Privacy Policy

Data-driven sales intelligence, on consumer behaviour and consumption habits, is becoming increasingly vital in ensuring excellent retail execution for FMCG brands.

To date, sales intelligence in the sector has been dominated by merchandising and planogram compliance software, invoicing solutions, and CRM solutions for field sales teams. 

Merchandising and planogram compliance

While there are several academic articles on the applications of AI in sales intelligence for FMCG, the primary focus so far has been in using image recognition software to monitor shelf stocks and layout, and for demand planning solutions. 

In fact, there are several solutions using image recognition to monitor shelf stocks. Some innovative retailers have even deployed robot-like devices to monitor out of stocks, planogram compliance issues and monitor spills/blocks etc in the aisle.

Demand planning solutions

And then there is the use of AI in demand planning solutions for retailers. Demand planning solutions for retail help retailers understand how much of each SKU to stock, to meet consumer demand. Traditionally, AI applications in demand planning for retail have been restricted to applying seasonality and events to historical data to predict future sales. While this has worked in the past, factors influencing consumer behaviour, and weather have changed so significantly in the recent 2-3 years that the applications need a rehaul. 

AI driven SKU recommendations

Another area that is getting a lot of attention is AI-driven portfolio recommendations for use by salespeople when selling into retail and into informal markets. Where traditionally, new listings and product launches in retail have been largely dependent on focus groups and buyer knowledge of the sector, the launch of AI-driven product recommendations is now arming the salesperson with data to convince the buyer of consumer demand and fit with the retailer’s current portfolio of SKUs.

Sales into informal markets and wholesale have historically been dependent on the performance of the brands/SKUs in organised retail (also known as modern trade), especially in emerging markets. AI-driven SKU recommendations are now driving listings and sales in this sector, through apps that use local demographic and sales information to drive recommendations for each store. Salespeople then use this knowledge to convince owners to buy the volumes needed to meet demand.

Several well-known FMCG companies are building/have built their own software and applications that can help with this. There are also 3rd party software providers that have their solutions for this sector.

How do salespeople and retailers (supermarkets, grocery stores, convenience stores/chains) ensure availability?

Currently, salespeople rely on retail supply chain managers to know how much to order. However, relying on supermarket & grocery customers to place orders for optimal volumes can impact sales and share of the market for brands, as these customers are more likely to place orders to ensure stock of the category as a whole and not necessarily of specific brands. Also, retailers are careful of ordering high volumes of stock, to avoid write-offs, working capital constraints and wastage. Unless there is a clear rationale for ordering higher than normal quantities, retailers apply their own judgement to demand planning solutions, which often results in lower quantities being ordered than those recommended by the software.

Also, Millennials and Gen Z now form a significant percentage of the adult population. The influence that social media and news have on how these 2 generations buy and consume products has still not been factored into how retailers place orders with FMCG brands. (Eg. L’Oreal went out of stock of their infallible range in the US due to their TikTok campaign, Weetabix went out of stock in several stores due to the Heinz beans and Weetabix campaign etc) 

Also, it goes without saying that 2020 has impacted consumer behaviour significantly and this, in turn, has driven, overstocking and stock-outs, simultaneously for different SKUs, in stores as supermarkets can no longer rely on historical sales data to inform what consumers buy in the future. With more people working from home now, consumption of food, beverages, personal care, and home care products is considerably different from 2019 consumption of these categories. Using historical data adjusted for seasonality and holidays does not work post covid.

Example: snacks and coffee/tea consumption, and toilet paper consumption are now more at home than at offices these days. So, people in sales should be focussing more on selling into grocery stores and supermarkets than into wholesalers and distributors who sell into offices. 

The combined impact of the pandemic and the unpredictability of a new generation of consumers has made it vital that salespeople have access to a new generation of sales intelligence tools that enable them to ensure excellence in retail execution.

By empowering sales teams with information on what consumers want and are likely to buy in the next 4-6 weeks or even in the next 7 days, not only can salespeople ensure 100% in-store availability of the brands they sell and in the right channels, but they can also reduce loss of share. Providing timely intelligence on consumer demand helps sales teams take advantage of opportunistic upsides that are also in line with long term strategy, leading to increased revenues of up to 40%, an increase in market share and increased consumer satisfaction scores.

By Veena Giridhar

Veena worked for several years within the FMCG industry for companies like PepsiCo and Diageo. Veena co-founded salesBeat with Alex in 2019. Alex has a history in tech start-ups and extensive experience in creating apps. The founders came together at Antler (a start-up generator) in Sweden. When the pandemic started in early 2020, they realised that availability of brands in retail would be impacted in the long term due to changes in habits during the period and launched the platform to eliminate stock-outs in stores.

Read more from Veena Giridhar