When your target market is massive and your sales team is overwhelmed by the number of leads, you will want to prioritize which accounts you reach out to. At this point, lead scoring is your steering wheel. Assigning a score for all inbound leads based on how likely they are to convert as a customer is the essence of lead scoring.
What about companies that have very selective targets? Lead scoring applies equally well there – not to qualify at the top of the funnel but to score intent and contextual fit across various stages of the funnel. When you do named accounts targeting (a.k.a account-based marketing), the stakes are high. A false negative could cost dearly.
Every company that is a going concern needs lead scoring.
There are two types of it: Traditional Lead Scoring and Predictive Lead Scoring.
Traditional lead scoring comes with a lot of rigidity. There is a lot of room for human/process legacy errors in this method. Let’s say a marketer analyzes the behavior of inbound leads. If the lead goes to the home page and then asks for a demo, he might have a high fit score according to the marketer’s qualification score template. However, it doesn’t tell anything about the spending power, intent and fit. At the minimum, this results in mis-segmentation of the leads and at worse, leads to under-qualification or over-qualification of the leads. This might result in mistreatment of a lead by the sales rep.
But, that doesn’t make lead scoring unreliable. The challenge here is maximizing your understanding of a lead. How can that be done? Predictive lead scoring.
Predictive lead scoring is what you get when you use a predictive analytical model for lead scoring. It analyses past data in your CRM. A good predictive lead scoring model takes into account data about deals that were won and lost and not just wins. It maps a pattern and helps predict the likelihood of each account being a win. A “fit score” is assigned to each lead. Higher the score, better the chances of the lead becoming a sales qualified lead.
But, then arises the problem of CRM data being incomplete. CRM data is complete only if the sales rep fills it properly. What if the data is poor? Most of the predictive lead scoring models fail because of 2 reasons:
- CRM data is incomplete because of low fill rates.
- Only a chunk of data is used to analyze, i.e – only the wins.
A good predictive model needs good quality data in large amounts!
And, what if the company is newly incorporated? They won’t have any CRM data. How can a predictive lead scoring model overcome the problem of lack of CRM data? PipeCandy solves the problem for you. Here’s how our Predictive lead scoring model looks:
If you lack CRM data, the second option – Attribute importance is your best friend! Decide which attributes are important for you and assign an importance level. The attributes could be revenue, industry, web traffic, etc.
Once the attributes are applied with necessary filters, each company in your universe is assigned a fit score.
For the ‘high fit’ leads you can then apply industry-specific insights models of PipeCandy and public/social insights models to assess if there is an explicit and immediate context that an account executive can exploit.
Why does Predictive Lead Scoring work?
Predictive lead scoring increases the efficiency of your sales team. Sales receives high-quality leads and it increases their effectiveness as well. The right accounts get nurtured by the marketing team. Your sales and marketing teams are aligned tighter than before.They’re now Smarketing.
Predictive lead scoring works mostly because it’s smart!
Like our kickass predictive lead scoring platform? Want a customized one for your team? Talk to us.