Use Case: How a soft drinks company increased sales through Echo’s mobility data

Mobility data for increased sales

Mobility data can help with increasing sales. Source: Unsplash.

Consumers are always on the move and companies need to be alert about where their ideal consumers are and where are they going. Mobility data provides real-time insights into our fast-moving world by providing higher accuracy on information such as consumers’ present location, their movement pattern, and where and when they are making a transaction.  Mobility data also gives information about how many visitors visited a place and how many times they visited that place. 

Our client, a soft drinks company, wanted to harness the knowledge provided by mobility data to increase its revenue. They had an already existing list of bars and restaurants where they sell their products. The interesting part is that these bars and restaurants also sold products from their competitors. Our client wanted to understand the foot traffic in these locations and cross analyse them with locations that had higher sales. This would give them a clear conception of how much revenue these places could generate. 

The Challenge

The soft drinks company wanted to create its revenue model based on foot traffic data. But to gain a competitive advantage, they were also looking to understand the point of sales of their competitors and how much sales the competitors were making. Based on the comparison of sales between the two products, our client could prioritize locations in their sales strategy that would bring in higher revenue. They were searching for tools to not only better target their customers but to be precise in their targeting across all locations.

Although the list of bars and restaurants, that our client provided, had the necessary contact information on them – such as telephone numbers and email addresses of these places – they were missing the mobility data which was essential to estimate the foot traffic in these places. 

The Solution

We took the POI datasets that already existed in our client’s CRM and enriched them with our POI datasets. Using their existing POI taxonomy made the process far simpler and quicker when we started filling out the gaps and adding missing data. Once we had a complete POI dataset we started adding mobility data to it. Our mobility datasets are curated from various sources which can include GPS-enabled devices such as mobile phones, cars, e-bikes, and watches. We also include data from non-personally identifiable information (non-PII) through app-based navigation systems as well as tap into sensory devices located in public spaces to determine the foot count in a particular location. 

We ensure that our client receives the datasets complete with all the attribution which may include the number of visitors, the time of entry and exit at a particular location, the number of visits, the places they went to before or after visiting this location, as well as how much time they spent in these locations. We provide all this information in an organized and comprehensive dataset to help our clients with faster formulation of their revenue model. 

What was particularly interesting for us in this case was that our client wanted to understand the number of sales made by their competitor. For this we had to extract out the data of the competitors. Not only did we find the sales record of both our client and their competitor but also how many people were purchasing both products. Our client took advantage of this knowledge to gain a competitive advantage as they were able to figure out which locations got them higher revenue. Through this information they could compare their revenue with their competitors in the same locations and decide on steps to accelerate their sales and develop a long-term  marketing strategy – such as pricing, product visibility, and price comparison – to address the gap in these locations. 

Our takeaway

Our client received a detailed insight into their point of sales which were eyeopening since this way they could take action to accelerate their sales in the low-sales locations. They were able to increase their sale at the preferred location by at least 15% which gave us the opportunity to further understand the relationship between mobility data and revenue generation. It was interesting to note that not every POI with high foot traffic brings in high revenue; in fact, there can be locations where the foot traffic is comparatively lower than others but the revenue generated is high. However, these are exceptions. Places with higher traffic can be ideal for gaining a better understanding of your customer and developing better advertising tactics.

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