3 Time-Wasting Missteps to Avoid When Collecting Data for Marketing Use
Being a “data-driven” marketer may make good cover letter material. But when it is actually true, it makes for incredible business gains. There are plenty of reasons for the gap between marketers’ ideals or promises and their actual everyday processes: Departments often work independently vs. holistically, access to disparate systems is limited, or (most often) data collection and reporting gets dumped for other priorities and projects.
While collecting data for marketing use can be a time-suck, there are common missteps marketers make that waste their precious time. Let’s address those time-wasters and provide some best practices for avoiding them.
1. Not Using Benchmarks to Set Goals
It’s impossible to look toward the future without evaluating the past, yet individuals and whole departments are guilty of setting goals without benchmarking data. Marketers can’t accurately know what should be improved or by how much without collecting information on previous performance. If increasing page views to a specific landing page is a goal, for example, it’s imperative to look at performance trends to establish a baseline for how much that number historically increases or decreases each year and what the goal should be for the next reporting period. Once a goal is determined, then specific campaigns can be evaluated for how they helped contribute or detract from the goal in order to optimize future marketing efforts.
2. Not Defining Standard Metrics Against Goals
Many of us are guilty of repeating the phrase, “Everything can and should be measured,” but the reality is that not only do marketers not have time to measure everything, but not everything needs a data point. The data pieces that should be collected are those that prove whether marketers and/or campaigns are hitting their goals or not. The misstep, however, is that often not just one person, department or region is responsible for a single goal. For example, if a company wants to improve website engagement, each person responsible for that goal will need to agree on the metrics that determine if the goal has been met or not. In this case, increases in time on-site and number of pages viewed may be used in conjunction with decreases to bounce and exit rates. Any number of scenarios exist, but the important takeaway is to define the metrics that indicate success with all stakeholders.
3. Not Automating Data Collection
One of the reasons data collection and use is the first to go when people get busy is because it’s often a multi-step process that involves different systems and a few Excel sheets. By automating data collection, it is more likely it won’t get scrapped. There are many ways to automate data collection, including analytics platforms where reports can be customized based upon the agreed metrics mentioned above. The problem, however, is that goals often span myriad channels. So while a preferred analytics system might work for website and advertising data, a social media platform would be needed for any goals related to that marketing tactic — and so on and so on.While there are sophisticated systems that can map and import data, artificial intelligence-based platforms are getting much better and more accessible to the everyday marketer. The use of a marketing automation platform, for example, can help individuals and companies not only automatically collect the data that matters to them, but also use it in marketing campaigns to test and optimize for even better performance. While data collection is the first step, it’s the use of that data in a way that both machine and marketer agree upon that makes it work to meet and exceed goals in real-time.
Working Harder and Smarter
Marketers struggle to collect and use data, often because their companies do not prioritize it. While executives want to see upward ticks across all areas, they set the tone about whether data-driven marketing is a priority or not. It starts at the top to not only allow marketers the time to strategize data for themselves and their department, but also allow them to invest in systems that can help them work harder in other areas by being data smart.