Man is not a purely rational being. A constant battle rages within him between deliberation and temptation. How can we determine the likelihood of a potential customer buying? With the help of lead scoring!
What is Lead Scoring?
Lead scoring helps you to separate the wheat from the chaff and to make a prediction about the sales and profit of a potential customer. For this purpose, the previous information and reaction behavior of all customers is used. This results in a set of rules that weights the results from explicit and implicit scoring differently.
Explicit and implicit scoring
In explicit scoring, you use information that you receive directly from website visitors, such as position, industry, and company size.
With implicit scoring, on the other hand, you access data that is generated by user behavior. This includes for example:
- the clicks on certain pages
- the length of time spent on certain pages
- the number of downloads
- the source or channel of the lead
- the client used (desktop/mobile)
How does lead scoring work?
First, define the scoring criteria for the leads and assign points for explicit and implicit scoring.
Then calculate the lead score for your leads.
Visitor A: 20 points + 30 points + 50 points = 100 points
Visitor B: 0 points + 30 points + 0 points = 30 points
Once your lead has (almost) reached the total lead score (set by you), it becomes a Marketing Qualified Lead (MQL) and can be handed to Sales for further processing.
Since the ruleset is based on heuristic assumptions, it is quite prone to errors and inaccuracies.
Heuristics refers to the art of achieving noticeable success with limited knowledge and little time. It is based on an analytical procedure in which statements about a system are made with the help of conjectural conclusions using existing knowledge about the system.
Lead scoring based on self-learning systems (“machine learning”) promises greater success. This involves searching for patterns in the characteristics of customers and non-customers.
However, humans must determine in advance which factors have a causal relationship with the likelihood of purchase. And that brings us back to the constant battle between weighing and temptation.