3 Steps to Data-Driven Marketing – and a Lesson from UPS

To create effective marketing in today’s environment, customer data is vital. Smart marketers know that. But many of us are so focused on delivering contextualized, personal marketing that we have not turned the tables and looked at what might be discovered from the data we generate while executing campaigns. And there’s a lot we can learn. To realize the full value of our marketing efforts, we need efficient, internal operations.
Optimizing internal operations doesn’t happen on-request, it is a cycle of continuous monitoring and improvement that requires collecting, analyzing, and using internal operational data:

  1. Collect important operational metrics. Data must be collected before any optimization can begin. Your company’s marketing automation system is poised to help transform your daily operations. Make sure that it is configured to capture and report on the metrics that matter to your organization. Some basic operational metrics that we recommend starting with include:
    • Duration
    • Iteration
    • Resource usage
    • Time to market
    • Team metrics
    • Role metrics
  2. Analyze your organizational data. Data doesn’t do any good on its own. Dedicate a person or team to extract the data from your software and analyze it. Slice and dice it in various ways to see if any important insights emerge. For example, don’t just compare time-to-market for each channel, include time-to-market for each product type, too. Data analysis or data visualization tools can really help in this step, especially when presenting the analysis back to marketers. Color and graphics will help people understand the data and notice new insights.
  3. Use the findings from your data analysis to make internal improvements. Using data in marketing operations means that operational decisions must be supported by data. Based on the data analysis from Step 2, experiment with various internal changes. The scientific method can prove valuable in establishing a cyclic system of formulating a hypothesis based on the data analysis completed above. It can also serve to test the hypothesis by making operational changes, and evaluating the results of the test by collecting and analyzing more data. One example of how this could work is to collect resource-use metrics. Analyzing the data seems to reveal uneven use or under-planned allocation among some resources. Re-allocate work load among these resources, collect more data and analyze again to see if usage is more even. If not, rearrange and try again.

UPS Takes Data on the Road
Many companies are engaged with internal metrics gathering. But some really stand out, like UPS—an excellent example of a company using internal data to make continuous improvements. By placing sensors in their delivery trucks, UPS is able to track drivers’ movements, which the drivers approve of because the sensors also inform them when maintenance is required. Analysts mine the data collected by the sensors in the trucks and compare them to data from the same sensors in other vehicles. This comparative analysis allows UPS data analysts to identify data patterns that indicate a truck is about to have a mechanical malfunction; they fix the problem before it leaves one of their drivers stuck by the side of the road with a broken-down truck.

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