Benefits of Market Basket Analysis

With the background information about Market Basket Analysis, which we saw in the previous post, let us try and understand how a retailer can leverage the information obtained by doing such an analysis and reap benefits.

a)      Pricing Strategies: When we know, which of the SKUs have greater affinity, we can devise pricing strategies accordingly. For instance, let us assume that there are 2 SKUs Bread & Eggs, which have a strong association. (Confidence of 80%). There is another SKU – Cheese Spread, which has a lesser association with Bread – Confidence of 60%. Let us assume that this retailer generally gives a mark down of Eggs on every Friday. Now by doing a Market Basket Analysis, if the retailer finds that the Eggs will be sold whenever Breads are sold (irrespective of the day of week), that may mean that the sale of eggs is not so influenced by the mark down on Eggs on Friday. Thus, the retailer can infer that instead of marking down the price of Eggs, if the Cheese Spread is sold at a discounted price, the sales dollars may go up. This is one benefit of doing a Market Basket Analysis, explained in an elementary way.

b)      Display of SKUs: In bigger retail store chains, the arrangement or display of SKUs in store shelves can be modified based on the inference out of Market Basket Analysis. For instance, there are 2 SKUs – A1 & A2 located far from each other in a store, but which are proven to have strong affinity. In that case, the retailer may choose to bring A1 and A2 nearby in the shelves so that the ease of buying for customer is improved. Sometimes it may so happen that the retailer may move out 2 SKUs having close association so that the customer is made to walk the stretch which may increase the probability that he/she will look at other SKUs which may be converted to an unplanned purchase. These two seemingly opposing strategies will be used differently by different retailers depending on several attributes of SKUs.

c)       Customized Coupons: If Market Basket Analysis is performed on a customer-to-customer basis, then purchasing behaviour of the consumer can be studied better. Many matured retailers do this and solicit coupons and offers based on what the customer “may” buy instead of publishing the same coupons for all customers of a store. For instance, if the customer is expected to buy a pack of 6 cans of beer and a Lays medium sized pack during every visit, then customized coupons such as $2 off on 12 cans of beer or $1 OFF on a Family Pack of Lays etc can be offered to him, instead of issuing a coupon to buy bananas at a discounted price, which he may not purchase at all. If the customer is purchasing only on Sundays every week, then he can be offered a coupon that expires on a Thursday in an attempt to increase the frequency of his visit to the store.

d)      Sales Influencers: MBA can also be used to study the trend in the purchase of a certain SKU. For instance, if the association between 2 SKUs has been very strong till some point in time and then suddenly decreases, that might be because of the following reasons:

1. The price of one of the SKUs is increased
2. The shelf-stock of one of the SKUs is decreased
3. A new brand of one of the SKUs was introduced
4. An old brand was removed from the catalogue etc.,

This will help a retailer to understand the influence of these activities on the sales figures.

These are just some of the ways that a retailer can put to use, the results of Market Basket Analysis. Hope this gives a fair basic understanding.

 

Market Basket Analysis

Market Basket Analysis (MBA) is a data mining technique that helps a retailer in several ways. Let us have a first look at the basics of MBA and I’ll reserve the benefits it offers to a retailer for my next post. In a basic fashion, if we want to define MBA, it is the analysis of a shopping basket or a ticket to determine the association or relationship between different SKUs. For instance, there may be an observable purchase pattern that says – whenever a person buys a pack of bread, she may buy a pack of eggs as well. To understand this better, we need to be aware of 2 terminologies namely Support & Confidence. Let us say,

IF (Shopping Basket contains = Bread), THEN Shopping Basket contains Eggs.

The first portion is called the support, ie the frequency with which Bread is bought. The second part is the Confidence, ie the frequency with which Eggs are bought whenever Bread is bought. If we wish to bring some numbers, assume that a store has 100 shopping tickets in a week, containing bread as one of the SKUs purchased. Out of these 100 tickets, 75 tickets have eggs as well. This means that there is a close association between Eggs and bread. (75% of whenever bread is purchased, eggs are purchased too).

One oft-quoted example is the case of Walmart, who determined that there is a close association between the purchase of baby diapers and beer cans. One can find the reason behind this in quite a few websites. But the point is that, many a times, the associations may be out of the reach of a retailer’s imagination. Thus, using a proper IT system to determine such associations is very essential. Now let us see a preliminary method to determine such associations.

First and foremost, the retailer should capture all the transactional data from the POS. In every shopping ticket, we can compare all the SKUs with all the other SKUs. If we do this exercise for all the shopping tickets over a period of time(say 12 months), we can identify different combinations of SKUs which occur more frequently in many shopping tickets. (We can perform a similar analysis for every customer, instead of every shopping ticket, if we want to solicit suitable offers for customers). We can sort the different combinations of SKUs in the descending order of their frequency. Now for each high frequency combination, we have to find out the number of tickets (or customers) that bought only one of these 2 items. Then we can determine, if there is a strong potential to sell the other SKU as well to those customers who bought only one of those SKUs. The result of our preliminary analysis will look something like:

This states that Bread & Eggs were bought 550 times together, but Bread was bought for a total of 700 times. Ie., There is a scope for selling Eggs 150 more times, than the normal sales.

This is a very elementary analysis, just to explain the basics. In real world, MBA can be far more complex. The reasons for complexity are as follows:

• The association need not be always 1 to 1. It can be 2 to 1 or 3 to 1. For instance, in our eggs & bread example, the purchase pattern can be something like:

IF (Shopping Basket contains – Bread & Cheese), THEN Shopping Basket contains Eggs. (Bread & Cheese, instead of Bread alone)

• The association can be dependent on the demographics of the consumer – For example, the association between Pepsi & Chips is strongly felt only when the purchaser is a male. Another example could be – The association between Noodles & Ketchup is strongly felt only in stores located in down-town and not in sub-urban region.
• The association can be dependent on the day of the week or season of the year. For example, there can be a strong association between Pepsi & Chips only on a Friday evening. There can be a strong association between paper plates and donuts only on the first 3 weeks of a spring season.

I hope this gives some basic idea about Market Basket Analysis. Let us delve into the benefits that this can offer to a retailer in the next post.