Imagine that your favorite hobby is maintaining a sparkling clean kitchen floor. You sweep, you mop, you run your robot vacuum, you deal with your dog’s muddy paw prints before they even start to dry. You could quite literally eat off your kitchen floor.
But what about that small patch of the floor under your refrigerator? Is it full of crumbs and dog hair? Is there a small leak that caused mold to build up? When’s the last time you thought about that part of your kitchen floor? Under your fridge lies an inconvenient truth.
There are many inconvenient truths about life sciences sales and marketing. Including this: Your data is likely not as clean as you think. Despite best efforts and good intentions, marketing and sales teams have data issues that are silently wreaking havoc on their ability to understand buyer’s journeys, engage their prospects, and, ultimately, close more business.
Solving this data problem is not as simple as pulling out the metaphorical fridge a few times a year and doing a deep clean. This is a problem that can only be temporarily alleviated if you first acknowledge it exists and then give it the time and respect it deserves.
Knowable Problem #1: Demographic Data
There are a few categories of “data problems” that are knowable and, for the most part, fixable.
Demand Gen Report does a nice job of summarizing these knowable data issues in the “2020 State Of Database Quality & Accuracy.” For example, when asked about the top challenges faced when maintaining data quality in their contact database, 78% of marketers said “data is old/outdated.”
This should not come as a surprise. Turnover among decision-makers is a nightmare for sales and marketing teams. Life Science Connect believes turnover is at least 25% per year in this industry. That means every four years, your entire database of names needs to be refreshed.
And that’s assuming the data was accurate in the first place. How many times have you been guilty of providing a fake phone number when you have to fill out a form? It’s OK to admit it – we’ve all done it in hopes we won’t be harassed by an overeager salesperson who follows up with our request to download content.
Life sciences marketers should apply a healthy dose of skepticism to all data, including what they get from third-party lead gen providers and media partners. We’ve seen a cringeworthy example of a pharma contact who had been deceased for a decade but was still sent as a “lead” from a paid media program. Regardless of the data source, make sure it’s clean before, during, and after it enters your database.
Fortunately, combating inaccurate demographic data is possible because this is a knowable, solvable problem. There are software solutions, internal processes, and outsourced labor available to verify that your contacts are still actively working at the prospect companies you care about.
Unfortunately, most marketers (and their third-party lead gen partners) don’t have the time or resources to ensure their databases are accurate. It’s not that they don’t want to pull out the refrigerator to clean underneath it. They just don’t have the time to do it nor the budget to afford the mop.
Knowable Problem #2: Firmographic Data
The first two categories – demographic and firmographic data – share a troublesome connection: They are point-in-time based.
When a contact enters your database, his or her information is based on where that contact is at that point in time. When that contact moves companies or changes roles within a company, the data quickly becomes inaccurate.
The same is true for firmographic data. Imagine a VP at an emerging biotech company downloads your white paper and gets put on your marketing list. That biotech company might be 10 employees with only a seed round of funding when your CRM or marketing automation tool first learns about them. What happens a year later when they have raised a Series B, have grown to 150 employees, and have a drug in stage two clinical trials acting like a rocket ship ready to blast off?
Do you have a process to update that company’s information? And when that VP leaves to start her own biotech, will her demographic and firmographic data be updated?
Having inaccurate firmographic data is only one obstacle. Not having enough data is the other. Consider what you need to know about a prospect to ensure the right fit. First, there are basic data fields like company size and location. Beyond that, each industry and niche will have its own set of additional data. For example, if you are selling to biopharmaceutical companies, do you have a way to append your data to know what therapeutic focus, route of administration, and molecule type those prospect companies specialize in?
It’s a tedious, time-consuming process to update that data before a lead enters your funnel. Will someone on your team look up each new lead online and update that data? Do you have a third-party data appending tool that you trust to do that for you?
When it comes to cleaning firmographic data, it’s much easier to do a quick, cursory sweep of the kitchen floor than to maneuver that refrigerator out of the corner to do a deep clean.
The Data Problem You Didn’t Know You Had: Behavioral Data
A third category of data problems is where marketing and sales teams are losing the most opportunity: Behavioral data. Most marketing thought leaders, consultants, and tech companies aren’t accurately describing this problem. They describe “behavioral data” and “intent data” as the next frontier for B2B marketers while applauding advances made in identifying previously unidentifiable buying activity.
That activity – the behavioral data life sciences marketers covet – is defined by things like opens, clicks, downloads, and website visits. Here’s the problem with that activity: So much of that data isn’t human behavior.
Those clicks showing up in your weekly lead reports and those charts on your Google Analytics dashboard are often spiders, bots, crawlers, spam filters, antivirus tools, and email client settings, all polluting your marketing data with misleading, robotic activity.
Here’s just one example of how this problem manifests:
Acme Biotech is an ideal prospect for you, so you build a targeted list to send a nurture campaign to.
Acme Biotech has an email plugin installed to weed out spam or potential viruses. This tool “clicks” on every email you send.
You then see that Acme Biotech has opened and clicked on emails in your nurture series.
You excitedly pass this information along to your sales team as a sure sign that Acme Biotech is interested in your product/service.
Your sales team grows frustrated when they can’t get a response from anyone at Acme Biotech. That’s because no one from Acme Biotech even actually read your email – the spam filtering software their IT department installed did instead.
That’s just one example. There are dozens of other cases where robotic activity sends sales and marketing teams down the wrong path. This is especially true if you are trying to reach big pharma, where IT systems and email filtering tools are especially sophisticated.
Think of this like a trade show attendee who has zero interest in your product/service but who scans his badge at your booth on his way to lunch just so he can be entered to win the AirPods you’re raffling off. In both cases, a real person does exist, but that person didn’t actually do anything to indicate intent.
Most life sciences marketers generally believe data is “clean” if it is accurate from a demographic and firmographic standpoint. There is widespread acceptance that Google Analytics data is also clean. And that the reports our martech tools generate for us are clean, too. But buyer behavioral data is just not as clean as marketers think it is.
Marketing automation vendors, lead gen services, and media companies are reluctant to admit the size of this problem. In fact, most are not even talking about this robotic activity. They can’t mechanize a solution, so instead, they sweep it under the fridge while they frame the conversation as one that only focuses on demographic and firmographic data challenges.
Next Steps: A Data Cleaning Chore List
“Many sales and marketing teams are still looking for that silver bullet,” says Michael Bird, Dun & Bradstreet’s president of sales and marketing, in Demand Gen’s “2020 State Of Database Quality & Accuracy” report. “They hire more people, run more campaigns or add to their martech stacks — only to find growth elusive. They haven’t succeeded because their focus has been on the tactic rather than the data driving it.”
Marketers can try to outmuscle the competition with tactics. That’s very much a short-term solution. The only way to truly outsmart your competitors in the long run is to focus on the quality of your data.
Start by stepping outside of the day-to-day tactical nature of marketing. Your data cleaning chore list might include these next steps:
Talk to your partners. What systems and processes do they have to ensure the data you receive is accurate? How can you collaborate to clean and append the data to make it effective for you? Ask for specifics and do so with that healthy dose of skepticism.
Educate yourself. Do you fully understand the sources of data you’re using to guide sales and marketing decisions? Do you know what processes are in place to clean your current data?
Be introspective. Build a list of what data you collect and from whom it’s collected. Where are there vulnerabilities? Ask yourself, “What can I do to validate this?”
Get your hands dirty. Pull a random sample of your current marketing database. Roll up your sleeves and look up those contacts on LinkedIn. Are they still at that company? Are their titles accurate? Is their company still a fit? Run this drill for 100 contacts and analyze the results. Don’t be afraid to look under the fridge. The sooner you see what is lurking underneath, the sooner you can start cleaning it up.
The next article in this series on data challenges will dive deeper into the history, causes, and future solutions to combating robotic activity.
For now, know that these data challenges don’t have a permanent fix. Keeping your kitchen floor – and your marketing database – clean will continue to be an ongoing effort.
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