It was only a few years ago that people were proclaiming that the future of B2B marketing had arrived in the form of predictive lead scoring. This new frontier would radically change the face of sales and marketing. Has predictive lead scoring measured up to the hype? Well not quite, though that might be a problem more to do with the amount of hype than the actual implementation of predictive lead scoring.
If you haven't read our "What is Predictive Lead Scoring?" article yet, make sure you check it out!
What has predictive lead scoring has done is improve on traditional methods of lead scoring? By reducing the role human judgment plays in prioritizing leads, predictive lead scoring was always designed to improve the accuracy and efficiency of these traditional methods. Furthermore, it's supplied a data-driven reason for decisions made by marketing and sales teams, helping to align these two teams and improving the efficiency of lead prioritization systems. While not radically revolutionizing the face of lead scoring, predictive lead scoring has done what it’s promised.
Predictive Lead Scoring is Improving
Make no mistake though, predictive lead scoring is still the way of the future. Advances in technology will result in more streamlined systems being used. Machine learning algorithms that determine scores for leads will continue to evolve providing increasing value. For example, identifying with increased accuracy those qualified leads likely to convert. The next generation of lead scoring will be fuelled by APIs, more data, significant training, and constantly fine-tuned algorithms. Predictive lead scoring uses different models like logistic regression and random forests to determine the best match. This data will power models that are used in conjunction with the supplied data to produce more accurate results.
In addition, predictive lead scoring models are always improving, and the potential upside is large. Drawbacks and restrictions to those being able to conduct predictive lead scoring are disappearing slowly. Early predictive lead scoring models required big datasets and thousands of contacts with clean and accurate associated data in order to make predictions. Many businesses lack either the quantity or the specific quality of data necessary to train an accurate model. Newer models can pull more data from other third-party sources and integrate it with the data from a business’ CRM platform to supplement the data.
As the machine learning field evolves, naturally so too will the technologies for predictive lead scoring. For example, developments in neural networks are already able to provide a more accurate score. Neural networks can intelligently aggregate data from various sources at the same time. With the scale of your predictive lead scoring being able to grow and develop simultaneous with the growth of your business, it makes sense to start investing in it for the future.