Sightcorp logo

Retail Data Analytics

Retail Data Analytics

     

What’s in-store data analytics?

Retail data analytics provides insights associated with inventory, sales, customers along with other aspects that are important for decision making. This discipline entails a number of granular fields that help create a wide picture of the health of retail business, sales, and other areas for reinforcement and improvement. Therefore, retail analytics help make better business decisions, operate businesses, and in the end give better customer service analytics.

It’s important to note that in-store data analysis is not just for large retailers like Target and Walmart. Small and medium-sized (SME) retailers can also take advantage of this technology and apply the insights obtained from data to get ready for future store planning, employee management, marketing, and inventory control. This is because data can be access from almost any touchpoint that retailers and consumers interact, including offline and online.

How in-store retail data analytics is used

Start with the correct tools
Lacking the correct tools for harnessing customer information can make life difficult for a retailer in-terms of establishing data-centric efforts. Challenges may also exist when retailers have these tools but don’t know how to use them correctly. Below is a short description of some commonly used tools for retail data analytics and what they are for.

Point of sale analytics
Point of Sale (POS) systems can be used for more than just ringing up sales. A majority of POS systems nowadays are designed with reporting features that can be used to shed some light on essential metrics including basket size, customer counts, profit margins, sale trends and many more.

Email marketing software
For retailers using email marketing to reach out to customers, it’s important to track open rates, time of engagement, and clicks. The email software used should have the capacity to provide this information. Therefore, retailers should at all times dig into this data whenever marketing communications are sent to customers.

Footfall analytics
Footfall analytics uses people counting technology, giving retailers access to meaningful in-store customer experience insights. Tools such as Face Analysis software (using in-store video cameras), beacons and people counters can help you gather information like dwell times and customer counts. This data can help you glean more info on how much traffic your store gets, and what parts of your store are getting the highest or the lowest amount of traffic.

Predictive analytics
While traditional analytics can tell you what customers like now, predictive analytics can inform you what a shopper will want next. Through multiple techniques such as data mining, statistics, modeling, machine learning, and artificial intelligence this form of advanced analytics can be used to make predictions about future customer behaviors. Predictive analytics helps retailers stay ahead of customer preferences, streamline their supply chain management and reduce inventory expenditures while helping expand margins.

Benefits of in-store data analytics

In-store data analytics take the guesswork out of a retailer’s day-to-day operations and gives a reality of what really happens on the ground. There are a number of moving parts in a store ranging from customer experience to sales inventory and everything else in between. It, therefore, becomes important to make decisions based on some reality.

You get to know your loyal customers
By knowing who your customers, as a retailer, you will be able to offer products in the required proportions and avoid dead stock. You can also tailor discounts and other benefits once you get a copy of who your customers are.

Evaluate market trends to meet consumers’ demands
Understanding what customers want is one of the best uses of retail data analytics. You need to know when they want it beforehand. Retailers who deploy analytics are able to focus their efforts to highlight areas of high demand, quickly pick up on emerging sales trends, and optimize for better in-store conversion.

Understand your true costs
Understanding your overheads and focusing on your main revenue generators will give solid success.

Retail has become as much about anticipating customers’ needs as it is about simply stocking and selling nice products. Retail businesses that innovate with the times and harness the power of analytics can optimize their efforts and garner better results thanks to proactive strategies emerging from real-time insights.

Discover Our Products

Below are other articles that you might find interesting:
Online Shopping Behavior
Analytics Software
Programmatic Digital Advertising

 

 

   

Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.