What Twinkies Can Teach Us about Customer Acquisition and Third-Party Consumer Data


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Garbage in, garbage out. Never has the old junk food adage been more relevant than in the world of big data that underlies today’s digital marketing.

If your customer acquisition efforts are falling flat, you may need to get a deeper look into the consumer data powering those efforts.

Does your consumer data deliver maximum business impact?
In a recent IDG study, organizations cited “Poor Data Quality” (23%) as the most important challenge to address. Even more revealing, on aggregate, 42% of respondents cited “Difficulty in Extracting Insights” to gain business intelligence as most important to them. Clearly, gleaning actionable insights from consumer data is a significant challenge for marketers today.

One of the biggest mistakes we see with consumer data usage in the customer acquisition process is in the area of purchase intent modeling.

Purchase intent models can provide tremendous impact on lead conversion, customer acquisition, and sales outcomes. But, marketers are often tripped up by reliance on the wrong third-party data or by avoiding third-party data entirely, out of concern (rightful we might add) that it isn’t as accurate or timely as a company’s own data.

The pitfalls of insufficient or irrelevant consumer data
Data mined from a marketer’s own domain can certainly provide valuable, accurate insight into consumer behavior. As you map a user’s journey across your site you can learn a lot about what interests them.

But, what many marketers fail to understand is that relying solely on first-party data is like navigating with only half a map. If all you have is visibility into your own website, you have only captured a small piece of the consumer purchase intent puzzle. By its nature, first party data lacks the scale and depth of third party data. You can’t really harness the full potential of your in-house consumer behavioral data without knowing what is going on beyond your walls.

However, turning to third-party data aggregation to complete the picture has its own risks and can lead to just as many dead ends. You are still only seeing a small portion of the consumer’s overall journey. You are not seeing the consumer’s interaction with your brand in the context of their complete purchasing journey.

Purchasing packaged audience data from other sources can provide a foundation of voluminous, rich data on consumers. These sources can provide significant value for an automated, high volume programmatic ad buy. But when it comes to identifying an in-market consumer, these data sources, packaged and labeled as Consumer Intent, fall flat because they are often intended for top of the funnel advertising buys, not down-funnel conversion. Interpreting these top-of-the-funnel signals as intent is a flawed premise that can lead to wasted marketing spend.

The problem with these sources and most available measures of intent is that they are arriving at a proxy or “predictor” of consumer intent that may be based on inaccurate assumptions or insufficient data.

Extrapolating intent from the wrong data leaves too much room for error
Marketers need to look closely at what ingredients are packaged up as consumer intent and how the data provider collected the consumer data. Like highly processed foods, a long list of ingredients rarely means a healthy outcome.

For example, ‘automotive buyer’ or ‘in-market education’ data packages are rarely based on actual, recorded purchases. Instead, they are based on possibly relevant behavior like online searches that contain a lot of noise. Someone searching for a particular university may be an already enrolled college student checking out where their high school friends ended up. A person who repeatedly searches information on Porsches may be a big fan of the brand without the income to actually purchase one.

And adding volumes of consumer demographic and psychographic data into the mix in hopes of meaningful predictions does not necessarily improve sales outcomes. Data from third parties as diverse as credit reporting agencies and direct mail service providers can help build out buyer personas. But that data doesn’t necessarily lead to better sales outcomes if it is re-purposed and labeled as purchase intent.

How to identify the surest signals of purchase intent
A better foundation is witnessed intent that is based on the surest signals of intent – transactional data and modeled from online conversion data, which encompasses the entire online footprint of the consumer, including events along their journey, including those occurring on their own properties. Consumer purchase intent can be measured with much more precision by looking at it from the bottom of the funnel up – on people who actually converted.

Witnessed behavioral network data is becoming more readily available. This anonymized data tends to be better for determining intent because it’s not based on data re-purposed from its original, intended use or from demographic data alone. Instead, it is the end result of significant analysis on which consumers have converted and what behaviors those individuals exhibited to determine precisely what are true indicators of intent.

Witnessed online behavioral data enables marketers to more accurately target ‘act alike’ hand raisers who are likely to make a purchase. The resulting higher signal-to-noise ratio helps marketers grow conversions and lower CPAs.

Don’t get stuck extrapolating and guessing with ingredients that may look and taste like the real thing. Why not improve your chances by knowing definitively which of your prospects’ attributes signal purchase intent?


Ross Shanken
Ross Shanken is a digital technology business builder, thought leader, and patent holder who founded LeadiD to revolutionize the lead industry. LeadiD's consumer journey insight platform provides the highest-resolution view of the consumer buying journey and enables marketers to shorten the distance between data and decision making. Prior to LeadiD, Ross was one of the earliest employees of marketing intelligence leader TARGUSinfo and helped lead the company to a $650M exit to Neustar in 2011.


  1. Excellent article with an appropriate Twinkie analogy. Now more than ever, with so much data at our fingertips, it is important to clear the airwaves and find out which identifiers signal purchase intent.


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