Food manufacturers and retailers generate huge volumes of structured and unstructured data. But according to analytics and advisory firm Quantzig, more than half of firms in the food industry lack the required analytics capabilities to put unstructured data to good use.
What’s more, even when analytics capabilities are deployed they are often used to generate hindsight descriptions of what happened rather than to obtain progressive insights into food trends that can be used to make operative, managerial and crucial business decisions.
Intense competition for customer loyalty is influencing the need for food industry companies to develop the ability to draw deeper, consumer-centric insights from big data and enhance their decision-making capability, Quantzig explained.
In a case study shared by Quantzig, its client was already extracting insights from customer buying patterns and successfully incorporating them into its upstream business operations. However, a recent change in its customer loyalty programme had resulted in huge data volumes being generated.
The company needed big data analytics to gain actionable insights into the metrics that would help it improve customer satisfaction and reduce customer churn rates.
Quantzig developed a big data analytics operating model which not only generated insights aligned to the client’s business strategies and objectives but also proved to be beneficial in better identifying food industry trends and consumer buying patterns.
Amongst other things, the new operating model helped the company to track customer usage patterns across its online platforms, improve customer experience, and develop innovate product offerings.
As well as generating a greater return on its analytics capabilities, the company realised millions in operational cost savings.
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