Picture two campaigns running side by side. One brings in 500 leads a month, the other only 300. At first glance, the answer seems obvious, until you look at what each lead actually cost.
That question shaped how I worked across offline campaigns and locations in Kerala, working closely with the sales team to compare how leads moved through both funnels, ran ongoing trials on where and how leads were generated, and used that data to guide budget decisions and streamline reporting.
Scope: offline campaigns, Kerala region · Reviewed on a recurring cycle
- 56%lower Cost Per Lead
- 60%less time spent on reporting
The Problem
Lead generation was running across several campaigns and locations, but performance wasn’t consistent. Total lead count was the number everyone looked at first, and it was misleading. A campaign could bring in a large volume of leads while quietly costing far more per lead than a smaller, better-targeted one, or generating leads that rarely converted once they reached sales.
Without a shared view across both teams, budget and manpower decisions were being made on partial information. On top of that, the KPI reports used to track performance were rebuilt from scratch every cycle, leaving less time for the analysis that actually mattered.
My Approach
CPL alone couldn’t explain why one area outperformed another, a cheap lead that never converted wasn’t actually efficient. Understanding that meant looking at two funnels together: the one marketing controlled, and the one sales controlled.
The marketing funnel: from asset to lead
Offline leads started with a physical presence at a location (“asset”), for example, malls, supermarkets, hypermarkets, airports, railway stations, small stores, or clothing stores. Each asset had its own cost and lead potential:
- Identify relevant assets
- Assess each asset’s potential and cost
- Decide BDE (Business Development Executive) deployment, sized to the asset’s cost and the lead volume it needed to justify
- BDEs pitch to relevant customers at the asset
- Leads are collected
The sales funnel: from lead to order
Once marketing handed a lead over, a separate funnel decided whether it turned into revenue:
- Lead allocated to a sales rep
- Lead dialled
- Lead connected
- Demo booked
- Demo conducted
- Order placed
Working with both teams, I compared performance across both funnels, not just which asset produced the most leads, but which ones produced leads that actually survived the sales funnel through to an order. Some sources drove strong lead volume with a weaker path to a demo; others produced fewer leads but converted noticeably better. Looking at CPL on its own, without this second half of the picture, would have pointed budget in the wrong direction.
Ongoing testing, not a one-time fix
This wasn’t a single analysis done once. New assets and approaches were tried on a rolling basis, given a fair trial period, and reviewed against consistent funnel data rather than a single month’s numbers or gut feel. Some that looked promising early didn’t hold up once measured against full-funnel conversion and were phased out; others were given more support and scaled up once the data backed them. The process itself, test, measure, decide, adjust, repeated continuously as conditions changed.
Based on this recurring view across both funnels, budget and BDE deployment were shifted toward the sources that were both cost-efficient and converting through to orders, while support was pulled back from ones that consistently underperformed across full trial cycles. In parallel, I rebuilt the KPI reporting sheet into a standardized, semi-automated format covering both funnels, so the same view could be refreshed each cycle without being reassembled by hand.
A cheap lead that never becomes a demo isn’t efficient, it just moved the cost from marketing’s sheet to sales’.
The Result
Reallocating budget and effort toward the better-performing sources, based on this recurring test-and-review process, brought Cost Per Lead down by 56%, the same overall marketing spend produced leads at less than half the previous cost.
The reporting rebuild had its own payoff: what used to take hours of manual sheet-building each cycle came down by about 60%, freeing that time for actually interpreting the numbers rather than compiling them.
- 11funnel stages tracked across marketing and sales
- 20+asset categories evaluated on a rolling basis
- 2 teamsmarketing and sales aligned on one shared view
What I Learned
This was the project that pushed me from doing marketing to analyzing it. I started with Excel and Google Sheets simply to make sense of campaign performance, and that need kept growing, eventually pulling me into SQL, Power BI, Python, and statistics to work with data at a deeper level.
It also changed how I think about marketing work generally: execution and analysis aren’t two separate stages, and a decision is only as good as the number of review cycles behind it. The work gets done, the results get measured, the data gets read, and that reading is what decides what happens next.