Screen shot of web analytics data
Machine learning promises to help you better understand consumers, improve the customer experience and extend the reach of your ads. PHOTO: Igor Ovsyannykov on Unsplash

Hype about machine learning in the media industry abounds, but once you move past the buzz, there are also a lot of practical applications for the technology.  

Machine learning is gaining momentum over traditional analytics models, which are no longer effective and will continue to lag as disruptive forces the industry to evolve.

Most sellers of media still use traditional methods of managing data to place ads. They are looking to evolve, but the volume of data captured today is so high they are struggling to draw basic insights that help them understand their audience and target their ads effectively. 

Media professionals are so busy wading through historical reporting and manually trying to generate insights from data that they cannot quickly and accurately predict how much and what inventory is available to them, who are the right consumers to target, where to reach them and how to reach them.

The second challenge is related to the first challenge: How do you more effectively engage with consumers and offer a personalized, relevant and engaging experience. The critical piece is the capability to process incoming data to gain actionable insights to accurately forecast and deliver the right experience, to the right customer, at the right time, in the right place and in the right format. Otherwise, you lose the consumer.

Without addressing these challenges, advertising campaigns will continue to be ineffective, annoy consumers and fail to generate sufficient financial rewards. Brand reputation can be damaged if their adverts are targeted the wrong way.

The Opportunity to Transform

Machine learning can help you better understand the consumer, enhance user-advertising experiences, extend the reach and effectiveness of ads, generate more revenues and essentially transform advertising. It is capable of processing massive amounts of data with a precision and speed that was impossible before. If properly programmed, it delivers accurate and actionable insights.

Machine learning uses algorithms to extract insights from data to execute different predictive tasks automatically through streamlined processes. It analyzes the interest, behaviors, purchasing preferences and demographics at high speeds to anticipate consumer needs that lead to smarter advertising decisions. Machine learning can follow the entire consumer journey across different devices and formats to allow the marketers to reach the right audience with the right content, at the right price, to provide personalized and enjoyable experiences.

Take the Leap Forward With Machine Learning

While the technology to transform the advertising business is available today, use of machine learning across the industry is in its infancy.  However, it is gathering pace as companies recognize its potential, so it will likely become more prevalent in the coming months and years. Couple this with the speed at which behaviors and preferences change, and the number of choices consumers have continues to increase. Exploring and implementing machine learning is an inevitable step for advertisers.

3 Steps to Success

Machine Learning is not “the solution.” There are several things to consider when putting a strategy in place to change the way an advertising business uses machine learning. First, before even thinking about the technology implementation itself, you must understand where, how and when to implement machine learning. There is no point implementing the technology without understanding what business benefit you want. Here are three to consider as you look to invest  wisely.

1. Re-evaluate Your Systems Infrastructure

Look at your existing technology and determine what investments you must make versus what systems are needed to support your ongoing business. A data-driven strategy is a must for any business to be successful, so choosing the right analytics tools and integrating them properly is a baseline requirement. The data must be clean, and the systems must be tightly integrated to provide the business with a “single version of the truth” to make informed decisions.

2. Anticipate the Human Infrastructure

A critical part of the machine learning journey is the workforce. The introduction of the latest technology will demand a change in the roles and the skills you need as the use of analytics becomes more pervasive. Employees must be re-skilled to execute higher-value tasks rather than more manual admin-based activities they’ve been used to. They must also adapt a new fail-fast attitude to innovation and learn to run pilot projects within cross disciplinary teams before scaling new innovations across the entire business. This approach limits risks while testing the new concepts in an agile way.

3. Put a Clear Strategy in Place

Consider how much of the transformation you can achieve in-house and where investments are needed to partner with external organizations to make the process a success. Put a timeframe in place to determine what to do first and how to measure the success. Remember, change won't happen overnight and once you start it is easy to lose sight of where you need to end up.

If implemented correctly, machine learning will fundamentally drive revenues, boost growth for businesses, enhance your brand and improve customer satisfaction. What we are predicting for machine learning today may be only scratching the surface for its potential to revolutionize the advertising business.