7 Applications of Data Science in Retail Industry

Application of Data Science in Retail

Application of Data Science in retail abounds. If you could name one industry that is heavily based on data and functions on it, it would be the retail industry.

The retail industry has been applying Data Science in its operations since long.

Whether it is analyzing consumer behavior for good old product recommendation or optimizing the price as per customer’s demands, the retail industry always aims at improving customer experience, while constantly evolving in the competition.

Data Science in Retail Sector

The retail industry thrives on data.

Knowing customers comes handy. What do their customers like? Which is that particular object that is demanded more? These and many other answers come easy with the right data collected.

Collecting customer data makes sense when you know that your retail industry’s base is customer and his shopping experience.

Buying products of your choice is so easy just on the basis of product recommendations

Did customers make a shift from one brand to another? Were customers loyal to a specific brand?

With the application of Data Science in retail, such questions are easily answered and the businesses flourish in the highly competitive retail industry.

How important is the application of Data Science in Retail?

Let’s take an example.

A retail brand observed a trend that the business (in-store purchases) isn’t much on weekdays. Customers preferred to make online purchases to avoid spending time in commute, standing in long billing queues and rush in the store.

The brand rolled out attractive discounts and lucrative offers. These were valid only on in-store purchases made on Wednesday. What followed was a huge increase in customer footfall in the brand’s store.

How do you think the brand was able to work wonders for its business? Data collected through various channels and a proper analysis of it was the key.

Let’s now walk through 7 such applications of Data Science in retail.

1. Customer Behavior Analytics

Customer insights that are in-depth and data-driven have the might to solve issues, such as personalized campaigns to increase revenue, boost customer conversion rates etc. There is a catch here. It is not that easy to collect data and derive useful insights from it.

Yes, the data is unstructured as it comes from various sources; complex as customers connect with brands through several mediums and is in different forms.

This makes the process of collecting, organizing and analyzing data, complex!

When you finally get insights, be ready to be surprised. You will get to know such facts that might have remained in the dark. The analysis will give you insightful weapons like most valued customers, customers’ behavior and ideal time to reach them, etc. 

Just effectively launch these weapons and win battles of customer acquisition and customer loyalty.

2. Price Optimization

There are several optimization mechanisms that have helped in setting the right price for both retailers and customers.

Do you think that price is decided only on the basis of an item’s production cost? It is also based on the spending capacity of the targeted customer and the competitor rate. 

Price optimization tools tactfully deal with the crucial factor of price determination.

Real-time optimization model lets the retailers attract a customer and retain him and make customized pricing schemes.

3. Personalized Marketing

Online and offline marketing isn’t anything new. Almost every retailer carves out targeted marketing strategies to let out a word about their products.

Targeted Marketing is important as sending a list of high-end gaming laptops to an unversed person will be of no use.

Dynamic pricing, product recommendations and assortment optimization are some tools that will come handy.

With a lot of data collected around who bought which item and when initiating personalized marketing and realizing them will not be a task. As a retail industry professional, focus on monitoring customer’s purchases and also on whether your marketing tactics worked or not.

4. Market Basket Analysis 

This traditional data analysis tool is fetching handsome profits for the retail industry, for many years.

Market basket analysis functions well in organizations that have a large amount of data. This is collected through various customer transactions. Predicting future choices and decisions on a large scale is easy with the help of this tool.

Where to place which content? How will be the layout organization? What will be the suitable price for different products?

To know the answer to these questions, a retailer has to know the items in his basket and likes, dislikes and previews. 

The rule mining algorithm makes the analysis possible. A customized function stores the data, divides it on the basis of differentiating factors and removes useless parts.

Based on this input data, association links between different products are created. Association rule application makes this process possible.

Insights, thus collected, helps in improving marketing techniques and developing strategies of the retailers. All the efforts directed towards increasing the efficiency are also fruitful. 

5. Customer Journey Analytics

With the increase in the use of social media channels, mobile and e-commerce to do just anything, customers can now easily access any information in a few seconds.

Customers are now free to compare prices of a commodity, look at discounts offered (who is offering what percentage of discount) and then decide where to make a purchase from. 
With the application of Data Science in retail, customer experience is a determining factor in increasing sales, revenue and customer retention.

To provide a seamless experience to the customers, understanding their journey across channels is extremely important. Data-driven insights make it possible.

Worried about how to assess such an enormous amount of data? Feed all your data- whether unstructured or structured, into Hadoop. 

After this, you will be able to analyze all of it as a single dataset, irrespective of data type.

6. Warranty Analytics

Warranty analytics was introduced in the retail sector as a tool to monitor warranty claims, detect fraud, increase quality and reduce costs. To identify problem areas and claims patterns, text and data mining is an integral part of this process.

Data is molded into actionable insights, recommendations through segmentation analysis and real-time plans.

Not even a single suspicious activity can pass through because the process focuses on detecting anomalies in warranty claims.

Once the analytics process is complete, the application of Data Science in retail produces actionable reports. Genuine claims, fraudulent activities, claims disbursed, etc. can be known easily.

7. Inventory Management

Why is inventory analysis necessary?

In order to have the required products available at the right time and in a proper place and condition, the retail sector relies heavily on analyzing inventory. To avert the situation of non-availability of items, supply chains and stocks are critically analyzed. 

Data analysis platforms and robust machine learning algorithms, identify patterns and correlations among supply chains and other variables.

Data thus received is helpful in identifying high demands and ideating strategies for sales trends, optimizing delivery and regulating the stock.

Application of Data Science in retail drastically improved sales and also helped anticipate future trends and working accordingly.

To stay ahead in the highly competitive market, seeking dedicated methods to unleash the true power of data is imperative for the retail sector. 

Expect the future market to be even more customer-focused and target-oriented.

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