There’s a dirty secret lurking in the average B2B database. If you’re a demand-gen marketer, you may already know what it is, even if you don’t know why. A study by IDC showed more than 50 percent of leads in the average B2B contact database are obsolete. And when was the last time you heard a marketer say, “I love my database”? Forget conversions and nurture, if you’re marketing to people who aren’t there, you have nothing to measure. And no results.
The old strategies aren’t working
Marketers have tried to solve this problem by purchasing new lists, but many traditional data vendors face the same problem. B2B moves fast and static lists begin aging the moment they’re created. If you’re adding old, tired data to your already old, tired database, you’re only compounding the problem.
Enter predictive analytics
Predictive analytics, the process of using sophisticated algorithms to identify companies and people ready to buy what you sell, can help with scoring leads and sorting the wheat from the chaff. But predictive analytics are only as good as the data you point them at. An “A” lead is useless if she’s moved to a new company, changed jobs, or gotten a new phone number since the last time her contact record was updated.
Big data doesn’t solve the problem
Far from being a cure-all, the proliferation of data complicates the problem. There’s a reason you never hear a data scientist use the term “Big Data”—or a smart marketer. You don’t need big data.You need accurate information combined with insights about the contact to help you not only engage with him, but engage with the message he wants to hear when he’s ready to hear it.
Predictive analytics only work when they have accurate data to work with
So how do you get the benefit of features like machine learning and semantic understanding that form the basis of predictive predictive lead scoring and segmentation? Start by fixing your data problems.
In the Leadspace B2B Index, we’ve analyzed a sample of 43,000-plus B2B professionals. They’re a slippery lot. Every day, your target audience changes. Your only hope for keeping up with your prospects (and internal champions) is on-demand data, gathered in real time not just from static lists, but from the vital signals people and companies share on the web and social media.
Does account-based marketing solve the problem?
You can’t talk about B2B marketing these days without mentioning account-based marketing. ABM is a new way of applying proven marketing principles to force you to think about target accounts as holistic marketing challenges, not separate leads and contacts.
The B2B sales process is complicated, long and filled with multiple decision makers. ABM campaigns focus on engaging with all the influencers inside a company, and delivering messages tailored to each of them. The CMO at your prospect company has different challenges keeping her awake at night than the CIO and the VP of Sales. Your product or service may well solve all their problems, but they’ll never know it if you “batch and blast” an identical message to all of them.
But how does that scale? The easy answer is to target them with marketing automation. But in the words of Marketo and Engagio co-founder Jon Miller, the biggest roadblock to ABM is when CRMs separate leads from accounts. Consider the case of RingCentral.
An ABM success story, driven by predictive analytics and data
RingCentral has one of the most sophisticated demand-gen programs in one of the most competitive markets in B2B: business telecommunications in the cloud. They measure the time it takes to reply to a lead in seconds, not hours or days like most B2B businesses. When they wanted to apply an ABM approach, they found that more than 30 percent of their inbound leads were unusable because of missing or inaccurate data, and 20 percent couldn’t be routed automatically to the right account teams because they couldn’t match leads (people) to the accounts they worked for (companies).
They used Leadspace predictive applications to enrich their data, filling in gaps and ensuring accuracy, and Leadspace Lead-to-Account Matching to match people to accounts and route them to the right sales folks. The results were dramatic:
- 200,000 unmapped leads rescued
- 6x increase in lead-to-account match rate
- 3x increase in actionable leads
- 65 hours saved every quarter for every rep
Six steps to fixing your data problem
Whatever stage you’re in with B2B demand generation, whether you’re ready to try predictive or are just starting to identify the core issues sabotaging the success of your campaigns, here are some things you can do right away:
- Ask pointed questions of your data vendors – Where does their data come from? How many sources and how are they aggregated? How do they know it’s accurate? How often is it updated? If their answer sounds like marketing copy (biggest! best! trusted!), you may have your answer.
- Understand your lead scoring – If you’re scoring leads in your marketing automation platform, what criteria does it use? How does it actually work? Outside of behavior scoring, what other criteria are you using to evaluate and qualify?
- Perform a database refresh – This is a must; update both leads and contacts as well as accounts. This doesn’t mean adding more data, this means ensuring the data you have is up-to-date and accurate.
- Have a post-refresh plan – What happens to individuals who have moved companies? What do you do with leads that were disqualified, but now qualify? Good vendors will help you work through these scenarios.
- Call vendor references – Every B2B martech and data vendor has impressive testimonials. Call them. See if their use case is like yours, and if they’re still as happy as when they emailed their quote and LinkedIn profile picture.
- Take a hard look at ABM – There’s a reason everyone is talking about it. Done correctly, it works. Best of all, it’s easy to understand why it works. Just make sure you have your data house in order if you want to do it at scale.
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