retail location intelligence

The retail industry has adapted extremely well since the pandemic. From finding a way to organise curbside pickups, retail brands have now found new ways to be available where the customers are. A good example of this is BOPIS (buy online and pick up in store). BOPISis where customers purchase products online and locate the nearest store for pick up. A key technology that is driving this sales strategy is location intelligence (LI). 

As early as when the pandemic began In 2020, 53% of retailers indicated that location intelligence is either critical or a must for business growth. In a more recent study, Nasscom revealed that the location intelligence sector is likely to reach USD 32.8 billion. 

When we say location intelligence, we don’t just mean LI for store walk-ins. There are a number of other stages where LI can enable retailers to make intelligent business decisions. Let’s look at what they are. 

LI to Identify a Suitable Store Location 

Let’s say a retailer is exploring new venues or locations to expand their retail presence. Location data can help in the decision making by; 

  • Giving insights into the user demographics in the region 
  • Showing how many similar stores there are and revealing data into what footfalls they are experiencing  
  • Traffic patterns in the locality 
  • Seasonal and economic conditions in the region (this can affect supply chain, procurement and logistics) 

LI to Analyse Store Walk-Ins 

A second area where location intelligence can help is in tracking store walk-ins. In fact, this is a powerful data point that retailers can use to find out how each of their stores is performing, at what time periods the stores are experiencing maximum foot traffic, during which days, and such.  

Using this data, retailers can determine which stores are performing better, when they need more staff to assist customers or open more cash registers, what time the stores need to open and close etc. 

LI to Understand Customer Preferences 

A Merit expert says, “Earlier, when customers used to walk into retail stores, they used to spend a good amount of time strolling through aisles and finding out what products are available in each aisle. Post pandemic, the shopping patterns have changed drastically. With the same products also being available online, customers now prefer to order online and visit the store only to pick up their order, or walk into the store, pick up what they want and leave.”  

Given these circumstances, there is a dire need for retailers to design and re-organise their store layouts in a way that it is easy for customers to come in, find what they want and bill conveniently.  

To achieve this, retailers can use geospatial data and heat maps. These technologies can help understand which aisles are having the most footfall, which aisles customers are spending most time in, and likely bottlenecks they might be facing during the buying process. Retailers can also use this data to send relevant coupons and discounts on products their customers have shown interest in or bought at the store. 

LI to Make Staffing Decisions 

As a continuation of the above point, retailers can use location intelligence to determine how many staff members they need on the floor at any given time or period. For example, typically the customer footfall is heavy during the holiday season. So, LI can help retailers make a call on how many extra employees they need in specific aisles, at cash registers and so on. In fact, even on a normal day, retailers can determine shifts for employees based on the footfall over the course of the day.  

LI to Analyse Market & Competition 

Retailers can use location intelligence to gauge customer walk-ins in competitor stores and derive insights around stores where there is high or low footfall, if the latter, then what are customers looking for, and how another retailer can deliver it.  

For example, Subway, which typically sells only subs and additional items like soft drinks and cookies, started offering basic groceries like bread and cheese in areas where there was less competition and groceries were not easily available.  

Similarly, when the pandemic hit, Starbucks saw an opportunity (where other retail stores were getting shut down), in mapping their customers and expanding their delivery radius to make Starbucks home delivery available to a wider range of customers. 

LI to Drive Better Marketing Campaigns 

With a ton of relevant data around what customers are searching for online, and where they are spending the most time in retail outlets, marketing teams can drive effective, targeted and personalised campaigns to drive more sales and build loyalty with the brand.  

For instance, going back to the example of heat maps at stores – if marketers are able to see which aisle customers are spending a lot of time in, they can curate marketing emails which notify them of discounts or offers on the products they showed interest in. 

While location intelligence has been in the retail arsenal for quite some time now, it is picking up pace as retailers gain an interest in getting one step closer to customers and focus on delivering a personalised shopping experience. 

Merit’s Expertise in Retail Data and Intelligence   

Our state-of-the-art eCommerce data harvesting engine collects raw data and provides actionable insights   

  • Three to four times faster than standard scrapers   
  • At lower cost   
  • With Increased accuracy (up to 30% compared to standard scrapers)   

Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends.   

Merit’s eCommerce data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.   

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