Customer Behavior Prediction Models

What Is Behavioral Marketing?

Customers have the power in today’s competitive marketplace. Traditional loyalty measures no longer apply; instead, consumers are loyal to the brand that provides the best customer experience. Customers who find personalized experiences very appealing are 10 times more likely to be a brand’s most valuable customer. This hyper personalization that customers crave can be achieved through behavioral marketing.

What is behavioral marketing? Most often referred to in the context of digital advertising, behavioral marketing targets customers and prospects based on an entirety of interactions; website visits, cookies, search history, transactions, call center, etc. Behavioral marketing is the process of analyzing patterns with the intention of serving more targeted content that will ultimately improve a customer’s overall experience and drive higher profits.

Like a forensic detective investigating a crime scene for physical and digital clues that will piece together a narrative, behavioral marketing scans physical and digital footprints across all channels. Instead of capturing a thief, behavioral marketing captures an audience’s attention with more relevant content personalized to a unique customer journey, leading to higher conversions. According to Accenture, 91 percent of consumers are more likely to shop with brands that recognize, remember and provide relevant offers and recommendations.

Behavior marketing uncovers patterns. Detailed analysis of every possible variable unveils not just customer behaviors across channels, but finer behavior patterns that inform marketers how or whether one behavior influences another. If, for example, a consumer clicks off a page showing blue down parkas after 2.5 seconds, but had recently bought a pair of winter gloves at a physical store and had also searched for flights to Banff, analysis might discount the brief page visit as an outlier not representative of a totality of behaviors in a customer’s journey.

What are Customer Behavior Prediction Models?

Customer behavior models are usually based on the mining of customer data. Each model is designed to answer one question at one point in time. A customer model can be used to predict what a particular group of customers will do in response to a specific marketing action.

Behavioral marketing, as mentioned above, is only possible with customer data. Data that informs how a customer interacts and engages with a brand exists in any source and type of customer data, whether first-party, second-party or third-party; structured, semi-structured or unstructured.

Compiling data from every conceivable source provides marketers with a single customer view. A unified customer profile that is updated in real time, combined with advanced identity resolution capabilities, forms a golden record – an accurate, complete and holistic picture of an individual customer.

Knowing everything there is to know about a customer with a golden record includes an individual customer’s preferences, transactions, devices, ID’s – and behaviors – across every channel. It is the foundation for providing a real time omnichannel customer journey that is optimized with personalization and relevance.

The Importance of Behavioral Marketing On the Bottom Line

Once a brand possesses the most accurate and timely data for each customer they are able to understand behavior patterns and based on those the brand is able to deliver a superior customer experience.

Artificial intelligence (AI) and machine learning are also increasingly necessary for a marketing organization to deliver on this promise. Powerful advanced analytics embedded in these emerging technologies can deliver personalization at scale previously not achievable to provide customized offers and messages to all customers in real-time across the enterprise.

Use cases include next-best-action predictions that automatically score customers and automate segmentation to determine the optimal content type as well as the optimal timing and placement of that content. In this scenario, AI and machine learning are invaluable tools to deliver messages and offers in the cadence and the context of the customer journey, which is critical to create the personalization and relevance that resonate with customers.

For the satisfied customer, this could mean placing an online order for a same-day, in-store pickup and immediately receiving an offer for a relevant accessory. Or it could mean developing an ever-deepening bond with a favorite brand because each interaction triggers a welcomed personal touch.

According to a McKinsey article “Capturing Value From Your Customer Data,” companies that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in growth margin. The article lists examples of insights such as time spent lingering on a page, frequency of customer service calls and information on a customer’s purchases. While most companies collect this information, it says, few piece together the disparate data points to inform a narrative about a customer journey. Yet according to the article’s research, tailoring outreach with personalization based on behavioral analysis delivers five to eight times the return on marketing expenditure.

Behavioral marketing works. Each time a customer engages with a brand, they leave behind digital and physical clues about what matters to them. By finding patterns in behaviors, data-driven marketers are able to deliver personalized, relevant customer experiences across all channels that are proven to drive new revenue.

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