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KREATO CRM BLOG

Lead Management becomes smarter with AI driven Lead Prioritization

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One of the important area of focus for the sales teams is lead prioritization.

Sales teams have a larger amount of data at their disposal. Even though data and intuition show that some leads are better than others, sales teams struggle to effectively prioritize good leads over bad ones. When they fail with lead prioritization, your sales reps spend the same amount of effort on bad leads as they do on good leads.

Sales leaders should implement data driven AI technology that can help the sales team prioritize leads, ensuring the team focus their efforts on best leads.

Ideal Profile Scoring:

First step in the lead prioritization is the determining the profitability of the lead – analyse profile data to find out how well the lead fits your target audience and how aligned your product or service is with the needs of the lead.

AI driven Lead conversion success insights could tell you the profile attributes that indicates the sales readiness of the leads.

For example, if your partner referral leads convert 40% of the time, but your trade show leads convert only 10% of the time, you want reps to spend more of their time working partner referral leads first.

The Ideal profile scoring algorithm takes into account the attributes that determines the profitability and the attributes that indicates the sales readiness.

Still the Ideal Profile scoring is a static measure and it doesn’t reflect the buying intention.

 

Behavioral Scoring:   

Identify the prospects that most probably will get converted. Behavioral scoring helps you to dynamic measure the sales readiness of any prospective lead identified, based on the interest observed on various engagement channels (emails, calls & in-person visits) and any other engagement activities like demo or event participation.

 

Measuring the engagement behavior of the customer helps to gauge the buying signals. The more the customer is engaging the intention to buy is high.

Auto capture of activity data and customer engagement data will help. AI driven customer engagement pattern analysis helps you to determine the minimum level of engagements (by different channels) that leads to successful conversions. No more guesswork should be involved here.