Marketing has always been hard. These days, it’s even harder. As technologies have evolved, data has exploded in volume, and finicky consumers now have seemingly countless ways to find and interact with brands, the marketer’s job has grown increasingly complex and challenging.
Perhaps that’s why marketers have been drawn to Artificial Intelligence (AI) and Machine Learning early on. To be successful, marketers must understand how consumers become aware of their brands, move through the buying funnel, and make the decision to become a customer. In an age of personalization, they must also understand how to capture, analyze and use all the data generated by these consumer actions so they can tailor messages to be the right one at the right time for each consumer.
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Digital marketers don’t have the resources to do all of that manually. That’s where Machine Learning can help. Machine Learning can sort through data faster than any human, no matter how smart, to identify patterns, trends, and opportunities.
Savvy marketers are already using Machine Learning for:
- Recommendation engines
- Demand forecasting and sales projections
- Lead scoring
- Churn risk modeling
- Dynamic pricing
- Predictive selling
- Dynamic product ranking
- Merchandising placement
- Augmented reality (AR) integration
- Dynamic content
- Programmatic ad buying
- Social monitoring
- Social media marketing
All of these uses enable marketers to do more marketing with less budget and fewer resources because tasks can be automated. More importantly, Machine Learning helps marketers to do it all cost-effectively because Machine Learning can put the increasing amounts of data to use.
Four Ways Marketers Can Use Machine Learning
For examples of real-life applications of the benefits of Machine Learning, let’s look at four specific ways Machine Learning can help marketers put data to use for more effective—and efficient—marketing results: segmentation, lead scoring, pay-per-click advertising, and dynamic pricing.
Even in the days when direct mail dominated, list segmentation mattered. Not everyone should get the same message, and marketers have to segment their lists to ensure the right message gets to the right person. With the vast amounts of data, we have today; segmentation is theoretically easier. We can know where people live, how old they are, whether or not they own a home, the kind of car they drive, if they’re married or even expecting a baby, their income range, and much, much more. And Machine Learning can turn all of that data into insight that leads to targeted marketing messages.
But there’s more: Machine Learning can go deeper to distinguish between high-value low-value customers. It can find clusters of your best customers. It can determine which products to recommend to which customer groups. It can help you predict buying behavior based on criteria you would never have thought to consider. And it can do much more with all your data than you ever could on your own.
2. Lead Scoring
In addition to getting to know your prospects and customers on a deeper level so you can segment, so too must marketers be able to score leads to know how to proceed. This is especially true in the B2B space. And Machine Learning can help. As Bernard Marr says in Forbes, “Not only can machine help with the original gathering of information of lead generation, AI can analyze unstructured data such as emails, phone calls, and social posts to then determine patterns and define who is a good prospect. This info is vital for effective marketing campaigns.”
3. Pay-per-click Advertising
Successful pay-per-click (PPC) requires starting with a human-defined strategy but then changing bids as needed based on data.
“If there is one area that machines dominate, it’s bidding,” said top AdWords influencer and author, Brad Geddes, in his Simplilearn webinar Man vs. Machine: How to Future-proof Your PPC Job. As Geddes points out, bidding is based on pattern recognition and statistics, making it one of the best uses of Machine Learning in marketing. Machines can also determine how a consumer might interact with an ad based on previous behavior and the statistics for that particular combination of keyword, advertisements, and landing page.
4. Dynamic Pricing
Machine Learning also enables dynamic pricing online. Prices can change in an instant based on factors like supply, demand, competitor pricing, the consumer’s interest level, and prior engagements through previous marketing. Airlines and ride-sharing companies are two examples of business models using dynamic pricing to maximize revenue.
Incorporate Machine Learning into Your Marketing Strategy
To learn more about ways marketers can employ Machine Learning to do more with less and maximize results, download the whitepaper - How Machine Learning Can Make Any Business More Competitive. In it, you’ll find an overview of the types of data you’re probably already collecting, methods for storing those huge amounts of data, and specific ways to use that data for a competitive advantage in business processes, marketing, and customer service.
Regardless of how you put it to use as part of your marketing efforts, Machine Learning will help you save time, money, and resources by doing more than humans alone possibly can and by identifying opportunities as well as pitfalls that you otherwise might not see or act on.