Using Neural Networks for sales prospecting
“No one wants to be sold but everyone wants to buy.”
Most of us hate being sold. The moment we know someone is selling something, we keep our guards up.
In the book, The Challenger Sale, authors Mathew Dixon and Brent Adamson surveyed over 6000 salespeople from around the world and found that ‘challenger salespeople’ outperformed every other group. Who are these challenger salespeople? These are the people who challenge the norm, are more knowledgeable and educate their customers. The customers trust them and ultimate buy from them.
“Prospecting is the first step in the sales process, which consists of identifying potential customers” – Business encyclopedia.
The most common approach taken by many ‘AI-based’ sales startups is to identify the next buyer by mining internet data. They look at what people are talking about in social media and then identify those who are searching for a given product or service. However, people who are already actively looking online are not the best potential buyers (or prospects) to sell to.
If you discover an opportunity late, in the customers buying process, you’ll more likely need to discount the price to earn the business – The Funnel Principle, Mark Sellers
Let’s try to see how top salespeople identify a prospect?
Top salespeople do proactive sales than reactive. They do not wait for potential prospects to reach out but instead identify their needs before they do. They identify patterns in their best customers and identify new prospects based on those patterns.
“The only way to sales conversation with high-value prospects is to interrupt them” – Fanatical Prospecting, Jeb Blount
Since Neural Networks can create an approximation of any function, we will try to approximate the prospecting process.
I ask the following four questions to identify who are ideal prospects (taken from the book ‘New Sales Simplified’ by Mike Weinberg)
- Who are your best customer
- Why they became customers
- Why they still buy from you
- Why do prospects choose you over other similar products
The goal is to identify common features among successful and unsuccessful prospects. Normally this is done manually and intuitively.
If we had to solve the same problem via Machine Learning we need to use Neural Network Classifier.
Classification can be defined as the grouping of things by shared features, characteristics, and qualities or if you will simply dropping things into corresponding buckets, you could, for instance, classify the following geometric shapes based on their similarity.
Based on the four questions mentioned above, we try to extract relevant features from answers to the questions.
