Today, consumer goods companies are analyzing petabytes of data from multiple sources to understand consumers' needs, the key influencers in their paths to purchase, price points that trigger their purchase decision and their preferred modes of payment. By doing so, retailers look to leverage insights to make customized offers to increase the basket size or make personalized offers to induce trial and repeat. This effort needs to be supported by the upstream supply chain by not only integrating data into demand planning but also continuously analyzing the supply chain data to stay agile.
A global study commissioned by Wipro and conducted by The Economist Intelligence Unit (EIU) called The Data Directive, showed how companies across industries were responding to data available to them. For retail and consumer goods industry, as many as 40% of respondents said they understood the value of their data to gain deeper insights into consumer behavior and attributes. While that's good news, this effort must also go into ensuring that the data is used to enhance supply chain response, improve efficiencies through optimization of logistics, shipping, warehousing and inventory management - so they can deliver the promise made to consumers.
Consumer goods companies are aware of this need. For instance, major Home & Personal Care as well as Food & Beverage companies are already rising to this challenge. Take this scenario for example - using a hypothetical situation, a consumer goods company can now predict what a specific customer wants to buy in a specific category - say baby supplies. The company then examines that specific customer's buying patterns, browsing behavior and probable baby care needs. It would then craft a marketing campaign aimed at him/ her, ensuring the timing, promotional offers and best suited engagement methods. For example, a baby care company in the US rolled out dynamic personalized offers and increased conversion rates by 170% and average order value by 80%.
However, such improvements in sales can be sustained only when the campaigns are backed by a robust supply chain that is prepared for the consumers' decision and can ensure that products are available in the delivery model that the consumer prefers - be it buy-only pick-up in-store, online order at store delivered to home or the traditional buy-online-deliver-at-home models. Basically, in the scenario mentioned above, that specific customer's decision must be visible to an agile supply chain that can then trigger sourcing, shipping, warehousing and order fulfilment activities to deliver a superior consumer experience.
Most consumer goods companies rely on multiple sources of information which do not always present the same version of truth. Consumer goods companies have built their data and analytics engines to address a variety of problems - with each system working independently. For example, one system predicts consumer behavior and dictates SKUs and pricing; another data set is used by marketing to create offers and promotions across channels. And a third platform leverages data to manage supply chain efficiencies. Each platform and operation has a different way to look at the business. The need to stitch the three into one seamless Big Data and Analytics platform has never been greater. Such an integrated platform would capture, manage data to generate actionable insights through advanced analytics thereby offering price, performance and time benefits and also ensure on-time fulfillment for continued consumer delight.
As consumer goods companies begin to invest heavily in building brand value and relationships with consumers, it becomes imperative for them to fall back on their core strength of effective logistics that would now need to cater to dynamic needs of today's consumers.