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MyTraffic > Case Studies > How Geoblink helped solve Danone's 70-year-old challenge and boost sales by 10% across the convenience channel network

How Geoblink helped solve Danone's 70-year-old challenge and boost sales by 10% across the convenience channel network

Technology Category

Analytics & Modeling - Big Data Analytics
Analytics & Modeling - Real Time Analytics

Applicable Industries

Consumer Goods

Applicable Functions

Business Operation
Sales & Marketing

Use Cases

Inventory Management
Supply Chain Visibility

Services

Data Science Services

The Challenge

Danone, a multinational corporation in the consumer goods manufacturing industry, faced a challenge in understanding the consumption behaviour associated with each point of sale format in its extensive convenience channel network. The network included small local supermarkets, neighbourhood stores, corner stores and kiosks. The disorganised nature of this network made it difficult to define the assortment of products to be stocked in refrigerators with limited space, which section of the cashier payment queue is the most strategic, the type of point of sale advertisement to be used, among other things. Danone had been trying to organise and explain the behaviour of its convenience channel for 70 years.

About The Customer

Danone is a multinational corporation that was founded in 1919. It has its roots in promoting healthy eating habits and caring for loved ones. The company was founded by a Balcanic immigrant in a small factory in Barcelona and has grown to become an icon in the consumer goods manufacturing industry. Danone plays a fundamental role in the distribution of yoghurt across the Western world. The company is known for its corporate social responsibility initiatives and works continuously to improve the product offerings across its diverse portfolio. To do this, they need to understand what consumers want in order to provide them with the healthy products they need at the right time.

The Solution

Danone sought help from Geoblink to organise its 13,000 points of sale. Using Geoblink’s “Territory Manager” feature and in collaboration with the Customer Success team, Danone was able to start organising its network. They used a technique called clustering to group objects into categories that were previously undetectable. This methodology allowed them to process hundreds of stock-keeping units (SKUs), finding the ideal classification for all products. Geoblink combined sales data with premium location datasets from outside of the store, making the clustering models capable of identifying specific types of behavioural patterns occurring around points of sale for all SKUs in the manufacturer’s inventory. They grouped the 13,000 points of sale into 12 clusters and then applied business savvy along with Geoblink’s clustering model to pinpoint patterns of stores that behaved similarly, coming up with three final clusters.

Operational Impact

Danone was able to put a chaotic and disorganised distribution network in order, classifying the points of sale into three groups: urban, rural and impulse.
They designed three pictures of success that took into account the particularities of each POS model, defining the key actions associated with success for each.
The solution was initially received with scepticism, but the positive results didn’t take long to appear.

Quantitative Benefit