Android’s latest campaign slogan “be together. not the same.” highlights the shift in consumer marketing towards individuality. Some call it 1:1 marketing, personalization, or “segment of one” and it’s all being driven by consumer expectations that brands know their unique interests and deliver communications to them that are relevant and personalized. Brands spend nearly $30 Billion annually on targeted campaigns focused on delivering the right message at the right time to the right consumer – to get them to act and to buy. With the compounded annual growth in spend of 12.4% on audience segmentation data and marketing technology, why is it so hard for brands to reach that holy grail of personalization to the individual.
Let’s start with a look at the data. Companies like Acxiom, Neustar, Experian, and Equifax gather and segment data on millions of consumers. Their sources vary from consumer’s online behavior via cookies, purchase data from credit cards, Point of Sale/product SKU data, demographic data, panel data, survey data, loyalty reward data and location data from carriers and ad networks – thousands of sources of data. This information is then analyzed and put into segments – grouping consumers based on “like” statistics. These segments are then sold to brands and agencies for advertising campaigns.
It seems like this should work well: detailed information about consumers grouped into segments so that brands can deliver the right message to the right person.
In reality, we live in a world where we all get the same ad from our favorite retailer for spring gardening (even if we don’t have a yard) or back-to-school specials (even if we don’t have children). Why is this occurring? They don’t know us as individuals and that’s frustrating, given that we as consumers are spending half our lives in the digital world, using mobile phones and giving access to information about ourselves.
Latest Insights on Location Intelligence
Sign up to get the latest from Gravy Analytics straight to your inbox.
Inference is the culprit. Traditional segmentation schemas infer that professional suburban women over 40 earning more than $100K a year love expensive wine, Godiva chocolate, gardening, and gourmet cooking. How do I know this? because I personally get bombarded with those ads. In reality, I drink beer, avoid chocolate, hold the record for killing anything botanical, and my kitchen is strictly for resale purposes only. I’d rather do my taxes than spend time gardening and will do anything to avoid cooking.
Between work and leisure I am connected digitally to the universe 18 hours a day, so why don’t my favorite brands know me? Because they INFER. They guess that because I like to spend money going to fine restaurants (for client lunches), I must like to cook. Because I have a home with a big yard, I must like to garden (I pay a landscaper). Because I searched online for Godiva chocolates (I give them as gifts), I must love chocolate. The truth of the matter is that they’ve got me all wrong.
For personalization to work and to truly engage consumers in an authentic way – brands and retailers need to KNOW, not infer. Generalized segments, panel data from paid participants, and cookie tracking don’t deliver the individualized data about what’s important to the consumer and what will drive their buying behavior.
So how can they KNOW? With geolocation data sourced from mobile devices, brands and retailers can get dynamic, pseudonymous, consumer behavioral data based on where consumers actually go and what they do in their daily lives. Using my own example, they’d be able to know—not guess – that I frequent Mexican restaurants, attend equestrian events, and go to my local community theater twice a month. Armed with these types of “real life” insights, wouldn’t you say that I’d be receptive to offers from a whole new set of brands?