Over the last few years, a great deal of media attention has centered around big data. Companies are amassing data at an incredible rate, thanks to online shopping, social networks and an ever-increasing number of apps. Marketers have more opportunities to really get to know their customers and find engaging ways to deliver personalized messages. However, the sheer volume of data makes it difficult to extract meaningful, actionable insights without having some excellent tools at your disposal.
While big data was receiving most of the attention in recent years, developers were quietly making major strides in the world of artificial intelligence. A long-time staple in science fiction, machines that could learn and initiate independent actions seemed impossible 50 years ago. That was before Alexa and Siri, two of the most popular voice-powered digital assistants available today. That was before Google developed DeepMind, a neural network that goes beyond predefined algorithms to make connections and derive meanings. That was before Netflix and Pandora could learn what users like and suggest appropriate choices. All of these examples leverage machine learning to find true meaning in big data.
Machine Learning and Big Data for Marketing
Consumers have become increasingly demanding over the last 20 years. They want personalized experiences that are relevant to them, but they do not want to actually tell marketers what they consider relevant. In short, they want marketers to read their minds. Databases may provide some clues, but by the time the marketing team can perform a manual search and analysis, the information may be obsolete. While basic automation may allow the database to be searched and analyzed quickly, no inferences can be made. However, with machine learning, the system can make connections between seemingly unrelated bits of information to arrive at a conclusion or prediction.
For example, suppose you operate a website that sells shirts that are available in a wide range of colors. Based on the click and purchase patterns, the company can determine the leading preferences by just analyzing some basic statistics. However, by adding artificial intelligence and big data, the company can offer the right shirt in the right color to the right buyer, creating a truly personalized experience.
Another example involves the use of artificial intelligence in a project that EX Squared Solutions recently developed for a customer. The product could learn from millions of images online, automatically tag thousands of them with correct titles and make them searchable. This provided users with better experiences while reducing manual labor, and as a bonus, the customer reaped SEO advantages.
Machine learning and big data can also help marketers reach "lookalike" audiences — groups of people who share certain interests or similarities. For example, suppose you know that most of your customers live in a rural area, are between the ages of 40 and 60, are primarily female and have household incomes of at least $50,000 per year. You could target your marketing messages to people with similar demographics, but you could take your targeting to the next level by harnessing the power of big data and machine learning. Suppose you launch a Facebook campaign and analyze the interests of the people who click on your ad. You discover that many of them are also interested in country music, horses, baking, gardening and collectibles. Based on these new insights, you could target audiences that share these interests — even if they do not match your limited demographics.
Advances in technology will continue to drive new marketing strategies and fuel consumer expectations. Big data offers opportunities to hone your marketing campaigns, but without machine learning, your efforts will be less successful and more time-consuming.