Would you buy from an AI sales robot?

It's clear who's frustrated in sales, and who's using AI to stay ahead of the game. With all the competing attention, it's becoming exponentially more difficult to sell online. Here are the top 3 ways to apply AI to sales.

1. Predictive Sales

AI can be used to score leads and prioritize action-steps based on those scores. This is done by leveraging anonymized data of specific customers, and placing them in "buckets" or stages of the funnel.

You've probably heard of the Pareto Principle:

"Roughly 80% of the effects come from 20% of the causes"

This applies to sales leads as well - 20% of leads generate value, while the remaining 80% burn resources.

Correctly labeling leads, removing efforts on the 80%, and putting those resources into the 20% is a 10x marketing strategy. This is simple optimized allocation of resources, perhaps the most important business skill.

Besides predicting whether or not a lead will convert at a certain stage, you can detect churn to prevent leads from leaving.

For instance, you could build a clustering algorithm that groups users that have churned or not churned on similiar attributes. Doing so, you may find clear distinguishing features that impact churn.

Churn detection via clustering algorithms can be applied to a variety of fields, but the idea is the same:

Another explainable and fairly simple model is with decision trees. With every decision along the sales funnel, the probability of a lead converting or churning changes. By pin-pointing stages of high and low probability of churn, you can optimize the funnel.

2. Lead Generation

All your sales leads are in various stages. You've heard the simplest version, "cold leads and hot leads." A more detailed version is:

Lead Generation

• Names
• In-profile leads
• Marketing Qualified Leads
• Sales Qualified Leads

Lead Conversion

• Customers
• Loyal Promoters

Lead Retention

• Continuous Value

AI can be applied to improving lead generation in many ways. For instance, given a list of leads (e.g. sourced online from LinkedIn), they can be ranked according to how well they match the ideal client (e.g. those with data history of repeated sales).

Further, data points can be verified and decision-makers identified. By focusing on relevant, important leads, you can further generate derived leads, consolidating and simplifying lead sources.

3. Chatbots

Saving the best for last... chatbots.

By combining natural language processing (NLP) with end-to-end bot platforms, you can create a conversation interface with hundreds or even thousands of users simultaneously. No longer do you have to directly message potential leads one-at-a-time.

bitgrit is building a chatbot with the SAP platform, which will teach users data science, give them relevant resources, connect them with projects, and more!

To stay up to date on our progress, join our community.