Many companies are now lead scoring to help understand the stage of a lead and optimise the next action to take. There are 2 main types of lead scoring; Rules based or Knowledge based (Predictive). Most built in lead scoring within marketing tools offer a basic rules based lead scoring mechanism e.g. if a prospect interacts with more than 2 emails and requests a download within a month then score them as a ‘Hot Lead’. Such rules need to be defined in advance. Knowledge based lead scoring, takes a much broader set of data and then uses a machine to learn what activity influenced the leads that actually closed. It then uses this knowledge to predict the best score for new lead activity. Predictive lead scoring is far more accurate and far more detailed in how it can score leads.
Go beyond the scope of your internal data
By analysing a prospect’s previous interactions with your company and considering external signals, considering demographics and behaviour attributes from various data sources and thousands of data items, it is possible to predict the potential future value of the prospect, hence the predictive lead score is not only considering activity influencing conversion rates but also the potential future value of the customer.
By combining external and internal data, at both Company (account) and contact level, marketers can get a much richer, 360 degree view of all buying signals.
Using science to score leads throughout the sales cycle, provides a good way to focus efforts. This also has a subtle side effect in that by engaging sales to critique the scoring, they are forced to question why they qualified a lead or deal, providing great feedback to support continual learning of the processes used, by both humans and machines, to qualify leads.
Marketing Automation Accentuates Lead Scoring Difficulties
Many companies using marketing automation would have already implemented some form of lead scoring. But these are often flawed because they don’t have enough data to correctly understand buyer behaviour. Marketing departments continually generate marketing qualified leads (MQLs) that will never close. This basic lead scoring still requires a lot of gut feel and instinct from marketing to convert a lead to a deal. No wonder many marketing automation tools are struggling to provide the return that was anticipated.
B2B Sales Departments Ignoring many Marketing Signals
B2B marketing is now generating lots of inbound leads, but these don’t enter the funnel pre-qualified and ready to convert and much effort is still spent qualifying such leads by sales teams, even though they are inbound leads with a history of various marketing campaigns and significant data available.
Predictive lead scoring does not remove the need to buy new lists of prospects, but it does improve the bottleneck between lead acquisition and conversion. It helps make the funnel less top heavy with lots of unqualified leads:
Don’t just do Lead Scoring – Combine with Predictive Customer Marketing
So if you are looking to do predictive lead scoring, it can be significantly improved by including information gained by analysing your existing customer behaviours. This advanced, micro, customer centric segmentation influences lead scores, therefore don’t do either of them in isolation. Intelligently utilise predictive modelling to improve both lead scoring and customer marketing (expansion and retention).
Want to read more about how BrightTarget uses Predictive Lead Scoring? If so, click here