Spencer argues that CRM data quality matters less for perfection and more for correctly identifying who to exclude from campaigns. Clean email, LinkedIn, phone, and title fields let you target accurately. AI can automate qualification, but only if your rules are clear and your team trusts the logic behind your scores.
Chapters
Today we're going to talk about data in your CRM as well as for your campaigns. How clean does your data need to be before you actually run campaigns? Well, as clean as possible, really, because if you're going to strap things onto your data and run campaigns, well, it's not great to do that on top of dirty data. There are some really big problems, and I hate to be sitting down and doing marketing videos about it, but there are some really big problems about data quality in the marketing world. A lot of sales professionals know about it. They complain about bad quality leads. from marketing. Marketing says that they pass leads to sales. But what is the quality of the data that actually underpins them in the system? In revenue operations, everything that we do comes back to infrastructure and data and feedback loops at the end of the day. What my job is and what you guys should look for as far as underpinning data quality the right way is how much of it becomes disqualified. I don't know, that's a really weird place to start, but here's my opinion on the matter. If you are able to correctly disqualify people, Then you're correctly able to qualify people. It's like in life, it's easier to figure out what you don't want rather than figuring out what you do want. At least it is for me. In marketing, it's roughly the same story. You do know roughly what people do not want. They do not want to be solicited. They do not want to have an offer that's not calibrated to them. And they also don't want to just field random suggestions that don't make sense. Sales feel the same way most of the time. They don't want to have unqualified leads. So what actually makes a qualified lead, or as I like to put it, an SQL or an MQL. A lot of companies in the enterprise space, or as a lot of people know, and even in the PLG space, qualify leads differently. In B2B and B2C, they're completely different. But at the end of the day, it comes down to how clean your data is. As an example, what you can start to look at in the data world and how it affects marketing is disqualification criteria and qualification criteria as well. Inside of CRMs like HubSpot, Salesforce, you have qualification criteria. You also have lead scoring, and you also have qualification or signal-based criteria in today's modern world. Wow! I'm sitting here with an AI agent running on my computer that's qualifying some accounts for me. You may or may not be at that stage, but the problem is, is that that criteria needs to be formulaic, and it needs to be something called deterministic. If we can determine that our VP of Revenue Operations is the correct buyer, but they might not be the signatory. Then maybe we need to exclude all the signatories and go for the introduction into the VP of Revenue Operations. On the same side, if you're selling a legal product and you wanna introduce your product into attorneys, then you probably wanna go start to talk to the attorneys and probably not, you know, anybody else at that type of organization until they're ready to actually buy into the deal. So understanding how clean your data needs to be at the end of the day to be able to disqualify people and take them out of the targeting list is very, very important. The question that I get asked a lot is, how clean is clean? At the end of the day, we only have emails, LinkedIn accounts, phone numbers, first names, job titles, basically business card level information. Everything else is a speculation or it's sourced from online or it's sourced from third-party or first-party information sources through conversation, through events, through, you know, remembering everything, putting inside of the CRM. But the more data context that you have inside of your CRM, inherently the harder it is for you to actually keep track of it. This is where revenue operations actually comes into play and where marketing and sales can actually take advantage of that data, starting to say, yes, this is clean enough to run campaigns, but at the end of the day, they just want to know what not to include, or at least that's what they should be looking for, in my opinion. When we start to talk about how clean the data actually has to be is, is this data up to date? Is it accurate? And why is it accurate? No sales professional that I've ever worked with has ever said to me, yeah, I don't need a reasoning why this number is a score out of 0 out of 100. So parsing a data layer that separates the numeric value of a potential score of saying this account or this persona is scored this proper way out of 100, and here's the reasoning why, as a string value in the CRM brings a lot more value to a sales professional and a marketing professional than it might for a revenue operations person. Likewise, before you start sending campaigns on the marketing side of the field? Are they opted in to receive marketing communications? Have they clicked your ads? Are they at a target account that you would actually like to do business with? So it all depends on your qualification criteria, and it all depends on the quality of the data that underpins it. So to answer the long-form question of how clean does your data actually need to be before you start running marketing campaigns, the answer really comes down to it depends on what the product and the solution that you're selling is and how it affects your potential audience. If you need help with your revenue operations or understanding systematic builds, my name is Spencer, and you can always look me up on my with Nathan.
