In 2002, the mobile phone camera hit the US market featuring a 0.3 megapixel capability. In 2019, the most popular mobile phones have well over 12 megapixels.
A mobile camera’s quality is often measured in megapixels – the points that make up digital images. More pixels generally better quality because more information can be captured with more pixels. When we say that a photo is “pixelated”, it means that you can see the pixels. Noticeable pixels obscure finer features.
Yes, it’s shocking that phone cameras are only 17 years old. In technology, a lot can change in a short time.
Marketing analytics has gone through parallel development. The marketing analytics of 2002 was concerned about rough, pixelated segments of customers. In 2019, companies – particularly B2C companies – use customer intelligence analytics to get a crisp, useful picture of customers.
Most importantly, when you see your customers better, you stop seeing the “pixels” – like the distortions of zip-code demographics – and begin to see and anticipate the behaviors of real people. When you anticipate behaviors with customer intelligence, you can act differently: you can be a lot more helpful to your customers, and that’s good for business.
Marketing Analytics Give Way to Customer Intelligence Analytics
The marketing analytics of 20 years ago grouped people with similar needs, built profiles for the groups, and then targeted profiles for mass campaigns. Large numbers of people received the same treatment. Compared to broadcasting the same message to an entire population, targeting in this era of marketing analytics helped optimize campaign profitability.
Today, digital channels and data collection have progressed to the point that leading companies care more about the profiles of individuals rather than the profiles of segments. The term ‘Customer Intelligence’ reflects the fundamental shift in grain: we can see not only markets, but customers.
Customer Intelligence Helps Companies Understand a New Customer Dimension: Behavior
Behaviors are the distinguishing characteristics of different customer needs. Do I click on this text or alternative text? Do I tend to research online and buy in the store? Do I share my reviews with friends on social media? What other websites have I visited before arriving at your website?
Customer intelligence analytics includes behaviors in profiles to distinguish otherwise similar individuals. Two people may look the same based on demographics, but their browsing behavior may suggest that one person is planning a wedding or a family while the other seems to be dedicated to a single life for the foreseeable future. The company that treats these two demographic twins differently based on behavior will win in the market.
Digital Engagement Optimization Closes the Loop Between Behavior and Customer Intelligence
Increasingly, we understand preferences through digital engagement. As marketers, we still do surveys, focus groups, and other studies. Digital engagement, or tailored treatment online via a desktop browser or phone app or other means, uncovers what people do instead of what they say they would do (there’s a difference).
When you optimize digital engagement, you deploy customer intelligence to the point of customer interaction, creating a virtuous circle: I get an offer for a product based on customer intelligence, my response to the offer is captured as a part of my digital engagement which is fed back to my customer intelligence profile, which then optimizes the next interaction, and so on.
This recursive approach inevitably brings people into smaller groups and sharper focus. If you are doing marketing analytics circa 2002, you only see some of the picture.
The experts at Syntelli Solutions have delivered customer intelligence projects for leading organizations. Let us know if we can help you.
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