Retail merchandising strategy is paramount for small and medium sized retailers to boost sales of their products. It’s imperative to explore every corner of business strategy landscape to survive tough competition. For example, determining the place where a particular type of product needs to be placed so that maximum prospective customers can see it with ease, which can be the difference between a sale and non-sale.
Retail merchandising involves:
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Launching right product
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Setting right price
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Positioning Products at the right places
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Placing products in right amount
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Knowing types of customers buying a particular product
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Drawing traffic to particular products
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Improving customer relationship
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Buying trend
The problem is that with all this, there comes a huge volume of disparate data, which is basically useless if one can’t run proper analytics on it to figure out sales patterns, optimum positioning and buying behaviours of customers and perhaps more importantly so, the prospects.
Use of analytic tools could do wonders for small and medium sized retailers. Many of them often struggle to come up with an effective merchandising strategy because of being deprived of information they can extract from their own databases, forget about the vast amount of useful data floating around on the interweb.
Social media, industry forecasts, existing customers records and web browsing patterns can help retailers predict products a specific segment of customers is more likely to buy. For example, Kohl’s had announced personalized offers for customers in five of its stores. Smartphones were all required for customers to opt for the offer while they visited one of those stores. A customer who had looked for a pair of shoes online but never went ahead with the purchase would receive a coupon based on the same shoes. This had increased the chances of the sale of the shoes for Kohl’s by many folds, as customers have a very high likelihood to avail an offer when they get it at the time of purchase while they are shopping.
With increasing use of the internet on cellphones worldwide, experts predict that 25% of the world will be soon on social network. This creates big opportunities, but simultaneously, it also creates problems owing to unstructured, semi-structured and muddled nature of data. Here pops up a question how to use big data to help small and medium retailers devise marketing strategies that improve customer experience, boost sales, understand buying trend inside a retail outlet etc.
But first, let’s get some concepts right about Big Data. Let’s start with the three Vs of big data - Volume, Velocity and Variety.
Volume - Nowadays, a lot of data is available in the form of videos, musics and large images on social media channels. The volume is so large that normal computer systems are incapable of processing it.
Velocity - Data movement has become very fast. Gone are the days when data of 24 hours ago was considered recent. Now, people don’t rely on newspapers to stay updated, they rather get the latest news through social media, which even tells you what happened half an hour ago. Updates are now made almost every second as data is being accumulated across the world on various platforms. This fast movement of data represents big data.
Variety - Data is available in many formats, like database, excel, csv or access. It’s sometimes even available in the the form of video, SMS, pdf etc. It’s a big challenge with big data analytics to arrange data available through different formats in one format.
IBM is among many companies that offer big data solutions to retailers to help them devise personalized marketing campaigns. IBM’s big data solution helps retailers understand customer shopping behavior, improve cross-selling & upselling, analyze product and customer data to avoid stock-outs and overstocks etc.
However, these full-fledged big data solutions are very expensive for small and medium sized retailers. The best remedy to reduce high costs of big data solutions is to go for customized solutions. Evon Technologies offers aws data integration services and custom-made big data solutions to retailers at very nominal prices, thereby providing them an affordable way to make their business more agile and robust. Having a tool to understand big data is next frontier for small and medium sized retailers in order to ensure their survival amid cut-throat competition.
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