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 and clues that will piece together a narrative, behavioral marketing combs 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.
Behavioral Marketing: Beyond the Web
A big misconception about behavioral marketing is that it’s limited to analyzing a customer or prospect’s behavior on a website: click-throughs, time on page, etc. for the purpose of serving up first-party or third-party digital advertisements based on behaviors on a single channel. An example of this type of behavioral marketing would be a user on the real estate section of a newspaper’s website then being shown a realtor’s listings in her area.
As a contrast with direct marketing, where that realtor might send a listings brochure to everyone in a ZIP code, marketing to a segment based on one behavior will likely have the desired effect of reaching a more targeted audience with a higher level of interest.
Enhance Behavioral Marketing with a Multi-Channel Approach
A more nuanced form of behavioral marketing is analyzing a customer or prospect’s behavior across a wide range of channels. To borrow the forensic detective analogy, a search of the basement may yield an evidence windfall, but a thorough detective will still look in the attic and pull cell phone records. An accurate prediction of intent depends on analyzing a full range of behaviors. An individual behavior may have significance as to a customer’s intent, but pieced together over time behaviors tell a fuller, more complete narrative.
In a Harris Poll commissioned by Redpoint, 52 percent of consumers surveyed defined a personalized customer experience as receiving special offers only available to them, with 43 percent of consumers defining it as a brand knowing that they’re the same customer across all touchpoints. This level of recognition is possible only by analyzing how a customer engages across all channels, which means detailed analysis of customer behaviors at every interaction.
Behavioral marketing becomes more effective when marketers account for behaviors across a multitude of channels. Behaviors do not exist in a vacuum, but rather each is an influencing factor in a customer’s omnichannel journey.
Behavioral Marketing and Identity
Behavioral marketing becomes even more powerful when behaviors are attached to a customer’s identity. A realtor who segments an audience based on a prospect’s visit to a landing page showing availability in specific neighborhood may enjoy a better ROI than a bulk mailing, but a realtor who goes farther will likely produce better results. For example, by knowing the visitor’s household status, age, income, demographics, ID’s, etc. a realtor may send even more relevant listings, such as only those with four bedrooms, a two-car garage, an in-law apartment or within walking distance to a grammar school.
Compiling a more complete set of behaviors would entail more stringent analysis far beyond a single website visit. Perhaps the prospect is active on home improvement sites, or they’re inquiring about certain neighborhoods on social media. Have they commented on open houses they’ve seen? Compared mortgage rates?
How Does Identity Resolution Fit in With Behavioral Marketing?
Advanced identity resolution is a powerful tool for amassing a complete picture of a prospect or customer. Identity resolution goes beyond using a cookie match or a device identification as the basis for providing a customer with a specific offer – limited to a match at a moment in time such as a realtor segmenting an audience based on one website visit. Identity resolution empowers a more successful approach to behavioral marketing because it accounts for the fact that consumers have different identities in different channels, identities in digital and physical locations, households and other variables such as different name spellings.
Without a full accounting of each and every variable of an individual identity, behavioral marketing will by definition have an incomplete view of a consumer. An incomplete view introduces the potential of incorrectly analyzing a consumer’s behavior. This may include attaching a misplaced importance to a behavior on one channel because you do not have a view into a potential competing behavior on another channel.
Types of Behaviors for Behavioral Marketing
Now that we’ve seen that behavioral marketing as a process extends beyond mining digital and physical clues across multiple channels, it’s also important to understand that the clues themselves are highly granular. For instance, surface-level website behavior such as content viewed, pages visited or shopping cart activity might provide a foundation for behavioral analysis, but other digital clues might tell a competing story. A retail customer might click on a landing page that shows blue down parkas, but how will the customer immediately clicking to a different page affect an intent calculation? Similarly, what does the time of day of the activity, the customer’s geolocation or another potential variable tell us about the story behind the content viewed? Additionally, the action a consumer takes while on a page forms an even more complete set of behaviors. Did the consumer click on an image, download content, submit a product review?
There are countless behaviors a customer might take. Each website visit, each in-store visit, call center interaction, service request or any other engagement on any channel provides a gold mine of intent signals that astute marketers are able to mine for behavioral signals to better predict how a consumer will respond to specific content, whether an ad, an offer, or action that is most relevant to the consumer’s unique journey at a precise moment in time.
Behavioral 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.
Behavioral Marketing and a Single Customer View
Behavioral marketing, as you may have guessed, is only possible with customer data. Data that inform how a customer interacts and engages with a brand exist 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.
Behavioral Marketing and Automated Machine Learning
Optimizing customer journeys with personalization and relevance at scale requires automated machine learning. Automated machine learning extracts meaning from customer data; self-training, code-free models that are tuned to drive a business metric determine how one customer behavior relates to another, which behaviors are more indicative of intent, and discerns patterns that are far beyond the ability of human comprehension. If customer data provide marketers with random sets of clues, automated machine learning pieces the clues together to solve the mystery of what a totality of customer behaviors mean within the context of an omnichannel journey.
With an accurate indication of a customer’s intent, marketers are empowered to react at the exact moment an intent signal appears, infusing each interaction with a relevant, personalized experience that is in the context of the customer’s journey.
Behavioral Marketing and Sales Growth
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.