Lead scoring allows the marketer to optimize the use of their time and other resources to greatly improve the prospect to conversion ratio.
But, the important question on everybody’s mind is how should we score leads? Especially with limited resources.
A lead by its very definition indicates probability or likelihood. The straightforward answer as to how to score leads is to use customer surveys that directly or indirectly track behavior and conclude whether the person would buy the product or service offering. But, experience suggests that people often over exaggerate when responding to such surveys or when supplying any data on their own for that matter. Marketers who have relied extensively on such surveys have found this out the hard way.
Actions never lie. The key to getting lead scoring right is monitoring prospect behavior and assigning scores based on the extent to which the prospect exhibits the desired behavior. Keeping track of the visits on websites, and their activity on it, including the whitepapers they download, the webinars they attend, the YouTube videos they see, the ads they click, etc. helps gain perspective of the prospect’s behavior, wants, and needs. Tracking the prospects sentiments, generally through comments posted on the public domain such as social media websites, comments section of blogs and articles, and similar ways, is the next step.
Needless to say, it is impossible to monitor and score each prospect manually. Automation is the way to go, and there are scores of intelligence software that facilitate such tasks.
Such automated lead monitoring and scoring very often fuels further success. Over time, companies gain perspective on the segment most likely to purchase their products or service and an insight into the general behavioral patterns of such segments. Lead scoring, as a whole, helps fine-tune and hone in marketing messages and engagement strategies to improve conversion ratios across the board.
We’ve seen a big impact on our sales process by effectively scoring leads using analytics data. What are some of your most successful lead scoring approaches?