The first task is to look at the raw data in order to define a score between, say 0 and 100 points. This is not as simple as more activity leading to a higher score; some areas of activity may in fact be worth more score than others, and time needs to be taken into account in order to ensure that scores do not grow indefinitely over time. The machinations of this scoring algorithm, however should be kept separate from the sales organization.
For a sales organization to be comfortable building a process, they need a stable definition to be applied to the leads that are sent their way. This is where the fit/engagement matrix is highly useful. A value for the lead’s “fit”, in other words their title, industry, and size, can be mapped to a standard definition of A, B, C, D, where A is a high fit, and D is a low fit. Similarly for a lead’s “engagement”, or their activity on the website, a standard definition of 1,2,3,4 can be applied, again with 1 representing high engagement and 4 representing a low engagement.
The sales team can then understand leads as A1s, C3s, or B4s. The underlying scoring definition of what earns a lead points, how those points are adjusted over time, and which range of points maps to the each rank, does not need to be visible to the sales team at large. A core group of key individuals within sales and marketing can debate the definitions and make necessary adjustments each quarter.
With a clear definition of what makes each lead rank, the discussion can then progress to which leads should be passed from marketing to sales, and to which sales team if there are multiple teams involved. A1 leads will obviously be passed directly to sales, likely to a field sales force, but a mapping is needed for where each other lead rank goes. Some may be passed to an inside sales team, some may be passed to a partner channel, and some may be held back to be further nurtured. The set of leads that are passed to sales from marketing are deemed marketing qualified leads (MQLs). This higher level definition is useful in looking at a higher level view of your marketing analysis.
There are two key questions that these efforts in lead scoring allow you to tackle in analyzing your marketing programs:
Does our scoring accurately correlate to a higher propensity to purchase?
- A lead scoring algorithm should be continually revisited in order to ensure that a higher score actually correlates with a higher propensity to purchase, based on both fit and engagement.
Which leads are worth sending over to sales based on the sales team’s ability to engage them in relevant conversations?
- Adjustments in your marketing, sales, and channel mix can mean that you may
wish to send more leads or less leads to sales. This adjustment comes into play
as you adjust which leads, A1, B2, C3, etc, are sent to sales as Marketing
Qualified Leads (MQLs)
In order to better enable analysis of what is working and what is not, it’s a good idea to keep the lead score and lead rank tracked. As these are values that change with time the best way to do this is to stamp the values at the point in time that the lead is passed to sales. This value pair can then be analyzed against later in order to understand whether the score and rank at that moment in time accurately indicated an intent to purchase.
Setting up a lead scoring and lead handoff process in a way that allows you to both analyze and adjust it as you learn and your business grows, sets you up for long term success.