Sightcorp logo

Retail Analytics

Everything about Retail Analytics

     

Retail Analytics

Retail analytics have been available for some time now in brick and mortar stores. However, the spotlight for data gathering and analysis has been in the digital world.

Retail today is being shaped by the empowered customer, who demands convenience, customization, collaboration, and consistency. To deliver this demand to the customer, retailers employ retail analytics, which offers insight into how well marketing is working, what customers actually do when they enter a store and whether there is an end result to their visit, such as a sale (conversion).

What is Retail Analytics

Retail analytics can be described as an analytics process of investigating supply chain movements, customer demands, patterns, sales, inventory levels, and other processes that are important for making marketing, sales-related decisions, and other business-related decision-making processes. 

Retail analytics is able to provide you with information about your customers, their behavior, needs, and demands. Moreover, it will help you to understand your clients better than ever. Once you understand your customers and their touchpoints better, it is easier to enhance their customer experience and provide them with the level of service that they deserve . Apart from customer knowledge, retail analytics is also able to provide you with insights into your business and processes within your company. That way, you will be able to uncover what areas need improvements and what works well. Get to know your business better and set up accurate key performance indicators (KPIs) to analyze your performance and get ahead of competitors. 

Essentially, retail analytics is used in order to help the business owner or manager to make data-driven decisions, optimize business processes, increase efficiency, and enhance the customer experience.

Retail analytics is far beyond simple data analysis. Nowadays, retailer use various data sourcing technologies such as wifi tracking, 3D sensors, infrared sensors in order to understand and target their ideal customer better. Video based retail analytics can be used to get demographic insights into target audiences which makes customization of shopping experiences even easier. By combining different types of technology, retailers can gather information on buying patterns, location, age, gender, and other characteristics of their customers. This data can be in turn compared with sales data to measure the effect of targeting efforts such as in-store advertising through digital signage on conversion. 

How does it work

Retail analytics can be done in many ways from audience analysis, sales examination to complex systems that involve face analysis software. 

Analyze historical data

Analyze your best selling products from last season, get to know the customers who have spent the most, what is their profile and their characteristics. This information will help you with optimizing your marketing campaigns, product placement and delivering a superior service to every visitor.

Analyze your current audience

Recent advancements in AI technology allow retail owners to make use of face analysis software. This technology is able to analyze a crowd of people through a simple USB or IP camera placed at the entrance/exit or around product shelves and displays.  

The analysis process is pretty simple; the camera detects a face in a video frame and based on its facial features it can determine age, gender and measure mood and attention. The same software can be used for counting people and impressions when combined with digital displays. Knowing the store traffic (high, mid and off peak hours) together with the demographic breakdown gives the retailer enough information to  tailor their product offering, store layout, digital and traditional advertising to enhance and maximize customer engagement and loyalty. 

If you are interested in learning more about face analysis, how it works, and how can it be applied in your retail environment , click here.

Use human insights

Technology can provide a lot of information about the natural shopping behaviour of   your customers, but it should not completely replace the value of customer opinion. Even when using analytics software,it is still important to listen to your clients and ask them for direct feedback about your business and your product offering. The two work best when used together. Gathering information from more than one source is essential for understanding the bigger picture and being able to take a holistic approach to the shopping experience as a whole and making improvements where they matter most.

Retail analytics techniques/examples

In the past, the marketers’ ability to track the performance of media and promotions in driving in-store traffic, sales, and brand recognition was largely limited to analyzing sales and traffic trends. Now, however, there are a number of ways that analytics can bring powerful insights to a retail organization. There are  several components which can be used in different ways for different scenarios. Here is a list of the most common ways to carry out retail analytics:

People counting:

People counting is one of the basic and most commonly used retail analytics technologies in today’s market. There are many different types of people counters which generate information on the number of visitors in and out of a store at any given time. This data gives insights into how many people entered the store and how many left without buying, in ecommerce terms cart abandonment. This information comes in handy when calculating the store conversion rates.  

Hot zone and dwell time:

Another way of conducting retail analysis is by measuring where and how people move within a store, where they dwell and for how long. This is known as a hot zone and dwell time analysis. This trajectory analysis which is usually expressed in a form of a store heatmap can enable store managers to optimize store layout for better product placement. In addition, this data can be used to evaluate or enhance the effectiveness of sales or advertising displays.

Customer behavior:

No retail analytics campaign can take place without customer behavior analysis. This technique can provide relevant information about how customers interact with products and advertisements within a store. Through the analysis of customer behavior, the retailers will be able to gain a better understanding of the customer’s decision-making process and therefore adapt the store or business to fit the customer preferences. Nowadays, businesses use several methods to contact the customer such as mobile push ads, social media, mortar stores, e-commerce sites, and more depending on the industry. This makes the retail experience much more complex and the diversity of information that managers have to analyze and accumulate is increasing as well. Therefore, many businesses are starting to utilize omnichannel services for customer analysis that make a complicated process simpler. Learn more about omnichannel retailing here.

Gaze analysis:

Gaze analysis is another method that can be used for understanding customer behavior in terms of attention, feeling, and desire. The principle behind the method is the following: when a person is in the field of view of a static camera, for instance embedded a digital Point-of-Sale screen, gaze can give information about the focus point of that person. From here, retailers can learn which parts of the  advertisement are attractive to the audience and also how long they spend viewing it. This can be very easily applied to in-store shelves as well. Consumer attention estimation can also help retailers to define the optimal product positions within the store, help collect statistics about the most interesting products and contribute to enhancing user experience.

Demographics:

Understanding the demographic makeup of shoppers at stores across a chain or at a particular locations assists in determining the success of campaigns designed to engage target segments. This means that advertising messages displayed through digital signage, placed at Point-of-Sale, for instance, can be tailored to a specific target audience, which increases engagement because of the high relevance of the message. It is no secret that targeted ads perform better than generic ads. Ad targeting is a very common practice in the online space, however the offline environment still has a lot of catching up to do. Dynamic advertising in physical retail can be delivered through the integration of audience measurement software with content management systems (CMS) used to schedule and manage digital content. Digital screens are becoming increasingly popular withing retail stores primarily thanks to their flexibility and the ability to change ads on the fly. Learn more about using audience insights for targeted advertising here.

Best practices 

There are many ways through which retailers are able to gain information about their market and conduct retail analytics.

  • The retail owner can start running pilots to measure the impact of different marketing and merchandising tactics on customer behavior (e.g product A/B testing). This method might result in enhanced market knowledge and higher sales. Moreover, this method can also contribute towards reducing sunk costs in inventory that is not as popular as some of the other products within the product portfolio. 
  • Start observing customer behavior within the stores. Analyze and study gathered data to optimize the product offering for the right customer.
  • Adapt the in-store product displays towards customers, through studying the purchase and browsing history of your clients. This method will help to identify customer needs and interests. Moreover,  this method is especially useful when the retailer wishes to foster impulsive purchases.

Discover Our Products

You might also find the following articles interesting:

 

   

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.