Data-driven Retail: The Value of In-Store Insights

- 6th Feb 2018

Retailers today understand the potential of big data — ‘data-driven’ business decisions is the key to success. Analytics can help predict and influence consumer behavior. The problem lies in the intricacies of data capturing and retailers are still struggling to translate analytics into effective and actionable insights.

The Challenge

The quantity of data isn’t the challenge for retailers anymore; it is the data accuracy and integration. The rise of digital technologies including ecommerce, mobile apps and social media have enabled retailers to capture phenomenal data into online customer behavior. But to build a consistent, well‐branded customer experience, they need to go beyond digital and understand customer behavior in the physical store as well and integrate this data with what they already know about the customer in the digital world. And there’s the problem.

Most retailers continue to depend on legacy POS and CRM systems or manual surveys to gather in-store business insights. A few of them have invested in in-store tracking solutions such as video analytics, sensors, thermal imaging, laser technology etc. While all these technologies are good at people counting and descriptive analytics, they fail miserably when it boils down to capturing the actual insights into in-store customer behavior. Using such tools retailers will only get an aggregated view of number of people walking into their stores and do not know customer’s business identity (such as name, loyalty number, contact details etc.) until and if that person reaches the POS to make the purchase. This gap severely limits a retailer’s ability to understand customer behavior patterns within their stores.

The Solution

Retailers first need to understand where they stand in their journey to becoming a truly data-driven retail organization. Inadequate technology adoption is one of the key reasons why retailers have been failing to make better decisions using data. Data capturing is important across all the channels but for retail, capturing accurate in-store data is the most important one .

To better understand in-store customer behavior, retailers should invest in an effective location analytics technology that can capture customer’s behavior with the business identity from the moment they enter the store and map it with data from POS/ CRMs providing a single view of the customer. Until recently, availability of such technology was limited. However, now with the popularity of cloud based solutions, location analytics tools such as Proximity MX are available to the retailers. Using such solutions, retailers can collect real-time data about in-store customer behavior, analyze behavior patterns and seamlessly integrates this data with backend enterprise and CRM systems using their existing WiFi infrastructure. Retailers can also deliver real-time data‐driven marketing campaigns while the customer is still present in the store.

Why WiFi Analytics?

Let’s compare WiFi analytics with other platforms/processes ability to capture at-location customer data and engage with them real-time.

If you look that the above table, WiFi based analytics and engagement solutions have higher data accuracy than other platforms/processes.

However, not all WiFi based location analytics and engagement platforms provide accurate, deterministic and reliable data.  Most of them promise to provide in-store analytics regardless of customers connecting to the WiFi or not. But the data gathered on unconnected customers is anonymous, aggregated and unreliable since customers’ device MAC addresses are randomly generated and are not unique prior to the connection to network.

With Proximity MX, we recommend retailers to encourage their customers to connect to WiFi using creative in-store signages and stickers. The connected customers are then progressively on-boarded on the Proximity MX platform using smart captive portals to gather enterprise business identity of each customer. Once the customer’s business identity is gained, the platform builds customer personas based on their in-store behavior such as visits, dwell time, zones isited. With access to these accurate personas, retailers can trigger personalized and contextual notifications to their customers via multiple channels such as Email, SMSes, Smart Captive Portals,  App Notifications or even trigger APIs. All this while customer is still present in your store. Retailers can also integrate their marketing CRM or POS data within the platform to get a full 360 degree view of each customer walking into their location.

The Value

In today’s highly competitive retail environment, accurately understanding customer behavior is a major differentiating factor between retailers that delight their customers and those that struggle to entice them. Capturing in-store customer behavior can unlock valuable insights that will help retailers in the following ways:

Segmentation

With access to behavior patterns connected with business identity of each customer walking in the store, retailers can easily distinguish between different types of customers based on their visits, time spent, spending, purchases and loyalty.

Personalization

There’s a tremendous shift in customer behavior, they’re more demanding, each expecting their individual experiences to reflect their own personalities and desires. Starting from digital, the concept of personalization is becoming a new norm in the physical world as well. Armed with in-store insights based on customer personas, retailers can effectively personalize experience for each customer walking in their stores.

Performance

Using in-store analytics retailers can gather performance metrics across stores in different zones, geographies or even areas within each store. This data can help retailers optimize costs, increase revenue/sales and target their customers better.

Loyalty

The accurate understanding of customer personas can help retailers identify the right time, the right message and the preferred channel of engagement to better plan loyalty marketing campaigns. Also, by integrating loyalty data with in-store analytics, retailers can derive customer preferences and habits that will help them retain the existing loyalty members.

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