse of how most marketplaces are built. What didn’t change was the company’s existing discipline: data had always been central to decision-making in its offline business, and that same rigor needed to carry over to the new e-commerce platform from day one.

Bajaj Tech.AI built a comprehensive data tracking foundation on Google Analytics (GA) Premium, covering event tracking, dashboarding, and integration with the company’s broader data warehouse. Over the following six years, that data foundation helped the marketplace scale more than 100x in users and transactions.

Business Challenge

Three distinct data challenges had to be solved for the new marketplace to operate with the same data discipline as the company’s offline business:

• Capturing all events along with their key attributes. Simply tracking that a click happened wasn’t enough, every action needed enough contextual detail attached to generate genuinely useful behavioral insight, not just raw event counts.
• Creating relevant dashboards for Marketing, Campaign, and Centre of Excellence (COE) teams. Different teams needed different views into the same underlying data, tailored to the decisions each team actually needed to make.
• Getting all Google Analytics data into the company’s Data Warehouse. Web analytics data on its own has limited value; the real insight comes from combining it with CRM and offline data which meant building a reliable pipeline, not just a reporting dashboard.

Summary: The platform needed granular, attribute-rich event tracking, team-specific dashboards, and a reliable pipeline into the company’s broader data warehouse, not just basic Google Analytics reporting.

Solution Approach

Bajaj Tech.AI addressed each of the three challenges with a dedicated, purpose-built workstream:

• Event tracking built for real insight, not just click counts. Working with the COE and Marketing teams, Bajaj Tech.AI mapped every CTA that needed tracking and finalized the key attributes required for meaningful analysis. Google Analytics’ standard datalayer codes for core e-commerce events, product detail views, add-to-cart clicks, checkout, checkout completion, banner impressions and clicks were extended with full custom datalayer design. One core principle ran through every datalayer: capturing the logged-in customer’s unique ID consistently, while using GA’s limited dimension slots deliberately rather than wastefully. Critically, tracking requirements were built into every new feature’s specification from the start, so new capabilities launched with tracking already in place rather than being retrofitted later.
• Dashboards built around real customer journeys, not generic reporting. The customer journey was broken into three critical touchpoints, giving Marketing and Campaign teams a holistic view of each funnel stage. Dashboards covering both absolute numbers and ratios were built in Looker Studio, including hourly views for intraday trend analysis. Campaign teams could break funnels down by channel, medium, and campaign, and further by urban versus rural markets. Category teams got dedicated dashboards tracking product views, add-to-carts, and transactions by category, brand, and top-selling SKU.
• An automated pipeline into the Data Warehouse. Google Analytics data was moved from BigQuery (the cloud database underlying GA) into the company’s Data Warehouse through a nightly scheduled pipeline, with data formatted and structured for integration with CRM and offline datasets. From there, detailed MIS dashboards combining online and offline data were built in Power BI and shared across teams. The entire pipeline ran with minimal manual intervention, freeing teams to focus on analysis rather than data wrangling.

Summary: The solution combined deliberately designed event tracking, journey-based dashboards for each business function, and an automated pipeline connecting Google Analytics data to the company’s broader data warehouse and offline datasets.

Business Impact & Results

The data foundation built in this engagement didn’t just support a single launch, it scaled with the business over the following six years:

• The e-commerce marketplace scaled more than 100x in users and transactions over a six-year period, with the data infrastructure evolving alongside the business rather than becoming a bottleneck to that growth.
• Marketing, Campaign, and Category teams gained self-serve visibility into funnel performance, channel effectiveness, and product-level metrics enabling faster, more independent decision-making across teams rather than centralized, ad hoc reporting requests.
• Tracking-by-default became standard practice, with every new platform capability launching with the necessary datalayers already built in meaning the business never had to retroactively figure out whether a new feature was actually working.
• A unified data foundation enabled deeper analysis, from clickstream behavior analysis to propensity modelling that combined online and offline data capabilities that wouldn’t have been possible with Google Analytics data alone.

Summary: The data tracking and warehouse integration built in this engagement supported the marketplace through a 100x scale-up in users and transactions over six years, while giving business teams the self-serve analytics infrastructure to keep pace with that growth.

Key Takeaways

• Treating analytics as infrastructure not just a reporting layer is what let this data foundation scale alongside 100x growth in users and transactions over six years, rather than becoming outdated within a year or two.
• Building tracking requirements into every new feature’s specification, rather than retrofitting analytics after launch, ensures no capability goes live without the ability to measure whether it’s working.
• Web analytics data becomes exponentially more valuable once it’s integrated with CRM and offline data, a marketplace’s full customer picture rarely lives in Google Analytics alone.
• Different business functions need different dashboard views into the same underlying data; a single generic dashboard rarely serves Marketing, Campaign, and Category teams equally well.
• Automating the data pipeline (rather than relying on manual exports or ad hoc reporting) is what keeps a growing data foundation sustainable as both traffic and team headcount scale.

Conclusion

Six years and 100x growth later, this engagement is a clear example of what happens when analytics infrastructure is built as a long-term foundation rather than a launch-day checkbox. By combining disciplined event tracking, journey-based dashboards, and a fully automated data warehouse pipeline, Bajaj Tech.AI gave this marketplace the data infrastructure to scale its decision-making at the same pace as its business. Any organization scaling an e-commerce or digital platform faces the same underlying question: is your analytics foundation built to grow with you, or will it need to be rebuilt at the next order of magnitude?

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