Director, Client Consulting
Lead scoring, the process and methodology of attributing a value to a lead based on a variety of factors and behaviors, is an essential aspect of B2B marketing today. Businesses have access to more data now than ever before, which should make lead scoring easy… right? Unfortunately, more data doesn’t always mean better data.
No matter the process used for scoring, the insight gained is only as good as the data used in the first place. Without good data, a lead’s score is just that: a number.
Recently, one of our clients shared with me the difficulties that come with getting “good data” to be used in lead scoring. “We aren’t great at cleansing data,” she said. “We don’t look within our database to double check who someone is – so if someone says they are a student, that is often taken at face value.”
Unfortunately, leads themselves can influence data collected via form fills on vendor sites in order to avoid being contacted, making lead scoring that much harder. For example, writing in “student” for a job title is one way to do so, knowing many companies won’t contact a student regarding their next enterprise technology purchase. Because of this, baseline demographic data, which is often first-party data, can’t be the end all be all. As a result, companies should be interested in “what you’ve done” just as much as “who you are”.
Enter behavioral data. Though someone can manipulate the job title entered when filling out a form, their behavior can be tracked, and that can be used for lead scoring. Knowing whether a lead is reading blog posts or viewing demos is certainly going to contribute to his or her score, and that is arguably more relevant than their job title anyway.
Still, behavioral data is subject to quality control as well. Though someone most likely isn’t manipulating their actions on a vendor’s website to game the system as they would entering their job title, the way that businesses score different actions can pose data quality issues.
In most cases, when a lead takes an action like opening an email, engaging with a certain type of content or visiting a specific web page, the score goes up. Higher scores often mean a lead is “more qualified,” or should be prioritized for follow up sooner than those with lower scores.
However, there is such thing as “over-scoring,” which can artificially inflate a lead’s score, and brings some behavioral data quality into question. For example, as mentioned above, email opens often contribute to a lead’s score. Though an email open definitely indicates a level of engagement with a vendor, it’s not the most reliable data point.
Think about it – how many times do you open an email just to delete it a second later? A better way to measure email engagement is email click-throughs or conversions, which should absolutely contribute to a lead’s score, as those metrics show real intent to engage.
In any case, utilizing behavioral data from your own website is definitely a step in the right direction when it comes to lead scoring. But what about what that lead is doing when they aren’t on your site?
According to CEB, buyers are spending 50 percent of their time with independent sources – much more time than they are spending with any one vendor. Luckily, there are many independent sources that are able to help vendors supplement behavioral data, like TechTarget. At TechTarget, we track every action our users are taking on our sites, from viewing our non-vendor aligned editorial content to downloading vendor assets and engaging with branded communities.
We look at activity data at the account level, as technology buys aren’t made by any one person, but by a buying team, consisting of a myriad of influences and decision makers. We also prioritize accounts by most active to least active around a specific topic in any given week.
This data, accessed through products like Priority Engine™, can be used for more powerful lead scoring. Data from outside sources can help to paint a much clearer picture of a lead or account’s overall online behavior, and can alleviate some of the data-quality issues vendors often face when relying solely on first-party data.
Overall, it takes multiple data sources for lead scoring to be truly effective, as data quality issues can pose problems in more ways than one. The key is getting as much of the “full picture” as possible by marrying information provided by leads themselves, first party behavioral data and data from sources outside your own ecosystem to effectively nurture leads and give your sales force the best opportunity at converting leads to customers.
What type of data do you use for lead scoring? Share your thoughts in the comment section below or feel free to connect with me on Twitter or LinkedIn.
1st party data, behavioral data, lead generation, lead scoring, marketing intelligence, Priority Engine, TechTarget
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