By Asad Haroon
jarmoluk / Pixabay
We are all familiar with algorithms that establish a quantitative measure (score) regarding the qualification of a particular lead so it can be handed to the sales team by transferring over the respective contact data to a CRM. When pursuing an ABM strategy, however, you are signing up to use appropriate metrics to drive revenue based on accounts, not leads.
With that in mind, consider instituting a scoring model driven by Buyer Group rather than individual lead to better gauge the level of interest and intent demonstrated by target accounts. Let’s discuss how to form a new algorithm that reflects the reality of Buyer Groups.
Buyer Group Scoring Model
Working with your list of target accounts, you want to be sure you’re reaching the entire buyer group (demand unit) and not just anyone at a company’s IP address. Are you reaching the actual user(s), researcher(s) and decision maker(s) or are you yet to get past the obvious gatekeepers? And what is the percentage of accounts engaged within your Total Addressable Market (TAM)?
Combine data regarding the level of responsibility (Job Title, Function Affinity, Span of Control) of Buyer Group members with data showing their respective level of intent (engagement frequency, intensity and how recent) to create an overall Buyer Group score for that account. While there may be a wealth of activity-based metrics that can be tracked, establishing a consistent scoring algorithm based on these factors will help you provide a more accurate picture of a Marketing Qualified Account (MQA).
Record the number of Buyer Group members that have engaged with your brand over a set period of time and capture the time each contact has spent engaging with your brand. You can calculate an average time spent, over a set period of time, for members that are considered either an executive in the account or at a lower level of responsibility.
It’s important to account for the fact that, based on the size of the target company, the same Job Titles likely to be part of a Buyer Group might have different levels of decision making responsibility. The level of decision-making responsibility should be quantified using a weighted factor that influences the scoring algorithm. Interactions between prospects and your sales can be used to verify your assumptions regarding the composition of Buyer Groups.
There are also previously untapped sources of intent that can be mined and used as input to the account score. For example, call analytics is an excellent way to evaluate intent during any phone conversation conducted with a member of a Buyer Group. And keep in mind that any Buyer Group Score is a snapshot in time. So use this score to optimize marketing decisions that move accounts, over time, from a relatively low score to a higher one.
Buyer Group Engagement Detail
Intent assessment might include the type of content accessed, number of page views, and length of time spent on each page. Is this time increasing or decreasing and how far back in time (or how recent) was the last Go to the full article.
Source:: Business 2 Community