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Machine Learning in Sales Processes
Latest   Machine Learning

Machine Learning in Sales Processes

Last Updated on July 24, 2023 by Editorial Team

Author(s): Prashant Sai

Originally published on Towards AI.

Machine Learning in Sales Processes

Sales funnel processes and segments can be different for different companies based on the type of service provided by the respective company, whether it’s inbound or outbound sales. The content, campaigns, and sales pitch will align according to the status of your persona or Lead in your sales funnel (Buyer journey).

There are a lot of tools out there in the market which can help automate a lot of these tasks for you. But, how can you leverage various data points collected from your leads in this funnel process to predict user preference or help you sell better? The following are some examples where our own data scientists at Chatgen.ai have found proven use cases. Our tools have helped companies with structured data to help them cut down sales response cycle and improve retention.

Lead sorting

In general, the sales team would avoid chasing unqualified leads, which requires a time-consuming process of scoring and sorting the leads based on multiple criteria. Lead sorting is a methodology used to rank the importance of individual leads. Here is an example of action points by lead

Feeding additional data from channels like phone calls, email, etc., to the above-structured data will drastically improve the algorithm accuracy. With enough data from the leads on the above behavioral points, machine learning algorithms can significantly automate the entire lead scoring and sorting process with predictive insights, instead of relying on rule-based, manual filtering.

Lead de-duplication

There has never been more information available for us to work within today’s world of Big Data. Unfortunately, all this data is unstructured and hard to use. The simple task of figuring out who is who in a spreadsheet or database can be a daunting and time-consuming task. So, by using machine learning algorithms, we developed the most dynamic and scalable solution for de-duplicating and linking datasets.

Some of the significant use cases include:

1. De-duplicating customer records

2. Combining lists of addresses or businesses

3. Master data management

4. Merging different database systems

5. Creating a master list of products or parts

6. Cleaning up lists of names and emails

Automating pre-sales queries

A lot of insightful pre-sales lead queries data can be studied, which can reveal a helpful pattern. With the use of chatbots and Natural Language Processing (NLP), most of these queries can be automated and reduce the burden on your pre-sales. For a detailed approach on how to automate your support queries, please check this link

Improving Customer Lifetime Value

Analyzing a diverse series of factors to see which customers are going to churn or leave versus the ones that will renew is among the most valuable insights that AI and machine learning are delivering today. By being able to predict a Customer Lifetime Value Analysis for every customer, the company can prioritize where the health of client relationships is excellent versus those that need attention.

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Published via Towards AI

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