Your model should typically use a scale of 0 to 100 is used to quantify all the signals that might indicate an account’s readiness to buy. With that said your lead scoring system should be included in your account scoring system.
What’s wrong with traditional lead scoring?
Traditional lead scoring lacks context for how a decision actually gets made. When you score leads separate from the context of the buying council they sit in within an account, you miss a majority of the real picture. A higher lead score on an influencer could mean absolutely nothing to how close you are to a closed/won deal. In fact, if a qualified lead exists on their own in an account, we haven’t actually made much progress.
We have one person out of a group that could be as large as 25 that will need to be included in the decision to buy your solution. This one qualified lead represents 4% of the entire potential council. Ask any VP of enterprise sales and they’ll tell you, you have nothing and to keep going.
Alternatively, if you have multiple qualified leads that could be identified as an executive influencer, a technical buyer, a champion, and an economic decision-maker, then you have absolute traction in that account.
Your lead scoring efforts should roll up to an account score that captures the volume and quality of the engagements based on your goals in the sales process. A traditional lead scoring model missed the boat and fails to deliver a truly predictive lead scoring system.
Scoring at the account level represents how people actually make decisions in real life
You should score readiness to buy at the account level because it’s more reflective of how people in target accounts will actually make a decision to buy. Typically there are buying councils that can range from 3 to 25 people who could be “leads” in an account. Some of them can say no to your solution, a few of them can say yes, and others can simply influence the decision. Scoring at the account level allows you to truly represent what your sales and marketing teams will need to do in order to have maximum awareness and engagement in an account to actually get a deal done before sales cycles get wasted.
The north star of any account scoring system is to develop a predictive lead scoring system for different buyer personas in your target audience. From a marketing analytics standpoint, your goal is to understand what activities, content types, and channels in online behaviors and offline behaviors lead to a higher conversion rate with potential prospects and increase the probability of a closed deal.
As you develop what a lead score should mean based on the contact role of the person in the buying council of an account, you will need different ways to account for engagement, and this may even mean changing your definition of qualified leads, which should inherently carry less meaning to your organization. Marketing qualified accounts are truly what matter and we’ll show you why below.
Redefining marketing qualification changes everything between sales and marketing
At Ampfactor we follow a typical waterfall demand generation strategy in all our programs so our account scoring system is rooted in that construct. For us, there are 4 stages that lead to the fifth stage of sales qualified opportunity.
The first stage of measurement is our Target Accounts or TA
Target accounts are all those accounts that fit our detailed ideal customer profile perfectly. If an account exists here, the structure and behavior of the account make them perfect for us to do business with.
The second stage is Active Demand or AD accounts
Our collective goal as a business is to deploy as few resources as possible to beat out the competition and win in the accounts that have a high value to our organization. Accounts in the active demand stage have exhibited first-party or third-party intent, and are therefore suspected to be in-market.
The third stage is Marketing Engaged Accounts or MEA
Because we follow a buyer-led ABM model, all accounts we go after must have intent, they become marketing engaged accounts when members of the buying council have engaged with our marketing and outbound sales development campaigns on any channel. This in and of itself just means that we have penetrated the account. Accounts live here until the lead scores cumulatively reach a hypothetical level that means they are ready for a sales approach. For each of our clients, this number is different. Your brand, community development, and historical demand gen activities influence what this number ought to be. So we establish a base hypothesis and then test for how accurately that fits the behavior we see from accounts as they move through the stages.
The fourth stage is Marketing Qualified Accounts or MQA
Marketing qualified accounts have reached that hypothetical engagement score and are therefore ready to engage in an active sales cycle. All these accounts are subject to consistent outreach that asks for higher-level calls to action. At this point, we know who in the buying council is interested in solving which problems and can manufacture a faster selling cycle from the attention and interest we’ve earned. When we create an interaction that leads to an open discussion about an active opportunity or impending buying cycle, we classify the account as sales qualified and move to support sales as they sell into the opportunity.
These accounts are moved to the fifth stage, Sales Qualified Accounts (SQA)
At this point, you should be thinking about a couple of game-changing dynamics that will now ensue when this model is adopted. The first thing you should think about is that measuring marketing performance based on MQL’s is not only wrong but it misses the point. So with this structure sales will not be getting massive volumes of MQL’s which have a 1% conversion rate to sales qualified opportunity, rather they’ll be getting warmed-up accounts where deals exist.
We see an average 73% conversion from marketing qualified accounts to sales qualified opportunities when our model is deployed.
The third thing you should be thinking is that you probably have too many salespeople for the number of sales opportunities that exist.
Think about it. With less waste in the form of undeveloped accounts being handed over to sales, you have less “sales activity” to execute and therefore will need fewer salespeople to handle every deal that comes over from marketing.
At Ampfactor we’re leading teams to shift to a 70/30 ratio of marketing to sales departments who produce far more revenue. Achieving this state means that sales leaders can both shift some budget to marketing (very unpopular among sales VP’s) and simultaneously recruit absolute rainmaker sales talent because they need fewer of them.
Why develop a lead scoring strategy for your accounts?
How you score engagement matters. The point values assigned by sales and marketing teams are a collaboration for interpreting data points. You may want to use lead scoring software as a way to measure your marketing efforts, and sales development efforts, but the key for this to work is that the platform needs to be flexible enough to track all the inputs you want to include in your scoring model, and then assign point values based on the scoring criteria you select.
As you deploy marketing campaigns leading up to a sales process, your goal is to drive an engagement score for an account up in multiple ways. The objective is to develop a model that helps you avoid handing over unqualified leads or low-quality leads to sales departments. When this is done correctly, your marketing and sales teams are able to create multiple high-quality leads within an account that are representative of the mapped buying council that will make a decision to buy your solution
Three types of data in an Account scoring system
There are three types of data that paint a holistic picture of an account’s readiness to move away from its current solution, to something new. Here are those types of data and where they come from.
First-Party Intent data
Intent data is generally recognized in two ways, first-party intent data, and third-party intent data.
First-party intent data derives from actions that leads have taken on your website, ads, and content. Let’s take your website for instance. If your content team wrote a downloadable piece of content about how to solve a specific problem, and someone from your target accounts lands on your website and downloads that document, it signals that they have a problem or priority closely associated with that topic. Now suppose you had a video and social content about how to solve this specific problem as well. Your goal is to drive up intent (or test to see if you can) and send those other pieces of content to that prospect. If that prospect engages further, your lead and account score should recognize this and should increase.
Third-Party Intent data
Third-party intent data derives from data providers like Bombora, Leadsift, Zoominfo, and others who have massive networks of digital properties all around the internet, featuring similar content to yours about how to solve that specific problem, that someone from your target account has downloaded. If you’re sourcing this intent data from these providers they will send you a score around the topic you’ve selected to include in your lead scoring models and account scoring models. This data is only at the account level. The data providers certainly know who the actual person in the account was that downloaded the content but can’t share that with you because of GDPR compliance. What they can do is send you an audience strength signal. This should be taken lightly because there’s no way that they can know your buying council composition as well as you do for your specific solution, but it’s probably directionally better than not knowing anything.
No matter what type of intent data we’re ingesting, it’s intended to feed your lead scoring model so you can prioritize the accounts with intent data vs. accounts without intent data.
Engagement data should represent any warm interactions, from any channel, with your content from people in buying councils in your target accounts. If a c-suite person in your buying council engages with your efforts, that’s likely worth more than if a director or user does. We count this as more valuable because awareness in the c-suite has the ability to really accelerate a sales cycle. What we look for is repeated c-suite engagement over the time leading up to a sales cycle. When we track this at the buying council member level, lead scores reflect the quality of problem/solution/and timeliness fit, and we can be more confident that the account is leading up to a buying cycle.
Account coverage accounts for the total volume of engagement coming from all members in a buying council. When we sell into accounts and there’s meeting after meeting educating different influencers and decision-makers over months and months, we know sales engaged an account that had poor account coverage going into the sales cycle and this is one of the largest contributors to customer acquisition cost creep. In many instances, we see this can cause sales fatigue trying to make progress in an account.
How to implement an account scoring system
How you should implement an account scoring system with lead scoring depends entirely on where you are in your journey. There’s probably some lead scoring enabling technology required, your sales team will have to join in on the journey. Or, you could take an agency approach to learning before you build and partner with Ampfactor to get going today and run on our model on our tech stack. We call it renting the stack but it all comes down to how fast you want to level up and if your business objectives allow for internal change management, SaaS expense reallocation from existing contracts, and the pace that your competition is moving at. We’re happy to explore it with you, or just be a resource.