AI Lead Scoring Cherry Picks Today’s Best Leads

AI Lead Scoring Cherry Picks Today’s Best Leads

Struggling to get more productivity from your sales team? Properly prioritizing contacts for them using AI can translate into better performance.

There are many things eating into productivity today, so it’s tempting to take a shortcut in process. Scoring sales leads can take a lot of time, and that time could be better spent selling. But there’s no room for a shortcut at this stage of the sales process: sales leads must be prioritized. Otherwise, teams waste precious hours chasing leads that don’t intend to buy right now — or never seriously intend to buy at all.

There is a smarter solution to solving productivity problems than cutting out chunks of process: the automation of routine tasks. Take lead scoring. Data analytics and Artificial Intelligence (AI) can cherry pick the best leads at any point in time, so sales teams free up precious time and work more effectively. AI-enhanced analytics easily processes reams of CRM system data, sales histories, demographics, and company information. AI can then identify hidden buying signals to surface the low hanging fruit and determine the right time to contact each lead.

Often, specific questions need to be answered before sales reps can proceed with a contact. Below we consider three key questions and the lead scoring techniques that provide the answer.

How well does this contact match the target audience?

Ideal profile matching analyzes contact profiles. It looks at the big factors, like company size and type, product-contact suitability, and probable transaction profitability. It also looks at more granular items, such as the individual contact’s department, job title, and activities (such as filling out a landing page form).

By focusing efforts on the accounts most likely to mesh with their brand, reps automatically up their chances of success.

Are contacts really interested in your brand?

Engagement scoring is all about reading the buying signals: as a rule, the more someone interacts with your brand (via social media, website visits, etc.) the closer they are to making a purchase. For this type of lead scoring, auto-captured activity and engagement data is most significant. Pattern analysis can determine the engagement level of each prospective customer; the type of content he or she reads will be an important factor.

This type of lead scoring is more dynamic than ideal profile matching. It can tell reps where the buyer is on their journey and what types of contact will be most effective in motivating them to buy.

How close are we to making the sale?

Fruition scoring uncovers the degree of buying intent. It is a combination of the ideal profile score, the engagement score, and any milestones that the lead has completed. If reps know a lead is close to buying, they can follow AI-based product recommendations and create an attractive offer. On the other hand, if a sale isn’t imminent, reps can provide relevant content or other personalized attention to the contact.

Sales productivity is one place where your organization can’t afford to struggle. By giving sales teams the right tools, they can spend less time prioritizing leads and more time selling. If you haven’t checked out AI-guided sales tools, do so today. They can open the door to greater sales effectiveness.

Authored by Richa Kapoor, Marketing Manager, Absolutdata Analytics, and Promita Majumdar, Marketing, Absolutdata Analytics

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