Between 2020 and 2021, B2B employees made a massive shift to remote work (blowing up IP recognition software). Companies shifted their existing events online (without realizing that what works in person doesn’t translate well to an online forum). And everyone moved more of their budget to digital because that’s where their buyers were (making paid advertising more competitive and less cost-effective).
With the market uncertainty introduced by a global pandemic and social unrest, businesses continue to exercise more caution when it comes to spending as we see the same trends.
There are hidden costs of bad marketing data due to many factors, but what are the true costs? Essentially exercising more caution when it comes to spending isn’t a bad thing… as long as these same businesses realize they need to invest in a robust marketing analytic infrastructure.
The Effects of Bad Marketing Data
Decreased Efficiency
According to an article by Digital Commerce 360, marketers estimate they waste 21% of their budget. This waste manifests in various forms: inefficient spending, inaccurate targeting, customer loss, reduced productivity, and flawed performance reporting. These factors not only drain marketing budgets but also contribute to a reported revenue loss of up to 20%.
Marketing often seems intuitive, and that’s exactly what trips up many of us.
It’s easy to fall into the trap of assuming that the content we enjoy is what our audience wants, leading us to produce more of the same. Similarly, when we have positive interactions with a particular demographic, we might hastily conclude that they represent our entire target audience.
Unfortunately, assumptions unchecked by data are dangerous things.
Data-driven decision-making is crucial in marketing. While experimentation is necessary, it should be approached scientifically with continuous measurement and evaluation. Marketing’s impact on the entire business funnel means that errors can have far-reaching consequences (for an outline of how to measure your campaign effectiveness as you go, check out this podcast episode).
Poor data quality can significantly affect a company’s bottom line. According to industry reports, businesses may lose up to 20% of revenue and incur 20-30% of operating expenses due to bad data. Additionally, improper handling of personal data in marketing can lead to costly litigation, potentially damaging the career of marketing operations leaders.
To mitigate these risks, marketing organizations must implement robust data collection practices, effective tracking systems, and adhere to mass email regulations. This approach not only protects the company but also ensures more effective and responsible marketing strategies.
Impulsive Purchases
One of the classic mistakes I’ve seen play out over and over throughout my career is the following:
A marketer hears of a new tool that worked well for a colleague. They don’t want to bring in marketing operations because they’ll have to stand in line for a tool review, marketing ops will ask a bunch of questions, and then it will take forever to set everything up before they can use it. The result of purchasing without involving marketing operations is a new tool that doesn’t properly integrate with the marketing automation platform or do half the things the vendor swore it does, which means there’s a lag between when the activity occurs and when it goes in the system of record. This usually means that opportunities aren’t correctly associated with the action. We have no idea whether or not the new tool brought in more pipeline, which brings the purchase under scrutiny by the finance team, leaving marketing operations to deal with the fallout.
This example accounts for a great deal of the waste expressed in the article mentioned above. We have a tool that isn’t properly integrated (inefficient marketing spend and inaccurate targeting), which creates extra cycles for marketing ops (reduced productivity) and leaves us without a way to tie back the tactic to pipeline generation (inaccurate marketing performance).
The moral of the story is that marketers shouldn’t buy tools without marketing operations or revenue operations buy-in because it doesn’t matter how many cool stats you can report on in the tool if you can’t tie back your efforts to pipeline and revenue.
Time Inefficiencies
Marketing teams often spend significant time—up to two weeks per month—manually cleaning and integrating data from various sources like CSV files and Excel spreadsheets. This time-consuming process is necessary to meet executive reporting requirements and gauge program performance. However, implementing a robust analytics infrastructure can automate these data management tasks, including deduplication, standardization, and unification. This automation frees up valuable time for marketing analysts and operations teams. Instead of struggling with manual data integration, they can focus on extracting meaningful insights to enhance marketing efficiency and effectiveness.
Don’t just take our word for it. MIT Sloan states employees waste 50% of their time coping with mundane data quality tasks, and CrowdFlower states data scientists spend 60 percent of their time cleaning and organizing data.
Marketing organizations that force their personnel to spend 50% of their time in spreadsheets don’t realize the real benefit of having an analyst on the team. The biggest value an analyst provides is their ability to look at patterns and understand what is causing things to go well or not perform as expected. With a reliable infrastructure, they can stop wrestling with bad data and focus on maximizing the organization’s pipeline and revenue.
Human Cost
Imagine the frustration of being told that you have to prove you’re doing your job well but not having the proper tools to measure your impact. Then imagine spending 50% of your time proving your value by wrestling with CSV files and Excel to try to get the metrics that are being requested. Then imagine having very little time left over to do the kind of things that made you excited to get into marketing analytics or operations.
A reliable marketing analytics infrastructure presents key business indicators that the business regularly demands. The system runs passively in the background, collecting and cleansing data, and processing complex algorithms. This means your key players get 50% of their time back to do the things they love about their job—like making a more meaningful impact on pipeline and revenue. When marketers can easily identify what is and is not working, they can amplify the tactics that work and discontinue campaigns that aren’t producing.
Analytics competency is about more than reducing waste and increasing business productivity (although these things are vital to any business). A low-effort, high-value analytics infrastructure improves morale by reducing the amount of time people are forced to justify their work and decreasing the sense that the company undervalues them.
The leading cause of turnover is poor management. If your marketing leader does not value analytics and can’t (or won’t) effectively argue for the budget necessary for marketing analysts and marketing operations professionals to do their job, those employees will be four times more likely to look for a new job. A whopping 79% of people who quit their jobs cited being underappreciated as the main reason for leaving.
Don’t stop short when considering the true impact of a talented marketing professional walking out the door. The average employee exit costs 33% of their annual salary between a lapse in coverage due to the difficulty of finding a skilled professional and the amount of time needed for a new person to ramp up in their position.
