How is data science used in targeted advertising?

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How is data science used in targeted advertising?

Targeted advertising is a type of online advertising that is directed to an audience with specific characteristics based on the product or person being advertised. The targeted online advertising or digital marketing industry is bigger than you think, and there has been significant growth in the last decade.

Having online advertising provides instant feedback for companies and business owners will know more about their users. In the modern world, the presence of targeted advertising in various industries is a necessity of the sales department of any collection. In particular, everyone wants to spend the least money and get the maximum profit increase, which can be answered by using targeted advertising.

Targeted advertising can be demographic that focuses on race, economic status, gender, age, level of education, income, and employment, or it can be psychologically based on consumer values, personality, attitudes, opinions, lifestyle, and interests. They can also be behavioral variables such as browser history, shopping history, and other recent customer activities.

Methods of targeted advertising

Methods of targeted advertising

There are several ways to do targeted advertising:

1) Demographic targeting:

This approach defines the audience in terms of gender, age, income, location, etc., which is an old and efficient method because it is easy to predict customer behavior to categorize products. Demographic targeting is popular because it is easy to understand and implement, and provides transparency and control of the ads to the target audience.

2) Site targeting:

This method is a simple and popular targeting mechanism. The advertiser specifies the set of pages on which the ad is displayed. For example, a company that sells songs can display ads on a culture and art website.

3) Behavioral goal setting:

This model provides a way to provide ads to users using the user’s past behavior (searches, site visits, purchases). The most valuable resource for behavioral targeting is user-specific network traffic.

The more such data you have, the better your goal setting will be. Thus, even local ISPs can offer more accurate advertising to consumers than Google or Yahoo International platforms.

Improve the quality of targeted advertising using data science

Improve the quality of targeted advertising using data science

The use of data science techniques plays a key role in the process of optimizing advertising offers, increasing the accuracy of targeting and achieving the ultimate goal from the marketer’s point of view.

The main task of the machine learning system is to identify potential customers and online users who, after displaying ads, are more inclined to buy a particular product in the near future.

The main source of input features for behavioral targeting is the user’s browser history, which is recorded as a collection of web pages visited in the past. Target tags can be unique to each campaign and based on actual product purchases.

Visiting the site to determine purchase forecasts is better than clickthrough rate (CTR). We know that buying after seeing ads is a rare occurrence. The simplest and most widely used method is to introduce models trained with proxies. Currently, the most common proxy is click-through ads.

Click on the ad

Click on the ad

The effectiveness of ad campaigns is often measured in terms of clickthrough rate (CTR). As a result, they are moving towards increasing the optimal CTR. In this method, click on the ads is considered a positive example. So instead of conversion, the model designed based on data science techniques are taught using clicks, but the test set is still labeled with conversion.

Unlike clicks, visiting the site is generally a good proxy to buy. In particular, site visits serve significantly as a basis for building models to target the browsers they buy after the ad is displayed. The results show that site visitors are more likely to be buyers than click on the ad.

Another approach is text categories, where the web has several resources, both proprietary and free, that categorize specific web pages based on their content. These categories act as content-based groupings that can be used to reduce the size of the data.

Conclusion

Conclusion

This article gives you a brief overview of targeted advertising, which is a multi-billion dollar industry with significant growth. Most major players in the online advertising market work with real-time (RTB) systems, which connect advertisers and marketers.

RTB acts as an online auction that allows advertisers the opportunity to place online display ads for a specific user in real-time. Currently, in the industry, the main criterion for measuring the success of advertising campaigns is click-through rate (CTR), but recent studies have shown that the impact of site visits is better than CTR.

At first glance, data science and machine learning techniques for targeted advertising seem trivial. But after a closer look at the issue and despite a large amount of research that has been done, it has identified effective solutions to the problems mentioned and good models to predict success in future sales.

 

How to use data science in sales?

By |2021-08-18T21:23:53+04:3019th August, 2021|Categories: Business, DS knowledge|0 Comments

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