Lead-Scoring with Predictive Analytics – Sell smarter!

How does AI-based predictive lead and customer scoring benefit your marketing and sales team over traditional methods?

Do you use your CRM system for your lead and customer evaluation and perhaps even with integrated marketing automation? You’re on the right track, but you can do even better: the next step is predictive lead and customer scoring.

Lead and customer scoring have been around for a long time and have proven itself: according to a study by MarketingSherpa, companies that used lead scoring models already achieved a lead generation ROI of 138% in 2011. This performance is almost double more than companies that do not use lead scoring methods (with an ROI of 78%).

This benefit is due to customer or lead-scoring enabling Marketing and Sales teams (S&M) to focus on the most valuable customer. Organizations create a customer profile with as many criteria as possible (e.g. turnover, purchase frequency, contact channels, purchase times, quantity, purchase price and much more). Based on the profile, companies evaluate their customers by classifying them, for example, in a portfolio.

For many customers and products, companies need the support of software. 84% of the firms that use lead and customer scoring for marketing and sales do so via their CRM system.

In this article, we discuss the three unique advantages of a CRM system with integrated AI.

You receive forecasts instead of just the “status quo.”

The algorithms of the AI can capture and automatically integrate vast amounts of data from various sources.

Besides, the algorithms uncover common characteristics of leads that have become customers and that have not become customers. From this, the software can generate forecasts for future, new leads.

Practitioners describe this process as predictive lead scoring: Predicting the future from the past.

AI-based systems can create accurate predictions, also in further areas of customer scoring. For example, using existing customer sales data, AI-based software can predict future cross-selling potentials, pricing analyses and churn risk.

Save time and resources.

Companies have been trying to make predictions about sales, leads and customers, long before Predictive Analytics Software went mainstream.

However, these forecasts were often based on subjective assessments by sales and marketing employees. Such predictions are therefore very error-prone and time-consuming to produce.

A Demand Gen Report study shows that 61% of companies see “misleading buy signals” as the biggest challenge in traditional lead scoring. In contrast, AI technology can provide forecasts with a reliable hit probability using many high-quality data sources (such as ERP and CRM data, data from social media and competitor information).

Using data instead of intuitions also means that the manual bulk of the actualization of sales forecasts goes to the AI software. It automates and continuously updates new data and attributes. Automation saves sales and marketing much time.

The freedom gained can be used by sales and marketers for target-group-specific customer communication and thus work more efficiently.

AI enables your company to become more customer-centric

The advantages of using AI technology brings a new world of possibilities to the sales and marketing departments. Intelligent, data-driven software systems can, for example, answer the following relevant questions with an 80% probability of success:

Which customer has a high purchase probability?

The answer to this question is the original intention for lead scoring. By answering this question quickly and reliably, S&M can focus its resources on the most promising business contacts. And can achieve a higher success rate of business transactions.

Which customer could churn?

Usually, existing customers generate the most revenue. But once you have a certain number of running customers, you can quickly lose the overview. Changes in customer behaviour can go unnoticed until it is too late.

AI-based software is like an early warning system for future customer churn. The software can alert six months before a customer churns. These warning signals give S&M enough time to act.

Which offers are suitable for which customer?

By answering this question, marketing – or sales during a customer visit – can send customer-specific offers. A customer scoring based on AI concerning various criteria that indicate an offer affinity can cluster the customers. Thus, the software can make recommendations for which customers, which offer is probably suitable.

Which customer has the cross-selling potential for which product?

Amazon serves as a perfect example for this function: The reference “Other customers also bought” is nothing more than the suggestions of Amazon’s algorithm to exploit the cross-selling potential of Amazon’s customers.

Amazon’s recommendation algorithms have automated 35% of sales and 90% of customer support. As a result, Amazon has reduced costs by three to four per cent.

However, not only companies in the B2C sector can exploit cross-selling potential through AI. Also, in the B2B area, AI-based software is capable of giving your sales team concrete cross-selling tips.

Which customer is willing to pay how much?

The price is often one of the most effective levers in S&M to make a profit. A recommendation on this question can, for example, give the salesperson a certain degree of negotiation security during price negotiations. Besides, it helps the company to pursue a uniform pricing strategy since the algorithm can detect price discrepancies.


Sell smarter: Lead-Scoring with Predictive Analytics – Summary

AI-based predictive lead scoring has three significant advantages over traditional lead and customer scoring systems:

Companies can use multiple datasets nowadays and large amounts of data to create warning signals. The huge amount of different types of data enables a rich number of recommendations.

Furthermore, the automated, self-learning algorithms of the AI save S&M time and resources – time-consuming and recurring research work is relieved.

The last point concerns customer care itself: The specific recommendations for action enable sales and marketing to provide individual and target group-oriented customer care. This customer-centric care ensures that companies focus their resources on the most promising leads.

AI makes it possible to get the most out of customer and lead management and to perfect it.

Companies should, therefore, not close their minds to the possibilities offered by AI. Stay competitive and find out today how AI can support your company.

Do you have any further questions on Predictive Sales Analytics? We are happy to help!


Further Read:

Demand Gen Report (2016)

Hengsberger, A. (2019): Künstliche Intelligenz: Potentiale und Einsatz im B2B-Vertrieb

Vickers, Michael (2019): Is Your CRM as Intelligent as It Should Be? Ed. CRM Magazine