Predictive lead scoring is the next level in lead scoring methodology. It brings the power of predictive modeling to CRM data that is explicit and implicit.
Predictive Lead Scoring is the modern-day concept that unleashes the potential of AI upon your data, thus, extracting only high-potential leads for your sales reps
Utilizing predictive modeling, predictive lead scoring learns from successful leads that have closed into customers and finds similar patterns in all the incoming leads. This is done by pulling information from the CRM, marketing automation software, inbound marketing software and third-party lead enrichment APIs. Predictive lead scoring crunches this ocean of data to support marketing and sales teams, identify patterns and connections that would have been impossible to identify manually.
IS THIS HOW YOU MANAGE LEADS TODAY?
• Cold Calling
Collect them in a spreadsheet or a CRM
Contact every lead by emailing, calling and at times even doing personal meetings.
Too many ‘BAD’ leads. And you contact all of them. Sometimes you guess who to contact :)
With each day, leads increase
You hire more sales reps to address the demand.
Productivity still is capped.
You’re unable to prioritize leads.
Traditional Lead Scoring
Unable to offer a credible solution. Creates more problems. Eventually fails because…
Analyse lead data
Define rules e.g.
Website pages visited
Assign scores to the rules
Contact leads above the threshold score
Subjectivity, when assigning weights to rules. Personal experience of business team members might influence the scores.
How many rule can you define?
If a new factor pops up to qualify leads, you again modify your rules.
Becomes complicated in the long run.
What if a machine could tell which Lead to call or drop?
Predictive Lead Scoring tells you which Leads will close into Customers
How Predictive Lead Scoring works?
Squeezing Your CRM
Extracting Meaningful Data
Waiting for the green signal!
We’ll collect the behavioral data of your leads, spread across your CRM
Preserving important data is as important as removing noise from it.
Once the data is ready, we feed it to a model and come up with threshold that is specific to your data
This includes anything from the name of lead to the number of clicks he/she performed on a page
We dig deep into data, understand what it means and then perform necessary cleaning
All you need to do is wait for the model’s green signals!
This approach helps us build a sophisticated and a model just for your case
Hippo CMMS improved sales rep productivity by 236%