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By: Sree

Predictive Lead Scoring and Probability in Odoo CRM

Functional CRM


Lead is nothing but the conversation or communication with a probable customer. We can also equate lead with the business prospects. Any contact that a sales team maintains or gathers can also be called a lead. That means the lead can either be a prospective customer or contact.

In business, different companies maintain leads hoping that these leads or sales contacts would become clients at one or another juncture of the business. When we are talking about Lead from the ERP point of view, lead could be any information including phone number, mail id, or social media account of the prospective buyer. 

We get more and more leads through advertising, marketing, live chat, direct mailing, and other marketing efforts. We can get sales deals with the help of different types of activities including direct communication, internet communication, or mass media operation.

We can call a contact a good lead if the person's characteristics or buying patterns are more similar to that of your buyer groups. In such cases, the person is more likely to become a client from a lead.

In the modern business world, every company strives to generate a maximum number of leads or we can say they strive to generate a maximum number of good leads who are more likely to become good customers. 

In the ERP era, marketing wings are depending more on the ERP and CRM tools for lead generation and management. ERP can easily integrate different marketing tools and this can help us in lead generation. CRM and ERP tools for marketing and customer relationships can be integrated with email marketing, SMS marketing, and marketing automation tools. Besides, websites, blogs, live chat, and other features can also be integrated to gather as many leads as possible. The conversion of leads to customers can also be eased with the ERP tool.

What is Lead Scoring?

Once you have enough leads, the next task is to identify their level of interest. That means you may have 100 leads with you. But of the total, only 50 may be interested in your product and of the 50 only 10 may really buy the product. To make maximum use of leads and to plan conversations and activities, you will have to identify the interest level. Lead scoring can come to your rescue at this stage.

Lead scoring

We can define lead scoring as the act of using some values, especially values in numerical forms' to identify the interest level. This process is done automatically by recently developed and upcoming ERP tools.

However, to help the machine to learn the process well and give accurate results we have to give certain attributes. The attributes can be the state, age, or occupation of the lead. For example, a leader who is a college student is less likely to purchase child care products. At the same time, a lead aged more than 50 is more likely to purchase tools for geriatric support.

Lead Scoring Attributes

1. Demographic information of the lead

The geography of the place where the lead lives or the category to which the lead belongs decides the chances for the lead to become a customer. A user can give demographic information as an attribute as it will help you to set negative points if the lead does not comply with your demographic condition. Suppose we are giving country as a scoring attribute. Imagine you are selling warm clothes and you do not have any sale outlet or delivery service in India for the product. If your lead is from India, then your ERP will directly give negative points. This will help your lead management tool to pick out the lead from the probable customer list.

2. Company details

Details of the company or organization of the lead can also be selected as an attribute for lead scoring. It will help you to identify the type of industry and the size of the industry and point out the ones which are unlikely to be your client.

3. Email success rates

Those interested in engaging in a business with your firm will certainly click through your emails. If the email remains unclicked or the persons unsubscribe the mail then the lead scoring will give minus marks to the lead.

4. Predictive lead scoring

Predictive lead scoring can be considered as the approach that has taken the lead scoring method to its higher level. This method will help you to make use of the online data and machine learning technics and identify the characteristics of already existing customers. This will also help you to understand the characteristics of new leads. Besides, it will also help you to rank the probable buyers based on the analysis.

This will help the sales team to plan business tactics based on the probability rate.

Odoo CRM and Predictive Lead Scoring

Odoo CRM is a complete customer relationship management tool that has been used by many industries and businesses across the globe. It supports predictive lead scoring and helps the business plan different operations in an effective way.

In this blog, we can learn how Odoo CRM sets predictive lead scores to improve the accuracy of the prediction regarding buying patterns.

Go to Configuration in Setting.

Here we can check in Predictive Lead Scoring.


Under predictive lead scoring, we can find different fields. Some of the fields are State, Country, Phone Quality, Email Quality, Source, etc. These are some of the fields which are already added in the predictive lead scoring. But we can add or remove fields if we like.

In Odoo, the success rate of an opportunity is calculated based on the stage. However, the fields added to predictive lead scoring also play an important role.

Let us remove some of the fields and add some fields.

For example, we can remove phone quality from the list.

Instead, we will add Language to the fields. We can add the fields based on our preferences.


As we have completed the selection of fields we can save the changes and go ahead.

Now it's time for us to check how this feature helps to manage the reporting capabilities of CRM.

This is how Odoo CRM helps us to set predictive lead scores and manage lead generation and management effectively.

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