marketing success depends on many factors. You need accurate consumer research to build your branding strategy, engaging content to delight your audience, a firm grasp of behavioral economics, and a near mystical ability to intuit how people will weigh your message against those of your fiercest competitors. In the digital age, marketers can’t win without mastering data, analytics, and automation.
Fortunately, machine learning (ML) can already improve marketer performance on common tasks like customer segmentation, generating branded collateral, extracting and classifying relevant content, customer communication, and overall productivity and output. In the new economy, a marketing unit without machine learning mastery operates at a serious handicap.
BUT WAIT. Adopting an ML solution without understanding what it truly does can do more harm (usually expressed in wasted hours and dollars) than good. Machine learning is NOT magic and won’t automatically move the needle unless your team selects and configures the right ML solution for specific marketing challenges.
Many Martech companies shamelessly claim that their solutions are “AI-powered” or “use the latest breakthroughs in AI.” Some indeed exhibit cutting-edge technologies, while others use unimpressive and commonplace techniques.
To help you know the difference, here’s a primer on the top applications of machine learning for marketing:
Because marketing is a multifaceted field, machine learning can be applied in many ways using various combinations of techniques. Here are some of the more common techniques and applications:
Not all customers are the same. Unsupervised machine learning can help marketers group their audience into dynamic groups and engage them accordingly.Affinio’s platform, for example, analyzes billions of consumer interest variables, identifies specific customer’s interests based on their social media activities, then generates a visual report grouping people with similar interests. You then gain insight on which of your customers are die-hard foodies, who follows which series on Netflix, or who among them have similar travel plans.
A/B tests are effective ways of finding out which content option (email tone, web page layout, visual elements in an ad, article headline, etc.) resonates better with your audience. However, A/B Testing involves a period of “regret” where you lose revenue while using the less optimal option. You have to wait and finish the countdown before learning which option — the final answer — is better. In contrast, bandit tests mitigate regret (opportunity loss) through dynamic optimization where it simultaneously explores and exploits options, gradually and automatically moving towards the better option.
The right pricing scheme can make or break a product. Regression techniques in machine learning allow marketers to predict numerical values based on pre-existing features, which in turn enables them to optimize different aspects of the customer journey. Regression can also be used in sales forecasting and in optimizing marketing spend.
Using natural language processing (NLP), a machine learning system can probe text- or voice-based content, then classify each content based on variables such as tone, sentiment, or topic to generate consumer insight or curate relevant materials.IBM Watson’s Tone Analyzer, for example, can parse through online customer feedback and determine the general tone of users reviewing a product.
Marketers can leverage ML to extract relevant content from online news articles and other data sources to determine how people view their brand and/or react to their products. TheProtagonistplatform enables companies to gain full visibility into their customers’ values and motivations and how these attributes affect their buying decisions.
