Creating new business models with consumer behaviour data

Supply chain director for snack company graze, Ruvan Mendis, talks to The Manufacturer about how a new way of dealing with data has helped the company to create a disruptive and unique business model.


Snack - image courtesy of graze
The analysis of consumer behaviour data helped the company to create a disruptive and unique business model – image courtesy of graze.

Graze offers more than 200 snack combinations through subscription boxes, an online shop and retailers; and the company distributes thousands of snack boxes every day across the UK.

Due to a new way of analysing data with DARWIN (Decision Algorithm Rating What Ingredient’s Next), the company found a way of customising snack boxes based on the preferences subscribers enter on the site.

The Manufacturer recently spoke with Ruvan Mendis, supply chain director at graze, to uncover how data helped open doors to innovative new business models for the company.

He explained: “Innovation is at the centre of all our thinking at graze. Our original subscription direct to consumer model was disruptive and unique when it launched in the UK and the US, allowing us to build a large data set of consumers’ snacking preferences.

“We use this data to feed into our innovation pipeline as we develop the next set of graze’s new and exciting products in both geographies.”

Mendis continued: “We’ve seen both the type of data and the way we use it change significantly as we have transitioned into an omni-channel business over the last few years.

“In our online business, a lot of our data was coming directly from our end consumers, with high levels of engagement and fast responses due to the nature of the relationship.

“Individual consumers were invited to rate each of the snacks sent in their boxes each week, giving us millions of data points of real time feedback helping us to refine our product range and to know where to focus with our New Product Development.”

Predictive analytics and consumer behavior 

Especially in the food and beverage industry, the trend of customising products and aligning them with individual consumer needs is becoming stronger. Information about the consumer behavior is extracted from existing data provided by the consumers themselves.

This method, which is called predictive analytics, helps companies determine and understand the buying patterns of customers, and predict future trends for an organisation. Prescriptive analytics is another branch of advanced analytics, dedicated to obtaining the best course of action for a presented situation.

Mendis underlined that in the retail world, the data received isn’t as granular at a customer level, but brings different commercial insights from the point of view of graze’s retailers.

These are rates of sale, on shelf availabilities, and there is more of a lag in getting qualitative data on i.e. product quality through from consumers, he explained.

Mendis added: “We’ve worked to ensure we combine the best of both online and retail datasets into our innovation process to help us continue to deliver great new products for our retailers and customers.”

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