The Future of Affiliate Marketing in the Age of Analytics & Machine Learning
Marketing as we know has come a long way. What started off through simple ‘word-of-mouth’ is now becoming smarter, efficient and more personalised with the advent of technology. Of late internet has opened up several avenues to reach out to a wide range of audience. And the most recent developments in machine learning, business analytics and more have made affiliate marketing more powerful and efficient than ever before.
Ever since it’s boom, people have discovered diverse uses for machine learning and business analytics and have been using them to reach their target audiences. And it’s proving to be a great result so far.
Today, we are able to identify different user patterns for different age demographics, analyze and predict future trends, spot anomalies with accuracy and deliver results with great turnaround time than ever before – all this is being made possible with each passing day.
The Shift in Affiliate Marketing In The Gaming Industry
Marketing has been doing great miracles to businesses. Especially for gaming companies, given that they enjoy a lot of traction across different age groups, all over the world. And today, online marketing, in this domain, in particular, has evolved to such an extent that, ushered by technology, it has become a field where bots are playing a major role and are, in fact, acing it.
This has proven extremely useful for affiliate marketers, who promote games of other companies by the means of their own product and earn money out of it.
Mainly because, in the digital space, they have cracked the code to understanding consumer behaviour, deciphering trends and then using all this data to establish a relationship between brands and target audiences. And that’s exactly what gaming companies want too, so they retain a loyal customer base, while they look for new customer acquisitions.
Machine Learning and Analytics Are Re-imagining Affiliate Marketing
The way Machine Learning analytics is changing affiliate marketing is important because, today, as we all know, any marketing program is as effective as what we make of the data that we have gathered. Marketers may generate hundreds of data points with the help of clicks, links, and user feedback, but over time, it gets difficult to keep track of all these data points. And at one point it even becomes too confusing for us to understand these points and analyze the data in order to execute an effective marketing campaign. That’s where machine learning and analytics step in. They are proficient in analyzing details and coming up with trends that can be interpreted and acted on. All in just a matter of time.
Plus, these practices give brands one more great advantage. Once, we’ve gotten the data and the trends, affiliate marketers can reach out to different categories of customers differently with a more personalized and strategic approach.
In April 2017, New Base polled 1,019 marketers from all over the world to find out what type of technologies they plan to prioritize over the future. Thirty percent were keen on prioritizing machine learning and other spectrums of AI. Today, we can already see the impact it is making through content marketing, chat-bots, voice search and more. It all shows that the merger of machine learning and affiliate marketing is a bet that is really working in our favour.
Solving an Affiliate Marketing Challenge Using A Rule Based Engine at BizAcuity
We are firm believers of technology and we agree that machine learning gives a great boost to affiliate marketing when it comes to achieving the desired results. Recently, we chanced upon an interesting challenge.
When affiliates get customers to register on our sites through their websites or apps, they get paid. And these payments are referred to as CPAs (Cost Per Acquisition).
We noticed that extra CPA was being paid to a few affiliates for the same customer getting registered via different channels. This resulted in duplicating CPA cost. So we sat down to solve this.
A solution was developed, which could identify in-brand duplicates and cross-brand duplicates based on the uniqueness of customer attributes. Once it was implemented, suppressing unnecessary CPA payment, there was a substantial CPA cost saving for the client. And we’re sure we’ll come up with many more such helpful solutions for our clients – that’s what we’re best at.