How to apply Big Data in eCommerce

The answer is simple (on paper): every minute that passes on the Internet millions of interactions are being created. Since searches on different engines, engagement with social platforms or visualizations to, of course, shopping and visits to online shops. 

The great challenge is to add all that data and put them in value. If we can group and analyze them with enough speed (which should ideally be in real time) we will have an overview of the client as it had never been able to imagine so far. 

In terms of eCommerce thanks to Big Data we can know: 

This so detailed profile of an anonymous client can only be compared to that achieved in retail after many years of relationship between a person and a seller. 

Big Data in eCommerce is an opportunity to predict and customize the shopping experience, a way to apply strategies based on objective data in real time. 

Conducting a detailed analysis of the information generated by search engines, the results of our advertising campaigns(particularly relevant Adwords search terms), internal information and which is generated from the own shop and the feeling toward the brand in social media search engine we will have a very clear picture of what the consumer is looking for. 

This information must be crossed with our business which of our product satisfies him most? Should we add something new to the stock? Shall we create a category for this? 

Amazon knows a lot a bout this, as it increasingly uses behavioural content more and better behavioural, created on the user´s behaviour.

The most basic but not the less effective way to apply the BigData to customized shopping experience is through recommendations. 

Before, we were talking about having a complete profile of the user so we can adjust the products offers to reality and context of that person. In this way, we will greatly increase conversion. More and more we are moving towards different shops for each user. 

Depending on the market niche and season, competition can be fierce. One of the weapons that have always been used since the early days of the retail, supply and demand, is the strategy of repricing, which basically consists of adapting prices to market dynamically. 

Using Big Data, repricing has much more scientific than intuitive. We can again analyze vectors (stock level,percentage of conversion of a given article, predictions of sales...) and external variables already mentioned(stock competition, intended to search, engagement, trends...) 

Thanks to this we will be able, for example, to rise the price of a product with a strong demand and short supply, lower prices outside the market, create promotions, packs or bundles... the only limit is that you can think at the strategic level. 

One of the most delicate issues for any online shop is being able to predict the sales of their products. We always live with the doubt of if it will buy too and we have overstocked or on the other hand, we will break it and we will lose sales. 

This balance between the cost involved in producing and storing products which are not sold and the cost of the lost opportunity can be minimized thanks to Big Data. Again, you will find the example of Amazon which is able to use the historical sales to predict future sales. 

If we know the customer, we will treat him better. It is almost obvious, but a big true. 

As we deal with all the relevant information about him such as his purchase history, previous interactions with different departments and through social media, we have a clear vision of what to offer and how to do it. 

One example? The air carrier Southwest Airlines which features a great reputation precisely for its customer support ,uses real-time interactions of their customers on social networks,crosses them with data from its own customer service and allows that its agents are able to offer a quick and optimal solution for each setting(also they use Big Data when creating tailored promotional offers )