The function of a score is to determine the relative value of a record compared to other records in the database based on defined criteria. This is important because it translates complex patterns into something that is actionable by the rest of the business’s processes. In Salesforce, this is sometimes only applied as a Lead Score, but the practice of scoring can apply equally as well to other objects depending on the business process.
Setting up a score formula requires assessing and combining all the available criteria into a single number. Choosing what criteria is important and what impact it will have on the score has a tremendous impact on how effective the system is. Many people come up with a formula based on their business process and available criteria and then never touch the formula again. While some people may tell you that scoring is an art, the truth is that scientific rigor can be applied to maximize the effectiveness of a scoring formula over time.
Lets take a simple example. If you are looking your marketing performance data and you notice that a specific state, say California, has higher conversion rates than other states perhaps you should adjust your demographic score to increase the value of leads that are from California. If your sales reps are prioritizing their calls based on score, this will improve the overall conversion rate. The trick is figuring out exactly how much extra weight should be given to leads from California, since this criteria is only one variable in a complex system.
To figure out the optimum weighting for your different criteria you need to implement split testing. You can use an auto-number field and some adjustments in your score formula to create groupings. For this example we will have two groups, A and B. To split test our formula we apply the original score formula to group A, and the new score formula with added weight to California (group B). Depending on the volume and the length of your sales cycle you may have to be patient before measuring the data. However, once you have enough data you can determine the impact of the change by measuring conversion rates and revenue based on the groupings.
These adjustments can increase the efficiency of both the sales and marketing teams, and at the end of the day have a profound impact on revenue generation.
