6 ways ABM turns classical lead scoring on its head
Don’t be afraid to break the “rules” to drive the results you need, argues contributor John Steinert.
One of the most amazing things about the Account-Based Marketing (ABM) revolution is that it’s really, finally, bringing sales and marketing teams together. That’s because ABM focuses both teams on exactly the same objective: improved sales results from a defined set of accounts.
And while ABM can benefit from many of the tools in your total martech stack, to get the most out of what you have at your disposal, it’s important to reflect on what’s necessarily different about an account-based mindset and optimum account coverage behavior.
For me, nothing stands out in this regard more than classical lead-scoring concepts. Though they’re often put in place to help deliver overall efficiency and more predictive sales motions, they can actually reduce effectiveness in the ABM environment.
Common issues with classical lead scoring and ABM
• When scoring is about holding back. Scoring helps increase productivity in demand gen because it can reduce wasted activity lower in the funnel where costs per touch are higher. Therefore, in classic models, marketing typically passes leads only when they reach a certain threshold of activity or “stage” in the buying cycle.
But in ABM, even smart demand-gen scoring can have the negative effect of holding back important information about an account that could be very useful to a sales team, regardless of stage or activity.
• When scoring is about titles. To maximize their own productivity, salespeople develop all kinds of shortcuts that help them navigate away from dead ends. These commonly find their way into classical demand gen scoring because a high-volume lead-and-funnel-based process cries out for disqualification of individuals.
Generally speaking, sales doesn’t have the time to follow up with low-ranking titles, so it asks for title to be heavily weighted in a score. But in ABM, sales already knows which titles to go after. When you’ve built out your ABM list, you’ve likely filled in the missing contacts in part based on sales’ knowledge.
What sales doesn’t know is in which account a deal might actually be taking shape — and in which buying center — early enough for their skills to make a difference. So that is what marketing needs to find better ways of alerting them to. You need to develop account-based scoring that alerts sales to prioritize active accounts among many and the buying centers within them.
• When scoring is about disqualification. In ABM, many of the elements that go into a classic demand-gen scoring model have already been taken into account. When setting up your ABM program, you’ve built your account list to match your Ideal Customer Profile (ICP).
Working with sales, you’ve already determined which personas you’ve historically needed in order to win a deal, and you’ve populated them into your databases. By definition, your ABM list is the accounts you believe you can serve better and will deliver more revenue in return.
Because they are often based on who you should NOT pay attention to, classical qualification methods like BANT (Budget, Authority, Need, Timing) and other elements of lead scoring fall short for ABM. Instead, what you should focus on are signals that tell you when and where to move in closer to a specific account on your list.
Outgrowing the MQL
As part of the Account-Based Everything (ABE) concept advocated by Engagio’s Jon Miller, along with consulting firm TOPO, Miller has pointed out that traditional demand gen’s MQL (Marketing Qualified Lead) has a number of shortcomings when applied directly to ABM. By proposing we use the Marketing Qualified Account (MQA) instead, he’s helpfully pointed ABMers in a better direction.
As Jon describes it, the MQA focuses us on how engaged an account actually is with our company: Do they seem to want to do business with us? The strength in this is that it provides a lot more useful information than is contained in even the best individual leads.
Individual leads actually tell you very little about what is really going on in an account. That’s why they typically force organizations into an expensive and time-consuming tele-and-sales requalification process. So the MQA is a good start.
This is a much better approach to addressing sales’ need for information. On the other hand, because it relies so much on engagement with you, it can have the unfortunate effect of excluding account activity that your sales team could really take advantage of.
Help sales by simplifying complexity
Hull’s Ed Fry has done a great job recently of tackling how scoring needs to evolve for ABM. When you boil it down, it’s about doing the hard work necessary to evolve beyond lead-based approaches to a more collaborative model where marketing focuses intently on helping sales sell.
As Fry explains, marketing can help sales a great deal by extracting and translating the most useful signals about accounts into a form that sales can easily digest and more clearly implement against:
To create signals… to match your sales reps’ needs, you need to be able to run custom computation and data transformation on all your lead and customer data. This is often a challenge in traditional marketing automation platforms (which usually host lead scoring algorithms), particularly when combining multiple types of data (marketing engagement, in-app product usage, chat conversations, web analytics, social media engagement etc.).
Go beyond ‘predictive’ — Deliver more prescriptive guidance to drive sales productivity
Predictive modeling is super-useful when you’re dealing with the large volumes typical of classic demand generation. It can be great for disqualification — for getting all your teams on the same page so they don’t waste time chasing hunches.
If you have enough data, and you can afford it, a predictive model can be very helpful when you’re working to understand your ICP. But predictive can’t actually tell you which accounts are active in the market. It can only tell you about accounts where you are picking up signals in your own systems.
So, if there’s demand activity happening within your ABM account list in the market at large that you could take advantage of, you won’t know anything about it. This is where real purchase intent can give you the added insight necessary for substantive ABM success.
To maximize your ABM opportunity, you’ll definitely want to add this insight to further enhance the idea of Marketing Qualified Accounts. But seeing more of the active demand present in your list is only the beginning.
Prescriptive guidance — advice on how and when to contact leads — is also very important for improving sales productivity. While the most advanced teams and the most advanced systems infrastructures are already starting to try to automate prescribed actions, with the right information, any team can be in a position to improve performance significantly.
New intent-based data solutions are now coming to market that can clearly inform reps of what’s going on in their assigned accounts right now. And marketing is in a good position to use the same information to rapidly evolve both the rules and the substance by which sales gains can be achieved.
Simple account-based scoring and prescriptive plays
• Adjusting the standard SLA. Tele-based teams commonly have targets or rules in place for how many contacts they need to process (i.e., “smile and dial”) and what cadence (or number of contact attempts) they should use for a given lead.
To maximize yield from your ABM list, however, what’s actually going on in the account should trump volume-based rules. Thus, your scoring methodology needs to allow you and your tele-resources to change the approach when agreed-upon signals show the account has entered a buyer’s journey.
A tele-team needs to be given permission to focus on an account until they can truly disposition the demand that has been seen. Their incentives must not be aligned in a way that forces them to give up before they’ve achieved engagement.
• Provisioning sales to generate engagement. Although the classic demand-gen model suggests that a lead should be held back by marketing until it’s “ready” for sales, in ABM, this approach could well mean leaving significant numbers of deals within your target list entirely uncontested.
Although real purchase intent data can tell you that a deal is taking shape, it’s important not to reflexively equate that with a “late-stage MQL.” When intent data tells you there’s a deal underway within a company on your target list, but you haven’t been able to get that company to engage with you yet, trying to use only your late-stage content is likely to simply turn them off further.
In the account’s mind, you haven’t yet gained the right to be considered. To maximize your chances of gaining consideration, you may need to enable your sales resources to take actions typically reserved for marketing. Specifically, you may want to provision sellers with scripts and content packages that include your best top- and mid-funnel assets.
• Flexibility to adapt to new knowledge. There’s always a chance that the rules you’ve put in place to guide the team will be out of sync with what’s actually happening in the market. But historically, rigid scoring methodologies aren’t well suited to change.
When real purchase intent is available for ABM, both marketing and sales can monitor what is happening in a market, as well as in a particular account, nearly in real time. The teams need the ability to take advantage of what they see in an account, regardless of what a score or a prescribed play might dictate.
For example, attention being paid to a competitor or to a deprioritized value proposition could suggest the need for action or a different kind of action that your scoring hasn’t accounted for. Most importantly, in ABM, your scoring should allow your teams to adjust for insights that fall outside of the rules.
An infinite learning loop
Whether you’re doing scoring for demand gen or for ABM, remember that the primary purpose of these rules-based methodologies is to improve your business results overall.
Scoring was never meant to be a set-it-and-forget-it tool. You are supposed to evolve it as your learning grows and conditions change. In ABM, since you’ve already put so much work into creating your list, it’s important to recognize issues that a classic demand gen scoring approach can create.
Since ABM puts marketing and sales together into a much more collaborative construct, your scoring should reflect this so that you can take greater advantage of the opportunities this creates.
Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.