retail analytics

Retail analytics doesn’t exclusively apply to ecommerce businesses or brick and mortar stores alone. It applies to retail in every shape and form, and should ideally form the core pillar of a retail business strategy.  

Simply put, retail analytics is the process of collecting and drawing insights from data within a business (across operations, supply chain, inventory, marketing and procurement), and externally (competition, economic conditions etc.) to make effective business decisions.  

Today, retail analytics has become a complex process because every retail business is omnichannel – that is, it has a presence across multiple platforms and it likely sells across boundaries, which means that there is a ton of data available for businesses to draw conclusions from.  

To aid in classification and categorisation of multiple sources and layers of data, retailers largely rely on four different retail analytics models.  

In this blog, we’ll look at what those retail analytics models are, and real-life examples of how retailers have applied these models to bring more efficiency, customer-orientation and profitability to their businesses. 

Retail Analytics Models with Use Cases 

Descriptive retail analytics 

Descriptive Analytics is a model that retailers use to draw insights from past data. For example, during a yearly review, retailers use descriptive analytics to identify how a seasonal sale has performed in terms of revenue, inventory performance, logistical issues, and supply chain feasibility.  

Past insights can help retailers identify what went well, what challenges they faced, and how they can improve their processes, performance and profitability using this data.  

A classic example of descriptive analytics is Netflix’s data strategy. As many already know, Netflix relies heavily on data to customise every aspect of a viewer’s experience. The most common one, we all know, is data on what we are currently watching.  

While that is quite basic, Netflix uses layers on top of this to understand what time we watch it, how long we watch it for each time, our past watch patterns and shows that we have started watching, re-watched, paused or removed.  

At the macro level, they churn data on which actor/ actress or genre is trending in each region, and so on. They use these micro and macro level data points to personalise every viewer’s homepage, showcase trending content, and determine what will and will not work when it comes to deciding Netflix original productions. 

Diagnostic retail analytics  

Diagnostic Analytics is a model that retailers use to understand why something happened or didn’t happen. It depends on a few theories. For one, diagnostic analysis is based on a hypothesis that is formed to validate or invalidate an assumption.  

A Merit expert says, “The hypothesis can be that the sale of rain cheater jackets will increase during the rainy season this year, because of the seasonal demand for the product, and because the price is competitive compared to other brands or players. Secondly, identifying the correlation between two variables, or being able to determine the cause of a resultant data point is also a key aspect of diagnostic analysis.”  

Retail regression analysis 

Lastly, regression analysis uses historical data to help businesses forecast the future. 

An interesting example of where diagnostic analytics can be applied is in human resource departments at organisations. Through a series of questionnaires, anonymous surveys, existing data on performance of individual employees, and general retention history, HR departments can determine whether the current work environment is aiding employees, find out what employees do to be successful and motivated at work, see how their mental, physical and psychological health is and take insights from it and use it to sustain or enhance the work culture at the organisation. 

Predictive Analytics analyses historical data and draws inferences that retailers can use to make future forecasts.  

Target’s use of Predictive Analytics 

A well known example of predictive analytics is what Target did with its customers. Every customer who buys at Target has a guest ID, with card details, email address or their phone numbers. Every time they make a purchase, their purchase and product data is captured by the brand.  

In one instance, Target wanted to run a campaign to customise and cater some of its products to parents-to-be. So, it began analysing the purchase patterns of women customers along their pregnancy term. It noticed that women in their second trimester purchased more unscented lotions, hand sanitisers, calcium and iron supplements and the like.  

Once it had this data in place, it began curating campaigns to send promotional emails to parents-to-be, with offers or coupons on products they were likely to buy. The result? Its revenues jumped from USD 44 million in 2002, to USD 67 million in 2010. 

Prescriptive Analytics is a model in which retailers use technologies like AI and machine learning to determine what to do next. For example, using prescriptive analytics, retailers can determine how many cash registers need to be opened, what products of what quantities need to be stocked during seasonal changes or during events like Thanksgiving or Christmas and such. 

The use of prescriptive analytics to predict the buyer’s journey  

A home improvement brand once wanted to devise a strategy to increase sales of its products and drive relevant campaigns to meet this objective. The brand analysed what its best customers were buying and what led them to come back and make repeat purchases.  

What it observed was that its best customers had bought moving boxes, because they’re either moving houses frequently or remodelling their homes. These were customers who were also likely to buy other tools and appliances like power drills, paints and the like. Once they had this data in hand, they ran campaigns, offering discounts on moving boxes and their sales spiked significantly. 

Data, when used securely and without invading the privacy of a customer, has the potential to greatly enhance a customer’s experience with a brand. It can bring in a wave of convenience and loyalty for a customer, and growth and profitability for a retailer. 

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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