How Psychographic Data Can Help Brands Understand Advertising Disengagement

Current economic factors mean marketers must use their shrinking budgets wisely. Yet brand marketers continue to waste about 25 percent of these precious dollars because they’re not effectively using the correct data or insights to best understand their customers and unengaged audiences.

The Most Prominent Mistakes Marketers Are Making

“The definition of insanity is doing the same thing over and over and expecting different results,” said Albert Einstein. So, why are so many marketers still doing this today?

First, some marketers are still executing mass marketing campaigns that don’t reach a specific target audience, which makes it impossible to personalize messaging. For example, many marketers continue to allocate spending to channels such as traditional TV in hopes it's reaching some of their target audiences. To ensure these campaigns are reaching the right audiences effectively, marketers need to take the time, energy and budget to test and analyze the impact of different channels on their audiences. Despite all the data we now have at our disposal that can predict consumer behavior and purchase intent, marketers often ignore these unsuspecting factors in favor of gut instinct simply because it’s what they’ve always done.

Understanding Real-Time Purchase Intent

It's estimated that Americans are exposed to around 4,000 to 10,000 ads each day. The key takeaway: consumers are engaging with advertising. They might not engage in the channels a brand wants to prioritize, but they engage in the channels they want. Consumers choose channels that have content and experiences that match their interests.

For marketers to understand why consumers aren't buying from their brand, where and how they want them to, marketers need to access data about the consumers’ behavior, demographics, geographical location and real-time purchase intent to paint a fuller picture.

Sure, plenty of marketers have previously gotten by with buying data about consumers and appending it to their data segmentation strategy. But that data simply doesn’t cut it anymore as these rear-view mirror insights likely happened 30 days ago or more. Consumers have a myriad of factors distracting them, from their kids to careers to soaring mortgage rates and utility bills. If you're a marketer who wants to cut through the noise in a target consumer’s head, you must strike while the iron is hot.

In order to break through the noise, marketers need to understand consumer behavior and the motivations behind the behavior. The latter comes via psychographic data — i.e., information about a consumer’s values, attitudes, interests and personality traits. Brands can use this intel to create comprehensive profiles of an individual’s worldview, interests, and what motivates them to purchase.

Psychographic data can give marketers more context. For example, instead of just knowing that a customer or prospect has a pet, this data can also show the type of animal and that the pet owner watches a vast amount of content on leash training. This data goes beyond surface-level insights. It helps marketers understand a consumer’s current hobbies or understand that a consumer may be in-market to buy a kayak but it isn’t for them, it's for their brother on the other side of the country.

Putting Psychographic Data to Work

Once marketers understand consumer psychographic behavioral intent, they can assess nonperforming advertising tactics and reduce spending on campaigns that aren't getting a response.

Activating psychographic insights requires deterministic and probabilistic approaches. Even if marketers know who their target consumer is, these insights can enrich data with real-time intent. If they don’t know who the target consumer is, probabilistic data fills the gap by mining the content a consumer has ingested in addition to their location and recent transactions. Marketers can then look at the data and attach personas to it to define the target consumer for the brand.

Since consumer needs are constantly changing based on their life stage, geography, job status, relationship status and other factors, marketers need more than just this data. Brands must also apply machine learning to constantly update their understanding of their customers and prospects as they evolve. This will help better predict consumer behavior variables, such as the number of items purchased, the number of store visits, and the recency of store visits. Brands can then use this understanding in their marketing in several ways:

• Enhancing customer profiles: Utilize these deeper consumer insights to enrich profiles with the intent to buy. This includes moving consumers in and out of segments as different variables change so there's no wasted advertising spend on customers who have already converted.
• Personalizing messaging: Persuade current customers to adopt a product or service through targeted promotional messages. This can include layering in additional targeting approaches such as weather, day and time targeting, which allows marketers to restrict ad delivery to particular days of the week and times of the day.
• Reaching new prospective audiences: Utilize your new understanding of the consumer to reach prospects and high-value customers. This can include weaving into your messaging the brand values that resonate with your most loyal customers.
• Optimizing messaging: Reinforce product or service attributes that resonate most with higher-value customers and speak to lower-lifetime value ones with relevant messaging.

Don’t waste time or budget on advertising tactics that may have worked in the past but no longer apply today. Use psychographic insights to know with certainty which consumers you should target, when and where, and make every marketing dollar count.

Melissa Tatoris is vice president, retail at Zeta Global, a cloud-based marketing technology company that empowers enterprises to acquire, grow and retain customers.