Using Location Data to Get Ahead of the Competition
It’s no longer business as usual in the retail industry. It’s not enough to know your customers and to provide them with the best services and products. One tiny piece of information — where the customers are — is the game changer. Retailers can use location data to obtain this information.
Location data refers to the trajectories of people and objects. In the retail industry, knowing the location of their target market makes them more responsive to their customers’ needs and to environmental changes; hence, making them more profitable.
Location data presents a myriad of opportunities for business owners. Knowing where their customers are provides retailers with the power to access them and to effectively promote their products or services at the right place and time; increase their sales; redirect their resources in the best way; and overall, be able to make smarter business decisions.
Here is an example of how retailers can use location data to find their customers and increase competitiveness. In each example, anonymized location data around Bukit Bintang Kuala Lumpur between 1 October 2016 and 31 October 2016 was used. The data was available from DataStreamX.
Finding the right catchment area
For retailers, understanding their catchment areas is very important in running their business. A catchment is an area from which a retailer is expected to draw their customer and thereby increase footfall. Not focusing on the right catchment area can make a retailer lose most of their potential customers; and hence, their competitiveness.
In this example, we examine how Pavilion KL Shopping, a shopping mall located in Bukit Bintang, Kuala Lumpur, can use location data to find their catchment area and increase their footfall. Pavilion KL Shopping and Suria KLCC are two competing shopping malls located two kilometres apart from each other.
For Suria KLCC, it has been observed that 10% of the people who were approaching the mall were from the region within a 5-km radius (see above the left map’s innermost concentric circle). The second concentric circle (10-km radius) represents the most probable customers with a total of 44%, composed of the 10% and 34%. Similarly, the third concentric circle (15-km radius) covers a total of 78% (made up of 10% plus 34% and 34%). Finally, the outer region shows that 22% of the total people come from this region.
For Pavilion, 15% of the people who approached the mall were from the region within the 5-km radius (the innermost concentric circle). The second concentric circle (10-km radius) had a total coverage of 59% (15% and 44%) of the total probable customers. Within the third concentric circle (15-km radius) was 87% (15% + 44% + 28%). Finally, the remaining 13% of the total people came from the outer region.
What can we glean from this data?
From the images above, we know that most of the customers of Pavilion were concentrated within a shorter radius unlike the customers of Suria KLCC who were more spread out. For Pavilion, only 14% of the total customers came from the outside region compared to Suria KLCC’s 22%. From this data, Pavilion concludes that if it wants a bigger share of the market, it needs to improve its customer base from the outside region.
The figure below shows the geofence map of the two shopping malls’ customers in the outside region:
The green region represents the Suria KLCC customers while the blue region represents the Pavilion customers. The 22% of customers from Suria KLCC are mostly focused in the green region and the 14% of the Pavilion customers are focused in the blue region.
Based on this information, Pavilion can target more customers in the green region to compete better against Suria KLCC. Using location data, Pavilion can increase the prospects of finding potential customers and can increase their footfall.
The Smarter Way of Doing Business
As you can see from the case study above, location data is not only a handy tool to compete better in the retail industry; it is also essential to surviving in the industry. When a business has data about its customers, as well as its competitor’s customers, it can prioritize its targets. Opportunities to gain advantage are realized.
How do I get location data?
Obtaining location data for use in decision-making may be a difficult task for the uninitiated. Variables such as the time of day, seasonality, and local trends make location data nearly impossible to measure accurately by manual counting or sampling. These factors also affect the reliability of conclusions based on footfall data alone. As a result, location data is often used in conjunction with other types of data such as demographic data, allowing users to formulate and test their hypotheses.
Accurate and reliable location and demographic data can be obtained from mobile app companies who provide anonymous location and demographic information of their users at a small fee. If it is your first time purchasing data, consider using online marketplaces such as DataStreamX to help you get in touch with suppliers of location data easily.
Summary
We’ve seen how location data helps retailers find their customers to increase their footfall. In addition, retailers can leverage on location data to communicate with customers at the precise moment with the most relevant information. If you are considering getting hold of location data to help you make better decisions at your retail store, online marketplaces such as DataStreamX can help you connect with suppliers of location data quickly and easily. For more information, visit datastreamx.com.
Roger Ganga Sundararaj is a Data Scientist at DataStreamX, an online platform which serves as a global marketplace connecting buyers and sellers of commercial data. Follow Roger Ganga Sundararaj on LinkedIn to hear more on Big Data.
Find out more about DataStreamX here.
Originally published at https://www.linkedin.com on June 22, 2017.
