Is your organization leveraging analytics as a transformational force or a defense mechanism?
“There’s really very little excuse in today’s marketing departments to not use data,” argues LinkedIn head of B2B product Russell Glass. “If you’re not using data to make your decisions, or at least to inform your decisions, you’re probably not doing your job.”
Reproaches like Glass’ have compelled many organizations to construct facades of data-drive to conceal what Google digital marketing evangelist Avinash Kaushik calls a HiPPO (highest paid person’s opinion) approach.
This is particularly common in the healthcare marketing field, where companies have been slow to create data-driven decision-making architectures. While most healthcare marketers do use some sort of data analytics, only a select few are leveraging their data to its full potential.
The result? Insufficient analysis layered over decisions that have often already been made, a practice that does a disservice to organizations that could benefit from the full power of transformational data analytics.
Data as a defense mechanism
For many brand teams, data analytics have traditionally been valued as a tool of retrospection, an exercise that provides marketers with answers to questions like: Did this campaign reach our desired audience? Who were my most valuable customers last year? Did engagement spike at specific times or in specific locations?
Postmortems like these are invaluable. Marketers need to know what went wrong in a campaign in order to improve the next time around, but they also lay the groundwork for confirmation bias and retroactive justification.
Marketing organizations adhering to a HiPPO approach will use data selectively to retroactively justify their decisions, providing the appearance of data-driven decision-making without the underlying substance. This might work on occasion, but in the long run, it’s a highly unsustainable approach to modern marketing.
For the many marketing organizations that continue to take this approach, marketing decisions are driven not by data, but by the experiences, intuitions and hunches of their CMO (and/or other high-level stakeholders). Sometimes these gut reaction strategies are effective, sometimes they aren’t. Either way, they’re not truly data-driven.
Using data as a change agent
That’s not to say that brand teams shouldn’t use data to retroactively evaluate success—they absolutely should. But are those brand teams consistently using that retroactive analysis to improve their day-to-day operations? To create and inform new ideas? To identify new growth opportunities?
Rather than find a story in the data, genuine data-driven marketers use analytics to actively identify what doesn’t work. This approach enables marketers to consistently (and confidently) make tough decisions, such as scaling back spending on a once reliable but now ineffective channel, for instance. This kind of continual improvement is an essential step toward achieving long-term bottom-line growth.
In addition to using analytics to fine-tune ongoing operations, marketers must combine descriptive, predictive and prescriptive analytics to transform an organization’s entire approach to business, catalyzing what economists call top-line growth.
A transformational analytics program—one that uses data to look forward as well as backward—empowers organizations to address real needs and opportunities and helps them articulate critical questions that previously went unasked.
Beyond HiPPO
As data analytics become more mainstream across industries, more stakeholders are beginning to recognize the insufficiency of marketing approaches that prioritize data as a defense mechanism rather than a change agent. The most effective data analytics programs allow marketers to simultaneously improve their current operations and undertake new organizationally transformative projects, like refurbishing the first floor of a home as you add a second floor.
The sooner brands move from using data as a means to justify past actions to using data as a mechanism of organizational transformation, the sooner they will experience the benefits of truly data-driven decision-making.
