5 Tips for Winning at Demand Generation with Predictive Applications

In this special guest feature, Kylee Hall of Leadspace provides her top 5 tips for winning at demand generation with predictive applications. Kylee is the Senior Director of Marketing at Leadspace. Previously, she lead product marketing for B2B analytics companies focusing on web, mobile and retail analytics platforms. Her experience spans from small start-ups to large enterprise companies including IBM. She holds a B.B.A. in Marketing from The College of William & Mary and an M.B.A. in Econometrics and Statistics and Marketing Management from the Booth School of Business at the University of Chicago.

We’ve all seen the statistics. Marketers with dollar signs on their backs are having a harder time than ever reaching buyers. Nobody is happy with their database or the ROI of their marketing automation. Generating high-quality leads is the number one problem keeping marketers awake at night, except maybe the fear of getting fired.

We have the same challenges at Leadspace, and are lucky to have some of the most sophisticated and successful B2B demand-generation marketers as customers. They’ve added predictive analytics to their marketing stack to solve these problems. They’re seeing measurable results: more effective outbound campaigns, improved lead conversions, better inbound lead routing and ultimately, more pipeline. We see the same benefits in our marketing. But the best predictive algorithms are only as good as the data they have to work with. Most marketers still rely on static databases that are essentially outdated the moment they’re created. Their ROI from predictive is limited from the start.

Fortunately, there are ways to vastly improve the success of your predictive marketing.

1. Understand the different types of “predictive”

Marketers use “predictive” applications in a wide variety of ways, from deciding what content your audience wants to read to identifying customers most likely to churn. The most common use case for B2B demand generation is predictive scoring, which uses historical data to understand the attributes of your best customers and create an “Ideal Customer Profile,” and use it to identify companies and individuals who will be your best prospects. Companies using predictive to build outbound campaigns, in our experience, see an average 40 percent increase in lead conversions.

Marketers also use predictive applications to build net new prospect lists by searching sources outside their own databases (private databases, the Web and social media) to discover new leads that match their Ideal Customer Profile. This is a proven way to augment existing lists or build new lists with a high likelihood of converting.

2. Get your CRM house in order

I’ve yet to meet a marketer who says their CRM database is perfect. But there’s more to it than the right phone numbers and email addresses. Try this: pick a top customer at random in your CRM. Is Big Important Company listed as Big Important Company, Big Important Company Inc. and Big Important Co.? Is all the relevant account data up-to-date and accurate? How many reps are shown as the account owner? How many still work for you?

RingCentral found up to 30 percent of their inbound leads had missing or inaccurate data that kept them from being automatically routed to the right sales team. Those leads were being ignored. They fixed the problem by enriching their database with up-to-date information and employing lead-to-account matching to make sure inbound leads from target prospects and existing customers were routed to the right sales team. The bottom-line result: a 230 percent increase in actionable inbound leads.

3. Know where your data comes from

Marketers who rely solely on their own database start with a major hurdle. One oft-quoted study from IDC suggests more than half the leads in the average B2B database are obsolete. Frankly, lots of marketers are using the same tired old lists over and over, hoping for better results.

Every data vendor will say their leads are accurate and their sources are the best. Ask questions. Where does the data come from? How often is it refreshed? Is it validated against external sources? Does it take into account a prospect’s social activity and information beyond the business card?

4. Make sure you have the right data

Are traditional attributes like job title enough to ensure your campaigns reach the actual decision maker? Or do you need to go deeper into attributes like job function? Suppose (like me) you’re trying to reach B2B demand generation professionals interested in predictive analytics. In a smaller company, that could be a marketing manager, the CMO or the Head of IT. In an enterprise company, the Senior Director of Demand Generation. If your campaigns are based solely on title, you’re ignoring key signals that predict who makes the buying decision.

5. Make data accuracy a priority

There are any number of ways to accomplish this, but, not surprisingly, I tend to favor working with a vendor who specializes in providing accurate data and helping companies update (“enrich”) the customer data already in their CRM and marketing automation databases. Marketers should be able to focus on engaging with customers, not managing their data.

One of the biggest reasons we see predictive strategies fail is not the software or tools, and certainly not the users—it’s the data. Accurate, actionable data is vital for successful B2B predictive efforts. By focusing on high-quality data, aggregated from multiple B2B-specific sources, and using the right predictive tools to help you enrich it, score it and derive actionable signals, B2B marketers can start winning at the predictive marketing game.

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