How to increase sales revenue through AI-driven Lead Scoring?

Converting a potential lead to a paying customer is a tough task.

The Sales team must sort through a long list of potential customers every time to figure out how to spend their time. 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, the sales team spends the same amount of effort on bad leads as they do on good leads.

Are you struggling to convert potential leads to customers? AI-driven lead scoring to help you!

In the Lending Industry though effective lead prioritization is listed as the number one priority many of the methods being used are simple and rudimentary. These vary from online lead origination to tracking customer behavior on websites and social media platforms.

There are significant opportunities exist to prioritize your leads, but full-on adoption and buy-in remain elusive among lenders.

Sounds great! But how to achieve it?

Cool, that’s where AI & ML comes into the picture. AI-driven lead prioritization can help your sales team to outreach on people most likely to buy.

Technology and automation can help the sales team prioritize leads, ensuring the team focuses their efforts on the best leads. Best way to do this is by having a data-driven lead scoring system.

AI-driven lead scoring system to unlock the lending potential!

With the advent of AI & Machine Learning, businesses have a way of prioritizing leads based on the type of customer, buying journey, demographics, past performance, etc.

Data-driven lead scoring takes the traditional lead scoring approach to the next level by applying big data and machine learning algorithms to evaluate the key behaviors of existing customers and prospects and rank them against a scale that can distinguish customers and prospects who are more likely to convert, retain, or buy from the company’s products and services.

The first step in the lead prioritization is to analyze profile data to find out,

1) how well the lead fits your target audience.

2) how aligned your product or service is with the needs of the lead.

AI-driven customer engagement pattern analysis helps you to determine the minimum level of engagements (by different channels) that leads to successful conversions. The model evaluates the relationship between various attributes associated with customers and prospects and the identified behavior (i.e., customer purchase) and scores them based on the propensity to achieve the identified behavior. With that new scoring, the sales team can then prioritize their time, and only spend time on the leads that are highly likely to become leads.

How to leverage lead scores in business operations

Effectively segment customers and prospects based on the predicted lead scores and put them into distinct buckets.

Systematically monitor the relationship between predicted lead scores and customer purchase rate.

AI, the future of a business lead generation

In conclusion, the lead scoring model provides the necessary inputs to what a modern company needs to be successful. It not only uncovers key insights on highlighting promising leads and retain or even make a purchase based on the real-time data collected from existing and prospect customers, as well as products, and services, but also helps the company benchmark and reveal key optimization indicators and track key customer profile and segments consistently to paint the complete picture of what sells well versus not well the existing and prospect customer base.

How can Insight Consultants help you?

By using ML & AI we can help you build an accurate prediction model which allows you to score leads for your sales team and target offers at the right customers where they will be most effective.

Schedule your Free Consultation if you feel this may be of interest to you and your business.