Lookalike Modeling: An Effective Tool in Your B2B Marketing Stack

Whether you realize it or not, chances are good that you have been a target of lookalike modeling in the past.

You log into Facebook or read an article on The New York Times only to find ads on the right-hand side of the screen that are a little too pinpoint accurate for something you might want to buy (but maybe did not even know existed).

No, that is not your phone listening to your private conversations or Facebook creeping on your private messages. Instead, what you are experiencing is an effective customer modeling technique known as propensity (or lookalike) modeling.

While the examples provided so far have focused on the B2C marketing world, lookalike modeling is one of those predictive marketing tools that play a critical role in the B2B marketing experience, too.

How Does Lookalike Modeling Work for B2B Marketers?

Firmographic and behavioral data plays the same role for B2B marketers as demographic data on the B2C side. B2B marketers leverage insights about customer activity (behavioral data) and key firmographic details (like company size, location, industry, and annual revenue) to inform three key campaign decisions:

The Right Channel

What is the best possible avenue for your business to reach your target audience? Predictive analytics tools evaluate the right channel, whether it be display ads, social media, email, or some combination of the three, to get your content in front of your prospects.

The Right Time

Timing is about more than just figuring out the optimal day of the week for sending emails (though that is part of it). B2B marketers also need to consider the timing of campaign launches and successive touch points. Prospects at the top of the sales funnel should likely receive fewer emails, where those closest to a buying decision may get more of a “full-court press” of marketing activity.

The Right Content

Consider the type of content that works best for your audience (video? Webinar? Blog post?) as well as the subject of your content. Again, top-of-the-funnel leads likely need to see content with a soft positioning of your solution, whereas those closer to making a buying decision may see content with more direct offers and competitive analysis.

Of course, all three components rely on one key factor for accuracy, namely, quality data.

The Most Important Piece of Lookalike Modeling is Clean, Quality Data

Your company likely has access to a wealth of data about your customers. From firmographic information to behavioral activity and even psychological data, there is a tremendous amount of information out there about your target audience. If you are not using a good data management platform to collect that information, unify it on a single platform, scrub it clean of inaccurate or duplicative data points, and enrich it with additional information, you are likely missing out on critical insights that could have a significant impact on lookalike modeling.

Predictive marketing tools Lookalike modeling relies on clean, quality data to operate properly.

Those missed insights end up having a more significant impact than just missed opportunities; they can also significantly customer relationships. The beauty of lookalike marketing is that, for the most part, the customer really only sees content that is of genuine interest to them. When marketers determine a customer’s propensity to buy based on bad data, insights will be off and the wrong customers may be targeted with the wrong content (or on the wrong channel, or at the wrong time).

What Happens When You Do Have Clean, Quality Data?

Marketers leveraging clean, quality data to determine target personas for lookalike modeling have found campaigns perform two to three times better. That means higher click-throughs, leading to higher conversion rates, leading to more sales and a better customer experience. Not bad for just one of the tools in your martech stack.

Lookalike modeling can help boost revenue in a few key ways outside of online lead generation.

Through Offline Channels

The days of direct mail may seem behind us, but in fact, there is a strong argument to make for including direct mail in your campaign strategy. Prior to big data, businesses had no idea whether the recipient of a mailer really met their demographic specifications. With the insights businesses can glean from site visitors today, putting together a targeted direct mail campaign can actually be effective.

In Upselling Customers

If your business knows a current customer who has added onto their existing contract, what is stopping your marketing team from identifying the defining characteristics of that client in other customers? In other words, what works for one may very well work for others.

In Reducing Churn

Similarly, customers who churn your product likely demonstrate similar behaviors. If your marketing team can identify those behaviors early on and position at-risk clients in remedial campaigns to boost product usage, churn numbers can drop dramatically.

Conclusion

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean, and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.

 

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