Lead scoring is often a process of trial and error, but it can help to radically focus your Sales process by deciding which leads have the highest value. According to a 2023 report (1), mean conversion rates stand at around 10%, with a mere 1-6% of those turning from leads to customers, mostly due to a ‘low quality of leads in queues that sales teams work with’.
What is lead scoring?
Lead scoring means giving your sales and marketing leads a numerical score based on how high value you as a business perceive certain their actions to be.
This score is then used to create a priority list for Sales and Marketing. It can also be used to segment campaigns, with higher-intent leads being delivered more bottom-of-funnel content and lower-intent leads more top-of-funnel content.
The scoring systems vary considerably depending on your industry and context, but common ‘high value’ actions include:
- Visiting a pricing page
- Clicking on one of your Calls to Action (CTAs)
- Reaching out to Sales
- Attending a webinar
- Subscribing to your blog or newsletter
- Consuming a certain quantity of your content
The main challenges with lead scoring
1. Lead scoring doesn’t automatically equal success
Even with very effective lead scoring, you still rely on an effective outreach process. Your measurement of the success is therefore based on this variable, which is difficult to control for in an objective way – i.e. was it the wrong nurture track or the wrong lead?
“Without an appropriate inside sales lead management strategy, qualified leads that do not result in short-term sales often slip through and become lost revenue opportunities.”
Migao Wu, Pavel Andreev & Morad Benyoucef | Information Technology and Management
2. Getting the right data
Another major challenge in lead scoring is capturing sufficient rich behavioral insights into each prospect to drive a meaningful score.
Without high-quality behavioral data, many lead scoring approaches resort to basic ‘Lamb or Spam’ techniques or crude rules-of-thumb. These poor lead-scoring approaches reduce marketing effectiveness and sales efficiency.
Using behavioral data for lead scoring
Behavioral data is a record of all user activity on a site or app – click, view, time on page, and so on.
To effectively use behavioral data for lead scoring, you first need to identify prospects across all touchpoints, channels, and devices.
Having uniquely identified the prospect, the next step is to start capturing as much behavioral ‘signal’ from the prospect as possible, such as:
- Which pages they visited
- Their progress through a trial experience
- How far they scrolled down a whitepaper
- How much of your tutorial they completed
This behavioral data can then be used to calculate an accurate rules-based score for the prospect.
Using AI and Machine Learning with predictive lead scoring
Moving beyond this, predictive lead scoring replaces the hard-coded scoring rules with machine learning, to generate a predictive model based on the behavioral data. With predictive lead scoring, we can identify high-value prospects earlier in the buyer’s journey, and our lead scoring can learn as new patterns of behavior are uncovered.
From: The state of lead scoring models and their impact on sales performance
Why choose Snowplow for lead scoring?
Snowplow provides you with incredibly rich and granular behavioral data across multiple platforms (including web, mobile and AMP) – both in your data warehouse and in a real-time event stream.
Snowplow also provides you with a rich set of user identifiers allowing you to resolve diverse anonymous usage into a single prospect as soon as they have completed your call-to-action.
Putting this all together, you can custom-build the lead scoring system that makes sense for your business and buyer journey. You can even create a lead-scoring system that learns from its mistakes via machine learning techniques.
Most lead scoring systems will be based on crude determinations of customer value. With Snowplow, you can implement Single Customer View alongside your lead scoring and use a 360° view of your customer and their lifetime value to drive the correct scores for future prospects.
To test this for yourself, try Snowplow for free
Sources
1: https://link.springer.com/article/10.1007/s10799-023-00388-w