Key performance indicators: quality over quantity
Key performance indicators (KPIs) have become woven into the fabric of most businesses and pushed into the psyches of our organizations. In an effort to better understand and manage our businesses, we’ve created more of them and in the process have diffused their impact and value. And although a few organizations have evolved and cleverly devised leading KPIs, the rest of us have lost sight of their purpose and construction.
The answer then is not to create more KPIs but to create better KPIs.
The majority of today’s KPIs are constructed from operational data like sales, inventory, churn, network performance, etc. Some corporate KPIs broaden the lens by integrating customer demographic (firmographic) and psychographic information. But with increasing frequency, organizations are looking to integrate environmental data like economic trends, weather, and health statistics into their KPIs.
The result is that the further one moves outside the walls of the enterprise the more robust the data becomes and the more predictable the results. Interestingly, most corporate strategy groups integrate market research data into their plans. This is logical, as market research includes customer and environmental information, but the data seldom make their way into corporate KPIs.
To use an advertising example, accepted response rates are associated with radio and television ads. For example, it’s easy to imagine Campbell Soup running television ads at specified times in specified locations. Purchasing 30-second spots is predicted on the demographic expected to be watching or listening at that certain time, which may revolve around type of programming or time of day. Imagine now the integration of weather data. Where is it cold, rainy, and blustery? What about where it’s hot and clear? The former sounds like a perfect day for soup. The latter, not so much. Dynamic data such as weather significantly increases the predictive power of response when combined with programming and time of day. The challenge then becomes identifying, acquiring, and integrating dynamic data.
Identification is easier than it might seem. Take a static concept like a map and add the element of time to it. When did the oil pass a certain marker in the pipeline? How long before a vaccine expires while in transit? When does a customer most often visit a store or buy a product or service? “Where” and “when” become interconnected, and the combination of the two provides greater insight, opportunity, and revenue potential.
Believe it or not, most organizations already have access to geospatial data. Of business data, 80 percent are location-based but haven’t been entered into their system as such – address data, asset data, customer data, sales data, inventory data, human resources data, supply chain data, etc. The information is there, but it needs to be geocoded to reveal the added insight. Databases and data warehouses are now all spatially enabled, which solves one problem. Then there’s all the unstructured location-oriented data that organizations collect from market research reports, Excel spreadsheets that track business performance, weather, customer behavior, and news events.
Additional datasets such as imagery and base maps are needed to visualize the data on a map. These are now reasonably priced, widely available, and integrated into visualization platforms. For example, aWhere, Tableau Software, Spatial Key, FortiusOne, and Space-Time insight are just a few companies offering solutions.
Most organizations develop their KPIs after identifying strategic objectives. Changing the way organizations develop KPIs and integrate location intelligence starts with the questions executives ask themselves when attempting to measure the success of those objectives.
Being aware of dynamic data is the first step in updating your measurement system. Identifying, acquiring and integrating dynamic data is the second, but it requires executives to partner with their marketing and IT departments to bring to the surface the needed data. Our advice is to start slow. Identify a couple of KPIs that could be improved with location data and evolve them. The risk of not getting on this moving train is great. Someone once told me that the best way to fail is not to try. That axiom is more true today than ever.
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