CEOs don’t need to understand technology, but they better understand what technology can do for their business. Do you have data and technology to help you predict future business outcomes?
Data Driven Strategy
This series of articles focused on leveraging data to drive business strategy. That data goes far beyond typical market research or improving margins by a set percentage.
We emphasized data in two ways. First is the more common idea of translating strategic objectives into measurable goals. An example might be Improve customer retention by 6% this year by personalizing digital marketing. Defining measurable goals is a crucial step in translating strategy into operational terms that every employee can relate to.
The second way that we create a data driven strategy is by emphasizing the return on our data assets. Most businesses measure return on assets but most also forget that data is a critically important asset.
We described how data is the new gold and, in previous articles, gave examples of how businesses leverage data. We’ll wrap up the series here by digging a little deeper into how to leverage data to predict future business outcomes.
Predictive Analytics
A dear friend ran a company that built my first predictive model. She used to say that anyone can predict that you’ll sell more ice cream in July than in February, but how do they know how much pistachio versus strawberry or chocolate will be sold?
Every business in a given industry has similar data. But each business stores their data and links their systems differently. That means you can build a competitive advantage by analyzing your unique data set.
What are your biggest business challenges? Conversion, customer retention, inventory management? When I ran strategy for a large educational institution, we knew that only 50% of the students that enrolled actually came to school. Yet, we spent tens of millions of dollars trying to get those enrollees financial aid, housing, transportation, and jobs. We subscribed to census data and neighborhood credit information to marry with our own sales leads and built a predictive model. It proved to be 97% accurate in predicting which sales leads would become students in school. We were able to prioritize leads and reduce tens of millions of dollars of costs.
Other examples include predicting fraud, predicting retail buyer behavior (see ice cream example above), anticipating maintenance requirements to avoid breakdowns, or predicting illnesses in targeted populations.
If you think you can detect correlations between pieces of data, you may have an opportunity to predict business outcomes. In our sales lead example, we had a large spreadsheet that compared pairs of data elements such as proximity to campus and neighborhood credit scores. We found that students that showed up for school had certain detectable patterns in these data elements that were very different from the patterns of students that did not show. These simple correlations formed the hypotheses upon which the predictive model was built.
Opportunities
Think about some of your greatest business challenges. Consider what information you wish you had to solve those challenges. And ask your business process owners and IT folks to look for possible correlations in your data. You may be surprised with your findings.
Most businesses will outsource the development of predictive models. The firm you choose will help you analyze your data further and recommend data sources that you can add. And they’ll build and help you test the predictive model.
I speak with CEOs and business leaders all the time. It’s rare that we don’t discover an opportunity to leverage data to predict business outcomes. Where are your opportunities?
Conclusion
This series discussed how to build and benefit from a data driven strategy. We emphasized the role of data in determining the right strategic metrics, cascading metrics driven performance objectives through leadership and across every employee, and how to utilize your data assets to drive a competitive advantage.
Data is often a non-performing asset. We don’t expect the CEO to understand the detailed data in the company or exactly how to leverage it. But we do expect the CEO to challenge business leaders and staff to discover ways to measure and improve the return on data assets.
As always, if you are working on your strategy already, or want more information, email Emily Ford at Emily@WolffStrategy.com and she’ll be happy to set up a free 30-minute consultation with me.
Larry Wolff is the founder & CEO of Wolff Strategy Partners, a boutique consulting firm specializing in Enterprise Strategy Management, Digital Transformation, IT Leadership, and Executive Coaching. Larry has served as CEO, COO, CIO, Chief Digital Officer, and management consultant for public, private, international, and emerging growth companies. His specialties include corporate and IT strategic planning, technology led business transformation, business and IT turnarounds, merger integration and large-scale project rescues. His methodologies span industries and scale to companies of all sizes.
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