How to Conduct a Contact Data Analysis to Produce Better Leads

Jamie Shanks
Jamie Shanks
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Contact Data

Lead gen needs help, and contact data could hold the answers to a more accurate, effective approach. After all, right now, we’re working with a return rate of about 5-25 qualified leads per thousand generated, and, quite frankly, we can do better. We have the technology!

The problem, of course, is that we don’t know where to start. There are so many tweakable variables within the current lead gen model (content, distribution channels, targeting, landing pages, imagery, emails, etc.) that we live in a kind of analysis paralysis.

We tweak, and the number of leads goes up. We repeat, and it goes down.

But it doesn’t have to. What we need to do is take a step back and look at the one piece of data everyone has but no one looks at: Our current customers. They — and their contact data — hold insights that can help us refine our whole process.

Assuming you have a tool to keep contact data accurate, we can uncover highly relevant issues for eBook/whitepaper topics, get better, more granular insight into exactly which audiences we should target, more effective targeting strategies — the sky’s the limit.

And all from a few measly fields in a database you already have.

What you can learn from contact data fields

Every field in your contact database is filled with useful information.

Obviously, the [Name], [Phone] and [Email] fields provide personalization, delivery, and Lookalike (or Audience Expansion) options, but hopefully you’re already leveraging that to some degree. And while phone numbers can be useful for a location-based analysis (done through area code matching), unless you’re missing a large San Francisco or NYC population, it won’t be all that useful.

Instead, focus on the [Title] and [Company] fields. These are rich with data that lead in a variety of directions and can inform every step of your lead-gen process:

  • Sorting this data by [Title] offers insight on the most common roles of your customers. Armed with this, you can make informed decisions about who your most valuable targets are and can begin extrapolating about their pain points, the kinds of premium content they’d be most likely to engage in, and can use it to build stories from preference and trend data identified by Google Analytics.
  • Sorting the data by [Company] offers a breakdown of applicable industries, allows for identification ideal company types within those industries, and help to highlight which businesses should be your customers but aren’t.

But the insight doesn’t stop there. By combining the two and sorting in different ways, you uncover a variety of other insights including:

  • Common Roles + Common Industries, demonstrating where your messaging is the most impactful. By understanding this, you can make the necessary tweaks if you’re looking to reach into new industries, new customer bases, or both.
  • Common Industries + Current Companies, which show the direction of your message. This allows you to dive deeper into the impact, replicate results, and maximize gains on your highest performing channels.
  • Common Industries + Missing Companies, which allow you to develop channel and business-based hypotheses about the exceptions to your successes. These hypotheses typically lead to channel, messaging, and targeting innovations.

Putting it all together

Once you’ve considered these and explored all the trends relevant to your business, it’s time to get to the good stuff: Building new lead gen campaigns.

Here are the different areas you can impact with your new ideas:

Messaging: Because you now know exactly the kind of person that uses your product, you can tweak your lead gen campaigns accordingly.

Premium Content: Same as above. Now that you have a solid idea of who you’re trying to attract and how they can be attracted, you can re-evaluate the premium content you’re producing. You can produce more content toward those who are like the audience you currently have, or you can pivot to begin addressing roles/businesses that you think could benefit from your product but who haven’t largely been acquired through your messaging.

Distribution Channels: Because you now have a much more targeted approach to your ideal personas, a little bit of research in Google Analytics (and across Google at large) will give you a much better sense of how to deliver premium content to drum up leads.

Targeting: Based on the data you’ve received, you have the opportunity to run 2 types of targeted campaigns that are much more likely to convert than “sales people in tech companies”:

  1. Lookalikes of people most likely to become your customers (based on your role/industry analysis).
  2. Micro-targeted campaigns toward decision-makers who should be using you but aren’t.

For either, simply upload the appropriate emails as lookalike audiences in Facebook, Twitter, and/or LinkedIn, and you’ll ensure you’re delivering to exactly the people most likely to give your product a try.

Fuel all of your marketing decisions with data — learn how in our free eBook.


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