5 Ways to Better Leverage Your Data
Assuming you have already made some significant investments in martech over the past few years, it’s likely you are sitting on a goldmine of data. In today’s blog, we’ll take a look at five ways you can leverage the data you already have so that you can direct your burgeoning budget toward bigger and better payoffs.
#1: Make a List of All the Tech You Use to Capture Data
According to Think With Google, the overwhelming majority of leading marketers (72 percent) are more likely to invest in the quality and volume of the data they capture, a.k.a. first-party data. In order to make the most of what you’ve got, you actually need to know what you’ve got. Make a list of all the tech in your martech stack. Be sure to include major systems as well as smaller apps and tools that perform individual functions. Hold onto your martech master list, and leave room at the bottom of the list for future additions.
#2: Get Real About How You Use Your Tech
Gaining a firmer grip on the data you collect and use to make decisions may seem a little basic, but trust us; it’s good to conduct a martech stack inventory on at least an annual basis. As you consider each piece in your stack, answer the following questions:
- Why did we originally invest in this tech? Be honest. Were you swayed by bells and whistles and if so, do you actually use the bells and whistles?
- If you bought a piece of martech because you believed it would help you achieve a specific marketing goal, did you achieve that goal? Why or why not?
- Are there capabilities this tech has that you want to use, but never have? Why?
- Are there integrations that would make your tech work better with other systems?
Don’t forget to ask the folks on your marketing ops team, who use these tools on a daily basis, to provide their input on whether or not the tech they use is helping them achieve goals or providing them with the capabilities they need.
#3: Table Your Marketing Strategy and Align It With Your Martech Capabilities
If you like visualizing information by making tables (and who doesn’t?), here’s a simple exercise that will help you see gaps in your data collection and goals. Make three columns. At the top of your first column write Marketing Strategies, andthen list your long range goals such as: eliminate data silos, reach our total addressable market (TAM), create a more cohesive customer experience (CX), etc. At the top of the second column write the word Tactics. Underneath it, list all of the actions you plan to take to accomplish each long-range goal. For example, for the strategic goal of reaching your TAM, an action you would need to take would be to calculate your TAM. Finally in the third column, write the word Tools. Here, you will list the various martech tools in your stack that you will use to execute each of your tactics and strategies. Creating a table is one of the best ways to keep you and your team focused about what it is you are trying to achieve and what you need to get things done.
#4: Examine the Types of Data You Are Collecting
Now that you have a solid idea of how you plan to achieve your marketing goals and the tools you need to do the job, let’s look again at the list of the martech master list we created in step one. According to Deloitte Digital, the average business has 17 unique technology applications housing customer data. Whether you have more or less, it’s important to note the types of data each tool is collecting. We recommend writing this information down and then asking yourself the following questions:
- Do we have the data we need?
- Can we collect the data we don’t have, but want?
- Do other departments have data we need, and are they willing to share?
- Do we need to supplement our data by purchasing third-party data?
#5: Consider the Data Quality Measures You Have In Place
Data is constantly changing. Customers get promoted, change offices and move on to new companies every day. To account for all this change, you need to make perpetually clean, complete and standardized data a top priority. This ensures your data remains valuable and ready for use. A few data quality measures we recommend include:
- Connecting Data Sources — Integrating data sources across your stack is a smart way to enhance data quality across your enterprise.
- Centralizing Data Access – When there is a single source of truth, everyone on your team is better able to see the story your data is telling, react to trends and opportunities in a timely manner, and address issues before they become problems.
- Establishing Data Management Processes —Just as integrated data allows you to see the whole story, setting processes in place to keep your data perpetually clean ensures that you get the story right.