The following are seven key considerations -- a checklist of best practices, if you will -- to help marketers use their data to improve the performance of their media buys.
1. Understand and work with accurate and complete data sets -- whenever possible. Sometimes tracking all your media buys isn’t possible, particularly given the challenges of tracking offline media through online browser cookies. But this doesn’t mean that you should forfeit whatever offline findings exist just because you don’t have a holistic picture. Although complete sets of data are ideal, even slices of data can be synthesized with other data sources and used to uncover valuable insights.
2. Find a metric that works for you. While efficiency is a critical goal of any media buy, every marketer’s business objectives are unique. Find a metric that best correlates to measure the incremental value of your goal, even if that goal doesn’t appear actionable online.
For example, if the goal is to drive more sales, then it may be a good idea to monitor sales per thousand site visits, instead of using standard metrics like cost-per-click or cost-per-thousand impressions. Marketers understand metrics that best correlate to the performance lift of their campaigns, so figure out a way to use them. Even if you’re simply trying to get the most efficient conversion or reach the customer most likely to respond to your messaging, finding a metric that best correlates to incremental actions based on media spend can make all the difference.
3. Review the costs associated with third-party data. Enriching all first-party data with additional data can be extremely valuable. However, the efficiency this brings needs to outweigh the costs of acquiring the data. For example, perhaps the target audience that’s most likely to respond to a campaign is 18- to 34-year-olds who drive a Mercedes, shop at upscale boutiques and subscribe to The New York Times. While acquiring this exact data set is possible, combining these data points can be costly. It’s likely that a hyper-targeted campaign might lead to higher conversion rates, but it may not be more efficient than the standard run-of-network targeting.
4. Test the impact of every data analysis to continually verify your decisions. While testing constantly isn’t cost-effective, conducting small tests on all big decisions can keep marketers on the right track as you evolve your media buying strategies. For example, if your agency or data platform provider decides to add a new publisher to the mix because analytics show that serving these users impressions might yield a better conversion rate, it may be valuable to conduct A/B tests on the results to ensure that the new publisher really does add value.
5. Remember that performance comes in many forms. Leveraging data to optimize media buys creates opportunities to measure performance in different ways. With data, a marketer can drive a certain type of customer to convert, versus a straightforward conversion. For example, a telecom marketer can leverage data to drive a higher volume of conversions from customers with strong credit scores. This data allows marketers to evolve media-buying performance beyond mere conversion to conversion of a specific type of customer.
6. Be patient when analyzing the results of data-driven changes. Optimizing campaigns based on insights derived from data and analyzing results can take time. Evaluate how crucial the findings are to your business, determine the cost and time you are willing to spend to find those results, and give your campaign the chance to either prove or disprove the data.
7. Don’t forget that negative results are results. Everyone likes to see a positive lift when testing the validity of data-driven decisions. However, negative lift can be just as important. Identifying the inefficiencies in a campaign change and eliminating them is just as valuable as finding out what works.