- How Does Predictive Lead Scoring Work?
- Is Predictive Lead Scoring Worth It?
Traditional Lead Scoring – This is a rules based approach based on how leads interact with you digital content (Behavior Scoring) and information you know about them (Demographic Scoring). It can also be based on the company where the lead works (Account-Based Scoring). For each of these categories the marketer decided on a set of rules to score the lead. Each action, or piece of information, will get a score based on these rules. The drawback to this approach is that not all marketers have a good approach for creating these rules, and for implementing them in their marketing automation platform. People make mistakes.
Predictive Lead Scoring – This approach uses the same information as traditional lead scoring, but lets a machine learning algorithm decided how best to weight each action, or piece of information. This alleviates the problem marketers have with not having an approach for creating rules. The drawback is that machine learning algorithms need well-structured data and enough of it to build meaningful models. Without clean data, computers make mistakes.
Recently, we discussed how to use predictive analytics to determine which sales opportunities will win and which will lose: 5 Attributes to Predict Sales Wins. We can use this same approach, but move it up the funnel, to build a predictive lead scoring model. The result would be an automated way to determine which leads have the highest propensity to buy.
How Does Predictive Lead Scoring Work?
Collect and Clean Your Data – Machine learning algorithms are great as long as they can read your data. The first step is to aggregate the data in your marketing automation platform, your CRM, and third-party data sources. Once all of this data is put together it has to be tidied up. Messy data will lead to messy results.
Build a Machine Learning Model – Advanced machine learning algorithms will process all of your historic data to discover patterns. The algorithm will identify which combinations of attributes are likely to convert to wins.
Test the Results – In most cases multiple machine learning algorithms are applied to your data. They are tweaked to fit the nuances of your company. After several models are tried, they are tested to determine which is the best fit for your data.
Deploy – The model that performed the best against your data is deployed across your technology stack. The model will run on a routine basis and will score leads accordingly. The outcome is a prioritized list of leads based on their propensity to buy.
Is Predictive Lead Scoring Worth It?
As with most things in life, it depends. If you don’t have many leads, or if they are not connected to won deals, you are unlikely to realize the benefits of predictive lead scoring. Without a sufficient amount of data, machine learning algorithms will not work and will often lead you astray. In this case, a traditional lead scoring approach will yield better results.
If, however, you have sufficient lead and deal volume, predictive lead scoring is worth it. This is especially true if you don’t already have a lead scoring process in place. The outcome will be a predictive model that automates your approach to prioritizing leads. The results will be a better prioritized list for your SDR team, more quality leads for your AEs, and higher conversion rates to win.
At ORM Technologies, we specialize in predictive analytics and optimization for sales and marketing. If you have any questions on this post, or would like help with predictive lead scoring, please email us at email@example.com.