Instead of slipping from retrospection to retroactive justification, genuinely data-driven marketers use analytics to proactively identify what doesn’t work, and in turn minimize those inefficiencies. In contrast to those that focus data analytics efforts on finding the positive story in the data, they consistently leverage analytics as a means to actively minimize inefficient efforts. This approach enables data-marketers to consistently (and confidently) make tough decisions—scaling back spending on a once reliable but now demonstrably ineffective channel, for instance. Striving for such continual (if difficult) improvement is an essential step toward achieving long-term, “bottom-line” growth.
In addition to using analytics to fine-tune their ongoing operations, marketers must embrace the transformative power of data-led innovation to fundamentally evolve their organization’s existing business practices and processes. This means combining 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 build out new capabilities and processes that address real needs and opportunities, and helps them articulate critical questions that previously went unasked.
Beyond HiPPO
In short, the most effective data analytics programs allow marketers to simultaneously improve their current operations and undertake new, organizationally transformative operations—not unlike refurbishing the first floor of a home as you add a second floor.
However, executing these tasks in tandem requires a firm commitment to data democratization. Siloing—of datasets, of teams, of brands—severely diminishes the ROI of data analytics, meaning marketers must lay out a clear project plan at the outset of every data-driven endeavor. Ensuring that everyone knows which stakeholder is accountable for facilitating access to which datasets not only increases organizational transparency and collaboration, but also improves the efficiency—and effectiveness—of the entire operation.
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.
Ultimately, 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.
Network
