Today, many companies are planning to leverage AI in their marketing
efforts. According to a 2019 survey by NewVantage Partners, 92% of companies
are increasing their pace of investment in big data and AI.
If you’re in the exploratory phase of an AI project, you’ve likely already begun to take stock of your customer data. Most teams begin by synthesizing customer data from internal systems, including CRMs or order management systems. And this is a great start.
But in order to truly transform your marketing campaigns — to go from one-size-fits-all to personalized, and rules-based to data-driven, and manual processes to automated ones — incorporating another type of data is key. This would be behavioral data (also known as clickstream data). According to research cited by McKinsey, organizations that leverage behavioral data outperform similar companies by 85% in sales growth and more than 25% in gross margin.
According to research cited by McKinsey, organizations that leverage behavioral data outperform similar companies by 85% in sales growth and more than 25% in gross margin.
What is Behavioral Data?
Behavioral data helps you understand how your customers (and would-be customers) are interacting with your business online. This includes your website, mobile apps, devices, advertising, emails, call centers, support systems, and more. Behavioral data can be accessed through a variety of marketing technology vendors like Adobe, Google, email, and CRM systems.
Behavioral data can answer the following kinds of questions:
- How many times did a customer visit a certain
page before purchasing (or not purchasing) a product?
- How many and what types of advertisements did
each prospect see? Which ones did they respond to?
- In what order did a customer view a certain
set of pages or products?
- Which types of pages, content, or products was
each visitor interested in?
- How many and what types of promotions did a
customer receive? Which ones did they engage with?
- How did each visitor find your site?
On their own, the answers to these questions are interesting; when synthesized, they’re transformational. By combining and analyzing these data points, along with internal customer data, you can harness AI to power personalized recommendations and determine the next best actions for each customer.
Six Use Cases for Behavioral Data + AI
Applying AI to your behavioral data can reshape your marketing efforts in a number of ways.
Here are a few concrete use cases:
- Multi-Channel Attribution: Successful multi-channel attribution qualifies the relative impact of each customer interaction (both online and offline) on revenue, giving marketers a picture of the full customer journey to more effectively allocate spend across channels. Behavioral data is fundamental to using AI to measure the relative contribution of different marketing activities and ensuring that both converters and non-converters are taken into consideration in the attribution models. Learn more about data-driven, multi-channel attribution here.
- Ad Targeting: Not only does behavioral data help create an initial audience for targeting your advertisements, but it can also reveal which visitors are most likely to return and make a purchase, helping you optimize your retargeting efforts and drastically reducing your media spend. For example, PC and smartphone vendor Lenovo’s used algorithmic retargeting to cut their net spend by 90% below their rules-based display retargeting effort, while they maintained a consistent top line. See the full Lenovo case study here.
- Product Recommendations: Behavioral data tells you which types of items customers look at, in what order, and which items they ultimately purchase together. Your team can use this information to improve product recommendations. For example, electrical retailer Dixons Carphone was able to increase their add-to-basket rates by 2.5 by implementing AI-driven personalized recommendations. Watch how in this video.
- Personalized Offers: In addition to product recommendations, behavioral data also enables you to send targeted promotions to your customers, based on what items they’re most likely to purchase. An electrical components retailer uses AI with their behavioral data to predict with offers will reengage inactive customers.
- Anti-Fraud: Behavioral data can help flag potentially fraudulent purchases by calling attention to abnormal visit behavior, navigation patterns, time on page, and more. In addition, behavioral data can help detect click fraud (where competitors, ad publishers, or other bad actors automated clicks on your PPC ads, driving up charges and skewing your conversion data).
- Subscription and Retention: Which customers are most at risk of unsubscribing from your service? Why are they signing up for them in the first place? Behavioral data helps you understand factors for customer subscriptions, identify which customers are most likely to churn, and craft retention strategies accordingly.
Behavioral data is an untapped source of extremely high signal for AI, making it a crucial component of a modern-day marketing strategy. In order to maximize its effectiveness, make sure to outline clear goals and understand how your data + AI will support those objectives.
Looking for help? Implementing a system to synthesize all your sources of behavioral and customer data is an important first step. However getting it all into a uniform and useful format is an arduous process. That’s why we’ve created a guide for Data Engineers titled Key Considerations When Building Pipelines for Behavioral Data. Share it with your team and then consider starting a free trial of Syntasa’s Marketing AI platform today.