Lead scoring

Lead scoring is the term used to describe eval­u­at­ing leads. The quality of the collected in­form­a­tion is compared. Both the amount of in­form­a­tion per lead (explicit scoring) and the reaction of the lead to the com­mu­nic­a­tion (implicit scoring) play a role in the eval­u­ation. Using this you can figure out how high the prob­ab­il­ity is that a pro­spect­ive customer will become a real customer.

Lead scoring is all about eval­u­at­ing how valuable each lead is. Not only the quality and status of the contacts are assessed, but also the like­li­hood of the company making a sale.

Ob­ject­ively eval­u­at­ing a contact for the sales de­part­ment is usually only possible if they are compared to other contacts. There are two main criteria used for this purpose: On the one hand, you check how complete the contact in­form­a­tion is on the user’s profile – this is referred to as explicit in­form­a­tion. On the other hand, you assess the lead’s response to the contact attempts made during the lead nurturing process – thereby gaining implicit in­form­a­tion.

The goal of lead scoring is to fa­cil­it­ate the co­ordin­a­tion and co­oper­a­tion between marketing and sales. This is all done at the beginning of the lead man­age­ment process by pre-sorting the leads received, and later on more thor­oughly, according to clear guidelines, which determine when a lead is ready to be passed to sales and how high its priority is. These rules and guidelines are sum­mar­ised in a lead scoring model, which is im­ple­men­ted in B2B and B2C marketing and sales areas es­pe­cially. The model helps determine whether a contact should be followed up by the sales rep­res­ent­at­ive i.e. passed to lead routing, or whether it needs to be further managed in lead nurturing.

The dis­tinc­tion between implicit and explicit in­form­a­tion has been mentioned above, but implicit and explicit scoring is also carried out – both of which are part of the scoring model.

Explicit scoring deals with the lead’s contact in­form­a­tion or profile. Relevant in­form­a­tion in the B2C area is so­ciodemo­graph­ic data, such as age, gender, and location. In the B2B area, for example, the position of the contact within their company, the industry in which the company operates, its number of employees, and the expected turnover, all play a role here. Each company decides for itself which explicit data it would like to use for the profile analysis. It’s important to work closely with sales and marketing. Together, both de­part­ments can determine the factors that make the optimal customer.

Define and weight cat­egor­ies

A general user profile emerges from these ob­ser­va­tions after the most important factors have been selected. In the following example, the in­di­vidu­al cat­egor­ies position, industry, and number of employees are weighted dif­fer­ently depending on in­di­vidu­al re­quire­ments. The position of the employee is the factor that is weighted the most for the company in this example:

Assign score points within the category

Different data, same idea: implicit data is also weighted within the cat­egor­ies. In this example we use landing page visits. The eval­u­ation is based on how up to date things are, and frequency. The maximum number of score points is again 10.

Determine the rating in ac­cord­ance with the idea profile

Just like with explicit scoring, the in­di­vidu­al pro­spect­ive profiles are then compared with the ideal profile to see how well they match.

A very active pro­spect­ive customer, who regularly visits the website, has already requested an offer, and has down­loaded an e-book, matches the ideal profile well and therefore receives the rating '1'.

If you plan to use efficient lead nurturing and later build a solid bridge to lead routing, you need lead scoring. A pro­fes­sion­al and well thought-out lead scoring model focuses on the leads who have the highest like­li­hood of pur­chas­ing something or com­plet­ing a trans­ac­tion. This is how marketers and sales rep­res­ent­at­ives organise and make the most of their time and resources. To do this, you first need to cat­egor­ise your requests and pri­or­it­ise them at the same time.

By identi­fy­ing the 20 to 30 percent of your contacts who are most likely to make a purchase, you can then put more effort into this lead segment. The remaining contacts shouldn’t be neglected because even a lead with a B3 score can be converted into a customer if the right measures are taken. A rather low-rated lead, however, has a lower priority in lead routing. This group is easily in­teg­rated into automated lead nurturing campaigns that help capture further potential customers.

Is the lead scoring now complete? It’s now time for the next step in the lead man­age­ment process, the final lead routing.