Big data and artificial intelligence in ecommerce are here to stay, it´s a fact. However, multiple error test tests are still being theorized and carried out to verify in which fields they work and for which objectives they are effective.
Predictive analysis is one of the fields in which technology of online sales services is most developed. This technique consists of analysing the data collected by traditional AI (artificial intelligence) systems to create patterns of user behaviour. These are invaluable for ecommerce if you know how to use them correctly.
What are the benefits of predictive analysis of the data in an ecommerce?
1. It allows managing the stock correctly before stages in which, historically, peaks of demand are generated. In this way, the out of stock is kept under control when, effectively, it is not included in the brand's pricing strategy.
2. It allows product price optimization with more sales forecast. In the same way, discounts and seasonal offers that suit the brand may be incorporated into the pricing strategy depending on the consumer's purchasing trend.
3. The monitoring of the competition is maximized, since it allows the analysing the prices fixed by the rest of the brands at each moment and act accordingly in upcoming events of similar characteristics.
Thanks to the predictive analysis of the data, the strategists of each ecommerce can make informed decisions, with a much more solid base on which to lean, to enhance the value of the brand in each of the stages of its sales funnel.
Introducing predictive analysis into pricing strategy
The key to predictive analysis is the knowledge about the audience; understanding how they buy and identify their needs at all times to anticipate and adapt the decisions of the brand to their needs.
For this reason, the most appropriate way to incorporate the predictive analytics technique is to use a CRM (which allows you to record the steps that a customer takes before a purchase, as well as keep track of their interests and the trend of these).
On the other hand, it is essential to have a price monitoring tool to check how the rest of the retailers act against the demand of the users. The comparison of all this data (the behaviour of the prices of the competition before a seasonal sales event), will be the juice with which the ecommerce nourishes the content of its pricing strategy. Good dynamic pricing software will convert this data relationship into the correct price in each moment.
The current user, who can access a multitude of services at any moment, values more and more the possibility of finding what they need, at the best price, in a few clicks. This is the fundamental reason why every brand should make use predictive analysis of their data. The only way to become a reference for the consumer is to offer what they need at any time.
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