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Artificial Intelligence in Negotiation with Matt Dixon, Ep #69

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Matt Dixon, Chief Product & Research Officer at Tethr and business writer and speaker, returns to the Negotiations Ninja podcast to discuss the science of negotiation, specifically artificial intelligence. How does artificial intelligence impact negotiations in sales, customer service, and conversational jobs requiring human interaction? Can artificial intelligence take on what we believe only humans can do? How can you integrate technology into what you do to improve daily negotiations or skills? Listen to learn more.


Outline of This Episode

  • [2:40] Learn all about Matt Dixon and Tethr
  • [5:03] Artificial Intelligence in Negotiation: 3 main impacts
  • [13:50] A practical application of AI for training purposes
  • [20:10] The potential automation of negotiation
  • [31:26] How to improve your skills with existing technologies
  • [39:09] How to learn more about what Tethr is doing

The use of artificial Intelligence in Negotiation: 3 main impacts

Artificial intelligence can help you leverage operational, financial, or CRM data to understand patterns that might yield insight into how you’re doing in a negotiation. It’s a way to understand data at a scale that humans can’t or is very time-consuming and is without bias.

Data can tell you discount rates between different reps, what pricing or terms and conditions they’ve negotiated, and how it varies across the sales force. It might yield insight into how you might “give away the farm” to certain customers. You can use that insight to avoid what you have been doing and position your value better.

Another area artificial intelligence is being used is to understand the ebb and flow of conversations. It can track how a salesperson is engaged in conversations with customers in real-time, so much so that it can suggest how to alter the conversation for better results. The technology already exists and some companies are leveraging it positively.

Tethr focuses on AI to mine the entirety of conversations. It’s less about prompting with real-time guidance and more about digesting conversation data across multiple people to understand where there are coaching, training, and value-proposition opportunities to be had. Then sales managers can take that data and coach their reps to handle their negotiation conversations differently.

A practical application of artificial intelligence for training purposes

Companies have recorded calls for a long time. If you call any call center, you’ll likely hear, “This call may be recorded for quality assurance purposes.” Companies take those calls, compress the files, send them to a data center, and never listen to them again. At best, they might go back and listen to 1% of the calls for “quality assurance purposes.” But now, you can transcribe those calls and teach a machine to listen for what you care about.

If you’ve just trained reps on a specific negotiation technique and you’re recording their calls and meetings, you can use this AI to see if reps are using the methods they’ve been taught. Does it have the desired effect? You can pair it with CRM data to see if you’re closing a discount gap or giving away fewer concessions. Which reps need coaching on the skill? Who is flat-out ignoring their training? It answers the question: did the training bring about the desired effect? 

The machine can report who’s doing fine, who’s struggling—and what they’re struggling with—and who’s ignoring the training entirely. It’s a great way to drive performance improvement in any organization.

The potential automation of negotiation

Tethr works with a large North American telecommunications company. They recently looked through their chat data to figure out why some chat reps—handling asynchronous chat exchanges—provide more discounts and rebates than others.

What they found was shocking. The customers being offered better discounts had hired chatbots to negotiate on their behalf. It’s a gameshare model. You get back a certain percentage, and they get paid based on what they negotiate on your behalf.

Service reps were giving twice the amount of discount to robots than to actual people. The chatbots were trained on negotiation techniques until they became great negotiators and won great discounts for the customers that hired them.

So is artificial intelligence in negotiation going to become more prolific? Matt believes that there are certainly some basic transactional things that should be automated or given to chatbots. But as you get into complex issues requiring human judgment, bots would probably struggle to negotiate a complex billion-dollar B2B contract.

How can you improve your skills with existing technologies? Matt shares some useful strategies in this episode. Don’t miss it!

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