Steps for getting started with data-driven marketing
May 10, 2019
No matter your business size or industry, doing more of what’s working and less of what’s not is a no-brainer. With data-driven marketing, you can not only uncover insights on your prospect and customer behaviors, but you can further leverage those insights to inform the strategies that impact the success of your business.
Check out these steps for getting started with data-driven marketing at your organization:
Step 1 - Define your goals
Setting your team up for success starts with defining the goals and outcomes you want to achieve. Whether you’re looking to increase the number of new customers acquired, increase website visits, or grow marketing-generated revenue, setting specific and measurable goals will help your team map out the strategies that will lead to a successful outcome.
When developing a plan for the entire team, select marketing channels in which actions can be attributed back to the goal when building out a strategy. Additionally, consider the available budget when mapping out the allocation of resources to help guide effective goal-related actions rather than inefficient activities. For example, a social media campaign creates tangible data points, while a billboard campaign has theoretical data points.
Step 2 - Identify the methods of tracking
Once the goals have been set, use them to outline the questions you need answers to. Questions like:
- “What should we measure?”
- “Do we have that data?”
- “If yes — how do we go about accessing it?”
- “How do we use that data as insightful information we take action with?”
This will help you determine the data you need to measure and track towards goal performance over the period of time you aim to achieve it.
Step 3 - Analysis, attribution, and A/B testing
With goals, metrics and tracking methods set, campaigns contributing to the overall goals can commence. As data gets collected from these various campaigns, your team(s) can begin to analyze the outcomes of their strategies in action.
During this time, the data may reveal an underperforming campaign that is not meeting the predetermined benchmark of success metrics. While removing the dead weight and continuing forward without this activity may seem like the most efficient, cost-effective option, allowing for time to assess the information may uncover variations to the campaign that will save you from needing to remove it at all.
Attribution assesses what’s driving results
Understanding which efforts are actually contributing to the overall goals is an important factor for any successful analytics-driven marketing initiative. To be sure that the methods you use to assign attribution align with the goals of the organization as a whole, keep in mind that:
- The attribution model(s) you use should be understood and easy to communicate by those using it.
- You should use multiple attribution methods only as needed.
- An attribution model is a way to set benchmarks and performance standards, and it is not how to track the outcome of every marketing dollar spent.
- If the model isn’t making the jobs of people who use it easier, it’s time to reassess.
Some of the different approaches to attribution include:
First Touch Attribution
First touch attribution credits all of the success from an achieved outcome to the first campaign it interacted with. However, this method only provides limited visibility into the other activities that may have affected the positive outcome, leaving your team with little information to strategize from when formulating new questions to ask of the data.Last touch attribution
Last touch attribution assigns all the credit of a successful outcome to the final campaign associated with it. While this method is easy to implement, especially when re-engaging with existing accounts in your database, the long-term effects of this method limit your ability attribute marketing efforts towards new leads in the funnel, which can cause bigger challenges down the road.Multi-touch attribution
Much like it sounds, multi-touch attribution spreads the credit across all campaigns. You can do this through linear distribution, which gives each campaign an equal cut of the credit from a successful outcome. Another way to do this is with weighted distribution, in which your teams can assign credit to the involved campaigns based on factors like time and campaign position as it relates to goal conversion.
By understanding the various factors contributing to the outcomes needed to achieve the set goals, your teams can begin to iterate on the tactics, tests, or campaigns themselves that are generating the best results. Proper attribution allows you to continue refining the questions you want answers to, leading to more specific measures of success that can be used to drive more positive campaign outcomes.
Test strategically
A/B testing are experiments based on differentiated campaign variables, which is why they are some of the most fun and most tricky parts of any analytics-driven marketing initiative. While they encourage creative and critical thinking, A/B tests must be developed with the overall learning goal in mind. From colors to wording, page flows, and heatmaps, developing and deploying A/B tests will help your teams generate better insights from variables in a given campaign.
Step 4 - Measure and share results
Whether your tests run for days, weeks, or an extended amount of time, assessing the results of these tests and the effect of your sample size on the results will allow your teams to measure the impact of their activities against the strategies tied to the overall goal. Sharing the results of these intentional marketing efforts with the entire organization will help educate and demonstrate the impact data-driven marketing initiatives can have on the business’s bottom line.
Go further with data-driven marketing
Download the Analytics-Driven Marketing for Action ebook for case-study examples of what marketing analytics in action looks like, or reach out to our teams to learn more about how Looker can help realize data-driven marketing at your organization.
