Online retail in India is growing at over 30%. Around 830 million Indians will be online by 2021 and the revenue from e-commerce will most likely grow to $200 billion within the next decade. The reasons for this historic transformation are many – increasing access to smart devices and the internet to a larger public, increasing consumer wealth, success of e-commerce platforms like Flipkart, and of course, the increasing number of data science applications in the retail industry.
This blog will give you the lowdown on how 10 applications of data science
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- Recommended Engine
- Market Basket Analysis
- Inventory Manager
- Sentiment Analysis
- Personalized Marketing
- Warranty Analytics
- Customer Lifetime Value Prediction
- Location Recce
are helping businesses make decisions based on insight to not only increase profits but also to increase customer satisfaction and improve their overall retail experience.
1. Recommendation Engine
Recommendation systems are useful across a variety of services. Spotify uses recommendation systems to recommend new music to over 100 million of their users every week! In retail of course, these systems are useful in recommending products that customers may find interesting and perhaps even setting trends for the market.
Recommendation systems are adaptive, and therefore, personal. Depending on the choices you make, machine learning algorithms make choices that may benefit you.
In retail, recommendation systems generally use two types of filtering – collaborative and product-based. Recommendations in collaborative filtering depend on purchasing histories of customers like you. In product-based, recommendations are products that are like the one you are buying.
2. Market Basket Analysis
A market basket analysis is an application of data science that helps retailers determine, which products relate to each other – or which products are likely to fall into the same basket. It is a technique that has been around for ages. The idea is simple. If you buy bread, chances are you want to buy butter or jam to put on the bread. Hence, the retailer will recommend this product to you so that you don’t have to go fetch! The recommendation makes the customer’s task easier and helps the retailer ensure a sale and avoid the risk of you forgetting to buy a spread for your bread.
3. Inventory Manager
Have you ever experienced the frustration of not being able to buy a product that you are really interested in because, guess what, the product was out of stock? Well, it’s unlikely to be a problem for long. One of the successful applications of data science in retail is helping inventory management.
With real-time information on both demand and stock of products and the means to quickly process this information, retailers are curbing the ‘out of stock’ problem using better foresight.
Knowing how much to price a product used to be a challenge for retailers. Not any more as data science applications are so much better at it! Knowing how much a customer will be willing to pay for a product is profitable. More importantly, it helps retailers build trust with their customers by ensuring it doesn’t seem like they are over-charging.
Plus, there is a greater level of flexibility that data science applications are capable of leveraging. Using insights on the different seasons, customer spending patterns and competitor pricing strategies, data science applications are helping retailers optimize their pricing and even personalize it to the customer’s liking. They are proving that customers don’t mind spending if they think it’s worth it.
5. Sentiment Analysis
With the plethora of options available for most products in the market, it is important for the retailers to narrow in on the few products that really match their customer’s fancy. Hence, it is necessary for retailers to learn how their customers feel about their products.
This was traditionally ascertained through a customer sentiment analysis using surveys or other modes of feedback. It’s easier now with the abundance of data available – on social media, on-site rating systems, and what not.
Using natural language processing and machine learning algorithms, data science applications are adept at giving retailers a picture they can rely upon to gauge how their customers truly feel about them.
An important aspect of the retail business has always been merchandising, which may be likened to the visual appeal of a product or brand that influences the way customers behave – especially in sales encounters.
Data science applications are making merchandising more dynamic and responsive to different environments. By effectively rotating the products on display, tailoring their packaging to the tastes of cultures and moods of different seasons, they can better grab the customer’s attention and engage them. This is a significant achievement in the retail industry and data science applications deserve much credit for this turn of situation. But they remain behind the scenes on most occasions.
7. Personalized Marketing
Now that businesses know how you feel, what you want to see, the products you like, other products that may be of interest, it is safe to say that they know you. They probably know you better than your best friend. I wouldn’t recommend replacing your best friend for Amazon though. Even if it could potentially lead to getting special offers, discounts and freebies.
What do they do with all this information on you? They use it for marketing purposes of course! Businesses and organizations have always created customer personas to better target their customers. Now they have exact caricatures of you and your friends and everyone else, so that personas are really persons. And their marketing messages and communications are personal. I love you too Amazon. Thanks for gifting me what I wished for my birthday.
8. Warranty Analytics
Using data science applications to analyze information pertaining to warranty claims benefits the retailers in at least 2 ways – detecting fraudulent claims or narrowing in on a serious product or service problem in case of several similar and genuine claims.
While detecting fraudulent warranty claims helps in lowering cost of repairs and replacement, identifying a defect in the product could help solve it in manufacturing or at another early stage in the product’s lifecycle. Data science applications prove in the latter case that in the retail industry, where product warranty is much sought-after, preventing claims is way better than curing them.
9. Customer Lifetime Value Prediction
The retail industry values you quite literally I mean. If you are registered on any of the e-commerce platforms, then they probably have an estimate of what you’re worth. To them, at least.
Traditionally, this evaluation was referred to as the lifetime value of the customer. It used to be a ball-park number with very little scope for change. It remains a ball-park number as the future is susceptible to change. But the scope of the retailer’s ability to track this change has increased drastically.
Perhaps for the first time then, retailers’ can track your worth to them as it grows or decreases. The next step would be to use this information to better understand the factors that influence change in the customer’s worth so that they can forecast the future better despite the many factors shaping it.
10. Location Recce
So, retailers know their customers (you and me) better, they can deliver better experiences, make more profit while doing all this and they are becoming better at forecasting. Is there anything left? Yes, there is still room for more. There is always room in retail for growth!
Expanding a business is always a big challenge. But with data science, algorithms can easily process information like profiles of customers in a certain demographic, their projected lifetime values, competition in the region, etc., to give retailers a sense of the viability of their services in any region.
Data science is helping retailers pick new locations to operate in and grow their business in a sustainable fashion.
Data Science Applications
As we have seen, the applications of data science in retail are several. They are as useful in this industry as any other. From helping you buy the right products at the right price to making sure they are always available, and all your needs are met in a timely fashion, data science is changing the way retail functions. It is changing the way you experience fashion, technology and everything you can put a price on that matters.