The global food and grocery industry has experienced tremendous disruption, thanks to the Covid-19 pandemic. As highlighted in our previous article on “Digital transformation in the food and grocery delivery industry in Kenya”, this disruption was experienced in an array of industries leading to the “tech-celeration” of digital innovations. Five years of technological advances were made arguably in just five months of 2019. In the food and grocery industry, we have observed and concluded that the status quo has been challenged. Customers are increasingly now more comfortable with shopping online.
As discussed in another of our articles on “Digital platforms & their adoption as growth strategies for businesses”, despite having an offline store, facilitating sales via an online platform is now a necessity. Third-party order aggregating companies such as Uber Eats and Bolt Food have witnessed a surge in the number of vendors registered. Legacy companies such as Java House are able to leverage the existing platforms to make their products available to their customers. Plugging into existing platforms is a much more cost-effective approach for vendors, to begin with. However, vendors are potentially losing out on very insightful and useful data that the third party platforms are keeping to themselves. This data is fundamental when it comes to optimizing many aspects of the business: from customers’ direct feedback and the ability to pull various e-commerce levers on the customers.
On the vendors that decide to go directly to consumers, we have noted in our opinion piece how brands have gone the direct-to-consumer route with varying results. One of the most critical items is getting the right digital talent to spearhead the direct-to-consumer agenda. Seasoned personnel poached from other departments of the physical retail business might not necessarily be the best fit. The right talent has to have both an innovative and disruptive mindset in the digital world. In addition, going direct-to-consumers calls for superior unit economics. It is paramount that companies intentionally take measures to optimize the whole customer journey: from pre-purchase, delivery, up to post-purchase. This calls for the collection and analysis of all customer data generated during the whole journey.
Having superior unit economics will highly depend on building an omnichannel experience around the customer. As discussed in our other article on “Going omnichannel to Drive Sales: Rethinking retail and e-commerce”, companies face cutthroat competition among themselves. As a result, while re-inventing themselves and their offering, customers have to be at the centre of their customer acquisition campaigns. An omnichannel strategy is able to combine all touch points on the customer’s journey and make the points “shoppable”. The customer is drawn into making a purchase every time they land on another channel within the ecosystem. The key to a successful omnichannel strategy is that data is shared among all different engagement points. A customer is able to pick up from where they left in another channel and finally make a purchase.
Optimizing the omnichannel experience shall need brands to deploy machine learning models in their ecosystems. Machine learning, , is able to learn customer patterns and hence make predictions. Studies have shown that 46% of online shoppers are price sensitive. Being able to take into account competitor pricing, time of the week and the current product’s level of demand, and finally pricing your products just right for your customers will enhance sales. Another way machine learning would be essential in increasing sales would be inventory management. Brands are able to take into account the optimal number of stock-keeping units (SKUs) and minimize wastage. Imagine a world where you have over 1,000 SKUs but have only 10% accounting for the majority of the sales. It would make more business sense to get this information and hence do away with the less-performing SKUs. The reduction in SKUs reduces supply chain and operational complexities. Customers are then able to enjoy the improved brand’s efficiency and hence improve their lifetime value. Having machine learning embedded at the core of your omnichannel strategy is non-negotiable in reaching superior unit economics.
Finally, brands need to find out how online platforms can complement their physical stores. It is certain that some companies are hesitant in running two models concurrently. However, with customers slowly shifting to the convenience of online shopping, if you do not provide the solution, your competitors will.
Author: Dickson Ndoro