How Predictive Analytics Benefits Retailers

Every business nowadays, Large scale or vying to be large scale tends to have a large amount of data with them. That data seems useless unless it is contributing something to the business, and of course, no company wants to spend money on needless data storage.

To take advantage of these large chunks of data, companies are implementing predictive analytics to see changes and make strategic decisions. As compared to other industries, one segment that has ample customer data circulating all over is the retail industry. Retail Industries are using the data to take out meaningful and insightful information out of it. This analytical information is the road to their business productivity and profitability. As an extended arm of business intelligence services, predictive analytics has turned out to be the catalyst in augmenting sales growth, envisioning client patterns, forecasting trends and building a strong rapport with their customers

There are a whole lot of use cases of predictive analytics in retail industry.

SHOPPER TARGETING 

Nowadays, retailers are addressing one of the biggest challenges in the industry is turning one-time shoppers into brand loyalists. So, personalized marketing is more actual than the wide range of marketing campaigns, but this effort in itself poses quite a challenge to retailers.

This is where retailers take advantage of predictive analytics and take personalized shopping to a whole new level. Many big giants like Flipkart, Amazon, and Grofers keep track of the customer behaviors like search history, shopping history, shopping preferences and more.

SMART REVENUE FORECASTING

Counting chickens before they hatch is what every retailer loves to do. With Predictive Analytics, this becomes a viable reality. Instead of banking on just the historical data of old clients and customers to predict revenue in the uncertain arena of retail where trends, tactics, and promotions are constantly changing from one year to the next, Predictive Analytics makes use of accurate forecasts that combine deep analysis of new buyers and their probable buying habits.

In the case of this shop, the days of the beginning of the month are the ones with the major activity. After the middle of the month the sales remain stable.

Also, it is also important to take a look at the number of sales by weekdays. The next chart shows the sales in this shop from Sunday (1) to Saturday (7).

Sunday is the day preferred by the customers to buy in this retail shop. During the rest of the week, the sales decrease from Monday to Wednesday and increase from Wednesday to Friday. Saturday is the day with the least number of sales.

All this valuable information we get just by looking at the line plots of the sales of shape which can be used to predict the sales for the upcoming days. Further, Machine Learning models/ other forecasting techniques are applied to forecast more complex sales patterns.

PRODUCT RECOMMENDATION 

You will come across recommendations that magically pops up on a feed promoting items that you had been looking for in online stores. Instead of recommending the customers products based on their purchase history, predictive analytics makes use of cumulative data to forecast what the buyer is likely to buy next and will generate product recommendations to match.

How Does Product Recommendation Works?

A product recommendation is basically a filtering system that seeks to predict and show the items that a user would like to purchase. It may not be entirely accurate, but if it shows you what you like then it is doing its job right.

All this is only possible with a recommendation engine. Recommendation engines basically are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. Or in simple terms, they are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross-selling and up selling.

Let’s consider an example to better understand the concept of a recommendation engine. If I am not wrong, almost all of you must have used Amazon for shopping. And just so you know, 35% of Amazon.com’s revenue is generated by its recommendation engine. So what’s their strategy?

Amazon uses recommendations as a targeted marketing tool in both email campaigns and on most of its websites pages. Amazon will recommend many products from different categories based on what you are browsing and pull those products in front of you which you are likely to buy.

Like the ‘frequently bought together’ option that comes at the bottom of the product page to lure you into buying the combo. This recommendation has one main goal: increase average order value i.e., to up-sell and cross-sell customers by providing product suggestions based on the items in their shopping cart or below products they’re currently looking at on-site.

Different Types Of Recommendation Engines

We will discuss basically the three important types of recommendation engines:

Collaborative filtering: 

Collaborative filtering RS works with the collaboration of users. If there are many users who liked some item then that item can be recommended to that user who hasn’t seen that item yet.

Let’s understand this with an example:

Let say there are four users and four items as depicted in the image above. All four users bought Item-1 and Item-2. USER-1, USER -2 and USER -3 bought Item-3 also but USER-4 hasn’t seen Item-3 yet. So, Item-3 can be recommended to USER -4. Now only USER-3 bought Item-4 so, we cannot recommend Item-4 to USER -4 because only USER -4 bought Item-4 and none of the other users bought this item.

THIS IS HOW COLLABORATIVE FILTERING WORKS.

Core-idea/assumption here is that the users who have agreed in the past tend to also agree in the future.

Here, all the three users namely USER-1, USER-2 and USER-3 agreed in the past that Item-3 is worth purchasing , hence in the future USER-4 may like Item-3 which is something USER-1, USER-2 and USER-3 agreed to purchase in the past.

Content-based filtering:

These filtering methods are based on the description of an item and a profile of the user’s preferred choices. In a content-based recommendation system, keywords are used to describe the items; besides, a user profile is built to state the type of item this user likes. In other words, the algorithms try to recommend products which are similar to the ones that a user has liked in the past.

The idea of content-based filtering is that if you like an item you will also like a ‘similar’ item. For example, when we are recommending the same kind of item like a movie or song recommendation. This approach has its roots in information retrieval and information filtering research.

Similarity Based:

There are broadly two types of similarity based approaches that we can deploy.

*  User-User Similarity

*  Item-Item Similarity

USER-USER SIMILARITY BASED RS

ITEM-ITEM SIMILARITY BASED RS

So, why should companies use predictive analytics?

In today’s online retail environment the use of predictive analytics technology is critical for retailers to succeed.

It might be that not every use of predictive analytics is relevant to your business but you can pick the areas that will create the maximum impact by reviewing your desired targets. You mostly need it for increased revenue, fraud prevention, optimized customer service, cost savings or better insights into customer behavior.

Predictive analytics can produce a huge competitive advantage for an online retailer, through the models have to be thoroughly tested before they are deployed.

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