Shelf Real Estate & Data Analytics

In the whole gamut of retail supply chain, there is only one moment of truth! The time when the customer looks at the product on the shelf and decides to buy it. As they say, its all prep work till register rings. Business wise, shelf is the only real estate property for any retailer. A place where the retailer has that unique opportunity to persuade the buyer to buy the product.
So how does a retailer do it? Traditionally couple of thumb rules has been practiced in the industry like putting the most profitable items at the eye level shelves and pushing others to the bottom. However with astronomical rise in the number of items and increase in competition among the brands becoming fierce, it is the need of the hour to assign right shelf spec to the right set of products so as to meet the underlying business objectives. These objectives can be as varied as minimizing the cost, Maximizing the sales, Increasing the assortment or a combination thereof. The skill is in arriving at that right number of items and variety to be placed on the shelves to meet these rules.

Now it may all sound very simple but the complexity of the problem lies in numbers. Considering the number of items that a category manager has to carry, the possible combinations of facings of the items that can be placed on the shelves is mind bogglingly high to be analyzed by a human brain. This is where data analytics can help to process the information at incredible speeds to arrive at the desired solution. This is a perfect opportunity area for human – computer participation where the computer’s data analytical skills supplemented with category manager’s business know-how can do wonders!

Does Etail Has a Future in India?

This is a difficult one to answer. On the face value, it may appear that online retailing is for the neo-rich upwardly mobile variety who are always hooked on to the internet and love to explore the new trends.

Well- that is true but that is only the tip of the iceberg. There is also a majority of population who can benefit from online retailing if it is done right. These are the average low and middle class segment which form the majority of indian population. The reason being twofold.

One – the bad state of physical infrastructure and increasing real estate prices leading to a scenario where customers have to pay more and have a less than satisfying experience at the retail stores. Think of the number of times you curse yourself while standing in a queue in Big Bazaar.

Two – The death of distance! While shopping online, the distance is of insignificant importance. It works favorably well for India where the travel is a big pain. Roads are in bad shape, pollution and traffic on the road is mind numbing etc. In these situation, having no worry to travel is a big boon. This is where etailing has a huge prospect.

So why its not happening? Well, first of all the etailing model have to be tuned towards the local needs. For e.g. – availability of information in regional languages to reach the masses. efficient logicstics operations to make sure delivery happens quickly. Measures like this will put the etail shops on the mainstream and industry will swing along the positive feedback loop.

Know Your Store

Perhaps every retailer understands that not all their stores are created equal. Due to demographical and many other socio-economical differences, sales pattern varies dramatically. So – why is it that most of the retailers still have one- size-fit-it-all assortment mix and merchandise plan?

The unpleasant truth is, its really hard to intelligently segment the stores into manageable chunks which makes business sense.

Guess what? With advent of technology in the field of Business analytics, it is now well within the boundaries of reality to achieve the holy grail of merchandising. However, to achieve it, there are two fundamental tenets that one must adhere to.

1. Data don’t lie

2. make sure your data is speaking the truth

However much these two may sound contradictory, they are both correct. Let’s see how.

When I say data don’t lie, what I mean is that data is not biased with pre-conceived notions. If intelligent analysis is applied on top of it, it can show key insights. For e.g. – a good data mining algorithm can identify the sales patterns and help group the stores which have similar propensity to sales in the same bucket. The benefit is, such type of analysis is completely unbiased and can at times help in correcting the conventional wisdom.

Now, that being said, it is also important that the data is captured in clinical condition and free of any type of corruptions like negative sales, missing stales information etc. If the data is not cleansed it is easy to understand that all the subsequent analysis will have inherent errors which may grow as we delve deeper. Always remember the GIGO law of computing – ‘Garbage in – garbage out’.

Now assuming that both the fundamentals have been adhered to, there are several exciting things that can be done. for the scope of this article, let’s just talk about store grouping. You can use the data mining packages to identify the group of stores which have similar sales profile. Once you have the list, you can use it for any number of things. Following are some examples that immediately come to my mind:

1. Specific assortment mix for each logical group of stores

2. Localized advertising plans for each cluster. for e.g. – Hispanic heavy ADs in Hispanic heavy stores.

3. Reorganization of distribution and item authorization plans to minimize the inventory and logistics cost

4. Focused merchandise plans for each chunk

It is obvious that the application of such a bottom – up store grouping process is only limited by the creativity of the users. So go ahead and figure out what you want to do with your set of store groups.