IT Leadership

How AI and ML are Helping RJ Corp Enhance Sales Efficiency and Productivity

After successfully undertaking sales force automation, multi-business conglomerate RJ Corp is now charting out a digital roadmap towards sales force enhancement for increasing sales efficiencies and improving sales force’s capabilities and productivity. Integrating the entire supply chain and distribution will be next on the group’s agenda and targeted for completion by end of 2019.

RJ Corp is the largest bottler for Pepsico in India and a diverse business group with interests in beverages, fast-food restaurants (Pizza Hut and KFC), retail, ice cream (Cream Bell), healthcare and education.

For two of the group’s biggest businesses, Pepsi and Cream Bell ice cream, Kamal Karnatak, Senior Vice President and Group CIO, RJ Corp. is enhancing the existing sales automation apps and building in intelligence with the integration of Artificial Intelligence (AI)/Machine Learning (ML) to enable the sales force to sell more.

Suggest Sales Orders

For the sales people out on field for sales calls to retail outlets, traditionally the mobile sales app (Saamna app for the Pepsi business and Cream Bell Saamna app for the ice cream business) has been providing the required information on the outlets – past orders, payments status, etc. along with information on product pricing and ongoing schemes. Now, with AI/ML capabilities built in, the system can analyze all the past information and based on that provide a suggested sales order to the sales person.

Based on what the retailer has been buying from the company in a particular season along with other information, the system gives the sales person recommendations as to which products and which SKUs a particular retailer is likely to buy on a given day, and therefore, he/she should try selling those. Thus, maximizing the sales efforts. According to Karnatak, with 125 SKUs under the Pepsi business and 200 SKUs under ice cream business, it is important for the sales people to figure out which SKUs the retailer is most likely to buy and therefore focus his/her efforts towards pitching those. This gives an informed direction for selling to the sales people, who were earlier depending on gut feel for making their sales pitch.

Predict Sales Forecast

Another area that the group is working on applying AI/ML is doing predictive sales forecast. The system takes 14 days’ weather forecast data along with any event data (such as Independence Day, Diwali, IPL, etc.), upcoming promotions, etc. and embeds these into last five years of sales data. It then runs AI/ML algorithms on this data to predict SKU wise sales numbers in the different markets. According to Karnatak, the system gives 14 days’ forecast, out of which the first seven days is firm forecast and the next 7 days is rolling out forecast, which gives indication. Currently under pilot in two territories, the system has achieved almost 80-85% accuracy in forecasting sales numbers.

“Earlier we used to face the situation of stock out, especially in season time. The retailer would be looking for a product but the depot would be out of stock because we didn’t know what kind of sale forecast would come. Thus, losing a sale opportunity. With data driven sales forecasting will not only be able to meet our demands effectively but will also be able to do cross selling, up selling and range selling,” explains Karnatak.

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