In our age of complexity, it’s easy to consider insights the ultimate upgrade from data—a destination that, once reached, alleviates a marketer from the burden of meaningless numbers, graphs, columns, and rows.
In some applications, that opportunity is very real, but the truly data-driven marketer knows that insights are only one path to mining value from data. There is much more value to be had.
Data—like technology—is intimidating. And rightly so: It demands hard skills, it catalyzes beliefs and actions, and it can be dangerous in the wrong hands. Even for highly skilled data-driven marketers, there is always something new or unknown: a question that cannot be answered, a number that doesn’t match, an expectation that cannot be met. Thus, it makes sense that so many marketers favor and focus on insights as the holy grail: Insights are pure and finite—their application focused and clear.
But marketers who truly want to use data as a competitive advantage need to be comfortable (or find someone who is) with all its vast, scary, messy complexity.
Digging for Data Gold
Because of the intimidation factor, many marketers succumb to one of two misconceptions: (1) data is simply poorly branded insights; or (2) if insights can be achieved, then data is no longer important. But just as a necklace is only one of the many valuable objects you can make from gold, insights are but one output from data.
As a comprehensive tool, data is not only the catalyst for insights but also the input for predictive models, learning algorithms, and beautiful visualizations; a vehicle for personalized experiences; and an asset of quantifiable value.
That’s why conflating insights and data or discounting data in the face of insights is limiting the potential value data offers to marketing teams.
To maximize this value, then, marketers should focus on implementing the following three strategies.
1. Establish a corporate data strategy
Recognize that data has commercial value, and consider how you can better put data to work. How can it inform better business decisions? In what ways can it enable more effective or efficient marketing? Is there an external market for it that could translate into direct revenue?
Focus on harnessing and using data for as many outcomes as you can, in order to increase value and achieve business and marketing goals. Fitbit’s entire product suite, for instance, foundationally runs on data, and the advances in its technology and offerings are the result of examining the data consumers have logged while trying to understand what next things would best serve those consumers.
Overall, making data a part of your business strategy—with a budget and an owner—can help you obtain and maintain data that the organization determines is highly valuable to it.
2. Make data planning part of marketing planning
When is the right time for insights? Sometimes. When is the right time for data? Always.
Great insights are crucial to the planning process—to inform strategy and inspire creativity. But there is still so much potential for data to be applied after the brief has been written.
Consider, for example, a cellphone’s weather app, which automatically uses GPS data to provide weather reports tailored to a user’s current location—a straightforward interaction that doesn’t require insight or even much data manipulation, but generates high value using a simple point of data.
Pushing GPS and weather technology a step further, fashion brand Burton used a weather API to serve up clothing recommendations reflecting visitors’ local weather—anywhere in the world.
A good data planner is trained to ask, “What data should be collected, stored, and used to get the most value from every marketing campaign, program, and touchpoint?” Making that question part of the standard process is the key to making the consumer experience as brilliant as the initial insight.
3. Put analysts at the center of the creative process
Ultimately, tactical decisions made with supporting data are far more productive, allowing you to ask better questions, extract more meaning, and resolve concerns. Having analysts embedded in the creative process ensures that data can be mined and questions can be addressed at every point along the executional journey.
Moreover, those analysts will be able to spot the opportunity for a model or algorithm to create better experiences. Amazon analyzes user behaviors across all its touchpoints. Digitally, the brand presents product recommendations based on what customers view and put in their carts. The company doesn’t need shopper insights to do that, but it does need an active learning algorithm that correlates behavior and product data, which can be accomplished only with a good data scientist.
Some three months into its foray in the brick-and-mortar business, Amazon Go, Amazon had its analysts exploring the store’s trends—what works and what shoppers’ sticking points are—to optimize the experience and make it feel as seamless as its digital counterpart.
Beyond the Insight Mindset
By employing the three strategies outlined here, and by focusing on the full potential of data, marketers can advance beyond competitors that are stuck in insight-driven mindsets. In turn, they’re sure to provide more value to both their companies and their customers.
You shouldn’t need an algorithm to recognize that that’s a winning solution.
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