This year we saw a lot of change. B2B employees made a massive shift to remote working (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 are (making paid advertising more competitive and less cost-effective).
The biggest change I’ve seen in the landscape is likely to stay, and that’s a more intense focus on proving a return on each marketing investment. With the market uncertainty introduced by a global pandemic and social unrest, businesses exercise more caution when it comes to spending.
This isn’t a bad thing as long as these same businesses realize they need to invest in a marketing analytics infrastructure to get the answers they’re looking for.
In an article by Digital Commerce 360, marketers estimated that they waste 21% of their budget. This waste takes many forms, including inefficient marketing spend, inaccurate targeting, lost customers, reduced productivity, and inaccurate marketing performance reporting. In addition to draining marketing budgets, these factors contribute to a reported loss of up to 20% in revenue.
Marketing seems pretty intuitive, and that is precisely what gets a lot of us into trouble.
Many people assume that the content we like is what other people want, so we should just do more of the content we like. When we have positive experiences with a specific demographic, we assume that is our target demographic.
Unfortunately, assumptions unchecked by data are dangerous things.
Marketing does require us to throw things at the wall and see what sticks. Because what works is constantly evolving, this is understandable. However, we should approach these experiments scientifically. Measure as you go and circle back at the end to determine whether a tactic met your goals (for an outline of how to measure your campaign effectiveness as you go, check out this podcast episode).
Marketing is an essential function that touches all other parts of the business. This means what goes wrong at the beginning of the funnel balloons out to impact other parts of the business. Kissmetrics states businesses lose up to 20 percent of their revenue because of bad data, and Pragmaticworks states 20 to 30 percent of operating expenses are due to bad data.
One of the big risks carried by marketing organizations comes about because of the collection and storage of personal data (this goes beyond email data and extends to IP addresses). If marketing organizations do not have the proper data collection practices, tracking in place, and mass email rules, they open themselves up to expensive litigation—which can have a lasting negative impact on the head of marketing operation’s career.
The Costs of 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 end result of making a purchase 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.
We’re still hearing that marketers spend up to two weeks out of the month cleaning and tying together data using CSV files and Excel to meet the reporting expectations of the executive team and understand how their programs are performing. With the proper analytics infrastructure in place, deduplication, standardization, and unification occur in the background, giving those two weeks back to marketing analysts and marketing operations. This means that those team members are freed up to focus on uncovering insights that can help marketing run more efficiently instead of wasting time trying to connect disparate data sources manually.
Don’t just take our word for it. MIT Sloan states employees waste 50 percent 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.
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.
No wonder 81% of marketers want to leave their job in the next three years. Also, consider that remote-only work options cropping up has made changing jobs easier than ever.
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, 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 are able to 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 obviously 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 job cited being under appreciated 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.