Use predictive marketing to achieve your goals

The digital revolution is something that happens worldwide. With the great crisis, with smaller investments for many entrepreneurs, the importance of having accurate information is extremely important. Build strategies that make the connectivity of hitting the right audience with the right marketing. Technologies such as cloud computing and machine learning are essential if this goal is to be achieved. We are talking about Predictive Marketing. Using data science and statistical information techniques so that the percentage of success is increasing.

With the large mass of data circulating throughout the virtual world, what is the best way to analyze the behavior of people and organizations? Through information analysis.

Learn more about predictive marketing

This type of marketing is a more complete analysis with more information, which precedes which company action. When it is time to take action, there are many more arguments for the campaign to succeed.

When you have more information and better understand your customers’ behavior, you get to know more about their tastes, as well as predict certain attitudes. 

Which of the techniques to use: Analytics or Predictive?

There are major differences between these two techniques. The predictive you can analyze estimated data for the future, a negative point that Analytics cannot.

Predictive is a good way for companies and entrepreneurs to take previous actions before their competitors. With good data analysis, you get great results.

For these factors, predictive analysis stands out a lot in several companies.

How to use predictive marketing?

The first step is to take the information you already have from your customers and unite them all for analysis with all the sectors you have, such as tax, sales department, after-sales, customer complaint, and production.

Then understand how their buying behavior is, if most buy more than once, why are they buying your product and what have you been doing for them to buy?

Make Big Data technology to your advantage

When you combine several pieces of information that show certain types of customer behavior, based on different actions, you can build great strategies confirmed by Big Data.

In other words, it shows that it is not enough to just check how much a customer is buying and which purchase interval, other factors must be analyzed, such as feedbacks, if satisfaction is good, where they live and how old these users are.

Effective features with Predictive Marketing

Campaign ROI

It is common for companies to base their future attitudes on data from the past coupled with technology, so they can better analyze where to invest and how the investment will be.

It is possible to predict ROI even before starting a campaign. With good data analysis and good planning, the chances of getting a good campaign right can be over 70%.

Lead generation

Most companies use strategies to create leads, however, it is not very effective in analyzing these contacts. The sales team is small for a very large amount of customer calls.

The technique of ranking leads that have conversion indexes is very important for investments and efforts to be effective and very profitable.

With that, the financial cost decreases, and time is saved as well. Work gains productivity and is also more personalized.

Analyze customer navigability

When showing something to an online customer who looked at a certain product, but did not purchase the product, through retargeting, it may not be very efficient.

Through the contextual offer, it is possible to analyze and understand how the purchase journey was made by the customer and create conversion strategies.

This main route, leaving only what is essential for the customer to have all the information to close the purchase and no other information that makes him lose focus.

Focus on what’s going wrong

If before you needed to analyze what is the best way to leave it to the customer, now you have to analyze everything that is preventing him from buying in general.

It is important to analyze the path of those who closed the purchase and those who did not, what was the difference in their navigability, and make possible corrections.

Too much information in a non-strategic way only delays or prevents the customer from making a good purchase. There are ways to understand why this failure occurs.

Learn what the Churn metric will look like

Churn is a way that analyzes the loss of customers and it is possible to analyze the causes. Other factors that can also be analyzed are the possible loss of the customer by mathematical analysis looking at how the customer’s biography and behavior in the virtual world is.

If a customer with this profile is discovered, it is possible to do something to change the situation, such as a promotion or even diagnosing the causes of the problem.