An organisation is always looking for improving its sales or efficiency of work. If an executive can foresee which lead is most likely going to be converted to an opportunity it would help the executive to focus and prioritise the leads accordingly.
Predictive lead scoring uses a predictive machine learning model to calculate a score for all open leads. The score helps salespeople prioritize leads, achieve higher lead qualification rates and reduces the time that it takes to qualify a lead.
Using this score, you can:
- Identify quality leads and convert them into opportunities
- Spend time on leads that have low scores and convert them into possible opportunities
For example, say an executive has 2 separate leads named Lead 001 and Lead 002 in his/her CRM to work upon. Post implementation of lead scoring model, scoring model applies a score of 90 for Lead 001 and 30 for Lead 002.
By looking at the score, you can predict that Lead A has a greater chance of being converted into an opportunity, and you can engage it.
Also, there are other factors that can help you to improve the score such as by referring at the top reasons influencing the score and deciding whether to improve this score.
Important Notes and Prerequisites before using predictive Lead scoring:
- Advanced Sales Insights features must be enabled in your CRM system
- Your CRM must have minimum of 40 qualified and 40 disqualified leads within the past 18 months
- If you’re using predictive lead scoring that pertains to a version prior to 2020 release wave 2 for Dynamics 365, delete the model. Otherwise, the previous version of the model will be applied on all leads in your organization, and the newly generated models won’t have any effect on the leads
- From 2020 release wave 2 for Dynamics 365, the application writes the lead scoring related data to msdyn_predictivescore table and has stopped writing to the lead table. This table is common for both lead and opportunity scoring.
- You can add custom fields to generate an accurate model for predictive lead scoring. The custom fields can be specific to your organization so that you can decide the impact of the outcome.
Lead Scoring Grading:
You can set the grading points structure as per your feasibility. The figure below shows that the grading points are editable.
Lead Scoring Widget:
This widget displays your lead score depending on the factors mentioned in your model.
Below is the location on your lead form where lead scoring widget is visible. One would see the below image when you do not have a published working Lead scoring model in the system.
Once you have the working lead predictive logic implemented, you will see the lead score widget as below. The image reference https://docs.microsoft.com/
Conclusion: PredictiveLead scoring would help organisations to focus on high scoring leads and also would let business understand the reasons behind low scoring or low success rates of lead conversion to an Opportunity. This would immensely help the organisations to take appropriate steps in improving the performance
Thank you, Ashitosh for bringing this topic to light.