Big Data Has a Big Problem: It’s Time to Pre-Identify Demand

Big Data Has a Big Problem: It's Time to Pre-Identify DemandBig Data Has a Big Problem: It's Time to Pre-Identify Demand

Within the first few months of your first mortgage payment, you’ll find yourself wandering the aisles of your local hardware store. Home ownership, after all, means home maintenance. There’s a causal connection there, just as there’s a causal connection between your first day of work and your first withdrawal from your 401(k), or the sports injury you suffered in high school and the arthritis treatment you seek 30 years later. In life, one event follows the other, even if the data points that map our lives have, up until now, been scattershot and incomplete.

As Big Data transforms fields as diverse as healthcare, education, and real estate, we are coming to the point where life’s complex tapestry — who we are, our needs, and our demands — can be revealed in data. Naturally, that’s an exciting promise for marketers. But that promise raises an important question: Will marketers continue to be satisfied to mine a narrow slice of that data, or will they dive into the deeper end of the demand curve in order to better define their customers and understand them in context?

The Current Paradigm is Thin

Take the hardware store. Sophisticated hardware brands use an array of digital channels to draw a laser focus on intender data. An online search for a kitchen remodel leads you to content marketing that’s rich with tips and advice. Meanwhile, pre-roll and banner ads featuring kitchen remodels stalk you across the internet. And when that weekend finally arrives, and you’re ready to visit the store, all of that online behavior is retargeted onto mobile, where you see coupons redeemable with your phone as you approach the closest hardware store to your home.

Right now this is probably the best that the current ad tech multichannel playbook has to offer. If the advertiser pumps enough dollars into the right vendors, they’ll probably see some sales lift. It’s certainly better than the previous analog advertising model where there was little in the way of attribution or accountability. But it’s not exactly data-driven. To be precise, it’s a set of behavioral triggers that are (at best) driven by thin data. After all, the person who was the target of all that marketing firepower could easily have been a renter daydreaming about buying a new house someday. Or it could have been a kid. Arguably, each campaign dollar that doesn’t convert is an example of someone whose online behavior didn’t manifest in the desired intent; proof that a point-in-time snapshot doesn’t necessarily reveal the behavior of a person.

Big Data that Knows You

A digital persona doesn’t remodel a kitchen, but a person does. The problem with ad tech’s current use of Big Data is that advertisers mistakenly think they can determine who you are by extrapolating a target persona out of your online behavior. Consequently, the data is thin, even if it’s seemingly accurate. But what if the data was thick — and deep? What if the advertiser could see you not as a fleeting cohort, but rather a connected line of cohorts, understood as a collective, algorithmically comparable to your peers?

Thinking back to the example of the hardware store again, the question of who is behind that intender behavior comes into sharper focus. Enhancing the picture with IP data will tell you whether the neighborhood in question is one where homeowners outnumber renters, therefore making it much more likely that their online user behavior is relevant to a kitchen remodel. Drilling deeper, you might apply data qualifiers relating to the age of the homes, recent home transactions, and family financial profiles because you know from your CRM database that homes over 15 years old and couples between their 10th and 15th year of marriage purchase the vast majority of kitchen remodels. Finally, you might apply data to gauge household wealth and discretionary income, recognising that customers with limited discretionary income are far less likely to take on a remodel.

As the world becomes more connected and automated, marketers will increasingly be able to see all of those public data points and be able to connect the dots. Instead of data as isolated life events, marketers will be able to map transactions as part of a life story — a continuum where products and services relate.

The Life Journey will Replace the Buyer’s Journey

Over time, as marketers come to build a more nuanced understanding of consumers, the old template of the buyer’s funnel will begin to fade away. After all, it was a simplistic marketing construct designed to fit the narrative of the available cookie-based toolset of intenders and online site behavior. Transactions are, in actuality, the result of a life journey that is both nuanced and more complex. People don’t remodel their kitchens because an ad campaign was on target. They remodel their kitchens because they refinanced their homes, their dishwasher broke again, or their last child just graduated from college. There is no four- or five-step funnel that accurately identifies when a household has reached a point in their lives where it just makes sense to remodel.

The more marketers apply Big Data to understanding the lives of their customers, the more likely they’re going to be able to see that kitchen remodel coming. In fact, with enough data, the hardware store should know when you’re ready to remodel before you know. After all, they sold you your washer/dryer, replaced your disposal, and they sold you the paint to freshen up your new home — they should know when they will eventually sell you that new kitchen.

Deep consumer insights can be incredibly powerful tools for marketers and they will change the dynamic between the brand and the consumer. If brands can use Big Data to define each stage of a customer’s life and effectively pre-identify demand, that insight could also be used to redefine the business model. Changing the relationship from a series of isolated transactions to a stickier and more long-lasting relationship is better for the brand and the consumer. Put simply, once your hardware store understands and maps your demand curve, it can also adapt to meet your prescribed needs. Its business relationship with you will shift from what is today a high-cost marketing-driven model to an automated subscription service that is both more responsive to you as a consumer and more profitable to them as a brand.

ExchangeWire: https://www.exchangewire.com/blog/2016/01/25/big-data-has-a-big-problem-its-time-to-pre-identify-demand/

About The Author

Ray Kingman | CEO

Ray has been at Semcasting, Inc. since its inception, leading the company in the development and commercialization of its automated targeting and data offerings. As an experienced innovator in content management, analytics and data visualization fields, Ray directs the day-to-day operations of Semcasting, Inc.