Data Analytics in Retail Industry: A Complete Guide

by | May 11, 2017 | Data Analytics

10 Min Read. |

With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival.

Data Analytics in Retail Industry

For big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others. With this, identifying customers who would likely be interested in certain products depending on their past purchases, finding the most suitable way to handle them via targeted marketing strategies and then coming up with what to sell next is what data analytics deals with.

Strategic Areas in Data Analytics for Retailers

There are some strategic areas where retail players identify a ready use as far as it is data analytics. Here are a few of those areas:1.) Price Optimization

Of course, data analytics plays a very important role in price determination. Algorithms perform several functions like tracking demand, inventory levels and activities of competitors, and respond automatically to market challenges in real time, which make actions to be taken depending on insights safe manner. Price optimization helps to determine when prices are to be dropped which is popularly known as ‘markdown optimization.’ Before analytics was used, retailers would just bring down prices after a buying season ends for a certain product line, when the demand is diminishing. Meanwhile, analytics shows that a gradual price reduction from when demand starts sagging would lead to increase in revenues. The US retail Stage Stores found this out by performing some experiments and was backed by a predictive approach for determining the rise and fall of demand for a certain product which beats the conventional end of season sale.

Retail giants like Walmart, spend millions merchandising systems on their real time with the aim of building the world’s largest private cloud so as to track millions of transactions as they happen daily. As stated earlier, algorithms perform this function and others.

2.) Future performance prediction

This is another important area when looking into data analytics in retail industry since every customer interaction has a very big impact on both potential and existing relationships. Dishing out the full idea to the full sales force personnel might be risky because making a wrong decision could result in an immediate or prolonged loss. Rather, top business organizations have discovered the best way to contain cause-and–effect relationship between key performance and strategic shift indicators by using a test-and-learn approach. This is carried out by customers or reps who compare the performance of the test group to performance of a well-matched control group. This is the data science involved behind the study. 

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3.) To accommodate small-scale retailers

Data analytics in retail is important for small-scale retailers, who can get assistance from platforms who provide the services. Apart from this, there are organizations, mainly start-ups, who offer social analytics to create the awareness of products on social media. Therefore, small-scale businesses can take the advantage of data analytics retail without spending too much in order to avoid hurting their finances.

4.) Demand prediction

The moment retailers get a real understanding of customers buying trends, the focus on areas that would have high demand. It involves gathering seasonal, demographical, occasions led data and economic indicators so as to create a good image of purchase behavior across target market. This is very good for inventory management.

5.) Pick out the highest Return on Investment (ROI) Opportunities

Retailers use data-driven intelligence and predictive risk filters after having a good understanding of their potential and existing customer base, for modeling expected responses for marketing campaigns, depending on how they are measured by a propensity to buy or likely buy.

6.) Forecasting trends

Retailers, nowadays have several advanced tools at their disposal to have an understanding of the current trends. Algorithms that forecast trends go via the buying data to analyze what needs to be promoted by marketing departments and what is not needed to be promoted.

7.) Identifying customers

This is also important in data analytics retail because choosing which customers would likely desire a certain product, data analytics is the best way to go about it. Because of this, most retailers rely so much on recommendation engine technology online, data gotten via transactional records and loyalty programs online and offline. Companies like Amazon might not be ready ship products straight to the customer’s before they order; they are looking in that direction. Individual geographic areas depend on demographics that they have on their customers which imply that demand is forecast. Therefore, it means that when they get orders, they are able to fulfill them more efficiently and quickly while data gotten depicted how customers make contact with retailers is used for deciding which would be the best path in getting their attention on a certain product or promotion.

Role of Data Analytics in Retail Industry

Other key areas where data analytics play a key role are:

a.) Discount Efficiency

Almost 95% of shoppers have admitted that they use a coupon code when they do shopping. For retailers to gain from offers, they need to first ask themselves how valuable such deal would be their business. Such promotional deals definitely will get customers rush in but might not be an effective strategy to sustain a long-term customer loyalty. Rather, retailers can run analysis on historical data and utilize it in predictive modeling for determining the impact such offers would have on a long-term basis. For instance, a team of data analysts and scientists can make a history of events that might have occurred if there was no discount. They then make a comparison of this with the real events when there were discounts to have a better understanding of the effectiveness of each discount. After getting this knowledge, the retailer will now readjust his discount strategy by increasing the number of discounts on various categories and removing less profitable deals. This would certainly boost the average monthly revenue.

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b.) Churn Rate Reduction

The creation of customer loyalty is the main priority among all brands because the cost of attracting a new customer is more than six times expensive than retaining the existing ones. It is possible to represent churn rate in various like percent of customers lost, the number of customers lost, percent of recurring value lost and value of recurring business lost. With the help of big data analytics, insights got like things customers are likely to churn, retailers can find it easy in determining the best way to alter their overall subscriptions to prevent such scenarios. For instance, a retailer takes an analysis of customer data after a monthly subscription box and can use it to get new subscribers who might likely end up as long term customers. This would result to the retailer decreasing the monthly churn significantly and would make brands be able to calculate lifetime value and make money back on marketing costs that are steep.

c.) Product Sell-through rate

Products that are data related can be analyzed by retailers to find what pricing, visuals, and terminology will resonate with the potential and existing customers. An alteration of the product showcase depending on the data sets that are analyzed, retailers will obtain improved sales rate. Take, for instance, Uber’s whole business model depends on big analytics for sourcing of crowd and sell-through of products. With customers’ personal data, Uber is able to match them with the most suitable drivers depending on the location and rating of their customers. Customers, therefore because of such personalized experience, would prefer to take advantage of Uber’s personalized offers against offers by competitors of Uber or even regular taxis.

Getting the right customers to stores is very important too, something a US department store giant recently discovered. Because of the way their analytics showed a dearth in vital “millennials” demographic groups, their One Below basement was opened at their New York flagship store. Promotions such as “selfie walls” and while-you-wait customized 3D-printed smartphone cases were offered. All these were just ideas for attracting young customers to their store with the aim of giving them an awesome experience.

Opportunities in Retail Analytics

Also, there are several opportunities in retail analytics:

1.) The promise of big data

Yearly, retail data is on the increase, exponentially in variety, volume, value, and velocity every year. Retailers who are smart know that each interaction holds a potential for profit. So much profit.

A report in 2011 states that retailers who use big data analytics could increase their operating margins by as much as 60 percent. This, therefore, has created the need for the data scientist whose job is to make big data (structured or unstructured, external or internal) clear. This is to help retailers take actions that will help them increase sales while costs are reduced.

2.) Marketing

  • Online behavioral analysis and web analytics that create tailored offers.
  • Personalized and location-based offers on mobile devices.
  • Targeted campaigns that use analytics for segmenting consumers, identifying the best channels and eventually achieving an optimal return on investment.
  • “Second by second” metrics used by real-time pricing.

3.) Customer Experience

  • Multi-level reward programs and personalized recommendations that depend on online data purchase preference, smartphones apps, etc.
  • Sentiment analysis of call center records, social media streams, product reviews and many others for market insights and customer feedbacks.
  • Predictive analytics for customer experience enhancement on all devices and channels, online and offline.

4.) Merchandizing

  • Detailed market basket analysis that yields more rapid growth in revenue.
  • Identifying shopping trends and cross-selling opportunities with the aid of video data analysis.
  • Rise in daily profits via a combination of external and internal data such as seasonal and holiday trends, economic forecasts, traffic and weather reports.

5.) Omni-Experience

The main aim is a streamlined and seamless experience for everyone involved. This is from when the product leaves the manufacturer, to the store floor or warehouse, to it being purchased, the retailer wants maximum efficiency in all departments.

It is no longer news that the retail industry has gone through a lot of operational changes over the years due to data analytics in retail industry. The solutions of big data analytics in retail industry have played an important role in bringing about these changes. Therefore, the adoption of these analytics solutions is growing rapidly making more retailers work tirelessly in order to enhance supply chain operations, improve on marketing campaigns and raise the satisfaction of customer as well as achieves a high success rate in retailing.


A lot of issues would be acknowledged to optimize data analytics in retail industry full capacity. Factors such as security, privacy, liability policies and intellectual property have to be stringent when talking about analytics. Analytics and big data are inter-related and therefore professionals who are specially trained would need to be included in the team so as to functionalize and utilize big data analytics.

Also, companies would find it pertinent to incorporate information from various sources of data, mainly from third parties, and aid such environment by deploying efficient data.

Finally, companies often make the mistake of falling in short-sightedness, making them fail in implementing the insights gotten from analytics. Of course, this could be fixed by continuously altering retail styles where a particular team is given the task of arranging and implementing insights.


Retailing has gotten to the platform for more disruption that is data-driven because data quality gotten from several sources such as social network conversations, internet purchases, location-specific interactions from smartphones have transformed into a new entity for transactions that are digital based.

The benefits that organizations would reap from utilizing data analytics are better risk management, improved performance and being able to discover insights that may have been hidden.

With the big return of interest data analytics delivers in the retail industry, most retailers will continue to utilize solutions so as to increase customer loyalty sustenance, boost the perception of their brand and improve promoter scores. Data analytics retail allows retailers and organizations gather information on their customers, how to reach them and how they can use their needs to impact sales. As technology continues to dominate retail industry, one thing is certain – data analytics is here to stay!

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