40 zettabytes. That’s how much data the human race will have generated by 2020.
Data is simply raw facts and statistics measured and recorded during your business’ activities whereas information is data with context, allowing for informed decisions. Both data and information have special importance for retail businesses in areas such as merchandise selection, inventory management and space planning.
Unfortunately, just understanding your data is not enough – you have to be able to make decisions based on these insights and apply them to the areas of your business which matter most. POS data, for example, will help you understand which products your customers prefer, which price they are willing to purchase products at, and the rate those products are moving off your shelves.
As helpful as this data may seem a large percentage of retailers still make the mistake of completely neglecting their data in areas of vital importance, like merchandise selection – the selection of which products you would like to place on offer in specific areas during certain periods of time. This is often to their detriment, and in this blog post, we’ll examine four reasons why merchandise selection is dangerous without data.
Reason 1: Efficient Merchandise Selection is Dependent on Accurate Consumer Decision Tree Classifications
How does a retailer know which merchandise to select for their stores? Some will guess and go with their gut instinct, often getting poor results while successful retailers will opt for a far more calculated approach. The challenge here is that a calculated approach toward merchandise selection is not possible if your product data does not include classifications as per the consumer decision tree. The good news is that an accurate product classification is no more than a few data entries, here’s a quick example: A product falls within the “coffee” category and “instant coffee” subcategory and finally the “decaffeinated instant coffee” segment. This is no more than three simple fields of data, but they are critical.
Typically, retailers who fall within the highly calculated camp, follow a methodology which looks something like this:
- A database which includes both internal POS data and external market data forms the foundation of their decisions.
- A logical set of calculations are selected for each subcategory (at the very highest level).
- The selected set of calculations is then applied to the subcategory, segment or subsegment to determine an ideal selection of merchandise for the subcategory, segment or subsegment.
Naturally, this carefully calculated approach can’t work without product classifications.
Reason 2: Merchandise Selection is Interdependent on Other Category Management Elements
To operate successfully in retail, several moving pieces work and rely on each other to achieve various objectives and efficiencies – all so that we have the right product, at the right place and at the right time. If you remove one piece, the rest fail to move.
For each of the category management functions to work, there needs to be a constant two-way flow of data from one element to the next. For example, without knowing your space plan, you will have no idea how much space is available for each product meaning you may decide to add a product to your assortment while there is no space available for it. Or, your space planning team may remove a product from a planogram which is no longer marked as a ranged product Meanwhile your business still holds inventory of the product.
Reason 3: Run the Risk of Missed Opportunities
The primary goal of merchandise selection is to maximise product sales and customer satisfaction. There are many factors to consider to make a proper selection such as purchasing budget, available shelf space, seasonal items, etc. When it comes to capitalising on opportunities consumer trends are critical. There’s more good news - you can spot consumer trends within your in your POS data in a very simple way.
If your data is in order and your product classifications are up to date with your consumer decision tree, then you would be able to spot increased interest in a subcategory, segment or subsegment and you will be able to make decisions accordingly. For example, let's say you conduct some thorough data analysis for a particular category, and you noticed that there had been consistent sales growth for a segment within the category - this may be an indication that you could consider allocating more space variations of the product to the segment.
Now, if you were doing merchandise selection without data then the danger is that you would never have spotted this sort of opportunity, but your competitors may have.
Reason 4: Run the Risk of Damaging the Shopping Experience
Retailers are always looking for a competitive advantage as they face savvy customers and ever growing competition. These factors directly impact retailers in their ability to provide the right selection of merchandise for a superior customer experience.
Retailers must adapt to this new reality by embracing new tools. Technology has given us tools which can collect several sets of data and manipulate it into insights for decision makers. Customer insights can help retailers understand which merchandise to select, what to stock and how much - creating an opportunity for retailers to drive foot traffic in-store with a selection of merchandise customers want to buy.
For example, a customer walks in to buy a product from your store, but you no longer carry the preferred item, and the customer is not willing to transfer his or her demand to an alternative product. This customer will have to look elsewhere, and you have lost a sale and potentially the lifetime value of that customer. Ultimately, looking at accurate data would have informed you as to whether it is wise or not to remove certain products from your selection of merchandise.
Merchandise selection is a tedious and data intensive process, but with the right tools, you can gather, analyse and visualise data to assist you with choosing the best set of products.