Clearing up the Misleading and Multi-faceted World of Leads To fully explain a qualified lead, let's first talk about leads in general. A lead is someone who has provided their contact information and expressed interest in your company or content.
Every couple of years, a hot, new trend bursts its way into the collective lexicon of the B2B marketer. In recent years, predictive marketing and ABM took center stage as marketers looked to hone in on the data that underpins just about everything they do and activate it in a way that felt more purposeful.
While ABM continues along its maturity curve, it’s no longer the topic du jour, and predictive, at least as a stand-alone product, seems to have mostly gone away.
While the terms “AI” and “machine learning” continue to provide the undercurrent for conversations around all things digital, it’s the customer data platform (CDP) that’s seeing its moment in the sun right now.
In an attempt to wrangle disparate data roots into a single, centralized customer view, the CDP has risen as the “must-have” piece of tech for 2019/2020 and with good reason. With most organizations choosing to go with the technologies that best suit their given needs instead of buying into the all-in-one cloud platforms (Oracle, Adobe, Salesforce, etc.), customer data for even a mid-sized organization can be scattered across the enterprise.
Instead of bouncing around between systems and departments, the CDP promises to deliver actionable customer insights from across the entire tech stack so everyone in the organization can leverage that customer data to produce revenue. Beyond enticing, the value proposition is necessary for our current technology and data-driven marketing environment.
There is, however, one major problem with a CDP’s ability to deliver on its promise: garbage in, garbage out…and the “garbage” I’m talking about is the data.
Research conducted by Salesforce shows, “The number of data sources used by enterprise marketing departments is growing each year, and it’s growing fast. Research shows a 25% annual growth rate for significant data sources, but others are much smaller. If you look at this 25% growth rate and think ahead ten years, you’re going to go from 15 to 40 data sources.” Of course, more sources mean more data and, as the saying [kinda] goes, “mo data, mo problems.”
If you’re not putting tools in place to deal with your data problems in the first place then, you’re going to end up with yet another tool in your stack that’s going to perform sub-optimally. The tools you need to ensure proper data quality include both prevention and cleansing.
According to the Customer Data Platform Institute, “…if you’re struggling with a variety of different data sources, a CDP is where you’ll want to gather your data at one source, but before you do this, there is one important step you should take: Clean your data.”
Further, in writing about two of the major themes he sees in tech stacks today, Scott Brinker had this to say: “Yet even if we recognize the importance of managing omnichannel data, decisions, and content at a more foundational level, getting there still means deploying new technologies for which the enterprise may not yet be ready. Take CDPs — a very hot space — but it turns out unifying customer data is very hard, and it takes a lot of prep work to extend beyond an initial CDP pilot.“
Like me, I’m guessing you’re sensing a trend. The implementation of a CDP can be exceptionally beneficial, but if you’re looking for a quick-fix, you’re going to be disappointed.
Investing in a CDP means investing in a wholesale change in the way you deal with your customer data, which also means that you’re going to need to make some initial, up-front investments in both cleansing solutions and processes to be successful. This also means you’re going to need to set internal expectations around the time and expense surrounding the implementation process lest you end up with multiple stakeholders breathing down your back, looking for results that are some months off.
Finally, while the cleansing process is critically important, you’ll need to stop as much bad data as possible from ever entering your system if you want to ensure that your CDP continues to operate at its best. Not doing so is essentially like buying a high-performance sports car then running it with raw, unfiltered fuel. Sure, it’ll run for a while but, before long, you’ll end up with an engine that, over time, runs worse and worse until it – once again – needs a complete overhaul.
Essentially, when your company decides to invest in a CDP, it’s investing in a schema change surrounding the way data is captured, processed, and interpreted. As anyone who’s ever committed to getting into shape after an extended period of letting themselves go will tell you, a significant change is not easy, fast, or fun, but, in the end, it’s worth the effort.
Know what you’re getting into ahead of time, invest in the tools necessary for success, and commit to the process, and you’ll likely come out the other end of things with rock-hard data abs, ready to take on the world!
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