APPLICATIONS OF DATA SCIENCE FOR RETAIL 

(1-A) Store Segmentation

What is store-segmentation?

One blanket strategy for Sales, Marketing, Pricing and Ranging for a company having stores at multiple locations will not work in today's time of intense competition. Instead, companies use Store Segmentation which is the process of grouping stores with common characteristics. It enables optimization of all store level activities that are suitable for that segment of store. It goes beyond just finding the right location: “it’s about personalization for stores".

It is especially important when the company does not have enough data about customer-demographics or past transactions (E.g., while opening a new branch). 

Based On what characteristics are stores segmented?

Store-segmentation process necessitates a complete analysis of the entire market, not only customer’s needs and shopping habits but also knowledge of changing market conditions and competition.

Usually stores are segmented using clustering based on characteristics like socioeconomic profile of the trade-area, competition, sales-area and special services that stores offer. Some such characteristics are shown in figure above.

What next after store-segmentation?

After segmenting stores using clustering, frequent item-sets and association rules analysis will be applied for each cluster for the purpose of target marketing. This way, a retailer will be able to find the differences between the clusters in terms of the items sold at the stores. 

(1-B) Product Segmentation

What is product-segmentation?

One-product-fits-all approach no longer works in retail. Along with differing personality traits come different customer needs and preferences. To cater to them, companies use ‘Product-Segmentation’ i.e., offering different versions of its product to different groups of people. E.g.  a company might have a base product that provides the core functionality, and several versions built around that core that cater to individual industries. 

Companies spend extra efforts, time and money on product-segmentation as they view it not as expansion of one product but as expansion of its product range.

What are benefits of Product-Segmentation?

(1)Monitor individual products

When products more closely align with a given customer-segment, company is able to take each product in turn and monitor its performance. This allows it to get a better picture of how each customer-segment responds to its product by letting it monitor the product most closely aligned with them, rather than one nebulous product. 

(2) Cater to different customer groups

With one nebulous product  some customers often feel they are paying for features they do not need and others may feel they do not have enough features. Product segmentation, when built around core buyer personas, allows a company adopt balanced approach between these two extremes, acquire more customers, and improve customer retention. 

(3)Understand target market to determine the right pricing

Once there is no one-size-fits-all approach in product offering, the company can also abandon one-size-fits-all pricing. 

As a segmented product tailored to different customer bases, company is able to adopt a pricing strategy that is ideal for each customer base.

(4) Explore new growth opportunities

The great thing about segmenting product is that it makes it easy to uncover growth opportunities. 

As company examines the types of customers and feedback received from them, a picture will begin to form of how it can further segment its base product to appeal to a wider range of people and unlock new growth levers.

(5) Segment willingness to pay data

If company has "willingness to pay data" it can segment it by customer groups, but that data will be less than useful so long as there is only one identical product for everyone to purchase. 

Creating multiple products that serve different segments allows company to segment that data  as per product-segments and price them accordingly.

What are product segmentation strategies?

Once a company has decided that it wants to use product segmentation, it must consider following four key product segmentation strategies:

(2) RFM Analytics

What is RFM Analytics?

RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. RFM analysis is based on the marketing adage that "80% of  business comes from 20% of customers." 

It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM analysis is used to identify existing customers who are most likely to respond to a new offer. 

Judging customer value on just one aspect (Monetary value of transaction) gives an inaccurate report of customer base and their lifetime value. As a customer may buy big but only once, or his big purchase might have been long time back. That’s why, RFM model assigns each customer three numerical scores: Recency-Score, Frequency-Score and Market-Value Score. Then the three scores are combined to get a combined RFM score and customers are ranked based on RFM Score.

How are customers classified based on their RFM score?

Customers are classified into segments based on this combined score as shown below:

(3) Purchase Likelihood Analytics

What is the likelihood to purchase?

The likelihood to purchase (LTP) indicates the high probability of some visitors who are more likely to make a purchase within an expected time frame (normally 6-12 months). 

The exact time-point when customer's purchase intent will be converted to actual purchase depends upon where the customer is in the four stages of customer journey as shown below:

On which factors does LTP depend?

Likelihood to purchase  (LTP) depends on factors like

(1) Seasonality
(2) Satisfaction of existing customer
(3) Customer Demographics
(4) Advertising

How is LTP measured?

Likelihood to purchase  (LTP) can be measured by surveys like

(1) Brand tracking Surveys
(2) Product testing Surveys
(3) Customer Satisfaction Surveys
(4) Ad Testing Surveys

In addition, for e-commerce, continuous analysis of user's every interaction(E.g.Click-stream-analysis, wish-list analysis, abandoned-cart-analysis etc.) can provide useful information about where customer is in his journey and help in estimation of his LTP. Customers with high LTP are found by customer-segmentation using ML algorithms which learn from buyer behavior and predict the probability of a purchase.  

How is customer LTP related to CLTV?

A customer’s lifetime value (CLTV) and likelihood to purchase (LTP) are related and they fall into one of three categories: low, moderate, or high. 

Due to intense competition and increasing marketing costs, acquiring new customers is becoming expensive. As lifetime values decrease, companies and agencies can use LTP segments to bring more conversions, improve ROAS, and optimize marketing budgets. Growth marketers can make use of high LTP segments on costly digital ad-platforms to turn more clicks into purchases. 

What are benefits of analysis of LTP?

(4) Pricing Analytics

What is Pricing Analytics?

Pricing analytics is a technique that provides companies with the tools and methods to better perceive, interpret and predict consumer behavior w.r.t. price changes.

Why is Pricing Analytics Required?

Pricing merits a detailed analytics because pricing power comes from understanding ‘what consumers want’, ‘where they shop’, ‘which offers they respond to’ and ‘how much they are willing to pay for a product or service’. 

Further the retail space is becoming highly tech-driven with 80% customers using price-comparison tools. Hence, industry leaders are abandoning manual processes and adopting data driven pricing tools which utilize price-analysis data from various channels of a company’s operations, enable competitive pricing decisions and a better ROI. The latest trends in retail Pricing Analytics are shown below.

However this does not mean that modernizing technology alone is enough for deciding Pricing-Strategy of a company. It must choose a Pricing Analytics Solution that is able to answer questions like:

(1)How is Pricing Influencing Brand Associations?
(2)Is current Price-Brand Association Optimal?
(3)Does the solution provide comparison with competitors' Price-Brand associations?
(4)Does the solution takes both price and value into account?

What are other benefits of Pricing Analytics in addition to setting  optimal price?

(5) Cross-Selling And Up-selling

5-A Cross-Selling

What is cross-selling?

Cross-selling is the process of satisfying the additional needs of customers that the primary product is unable to fulfill. It means encouraging customers to buy complementary products, increasing the overall cart value. 

For example, shopping from the in-flight product catalog, such as a pair of headphones, would be a cross-sell. Some great companies which very successfully use cross-selling are Amazon, ASICS, IKEA, Apple, Sephora etc.

What are advantages Of Cross-Selling?

(1)Helps increase overall sales value.
(2)Better customer-comprehension and engagement.
(3)Greater convenience to customers via useful suggestions.
(4)Helps discover similar/complementary products/services.
(5)Creates an environment of mutual benefit for businesses.
(6)Helps to promote and sell a larger variety of products.
(7)Promotes innovation of new products.
(8)Increases customer lifetime value.
(9)Enhances customer-satisfaction.

What is up-selling?

Up-selling is the technique whose end goal is to increase the cart value not by introducing new items but by increasing the size or quantity of the initial purchase decision. For instance, upgrading an economy airplane ticket to business class would be an Up-sell. Some great companies which very successfully use up-selling are Spotify, GoDaddy, Booking.com and DELL.

What are advantages Of Up-selling?

Why cross-selling and up-selling are so powerful?

As shown above, it is far easier to get more business from existing customers than acquiring new customer. Both cross-selling and up-selling are so powerful as they are aimed at getting more revenue from already existing customer and improve customer-retention. Increasing customer retention by as just 5% can increase profits from 25% to 95%³.  

According to a 2022 HubSpot Blog survey⁴ of more than 500 sales professionals, 72% of salespeople who up-sell and 74% who cross-sell say that it drives up to 30% of their revenue.

In HubSpot survey of 2022 the response to question: “What percentage of your company's revenue comes from cross-sell or up-sell?” the answers obtained are depicted above which clearly show the power of cross-sell and up-sell. This is particularly true for e-commerce site. Up-selling can raise sales by 10-30% on eCommerce sites, while cross-selling raises profits by over 30% on an average¹. 

Moreover, reports say that 77% of revenue comes from up-sell, renewals and cross-sell² in the case of B2B businesses.

What are strategies for cross-sell & up-sell?

(6) Recommendation Engine

Introduction

Ever wondered how Facebook suggest "people you might know," and you know the majority of them! Or, how Amazon knows what kind of product you need and show you the most attractive options? Or how can Netflix figure out what kind of movies or series you might like? They use a recommendation-system. A recommender system is a delicate way of bringing user close to relevant content from fast sea of information.

Some other top companies which use recommender system are:Spotify, LinkedIn, YouTube, Instagram, Quora and Google.

What is market potential of recommender system?

The global market for the usage of Recommendation Engine was valued at USD 2.69 billion in 2021. It is anticipated to surpass USD 15.10 billion by 2026, reporting a CAGR of 37.79% during 2022-2026.

What is a recommendation system?

Technically a recommendation engine is a data-filtering system based on ML algorithms to recommend products, services, and information to users. They are combinations of information filtering and matching algorithms that bring together content and user.

From where do they get data?

The data can be explicitly or implicitly collected.  Explicit data is generated by a user taking a direct action that indicates user's preferences (for example, information about past activity, ratings, reviews, gender, age, income-profile). Implicit data is generated during interaction of user with website E.g. device used for access, click-stream, location, and dates.

What do they do with data?

Recommendation engines discovers data patterns in the data set by learning consumers choices and produces the outcomes that co-relates to their needs and interests.

How do the recommendation engines work?

Types Of Recommendation Systems

(1) Demographic Systems:

Such systems take available user data (age, gender, location, etc.), classify it into specific audience segments, and then put it in a bigger picture to fill the gaps in the data.

Demographic-based suggestions are widely used on content-aggregation websites and in the general eCommerce marketplace. Usually, this type of recommendation provides a background operation in case there is no other information available.

Advantage:
Simple and can be implemented with even a limited set of data to deliver broad suggestions. 

Disadvantage: 

To make it work, this system requires full-on market-research as a foundation. 

(2)Knowledge-Based(Cognitive) Systems

Knowledge-based systems are systems where suggestions are based on a user’s preferences and certain constraints that are based on a degree of domain-knowledge. Rules are defined that set context for each recommendation. These do not have to use interaction history of a user  but can include these as well. 

Advantages:

(A) Given the way the system is built up, the recommendations can be easily explained.

(B) It is better suited to complex domains where prior data is lacking. As prior data is not needed it doesn’t suffer cold-start up problems.  

Disadvantages:

Building up this type of framework can be expensive. 

(3) Content Based Filtering

A recommendation like ‘products similar to this’, is a typical instance of this type of approach. Here algorithm recommends products which are similar to the ones that a user has liked in the past. For example, if a person has liked an article on Lionel Messi then this detail is stored by the blogging site including rating given by him in his ‘Profile-Vector’. All information of other articles is stored in ‘Item-Vector’. The Algorithm finds ‘Cosine Similarity’ between these two vectors (either by top-n method or rating method). This Similarity can also be calculated by the ‘Euclidean Distance’ or the ‘Pearson Correlation’.

With an approach like this, the more information that the user provides, the higher the accuracy. Hence often the metadata collected from a user’s history and interactions is also used by algorithm.

(A) Advantages:

This recommender engine technique does not need any additional data about other users since the recommendations are specific to this user. Also, this model can capture the particular interests of a user and suggest niche objects that very few other users are interested in. Hence, niche eCommerce stores (Discogs and Artsy etc.) or content aggregation websites (Mashable and The NextWeb etc.) use this recommender system.

(B) Disadvantages: 

Due to privacy and regulatory issues often personal metadata and individual transactional data can be missing at the outset. So, a recommender system cannot draw inferences for a query due to lack of sufficient information (cold start problem)

(4) Collaborative Filtering:

The collaborative filtering method collects and analyzes data on user behavior, online activities, and preferences to predict what they will like based on the similarity with other users. So far as user data collection is concerned collaborative filtering casts a much wider net, collecting information from the interactions from many other users to derive suggestions for one user. This type of recommender system is common in the eCommerce marketplaces.

(A)Advantages: Collaborative filtering has higher accuracy than content-based filtering. 

(B)Disadvantages: 

Popularity bias: Refers to system recommends items with the most interactions without any personalization.

Item cold-start problem: Refers to when items added to the catalogue have either none or very little interactions while recommender rely on the item’s interactions to make recommendations.

Scalability issue: refers to lack of the ability to scale to much larger sets of data when more and more users and items added into our database.

(5) Hybrid Recommenders:

A content-based filtering model will not select items if the user’s previous behavior does not provide evidence for this. 

A Collaborative filtering cannot provide recommendations for new items if there are no user ratings upon which to base a prediction.

Hence hybrid recommenders that combine above two approaches are becoming popular. They take advantage from both the ‘representation of the content’ as well as the ‘similarities among users’. There are two main ways of combining 

(A) Combining Item Scores