From Prosper202 To Adroll: Evolution Of Machine Learning In Digital Ads

My uncle used to be a marketer in the 1970s. He recently pointed out that the effectiveness of marketing has improved significantly since then. Big data and the evolution of machine learning are some of the major reasons for the shift. According to research from McKinsey, advertisers that take advantage of big data can increase their ROI by 250%. This figure is likely to rise even more in the future, especially as marketers capitalize off machine learning.

Big Data Spurred the Digital Advertising Revolution

US digital ad spending is expected to exceed $120 billion by 2021. This is largely because big data and machine learning have increased its effectiveness.

Big data has played a key role in boosting the ROI of marketing campaigns. It has helped marketers get a much more nuanced understanding of their campaigns. Tools like Prosper202 have helped them track the ads, landing pages, and placements that drive conversions. These self-hosted tools contain very extensive data that marketers can use to optimize their strategies.

The first CPA tracking tools are over a decade old. They were around at the beginning of the big data revolution. The lessons that marketers learned from these tools paved the way for them to create other tools, which have more sophisticated purposes.

Deep Learning Takes Digital Advertising to the Next Level

Prosper202, CPVLab, Bob and other digital advertising tracking solutions set the stage for the future of digital advertising. However, they were only the beginning. Newer tools use even more advanced big data concepts to enhance the performance of digital ads.

AdRoll is one of the newest advertising solutions. It relies on machine learning algorithms to provide deeper insights into customers.

AdRoll is an e-commerce marketing tool that was released in 2007. It has a number of great features, which include helping marketers with remarketing campaigns.

How is remarketing using big data? Rajiv Bhat of The Next Web shares some insights.

Identifying the Most Likely Places to Reach Targeted Users

When an advertising platform tries to display customized ads to a user, it needs to process lots of data on them. According to Bhat, if the algorithms aren’t programmed to exclude people that are unlikely to be relevant to the ads, they need to process up to 100,000 requests per second. This can be overwhelming. It takes time to process all these requests, which means that the platform doesn’t have time to display custom ads. This can be a problem with remarketing.

An expert from Climbing Trees states that machine learning has found a more efficient way of displaying targeted ads. It identifies the places where the targeted users are most likely to appear. On Facebook, remarketing ads may show up when people are browsing relevant Facebook pages. With PPV marketing, they may only appear on certain websites.

Integrating Conversion Data into the Algorithms to Improve Timing

Many platforms like Bing Ads, RTX and PoF Ads allow advertisers to track conversions through their platforms, rather than requiring them to use a third-party tool like Prosper202 or CPVLab. While it is still a good idea to use a third-party tracking tool, using the dashboard’s conversion tracking pixels has a huge advantage.

The advertising platform will have a much better understanding of the conditions that drive conversions. Their algorithms can be tweaked to make sure that ads are displayed when users are most likely to convert. These conditions will vary from campaign to campaign since they are very niche specific. For example, advertisers promoting online dating sites will find that conversions are usually higher on weekends or evenings. The platforms will monitor the conversions for every campaign and optimize their ad displays accordingly.

Scoring Creatives

Testing ads are difficult. It is impossible to know with any degree of certainty whether a creative will perform well until it has been tested. The only way to know is by looking at data to see which creatives convert the best.

Advertisers can reduce their testing budget by taking advantage of existing data on ads. The advertising platforms they are using will have extensive data on the types of ads that convert the best. They can provide recommendations on various creatives, based on data on how previous ones performed.

Machine Learning is the Future of Advertising

Digital advertising is a complex field. It requires a lot of testing and access to valuable data. Fortunately, machine learning is helping marketers improve their campaigns significantly.

Ryan Kade is the editor overseeing contributed content at Smartdata Collective and contributes weekly column.