Evolution of Frontline Sales Data in FMCG in India
FMCG in India gathers a large volume of data at the distributor points. Though technology and interfaces have evolved over time, usage of data remains primitive. In the first article of the series, Vivek Bhatnagar writes about the evolution of frontline sales data.
Here is this FMCG company which generates 58 million pieces of sales data every month by the 900 distributor salesmen that it sends to the market every day across the country. It is almost 700 million pieces of data every year. That’s a lot of sales data getting generated! How does it help in business?
Let us go back in time for a bit.
Back in early 2000s, the Distributor Salesman collected the order on a paper order book. At the end of the day, he compiled these shop wise orders into a Daily Sales Report (DSR). The DSR was usually a A3 sized pre printed piece of paper.The salesman then sat with the accountant at the Distributor point and dictated the orders collected and invoices were raised. The DSR pad was preserved and used for data analysis by the Sales Officer or the ASM. The process of analysis of this data was also interesting. It went something like this.
Look through the Daily Key parameters, e.g. Total calls made, No of productive calls, Total value sales, No of lines sold, No of packs sold, Average Order size etc. which were usually filled up by the salesman in specific blocks made at the bottom of the DSR. Select a parameter from above, say number of productive calls, and flip pages to see how that number has moved over the month. A quick mental calculation was done to arrive at the average productive calls, minimum and maximum productive calls. This analysis was usually done for a month ie. with30 DSRs. One could consider along with the number of days worked in the market, or analyse route-wise sales trend by flipping thru the DSR of a specific route
It was a very basic analysis, though tedious, done sporadically and done by the more evolved and committed SO & ASM. This was usually the end of it.
We could not analyse data nuggets like , say, the average productive calls across territories, average packs per order, average order value across routes or even SKU wise sales trend across the weeks, because it was just not possible.
Those days, having a structured Order form and a DSR which was duly filled up, was considered good practice and even an asset.
What we were dealing with those days was – Some shop wise SKU wise sales data, in paper form, which was inconsistent in terms of availability across sales personnel. Any analysis, we could perform was sporadic and rudimentary.
We used to wonder and question:
- What if this data could be analyzed more frequently / more deeply using statistical tools for decision making?
- Would a different set of business decisions have been made?
- What would have been the quantum of benefits?
As time progressed and technology evolved rapidly, the most surprising part was its easy adoption by the stakeholders. Most of all by the Distributor Salesman. This least tech savvy link in the sales chain, took to Palm Tops and subsequently to App based order taking quite easily. Well, at least more easily than the companies and distributors expected.
Now coming to Circa 2020
In many companies, the Salesman is taking orders on Android Phone based capable applications. These applications work as an assistant to the Salesman by structuring the order taking process by sharing the previous purchase trends and company priorities on a real time basis. They even provide stock availability at the time of order booking. The process of invoicing is also simplified as the apps sync with billing system at distributor point and the invoice gets generated immediately.
Coming back to where we started. Our 700 million data pieces that get generated per year. Today, these data elements get used in:
- Standardised reports get generated using this data and get emailed to the sales hierarchy.
- Some specific analysis for some specific Route/Product/salesperson gets done sporadically and conclusions get drawn
Sure, this is a lot more effective than the analysis of the paper form data of the 1990s. The question still remains- Is there more juice in this data? Is it possible to analyse this humongous data and potentially derive significant outcomes to drive the sales revenue up?
Vivek Bhatnagar is an FMCG veteran in India, and is a partner at ZA Consulting which provides business consulting services India in areas of sales and distribution, new product launch and go-to market strategies. ZA Consulting is a 3nayan partner.
Are you collecting the right type of data in your FMCG Business? We would love to have a chat to explore how to make data driven sales decisions. Visit the 3nayan web site or write to us.
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