Build a lead scoring model
Build a lead scoring model
Your eye on the lead portfolio: Building a lead scoring model
Systematically qualify leads and consistently follow them up based on their level of maturity: This is one of the most important principles of successful sales work. Putting too many resources into a lead whose intention to buy was more of a pipe dream than a reality? Of course, this means that other interested parties have to wait longer for their first meeting. This also goes wrong: marketing and sales overestimate the progress of a lead in the buying cycle and take them by surprise with a premature personal touchpoint. A lead scoring model helps to avoid these mistakes in lead qualification. While some teams still rely on the gut feeling of individuals, lead scoring is based on objective facts. This allows the sales team to approach exactly the right leads, while the marketing team can further develop the others in a targeted manner. Convinced? Then the next step is to set up your own lead scoring model. And here's how to do it!
Lead scoring model: making the property profile tangible
A brief flashback to theory: models are a replica of real-life situations. In doing so, they must simplify or hide partial aspects in order to keep the complexity at a manageable level. The team must resolve the trade-off between modeling effort and quality of results in order to end up with a usable lead scoring model.
In plain language: What information is available? What additional information can be collected? And above all: what information do we really need to describe where a lead is in the sales funnel? Ultimately, choosing the right set of parameters is crucial to how well the model can depict reality.
When creating lead scoring models, users consider two categories of information: explicit and implicit.
1. explicit factors
These are comparatively constant characteristics of a lead. Put yourself in the shoes of a sales software provider: Does a lead manage the IT department or a team that will actively use your software? It is possible - but unlikely - that this information will change over the course of the opportunity. This also applies to company-related information such as sales figures, strategic orientation or industry. Leads often provide this information voluntarily during inbound lead generation in order to download gated content. Alternatively, the marketing team researches additional information to complete the picture of the lead. Keyword sales intelligence: smart research tools can take over this task automatically.
2. implicit factors
Not everything can be simply queried or researched on the internet. This is because the behavior of leads is just as important an aspect of the lead scoring model as the explicit factors. Just like in a sales meeting: the initial research already tells sales staff a lot about a lead. However, the picture only comes to life in personal contact: What questions does the lead ask, what challenges determine their way of thinking? These points are the real fuel for the sales process. And this is why behavioral aspects must be reflected in the lead scoring model - not in terms of conversations, but with a focus on online interaction. After all, this is best automated.
What content does the lead download? Do mailings languish unopened in the inbox or are they opened and shared? Does the lead look at certain key topics on your website particularly often? This information allows you to draw many conclusions about the phase a lead is in. For example, ask yourself this question: Is the lead still interested in a general understanding of your offer, or is he or she already more interested in questions about pricing and project management when implementing your solution? The only drawback is that as valuable as this information is, it can be complicated to collect. Activity tracking via cookies is not always easy in times of GDPR.
Calculate lead scoring model
Where are we now with the definition of the lead scoring model? We have a set of explicit and implicit factors that we can use to describe a lead. But how do the individual factors interact? That is the next question! After all, not all information contributes equally to the attractiveness of a lead.
Weighting: At the end, the lead scoring model spits out an overall score for each lead. Each defined factor is included in this evaluation, but only with the previously defined weighting. And it makes sense to make distinctions here!
Example
Experience has shown that your best leads are in the specialist area. This, in combination with a medium-sized company and a B2B direct sales organization, represents your optimal explicit lead profile. Rate these leads the highest? Absolutely! Conversely, hide all others? Clearly not, but prioritize them according to their attractiveness. In our example lead scoring model, the team decides that the position of the lead is much more significant for the prospects of success than the sales model, for example.
Factor
Position
Company size
Distribution model
Weighting
50%
25%
25%
In this simplified example, the position is therefore the most decisive factor for the attractiveness of the lead. How well a lead fits the ideal customer profile in the eyes of the team is reflected in the respective score points.
Position
Management
Head of Purchasing
Management Application area
IT management
Score points
6
7
10
8
Example
Let's take a look at a lead from the current portfolio: Here, the company size and sales model match the desired profile exactly. However, the lead's position (Head of Purchasing) only receives a medium rating.
Factor
Position
Company size
Distribution model
Example read
Head of Purchasing
Medium
Direct
This results in this score for a lead from the current portfolio:
Lead
Position
Company size
Distribution model
Score
6
10
10
Weighting
50%
25%
25%
Result
3
2,5
2,5
8
To obtain the final overall score, the lead scoring model adds the points from the activity tracking of the implicit factors. The scoring procedure is analogous here.
Negative scoring
The lead scoring model offers a great deal of freedom in the weighting and evaluation of individual factors. How valid are some factors compared to others and what do the statistics show? All of this can be incorporated into the model. Negative partial scoring is also possible. For example, you can deduct points from the lead to "punish" unwanted behavior or characteristics. The lead then receives a correspondingly lower overall score. By the way: Hubspot's lead scoring is constantly updated. Accordingly, it automatically removes the negative score as soon as a lead no longer fulfills the negative characteristic.
Set threshold value
When is a lead so close to being ready to buy that it makes sense to make initial personal contact with the sales team? This is a key question when using a lead scoring model. Ultimately, the model should answer the question of when the marketing team should hand over leads to colleagues from the sales department. A good and mutually accepted lead scoring model turns marketing & sales alignment into a stable process. Has the critical threshold value from lead scoring been reached? Then qualified leads from MQL become SQL and the sales professionals can finally take action.
Trimming lead scoring for success
This is also the critical point for the success of a lead scoring model: it must be a product of cooperation between marketing and sales. Only if the teams from both areas gain a common understanding of lead scoring can the model lead to lasting success. The sales team must have the feeling that they have exactly the "right" leads on the table at the end of the process. In the same way, colleagues from marketing need to accept the results of lead scoring in order to be able to develop their contacts in a targeted manner. Coordinating a lead scoring model is therefore not a one-off process! Instead, it means continuously developing the basis and constantly renewing the alignment of marketing and sales!
