For years, the marketing community has worked to establish, sustain, and extend relationships with consumers. Marketers have sought a means to not only understand current consumer behaviors but also to develop well-defined vision for the consumer's future.
Some 77% of 374 surveyed client-side marketers believe that within the next three years they will need to clearly define customer journeys to better understand and gauge the marketing program focus, according to a recent survey conducted by ANA (Association of National Advertisers).
However, only half of the 77% surveyed have the capabilities to do so today.
As more consumer information becomes available through Big Data, machine learning is the elusive puzzle piece that will enable marketers to complete the picture.
Machine Learning Defined
Although machine learning might be new tool in the campaign to develop better connections with consumers, it is far from a new business practice. Today, all over the world, machine learning is used daily to improve and expedite routine tasks, such as eliminating spam emails from our inbox and recommending new movies based on our choice. It can even be used to predict the winner of the FIFA World Cup.
Machine learning can be defined as "a subfield of computer science and statistics that deals with the construction and study of systems that can learn data rather than follow only explicitly programmed instructions."
That definition may make machine learning seem too analytical—almost counterintuitive in the effort to forge a better connection with the consumer. However, the opposite is proving true. If Big Data is the 800-pound gorilla in the room, machine learning has become the savvy animal trainer, working to not only understand the beast but also to optimize its performance.
Establishing a connection with the consumer is a mission-critical element in the success of any business... but fostering customer interactions cannot stop there.
Continuously monitoring the customer's engagement signals is equally, if not more, important. Failing to identify or recognize the evolving needs or desires of the customer will likely result in a lost business (i.e., lost customer). For example, marketing maternity clothes to a woman whose baby is now three will not increase her buying habits nor will it extend the retail relationship.
Using Machine Learning for Predictions
When applied to Big Data, machine learning can be used to predict the most effective message to serve to the right person at the right time.
Machine learning works with algorithms to analyze consumer behavior in real time to perform predictive segmentation and adapt the communication experience.
The predictive segmentation and real-time analysis allow for the personalization of messages. Consumer engagement data—such as age, gender, location, and buying preferences—is collected from mobile devices, computers, and smart TVs. In much the same way, the postal service uses machine learning to sort mail by ZIP codes, marketing professionals can use machine learning to "sort" consumers by consumer engagement signals.
Personalized advertisements can then be generated to best address the specific needs or desires of each group in real time. For example, a 20-something living in an urban area, conducting searches for a new car, might receive a coupon for a car insurance service while he scans his options.
Machine learning is key to fully optimizing big data information. When used to analyze Big Data, machine learning can provide marketers and advertisers with the opportunity to quickly adapt to the ongoing evolution of consumer engagement signals.
Multiple factors can affect consumer engagement. Many times, the influences on consumer engagement are driven by current events or temporary circumstances. The quicker the data is "learned" and analyzed, the better.
Machine learning allows for a rapid turnaround of Big Data analysis, which enables marketers to connect with the consumer and deliver personalized ads in a highly timely manner, greatly increasing the effectiveness of the advertisement and helping to drive online and in-store sales.
Major brands (e.g., online retailers, national lenders, and Internet radio providers) have already experienced increased ad effectiveness through the pairing of machine learning and Big Data.
For example, during a two-month campaign, a leading global tire manufacturer showed an average CTR of .32% versus a benchmark of .07% on display ads enabled with machine learning. Conversions on clicks also rose to 2% versus industry averages of 1%. Furthermore, the machine learning technologies helped the tire manufacturer optimize its ad spend based on real-time data collected and analyzed across multiple screens, increasing the company's ROI.
