How Nestlé is combatting data silos?

Data analytics

Following her session at CXN Live: Customer Data, Insights and Analytics, Ritanbara Mundrey, global consumer and marketplace insights manager at Nestlé, reflects on her extensive customer experience career, noting that the greatest lesson she has learnt during her career to date occurred at Nestlé: persevere until you unlock the true value of your datasets.

In a previous role at Nestlé, Mundrey was tasked with decoding the enormous amount of data the organization was sitting on. Upon analyzing some of the complaints data Nestlé had, Mundrey discovered that out of thousands of calls, 84 percent had been miscategorized as complaints. In reality, the topics of these calls ranged from product suggestions to issues as simple as locating the best before date.

“We were missing out on 84 percent of our customer insights,” notes Mundrey. “When we began to look into the way things were analyzed we realized that understanding the context and urgency of customer engagements is critical.”

Read: The Big BookOf Customer Insight And Analytics 2020

Overcoming data silos

Nestlé deploys huge caches of data in various functions and various ways. All data is collected and intended to deliver to the organization's business goals. However, it is diverse, disparate and indeed, difficult to reconcile. “It took us six to seven months to figure out what we were sitting on,” remarks Mundrey. “The data was really diverse, ranging from weather data for the supply chain to sales and consumption data. A lot of these datasets didn’t speak to each other.”

Data silos existed not only between departments but also within them. The data fragmentation was in fact impacting the productivity of the organization. For example in sales, in exchange of spending time performing core sales tasks, sales personnel had to spend time collating, navigating and making sense of the fragmented data so they had the required insight and context for discharging their role.

For Mundrey the challenge was to connect data sources in a meaningful way so Nestle had a single version and source of truth.

A project was rolled out in Asia to improve data integration for the sales functions, then it advanced to enhance data architecture within other departments and now connections are being drawn between these various data streams.

“We are now using algorithms and AI to automate our data analysis so the process is seamless and smooth, and we have built in elements of predictive analytics based on previous performance and sales and delivery variations,” explains Mundrey.

Going forward the team is working to optimize marketing visibility. Connections are being made between retail market-share data, internal sales data and consumer data to get a full business picture from one source.  Mundrey notes that good traction has been achieved so far, however, Nestle is continuing its mission to further enhance data integration across the enterprise.

For brands looking to integrate predictive and real-time analytics into their customer experience strategies, Mundrey advocates a high level of rigor to ensure data points are connected in a relevant manner. This will ensure that the conclusions and forecasts are robust and credible.

Aside from this, the most important thing is to ensure you approach the project with a customer-centric mindset.

“Get the human context, that is the trick, and the minute we can get this and marry it with the wonder of machines, then we will have the answers we need,” notes Mundrey. “You can have a lot of data and tech experts to work on building a system from scratch, but first you have to understand what the consumer really needs.”

To hear more of Mundrey’s insights and advice on customer data and gain a better understanding of how Nestlé have excelled in this area, watch Mundrey’s session on demand at CXN Live: Customer Data, Insights and Analytics.