smartboost · How to Predict Consumer Behavior Using AI Marketing
As a marketer, wouldn’t it be nice to know your prospect’s next move? Furthermore, would you like to react in real-time when they are researching a competitor, commenting on social media about a problem they’re looking to solve, or ready to pull the trigger and buy now?
With AI marketing, you are now able to. By leveraging advanced analytics and machine learning techniques, marketers can predict a consumer’s behavior often before they even know what they want or need themselves.
What is AI Marketing?
According to MIT Technology Review, artificial intelligence simply refers to “machines being able to learn, reason, and act for themselves.”
AI may just be the most misunderstood term of the 21st century, primarily because the definition is so varied, and used commonly to describe activities that can’t necessarily “act” for themselves.
For example, Forbes takes a high-level view of AI marketing, describing it as “a method of leveraging technology to improve the customer journey.”
Today, most companies take a broader definition of AI that encompasses advanced analytics and machine learning. The key takeaway is that these systems can analyze massive amounts of data and find compelling patterns that humans can’t. These compelling patterns can help inform many strategies throughout the marketing funnel.
Let’s compare and contrast how marketing is done with and without AI to get the full picture of the opportunities for AI within marketing.
AI Marketing vs CRM Data
Today, many companies rely heavily on sales to personalize the customer journey. This approach has revealed its shortcomings, specifically around human cognitive bias and scalability. In a Forrester survey, 74% of business buyers said they conduct more than half of their research online before making an offline purchase.
Additionally, many marketers believe that they are already well-versed at making data-driven insights using platforms like Marketo, Hubspot, Pardot and Eloqua. However, marketing automation platforms have proven to be largely ineffective at optimizing data-driven predictions.
Let’s take traditional lead scoring models for example. Lead scoring has relied on arbitrary data based on human feelings with little correlation to real-world results. The result is a model that is based on human error and bias.
Marketing automation has greatly advanced the capabilities of marketers, helping significantly reduce the time it takes to automate tasks. However, the current systems have reached their limitations, relying heavily on generalizations built on limited data markers. While platforms might be able to add your name to a website or recommend a new piece of content based on the piece you just read, they’ve failed miserably at “understanding” the full picture. This is arguably the most important aspect of converting prospects to customers, optimizing ROI, and increasing lifetime value. Ultimately, CRM data wasn’t built for this purpose.
That’s where advanced analytics and machine learning have entered the scene. AI helps generate insights that both predict what users are likely to do next and recommend what actions to take.
The ROI of Predictive Marketing
A survey by Forbes Insights has shown that nearly 9 out of 10 organizations that have used predictive analytics for at least 2 years have seen an increased return on their investment, with 40% seeing an over 10% increase. Some organizations have seen triple-digit growth.
Take ServiceMax, a field technician management software for manufacturers. They use machine learning to individualize the buying journey by predicting the most rewarding path for a prospect to take. They use all the data from an individual’s past and current interactions with the brand, as well as analyses of individuals that followed similar patterns.
Through their efforts, they have cut bounce rates by 70% and increased time-on-site and the number of product demo requests.
The market landscape is shifting rapidly, as 80% of all marketing executives believe that AI will revolutionize marketing over the next five years. With increased competition and an overwhelming focus on experience, the ball is now in your court to build trust and credibility with your audience by leveraging the insights you have.
Identifying Your Audience Using Big Data
Every AI project starts with data across the technologies you use in your business. There is a wealth of insights that live in your CRM data, Google Analytics, social media accounts, and even your help desk.
To help us understand what data to pay attention to, let’s take a quick look at the four Vs of data:
- Volume refers to the amount of data, such as keyword search queries on Google or website clicks.
- Velocity refers to the speed in which data is created, such as real-time search results, content recommendations, or stock trades.
- Variety refers to the various source types, including everything from social data to website data to customer support tickets.
- Veracity refers to the trustworthiness of the data, such as self-reported data versus data gathered automatically.
Now that we understand the types of data that are needed, let’s take a look at the type of user behaviors you should be reacting to and how.
What Behaviors to Focus On
To develop real-time insights, we need to collectively understand a prospect’s mindsets, behavior, and demographics. Just like you can’t understand a person just by knowing what city they grew up in, you can’t understand a prospect by just looking at their current job title.
You already have the data you need, living across your internal and external technologies. Now, it just needs to be harvested. While what data to pay attention to differs from brand to brand, you’ll likely want to incorporate factors like sentiment, cultural characteristics, social engagement, and the way a user searches for information. Some of the important data points to pay attention to include:
- Website data
- Loyalty program data
- Social media usage data
- Keyword data
- Product affinity data
- Conversions path data
- Device data
Machine learning models built on these data points can predict when an individual wants to be reached out to, when they are likely to buy, and when they are likely to churn.
For example, Norweigan Airlines was able to reduce its cost per booking by 170% by optimizing their ad spend and leveraging machine learning models. In addition to brand interactions, they took into account device data, location data, and contextual data of pre- and post-site behaviors. They learned that home-based users booked flights at a rate of 4X greater than users at work.
Without machine learning, creating context from the large swaths of data would be impossible. AI and machine learning help bring order to the chaos of ever-growing databases.
AI allows you to use behavioral segmentation on a micro-level. Rather than dividing prospects and customers into rigid categories, such as ticking “yes” or “no” boxes for various characteristics, you can define behavior down to minor differences.
Similar to how the human brain works, machine learning can leverage deep neural networks to rapidly make inferences and solve problems. Like humans, it learns as it goes, adding new data points to fine-tune its inferences and predictions. But unlike humans, it can do this process significantly faster.
Deep learning provides a way to train AI to predict outputs, given a set of inputs. If a deep learning system has access to all a user’s behavior listed above, it can accurately predict human behavior.
It’s what Tesla has used to learn and mirror drivers’ behaviors in their autonomous vehicles. Just as you see a person standing in the middle of the road and instinctively slow down, neural networks have been trained to react similarly through pattern-recognition and trial and error.
Customers already know what they want when they come to you. It’s up to you to show that your offerings are the right option for them. Deep learning has the potential to find patterns in data to help businesses understand what customers really want.
Predict Consumer Behavior Months in Advance
Merging big data with deep learning will help businesses create personalized marketing approaches that will appeal to anyone who might buy their product or services.
Marketers can take data, like customer sentiment data gathered by social media listening tools, to identify patterns that can help them forecast consumer behavior months in advance. Ben & Jerry’s used machine learning to read social and cultural trends to identify a new viral marketing opportunity: cereal-flavored ice cream. Using machine learning models built on unstructured data, they discovered that “at least 50 songs within the public domain had mentioned ‘ice cream for breakfast.’” Sounds like a pretty delicious data discovery!
Machine learning can also help predict what products to promote and why. Sephora used machine learning to customize their email communications on product recommendations. They used their most loyal customer base to train their models to be able to predict which products less loyal customers would be interested in.
By crafting micro-segments of your audience and hyper-personalizing your messages, your brand’s relevancy factor goes through the roof, as does your chances of turning a prospect into a customer or extending the lifetime value of that customer. You now can develop a full picture of who your prospects and customers are, rather than just relying on a lead or logo with a few disconnected data points.
The ability to predict wants and needs is the dream of every marketer. With access to the right data and deep learning models, it’s now possible. It’s more critical than ever to take advantage of the new frontier of AI and machine learning to engage your audience and make confident predictions about their behavior.