3 uses of data science in marketing campaigns

The digital age has definitely changed how companies use information. Today, with the tools delivered by data science, new business strategies have emerged that allow target audiences to be understood and identified with a very high level of accuracy.

Although companies have always had access to the data produced in the execution of their operations, it is the use made of this information that ends up making the difference. Data science transforms data into ideas that can be applied immediately to businesses to achieve better results.

Concrete examples of its applications in the area of marketing range from content marketing and SEO to customer engagement. Here are three ways you can make smart use of your organization’s accumulated data:

  1. Early lead identification

A very important point for the financial well-being of each company is to know how to identify potential customers, according to the probability that there is to close a deal with them. With data science you can create a predictive leads system, which works from an algorithm that calculates the probability of conversion. Thus, leads can be segmented into potential customers and, on the other hand, into customers with little interest.

By assigning an algorithm the execution of the same task, it opens the opportunity to analyze various types of information, such as behavior and other social characteristics of customers. Finally, we have data that allows us to detect and predict certain profiles of potential leads and, consequently, increase conversion rates.

These principles can also be applied to online campaigns, Pay Per Click (PPC), content marketing and SEO, in such a way as to identify and segment each lead early according to its conversion probability.

According to Forbes, on average it takes 47 hours for a company to respond to a lead. This may be due to a number of factors, but it usually occurs because salespeople are unaware that they are in front of a potential customer. With the use of a predictive lead system, the sales team can be notified in real time to a customer who enters the top 20% in the database, so they can act immediately and not miss a business opportunity.

Both human subjectivity – because it is very objective or based on experience – as well as the analysis carried out by a salesperson of the partial information available to him, distance themselves from the accuracy of a decision taken from the use of data in its pure state.

  1. Creation of user profiles

Although it is nothing new that companies collect data about the consumer, it still appears as an unknown what their intentions are for these consumers.

For example, a friend recommends on Facebook the new album by Bob Dylan, a famous artist you don’t know but are curious to hear. From this recommendation, look for the album in Allmusic.com to read some reviews. Then you go to Amazon to see what users think about the album. Finally you convince yourself and buy it through the Spotify app installed on your cell phone.


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All of this information, which gathers all of the consumer’s activity including history, interests and tastes, is stored by search engines under individual user profiles. Still, it remains a challenge to occupy this information to predict future behavior; it is not known for certain whether the same person who purchased Bob Dylan’s CD later will buy another CD from the musician, or whether he will use Spotify or another straming platform to do so.

That’s how the internet works. From an impulse, we can go from reviewing news of our business to checking on a social network how a friend is. There are algorithms that, taking this example, would classify the user in specific profiles (“professional” and “connector”, respectively). Thanks to this detailed segmentation according to profiles, the marketing team can greatly increase the success of digital campaigns by targeting products at specific profiles.

3- Generation of pricing strategies

Most marketers rely on factors such as competitive prices and product manufacturing costs to create pricing strategies. It is true that this often produces results, but it is still not based on pure data processed by data scientists, something that can generate even greater benefits for the company.

There are several benefits to employing data scientists in creating pricing strategies. On the one hand, factors not previously considered can be taken into account, such as economic indicators referring to the state of a currency or supply chain. The preferences of each customer and the historical relationship with your brand, including past business, also come into play. With all this information you can identify the exact factors that influence prices as well as the conversion potential behind each customer.

Thus, the pricing strategy acquires agile characteristics, allowing it to adapt quickly to the market and the competition.

On the other hand, thanks to customer segmentation, it is possible to analyse the transaction history in order to discover previously hidden behaviour patterns. This information makes it possible to adapt sales strategies immediately to generate offers that lead potential customers to close the transaction.

At the same time, with data science it is possible to generate models that, from errors and opportunities not taken in the past, notify in real time when a change in prices has a negative effect on demand.

Finally, the sales team is favored with the tools provided by data that shows how a change in price or discount on a product will influence the closing of a transaction or the conversion of a customer.