Poor lead prioritization can have huge implications for sales and marketing with missed sales goals and demoralized sales teams and ruined collaboration. Poor lead quality drives burnout and high turnover in sales positions leading to extra costs for training and more sales staff on hand to generate the required pipeline. Traditional lead scoring methodologies rely on interest from the prospect to determine a score, but the interest level of the individual does not necessarily provide an indication of their ability to purchase.
AI based machine learning models can score marketing leads using a wider variety of factors and learn from those leads that ultimately became opportunities and those that created revenue. By looking at more information about customer behavior, company size, industry, etc., each lead can be evaluated and scored with sales representatives receiving a ranked list of leads for follow-up. AI can also provide reason codes for each lead so that sales knows the key factors that make the lead valuable. This optimized process insures that sales is working the highest quality leads available which drives faster ramp up from working on consistent leads, quota achievement, less turnover and overall lower sales costs.