If you’re a B2B lead generation professional, chances are you’ve heard of account-based marketing (ABM) and understand its benefits. But as your company grows, implementing any effective lead generation strategy only gets harder.
ABM will help focus your company’s efforts; but even so, B2B marketers still struggle with the challenge of gaining—and maintaining—intelligence on their buyers at scale. The more contacts you accrue the harder it becomes to focus on targeting the right leads, or even identifying them at all to begin with.
This problem grows exponentially with your company. At first, it’s possible to manually keep tabs on a small pool of contacts. What information you don’t know about them can largely be gleaned by scouring the Web.
But have you ever tried Facebook stalking a mid-market business’s entire database (never mind an enterprise company)?
As your company grows, CRM and marketing automation become a crucial part of mapping accounts and managing campaigns. When that happens, the sheer numbers involved render manual methods of gathering information absurdly inefficient. There‘s just so much information to process: from first-party data (whether from inbound campaigns or outbound prospecting), to unstructured, anonymized data like online behavior and social media profiles.
Predictive analytics: A solution – but be careful!
Predictive analytics has helped countless B2B companies overcome these challenges, and is arguably a necessary prerequisite for an effective ABM strategy. It provides vital intelligence at every stage of the sales funnel: from information on existing leads to building an Ideal Customer Profile.
An effective predictive model will deliver higher ROI and greater efficiency, saving dozens of hours. Predictive models give marketing reps a far better idea of which leads are sales qualified, and which would be better off being sent for further nurturing – leaving sales to focus on only the most promising leads.
But there are still three underlying issues which traditional predictive analytics vendors can’t solve.
1. Maximizing the quality of the underlying data in your database
What if that formerly influential VP-level contact you’ve been targeting just got fired, or moved to a different company? How much time will you have wasted on a dead-end lead?
It’s hard enough keeping a clean database as it is, what with all the incomplete or false information from inbound marketing campaigns corrupting your system. Now throw into the mix the fact that B2B professionals are such slippery fish, and it’s clear just how out of date your databases likely are.
A recent Leadspace study showed that in a single quarter, slightly over 6% of B2B professionals were promoted. That’s aside from those who were fired, moved companies, switched roles, retired, or for another reason are no longer in the same place within the decision-making hierarchy they once were. They just can’t sit still.
Which begs the question: How can you enrich your data in real time to keep it relevant? Most predictive analytics options don’t have a solution to this problem – they simply build models based on your (gradually deteriorating) data to provide (gradually deteriorating) predictive models.
This is a really big one: Traditional predictive analytics systems take a black box approach – they tell you what to do, but not how they came to those conclusions. But such an approach isn’t entirely reliable, and not only for the reasons already mentioned above.
We all use GPS systems like Waze to help us get from A to B. But we also, you know, look at the road from time to time (well, most of us anyway).
The point is, no amount of technological ingenuity can replace good old-fashioned human knowledge, experience and intuition. It’s not enough to know who is theoretically a good lead based on previous behavioral metrics – you need to know why, and how best to convert them.
Once a lead makes it to a sales rep, they need all the information about that person they can get to know which leads to contact, and why. Without that, they’ll just be shooting from the hip – albeit (if your predictive model is accurate) to reasonably qualified leads. This problem becomes all the more acute if you’re pursuing ABM, where customized pitches are a requirement.
3. Understanding accounts, as well as the individual leads within them
The term “account-based marketing” can be a little misleading. While ABM views accounts as markets of one, in order to actually engage with them you obviously need to be aware of the key influencers within the company. That means to say: people.
You could have a brilliant list of prime accounts, but be pitching to the wrong people inside those accounts. Or your database could be filled with great leads, but you can’t match them to the right accounts. Either way your ABM strategy is unlikely to get very far.
So it’s clear that, despite its usefulness, traditional predictive analytics has some clear limitations.
In our ebook below, we’ll explain how Leadspace’s end-to-end predictive analytics can help you overcome each of these limitations.
In short: it’s all about empowering you with as much intelligence as possible, including even the most minute insights into your leads, alongside a uniquely intelligent predictive platform which is perfect for ABM.
Top image courtesy of iStock