What Can Dr. Oz Teach Us About Data Quality?

dr-ozAs I was writing my last piece on what the MarTech Revolution can do for media partners and publishers, I was struck by something that seems second nature to those of us used to running consumer-based media campaigns, but which is often missing in the MarTech Revolution: optimization. We all know the old adage “garbage in/garbage out”—this remains true in marketing. Frequently I see demand-gen organizations invest great sums on Eloqua, Salesforce, Marketo, you name it, spend the engineering time and resources to tie everything together, and then just shovel a bunch of worthless data in the front end. All the money in the world won’t fix data or products that were no good from the start.

What does Dr. Oz have to do with marketing?

Which brings us to the title of this piece and Dr. Oz who was recently back in the news for a trip he took to DC and a run-in with Senator Claire McCaskill, who was once again calling him on the carpet for scamming consumers. Those of us in the space have seen his likeness (both licensed and not) used to hock weight-loss pills, cleanses, and any number of miracle diet pills. For years, it seemed as though if you simply added his likeness to a program it would buy instant consumer credibility.

To that end, hundreds of millions have been spent online on what is essentially garbage. Dr. Oz and Harpo sued numerous people over the years for unlicensed use of his brand and successfully got much of it stopped. He has, however, often praised the value of many of these types of supplements on his TV show, once calling green coffee extract “magic weight loss cure for every body type,” when in reality, as we all know, nothing replaces hard work when it comes to losing weight.

You must control what you put into your systems

The same is true of demand gen; one can spend hundreds of thousands, if not millions, of dollars on implementations of new technology to funnel and measure leads—and many do. What I see few marketers do (or do well) is control what they buy.

Data governance is something many people pay lip service to, but rarely actually implement, or at least implement well (especially in B2B). Even a rudimentary data governance plan will yield massive results. Looking at it in simple terms, if you block 15% of the garbage from entering your funnel, you have saved 15% of your operational costs, not to mention what that bad data can do to sales professionals’ time and morale. Implementing these systems—like implementing marketing automation—takes time and effort, and having digested large integrations, many companies choose not to undertake a second round of expenses. Yet, a simple set of controls and good internal data segmentation can achieve massive gains.

What exactly goes into this you might ask?

Three-step approach to improving data quality

At a bare minimum, start by selecting a system that allows you to tag data with multiple identifying variables. Even if you’re not controlling what comes in, the deeper and tighter that you can segment your marketing spend data in your database, the more you’ll be able to evaluate what you’ve purchased. From there you can roll the quality evaluations up the chain and optimize. If, however, you want to keep the noise from entering your sales funnel, then you have to optimize more quickly than that allowed from sales results. That starts with a three-pronged approach.

Front-end verification

This has been common place in the consumer space for years, and although less effective in B2B, it can still clean significant amounts of your data. Scrubbing for location, phone, job description, etc., and immediately pinging an email address for validity will be your first data points—these all need to check out or you should junk the data then and there.

Mid-purchase analysis 

Assuming your leads pass muster during the front-end scrub, then we move on to evaluating the data using third-party sources. I particularly like leveraging social media to verify the status, activity, and position of individuals. Looking at our own CMO Scott Vaughan, if a lead came through with his name on it and I found his Twitter and LinkedIn, profile I’d have all the data I needed to verify him as a prospect. I appreciate that this is unfortunately time-consuming, but it’s incredibly effective and should be used on new data sources as they are tested. As an alternative, if you’re not working at the enterprise level, there are tools that will allow you to build and measure the leads you’re buying in real-time against your own top customers. These are much less time consuming, but also less effective the larger the purchase size and purchasing organization become.

Post-purchase human verification 

Lead velocity is critical. If you’re sitting on something for weeks before this final evaluation of your data, you’ve pretty much destroyed the data at that point simply through stagnation. For purposes of this piece, I’m operating under the assumption that all of the marketing tech you’ve purchased is working properly and you’ve sped up your leads sufficiently to get them in the hands of a real person while still warm. (If not, take a quick look at Scott Vaughan’s post on how to address and improve lead velocity). The marketer doing this final optimization step doesn’t need to be a sales person; it can be a prequalification team that validates interest, position, company size, etc. This creates another touch point in the funnel and provides better, faster data than optimizing from sales results.

Tethering these approaches together will not only screen a lot of data at the front door, increase data quality and allow your teams to focus more time on better prospects, it will also give you the tools to truly optimize the entirety of your spend. Pricing and timing can be adjusted more aggressively with this amount of data, because the risk of killing a valuable media spend is very low. Conversely, you’ll be moving away from inefficient spends more decisively, which will make marketing’s impact on sales that much greater. All of these tactics will help marketers better attribute revenue to their demand generation strategies and efforts.

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