Predictive lead scoring with a lead generation strategy offers a highly effective, efficient alternative to traditional lead scoring, giving you the ability to evaluate large quantities of data to find the best possible leads. Although there is no doubt that predictive scoring offers many benefits, from reduced guesswork to more accurate scores and a streamlined approach, this system also simplifies lead scoring significantly.

Avoid These Predictive Lead Scoring Mistakes

With that said, there are a few things that you certainly want to avoid if you are considering predictive lead scoring as an alternative to manual scoring. Keep reading to find out which mistakes to avoid.

Some of the top predictive lead scoring mistakes to watch out for include the following:

Mistake 1: Focusing on quantity over quality

By its very nature, this type of lead scoring uses large amounts of data to create a clear bigger picture of each potential lead. Unlike manual scoring, which can be done with smaller amounts of behavioural and demographic data, a wide range of data sources are used to calculate scores automatically. Make sure that you do not put your focus completely on quantity. Instead, set parameters that are most relevant to your scoring model. This can include third party data, behavioural data, demographic data, and any other data that helps to create a detailed score.

Mistake 2: Not having a scoring model in place

Without a lead scoring model, how will you define your scoring efforts? Without a plan in place, you are effectively shooting blind. You cannot use a cookie cutter model, either, because no two campaigns will be exactly the same in how leads are scored and nurtured. Before getting started with any type of scoring, you first need to go back to the beginning and define your scoring model. Have a look at our tips on how to create a lead scoring model to get a better idea of what to include in your model.

Mistake 3: Failing to consider drift

Another major mistake to avoid is not taking drift into consideration. This is a normal part of the process and cannot be avoided. Instead, you will need to refine and adapt your scoring efforts frequently, testing constantly to ensure that data is still correct. Bad data can very quickly throw off your scores. When you don’t take drift into account, you may end up with outdated data, duplicates, cold leads, and other similar issues.

Once you see how much this approach can help your lead generation efforts, you will quickly see the true value that predictive lead scoring has to offer for your nurturing strategies.