The importance of predictive analytics

In our days predictive analytics is rapidly evolving as machine learning contributes to the effectiveness of advertising and marketing. Chris O'Hara, global marketing director at Krux, believes that this revolution in data science is the most important trend. Here are a few of his comments on this:

"Getting an advantage means surpassing competitors in understanding where to place a bet and what it should be."

"Most marketers and agencies are not very competent in terms of predictive analytics and user scoring."

"We begin to see that platforms that have implemented machine learning and artificial intelligence (AI) in their user interfaces provide entrepreneurs with enormous opportunities, even if they do not have any statistical knowledge."

Advertisers want to use online data to their advantage - creating a model of a certain client with this information, they are trying to predict which of the representatives of the target audience can respond to a specific message or product.

Modern data-science allows you to process huge amounts of data. In fact, this is the main purpose of machine learning - the form of AI, which is able to "learn" on the basis of each new interaction between the client and the message.

 For the most part, a predictive analyst is reduced to modeling a person's predisposition to click on an ad and convert (immediately after a click). Although for many years companies have used this type of modeling for their own customer bases (to predict, for example, which of the users will most likely leave) - today it is used on the Web on a much larger scale, to also be targeted on unknown consumers.

This technique helps to plan ad impressions based on a number of factors, and now it can be used in real time. Advertisers can target different types of behavior and demographic data or create segments based on different buyer personas of current clients. These segments can be used in predictive analytics to understand which features should be looked at when working with a more extensive set of visitor data.

Companies with first-party data (model information collected by site owners) model potential customers better, since the information available allows them to create a more accurate image of their current customers.

Today there are more and more opportunities to track customers through several channels and devices, predictive analytics can be used not only in display advertising.

At one of the conferences in 2017 devoted to the discussion of the latest program trends, Chris O'Hara spoke about how difficult it is to qualify users on several marketing channels:

"Today, marketers are increasingly using data management technologies to mark users on device graphs, counting on the fact that they will be able to qualify these people according to their interactions in each channel.

 To do this correctly, marketers must connect users with all their devices and be able to store important attributive data for a long period. To solve these problems, advanced technologies are required. "

In the long term, marketers will try to mark out the entire way of the consumer to make a purchase (customer journey). Thanks to this, they will be able to understand, for example, at which point in the way certain versions and formats of content are the most conducive to the decision to purchase.

Predictive analytics and improved targeting do not always provide 100% efficiency. The quality of the site for placing an advertisement is also important, and private marketplaces allow advertisers to participate in closed auctions and fight for the best advertising spaces.

https://econsultancy.com/blog/68682-programmatic-101-what-are-user-scoring-and-propensity-modelling/

More business analytics posts you can find here: https://analyticsinbusinessworld.blogspot.com/2017/05/data-on-its-own-its-secret-of-business.html