Big Data Analysis And Retail: Creativity At The Service Of Data

Until a few years ago, marketing was the daily bread of creatives: winning campaigns were based on extraordinary ideas of brilliant personalities capable of making the right decision at the right time without being able to predict the outcome. Today, unfortunately, or fortunately, creativity is no longer the only fundamental element of a successful marketing campaign:  the basis is data, stored, aggregated, interpreted and used correctly. Marketers today can rely on data to design and test their strategies.

And while data can never replace human creativity, it can undoubtedly provide practical tools to improve the performance of a marketing campaign.

Big Data Analysis And Retail: Integrated Data For Multi-Channel Business

The retailers of 2019 are now present in physical stores and on the web. Otherwise, they do not exist. And they are presented with a strategy that must necessarily be omnichannel. That is, it must offer customers a unique shopping experience. There cannot be products or services for the web or stores.

The customer is demanding and knows what he wants: he wants to be able to start a purchase online and finish it in the shop, or vice versa, seamlessly. But omnichannel is not just a challenge that retailers have to face. It is an opportunity:  they can learn a lot from their customer’s online behavior. More: They can set up their marketing campaigns on important data by analyzing data on integrated purchasing behaviors (online and offline).

But it doesn’t stop there. They can set up campaigns and activate promotions customized to the needs and expectations of the individual customer. They can offer advice and an “intimate” and personal support: in this way, the act of purchase becomes an experience again. Big Data Analysis and retail: the customer and his shopping experience are at the center. The era of sizeable impersonal discount stores is over, with inexperienced and disinterested shop assistants in contact with the customer: today’s store managers will have to look more like the shopkeeper of yesteryear, who, when the regular customer entered the shop, already knew what his tastes were and what, probably, he would need that day.

Thanks to data analysis, this becomes possible again: the clerk can know which products the customer who enters the store has viewed online, which ones he wants (perhaps because they are on his wishlist), what he has previously purchased (online, in that store or the others of the chain), how often etc … And all this without ever having met him before. What about privacy? The expert consumer is willing to share his data with the brands he is linked to, as long as (and this is an essential condition) they use them to provide him with a unique, better service, to pamper him and make him feel special. Certainly not for sending massive newsletters.

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Seven Use Cases For Retailers Grappling With Big Data

But let’s get to the point, what can big data analysis do for the retail sector? Let’s look at seven use cases:

1. Provide a unique shopping experience: Retailers can predict what they will likely buy next based on a customer’s purchase history and e-commerce behavior. They will thus be able to use data analysis to anticipate their customers’ demand and offer a perfect customer experience (which will increase their loyalty).
2. Activate personalized promotional campaigns: the omnichannel customer expects personalized promotions and dynamic prices, both online and in the physical store. Thanks to big data analysis, retailers can apply aggressive promotional policies to clusters of targeted customers.
3. Make informed decisions: companies will be able to make informed decisions based on the only truly reliable source, the data of their products and customers. Thanks to these, they can make reliable decisions, for example, on promotions, prices and the choices of products to be included in the catalog.
4. Make accurate forecasts: thanks to the analysis of historical trends (increasingly real because integrated with machine learning techniques), stores will know the purchase volumes of products in the various periods of the year and, therefore, be able to organize supplies or production accordingly. Additionally, an analysis of sentiment and social media can help retailers to identify future market trends and, therefore, to determine in advance the items that cannot be missing in the store.
5. Increase your responsiveness: Thanks to continuous analysis processes, retailers can adjust their real-time decisions based on changes in the market and their customers.
6. Optimize prices: big data analysis allows you to learn from yourself and your competitors. Based on the fees charged by their competitors, retailers will be able to identify a winning pricing strategy for the company’s flagship products.
7. Improving the supply chain: Knowing the expected demand volumes is also fundamental to optimizing the storage of goods, the warehouse staging periods, and the delivery routes. By relying on precise demand forecasts, retailers can reduce logistics costs and thus apply more aggressive pricing policies than the competition.