Imagine the following conversation between a sales or marketing rep from your company, and a potential buyer:
“Hi there ____. This is some guy whose name you instantly forgot, from a company you’ve never heard of before.”
“You’re the Director of Human Resources, right?”
“Great! Well I’d like to talk to you about a product which you might be familiar with – or maybe not – which may or may not have anything to do with your job function.”
***Line goes dead***
Nobody actually pitches like this, of course (at least nobody employed). Engaging with a prospect effectively requires lots of prior research into who they are, what responsibilities they hold, where they sit in their company hierarchy, and of course insights into their company itself (or in reverse order for account-based marketing). Every lead generation professional knows that.
But it’s absurd to consider that if you were simply working off of your CRM or marketing automation system, the above conversation would often be pretty much the best you could offer.
The Data Dehumanization Dilemma
The problem is that traditional B2B databases – even the most sophisticated ones – have long since been outpaced by the scale of big data companies handle. Modern database management requires something more.
B2B sales and marketing today deal with humanly unprocessable volumes of data. While such tools are of course useful in managing that data to an extent, they have progressively evolved into something of a data dumping ground, with accurate leads lumped together with incomplete or inaccurate ones. That’s before considering that, as mentioned above, even the accurate, complete data in your system isn’t necessarily actionable.
The result is a dehumanization of your data, which means an uphill struggle to develop any kind of relationship with your leads, let alone convert them.
Who are all those people, beyond a handful of superficial data sets such as name, contact details (maybe), where they work and job title? And do you even know if that information is accurate? If it once was accurate, is it still now? Have they moved jobs since entering your database? Does their title really tell you anything about what they do, what their responsibilities are, or if they even have buying power? Given the propensity among B2B professionals for inventing creative yet meaningless job titles, that’s certainly not a given either.
So demand generation becomes a toss-up between casting a wide net and wasting sales’ time with lots of unqualified leads; or overloading your marketing and sales teams with long, tedious, manual work to identify individual qualified leads or accounts.
Not very efficient.
Humanize your data – it works better that way
The solution starts from the basics; it ultimately boils down to how you manage your database. Are you giving your data the proper attention? More specifically, are you relating to your data as people? After all, relationships – whether personal or professional – are always built between people. Even for account-based marketing, your actual point of engagement with the account will be the person or people involved in the buying decision. You still need your marketing to be human. And for that to happen, you need to humanize your data.
But where to begin?
To humanize your data, the most fundamental starting point is understanding the kind of granular information you actually need to engage with your leads. Leads are people, and people are complex – you can’t sum up a person with half a dozen data sets.
Humanizing your data requires rethinking your database management strategically. The only way you can truly know the people behind the data, is by drilling down into your leads and building a highly personalized picture of who they are, what they do and – most importantly – what their most pressing needs and pain points are likely to be. The more – and the more granular – the information you can get, the better,
To use the example of job title again: let’s say you’re looking for a head of HR. They could simply be called “Head of Human Resources,” but we all know that’s too simple for many B2B companies. So what are you looking for? Vice President of Human Resources? Director of Administration? Chief People Officer? Something else entirely?
What you actually need to be looking at is job functions, not titles. What they do, what their specific responsibilities are – or indeed if there’s somebody else more senior (perhaps with a correspondingly ridiculous title) who you should be contacting instead.
In the same vein, there’s no point talking about your product if your prospect hasn’t the foggiest idea what it’s for, or if their company as a whole isn’t capable of integrating it. Knowing what technologies prospects are familiar with, use regularly, or even just recently purchased, would prove invaluable for knowing if they have a use for your product or service – or if they’d even know what to do with it at all.
It’s important to note that this level of granular insight can’t be obtained via traditional data vendors. The intelligence you need is spread across multiple channels – from the open web to social media and third-party data.
To effectively humanize your data, you’ll need an end-to-end data solution capable of collecting and validating all of that information – while constantly refreshing it to ensure it doesn’t become obsolete. Just as importantly, your solution should be able to make actionable sense out of all the data, using tools such as predictive analytics.
Humanize your data for ABM, too
If your company is pursuing account-based marketing (ABM), all of the above requirements still apply – but with additional applications.
ABM begins with identifying named accounts to target, and then finding the key influencers (i.e. leads) within those accounts. So to start with, you’ll want all of the above information for both your leads, and their accounts (i.e. the companies they work for).
You need to know the account you’re dealing with in great detail, including where it might fall in any greater company hierarchy. Is your target account a subsidiary or franchise of a much larger business, for example – and if so, is that really the most strategic account you should be targeting? For that, you’ll need site-level matching capabilities built into your data solution as well.
Picture credit: Pixabay | CC0 Public Domain