Big Data is about human relationships. If advertising is art in service of commerce, big data is math in service to relationships. The fundamental underlying concept is that the better you know someone, the more relevant, useful, and valuable the message and the connection can be. Data science exists to try to duplicate the collection, sorting, weighing, and application of information that drive friendships.
Here are the relationship assumptions and human behavior patterns that inform complex computations and the application of marketing technology. Rather than think of big data as a scary, invasive “big brother,” think of it as a mechanism for insuring that the right people get the right message when they are engaged and ready to act. One potentially depressing reality, demonstrated by the use of big data, is that we are not unique and much more predictable than we think we are.
Birds of feather flock together. Like-minded people seek each other out. This is a pattern infused deep in our limbic brains and reinforced by cultural and ethnic orientation. We stick to people who are like us and share our backgrounds, education, and income. Most consumers can be found and categorized by their zip code. So by pinpointing target customers by geography, class, and ethnicity, marketers can make fundamental assumptions about wants, needs, and preferences.
Demographics are destiny. The apple doesn’t fall far from the tree. We are products of genetics, early experience, education, and familial behavior patterns. We are essentially an amalgam of who we came from. And while some people make a radical break with their past and many of us reinvent ourselves in unique and interesting ways, we are, for the most part, reflections of our psycho-demographics. So marketers understanding who we are can apply this data to what we want, what we do, where we go, and what we care about. If they do it right, we get information that’s valuable; if not, we are annoyed.
We are creatures of habit and patterns. Everyone filters, sorts, and prioritizes the countless stimuli we receive each day. Most of this process is unconscious. But each person determines the things we like, the things that give us comfort, the things that reflect our self-esteem, the stuff we hate, or the status we’d like to have. We build a series of preferences and coping mechanisms to make sense of the blooming buzzing noisy world around us.
In doing so, we leave identifiable patterns, many of which are similar to people like us. These patterns and repeat actions are discoverable and, to some extent, predictable using algorithms. So brands mine big data to identify patters and document the behaviors and attitudes represented by these patterns.
Change is challenging. Inertia is the Universal Competitor. Most of us operate in comfortable daily circumstances and repeat all kinds of patterns. Our default behavior is to do nothing. We shop at the same places, eat at the same restaurants, vacation in the same spots, drive the same routes, and, in general, vary marginally from practical, familiar, and safe routines. Changing something big — a hairstyle, a car, a sports fandom, a house, or a spouse — is a huge challenge, frequently upsetting an otherwise settled life.
Marketers use this information to determine who to address and how to address them using big data. There are predictive variables that separate people with a propensity to change or switch from those that won’t. Ideally this improves the relevance and resonance of the messages each person receives.
People buy into brands. Americans don’t just buy stuff. They identify with the brands that reflect their interests, needs, and sensibilities. Consumers buy into brands rather than just buying off the shelf. Conversely, brands are designed to appeal to specific cohorts with specific interests and characteristics. These affiliations, both attitudinal and behavioral, are routinely tracked and used in targeting campaigns.
Because people buy into brands and willingly give up personal data to get information, content, deals, and offers, they expect their favorite brands to know them and use the data volunteered to personalize their experience and super serve their needs. Mercenary shoppers armed with a what’s-in-it-for-me mentality (WiiFM) expect their favorite brands to know them and act accordingly by doing-it-for-me (DiFM) and delivering messages that are personally relevant, useful, and valuable. Collecting, processing, and using big data sets enables and improves on-going relationships.
RFM Rules. When we do something — like signing up, viewing a video, or buying something — we are much more likely to do it again, sooner rather than later. This notion of recency and frequency powers most loyalty and reward programs. Someone taking a given action is 10 times more likely to do it again than the average person.
The choices people make, the brands they select, the stores they visit, and the goods and services they buy can be tracked, categorized, and revisited by marketers. It’s not an accident that when you make a charitable donation or an important purchase you hear from the brand soon after.
Big data sounds much scarier than it is. Used legally, properly, and respectfully by ethical and conscientious marketers, it can make consumers’ lives easier, less cluttered, and more convenient.
Danny Flamberg, EVP Managing Director of Digital Strategy and CRM at Publicis based in New York, has been building brands and building businesses for more than 30 years.Prior to joining Publicis, he led a successful global consulting group called Booster Rocket, as Managing Partner. Before becoming a consultant, he was Vice President of Global Marketing at SAP, SVP and Managing Director at Digitas in New York and Europe and President of Relationship Marketing at Amiratti Puris Lintas and Lowe Worldwide.
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