A good lead-scoring model is an important part of a successful relationship marketing plan. The impetus for implementing a relationship marketing scheme is to build, develop and maintain strong customer relationships. Implementing and reacting appropriately to a lead-scoring model developed for your company is one of the first steps towards turning a lead into a
A good lead-scoring model is an important part of a successful relationship marketing plan. The impetus for implementing a relationship marketing scheme is to build, develop and maintain strong customer relationships. Implementing and reacting appropriately to a lead-scoring model developed for your company is one of the first steps towards turning a lead into a client. By knowing how contact and develop potential leads, you will better know how to nurture these relationships once they move from the lead phase into the client stage.
Types of data for relationship marketing
The most successful and accurate lead-scoring models use both explicit and implicit client information. Explicit data are hard facts about your leads that are often provided by your prospects themselves, such as gender, geographic location, company, company size, and title. Implicit data is collected from monitoring the behavior of your leads: web site visits, emails they open, and sometimes previous purchases.
Used together, explicit and implicit factors create a comprehensive picture of your prospective clients that help you make an accurate determination of their likelihood to purchase your products and services. Demographics and psychographic information are two important types of explicit data that are integral to the execution of a successful lead-scoring model.
The value of demographics data
The demographics section of a lead-scoring model categorizes individuals based on characteristics of both the individual, as well as the company for which they work. Some lead scoring models will rely heavily on individual information, some will rely more heavily on a lead’s company information, and some lead-scoring models will use a combination of the two. This will largely depend on the type of business you have, and the types of products and services you provide.
Demographic information collected for your lead-scoring can be extremely useful and help to shape the relationship marketing campaign for your company. If you sell beauty products online, you may discover that a certain brand sells better on the East coast than on the West coast. This will help you further tailor your lead scoring model, as well as your relationship marketing plan.
Demographic information can be extremely useful, but it does have a few pitfalls, of which you’ll want to remain aware:
Self-reported information is not always accurate
People often give answers that they believe are more desirable, such as overstating the size of their company or their salary. Beware of potentially aspiration data.
Company information tends to roll towards the mean
Leads at large companies may downplay the size of their company, or their role to avoid potentially hassling sales calls, while leads at small companies may pad their numbers to appear to be a bigger player on the scene than they are in order to be given more attention or to be taken more seriously.
Sometimes people lie
Unfortunately, not everyone will tell the truth on your data collection forms. For a variety of reasons ranging from embarrassment to annoyance or secrecy, some people are reluctant to reveal personal information. If this is a prerequisite for downloading something from your website, they may enter incorrect information.
Lead-scoring is a very successful tool, and demographic information is one of the most essential aspects of a lead-scoring model. Keep in mind that the data may not always be 100% accurate and figure this into your scoring system. It is also useful to remember that scoring doesn’t have to take place all at once – you can do it over time, and this may be a more effective way to run your lead scoring model, as part of an effective relationship marketing platform.