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Big Data applied in sales process

Big Data is the set of methodologies used to capture, store and process a large volume of information from different sources in order to accelerate the decision-making process and thereby bring competitive advantage. The different types of information that can be classified into:
  • Structured — Table of a DBMS and spreadsheets
  • Semi Structured — Json and XML Files
  • Unstructured — Books, social networks, videos, images and recordings

These data and statistics, once gathered in one place, allow a variety of information to be complemented and identified to make projections. Among the projection techniques, we can highlight:
  • Sales Forecast: Forecast the company’s revenue in a given period of the year.
  • Projection based on past experiences: In this forecast, past sales (minimum of 6 months and ideal of 12 to 18 months) are analyzed and if there was any campaign, action, product launch or seasonality that may have affected the number of sales.
  • Market-Based Projection: Used when the company is affected by seasonality or will launch a new product on the market. This analysis uses information from a similar business, the same area of activity, size and location.

Projection based on contribution margin: Indicates how much of the net value after sales is available to pay fixed expenses and what is the profitability rate.

Another way to use this data is in the sales process itself:
  • Mapping of the purchase day: It consists of the potential customer experience from the first contact with the company, during the qualification period until the closing of the sale and loyalty. Identifying key moments is crucial to the bid strategy and mapping the user experience.
  • Personalized offers: In this case, the differential is to allow the client to receive the offers and contacts more targeted to his needs and not generic information, making the contact more personal and effective.
  • Churn Decrease: Churn is the percentage of customers who pay for a service or product on a regular basis. The decrease in this indicator means the loss of customers who are loyal to the brand and, for some reason, have stopped buying. Several behaviors may indicate the intention of the cancellation or loss of this customer being: to enjoy the page of the competitor, increase of the complaints of the SAC and the increase of negative evaluations in the page of the company or social networks.
  • Customer Loyalty: Loyalty allows a long-term and more profitable relationship with the consumer. For this, it is necessary to know the individual needs and perform actions directed to them, integrate the purchasing history to make more interesting recommendations and suggest and offer at the most appropriate times, increasing the assertiveness of the contacts.
  • Cross-Selling: It is the cross sale between products and services. Shopping cart analysis can indicate which products can be sold together in a promotion, for example.
  • Shopping cart analysis: Helping to detect consumption patterns that can be used in recommendations and reduce Churn.
  • Upsell: When the customer is about to buy a product and he receives an offer for a similar product but from a category above.
  • Fraud Detection: This process looks for patterns to recognize the occurrence of fraud that has already occurred and to seek scans to predict fraudulent events before they occur. Avoiding a potential malicious customer who can take advantage of a company’s sales flow.

The companies themselves already have the necessary information, or ways to capture this information to carry out these analyzes and compete in the market. And you- are you prepared to use this data in favor of your company or customer?

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

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