5 Missteps To Avoid While Managing B2B Data

Big Data overload is arguably one of the greatest challenges facing B2B marketers today. We’re saturated with so much data, that it becomes gradually harder and harder to distinguish the qualified leads and accounts from the junk. That can make demand generation feel like an impossible challenge; how can you keep up with demand from Sales if you don’t even have control of your own database?

This challenge may be partially a technological one — as illustrated by an industry survey of leading B2B marketers. But on a more fundamental level there are 5 missteps in particular which, if avoided, can save you a lot of grief:

  1. You aren’t regularly enriching/refreshing your databases

Remember, the leads and accounts you’re working with aren’t abstract pieces of data — they represent real people and organizations who are evolving and changing all the time.

On the individual level, in particular, B2B professionals just can’t sit still. Think of how many people in your LinkedIn network alone change position or company each quarter. Now consider that multiplied by all the B2B professionals in the world who get promoted, change responsibilities, retire, move companies, move to different industries, lose their jobs, or even, tragically, pass away.

The same is also true, if to a slightly lesser degree, on the account level. Companies grow, flit in and out of buying cycles, get acquired, go through tough patches or even fail altogether.

If your marketing organization isn’t keeping up with the constantly evolving state of your data by regularly refreshing its databases, the likelihood is your data is degenerating as we speak.

We’ve seen plenty of customers whose greatest source of tension between sales and marketing lies in obsolete marketing data. In some cases it can cause marketing to field ostensibly “qualified” leads to sales, which ultimately turn out to be totally unqualified; in other cases, marketing becomes increasingly paralyzed by the sheer volume of “bad” or out-of-date data.

Read More: Radius Set to Revolutionize B2B Data Commerce With an Unlimited Access to The Network of Record

  1. You’re focusing on generic criteria like “job title”

Very often, companies with “too little” data will turn to third-party data vendors to fill their pipeline with net-new leads and/or accounts.

Sometimes — particularly if you’ve got your buyer profiles right — this can work really well.

But increasingly, we’re seeing companies burned by a chronic tendency — on both the vendor and customer sides — to focus on superficial datasets like “job title” when hunting for new prospects.

“What’s wrong with that,” you ask? Surely job titles form an integral part of any customer profile?

Well, yes and no.

Yes, looking for a specific job title can be an effective place to start targeting — but if you stop there or at similarly superficial insights, your data will still be a blunt object.

Your ideal customer profile may be a “CMO” in a particular industry for a good reason — but that doesn’t mean every CMO in that industry will be willing or able to buy. Do they have the budget? Are they even in an active buying cycle? Are their existing technologies compatible with your offering? Are they using a competitor already?

Then there’s the fact that a title — no matter how grandiose — often doesn’t give any indication of a lead’s actual responsibilities or buying power.

If you’re compiling your database on the basis of superficial insights, you could wake up to find yourself back to square one — drowning in a sea of surprisingly irrelevant data.

Read More: Radius Delivers Omnichannel Marketing to B2B Organizations with New Integrations

  1. You’re using multiple data sources

Finding a single source of truth for your data requirements is crucial to ensuring your databases remain coherent and usable.

But such solutions are hard to come by, and as a result, it’s very common for B2B marketing (and sales) organizations to be drawing their information on prospective customers from a wide range of sources.

You may be engaging two separate data vendors — one for leads, the other for accounts; or one for job titles and the other for social data; etc — while drawing on your CRM and Marketing Automation Platform, along with data painstakingly gleaned from manually trawling Google or social media profiles.

If so, you’re not alone by any means. But while juggling numerous data sources can deliver short-term wins, in the long-term it will only multiply your data woes with duplicate, often conflicting, data sets.

  1. You’re focused on leads or accounts — and forgetting the most important thing…

Leads vs. accounts is old hat. Today, particularly with the explosion in account-based marketing, most marketers focus on both without thinking twice.

But there’s an underlying significance to this paradigm shift many are missing. You aren’t marketing to accounts, or even “leads.” As we mentioned at the start, ultimately your marketing efforts are aimed at the people behind the data: your audiences.

That move towards a more holistic, audience-centric approach to marketing is starkly illustrated in SiriusDecision’s new Demand Unit Waterfall, which advises marketers to put aside a two-dimensional focus on leads or accounts, and instead combine the two — something SiriusDecision terms “Demand Units”.

Your “Demand Units” are essentially your target audiences: the small group of decision-makers inside your target accounts who you need to reach.

Rather than trawling the Big Data ocean for leads and accounts, marketers should be more targeted, fishing for prospects in a limited pool of highly-qualified audiences.

Read More: Eyeota Delivers Precision Targeting to B2B Marketers Through New Data Marketplace Integration

  1. All data, no intelligence

Finally, there’s no use having all that data if you can’t translate it into actionable intelligence to guide your campaigns.

To see through their fog of data, companies need to be investing in the right technologies. A Marketing Automation Platform and CRM may be the bread-and-butter of any effective B2B marketing organization, but that alone is not enough to build a truly successful demand generation strategy.

Analytics technologies are critical to understanding an otherwise indecipherable mass of big data B2B marketers are usually faced with. In particular, the rise of machine learning and Artificial Intelligence marketing tools offers real hope for marketers in this data-deluged world.

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