Matt Dixon

Matt Dixon is one of the world’s leading experts on sales, customer service, and customer experience. As Chief Product & Research Officer at the Austin-based artificial intelligence and machine learning venture Tethr, he has executive management responsibility for product management, innovation, and marketing and plays a leading role in helping to bring the company's data and insights to the business world through ground-breaking research, thought leadership, and public speaking.

Matt is the author of three best-selling business books, including The Challenger Sale and The Effortless Experience and is a frequent contributor to the Harvard Business Review. He is a sought-after speaker and advisor to management teams around the world, ha...

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Insight Selling: Driving Growth in the Age of Customer Empowerment

Matt Dixon’s first book, The Challenger Sale: Taking Control of the Customer Conversation, was a #1 Amazon and Wall Street Journal bestseller that has been lauded as “the most important advance in selling for many years” (SPIN Selling author Neil Rackham) and “the beginning of a wave that will take over a lot of selling organizations in the next decade” (Business Insider). 

Now, Matt takes audiences on a whirlwind tour through sales history—from product selling to solution selling to Insight Selling—and issues a stirring call-to-action for today’s sellers: adapt or become irrelevant. 

Drawing from dozens of studies and from his own extensive experience advising leadership teams around the world, Matt explains how customers today are learning on their own, engaging salespeople much later in the purchase journey than ever before, and explains why survival in this new world demands that we shift from a classic “solution-selling” posture (in which the seller seeks to diagnose what’s “keeping the customer up at night”) to an “Insight-Selling” posture (in which the seller seeks to show the customer what should be keeping them up at night)—from diagnosing needs to prescribing needs.  In a world in which customers can learn on their own, the value a seller must bring is to share with the customer those things they couldn’t learn on their own. 

This presentation covers the new behaviors, competencies, and activities required for sellers and marketers be successful in this new world and offers the unique opportunity to hear from one of the “founding fathers” of the Insight Selling movement. 

Books

  • The Challenger Sale: Taking Control of the Customer Conversation
    Buy
  • The Effortless Experience: Conquering the New Battleground for Customer Loyalty
    Buy
  • The Challenger Customer: Selling to the Hidden Influencer Who Can Multiply Your Results
    Buy

News

  • Use AI to fix call center quality assurance, not just automate it
    Use AI to fix call center quality assurance, not just automate it
    Jun 14, 2018
    Matt Dixon...
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    Use AI to fix call center quality assurance, not just automate it
    Jun 14, 2018
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    The problem with traditional call center QA

    Few processes are more broken in today’s customer service department than quality assurance, or QA for short. QA is the process whereby companies “audit” calls for how well a representative adheres to company-defined scripting and language during a customer interaction. Did the rep say the customer’s name three times? Did the rep thank the customer for her loyalty? Did the rep read the required disclosure statements after executing the transaction? Did the rep display empathy, friendliness and professionalism?

    The problems with call center quality assurance—as currently practiced by companies—are many. From the perspective of company leadership, QA’s biggest shortcoming is that it is a manual, people-driven process. And people are inherently inefficient and expensive. What this means in practice is that companies will end up listening to and auditing only a small percentage of customer calls—typically 1% of recorded calls will ever be audited by a company’s QA team. As a result, QA becomes a source of frustration for reps who feel that they aren’t being treated fairly and that the company is assessing their performance on too small a sample to be valid. So, it should come as no surprise that for every call center QA process in a large company, there is also an appeals process for reps to dispute spurious results and scoring that is perceived to be unfair.

    AI-enabled speech analytics as a solution

    For this reason, companies have latched onto new AI-based technologies (namely, machine learning-powered speech analytics) as an opportunity to automate QA—to stop having QA managers listen to a small percentage of calls and instead teach a machine to listen to all calls, without the cost and inherent bias of people. Understandably, service leaders’ eyes widen at the idea of automatically scoring all service interactions without any human involvement.

    As an AI AND MACHINE LEARNING PLATFORM company, Tethr is often asked to help companies automate their call center QA process. But, as attractive as it seems to use AI-based “listening” approaches to automate a manual process, our advice is that companies think twice before doing this. In our view, digital technologies are better deployed to fix call center QA, not just automate it. People are the problem in QA…but it’s less because of how QA is administered and more about how QA is designed.

    The far bigger issue with call center QA isn’t that it’s inefficient and expensive (which it is), but that it’s built off of assumptions, hunches and gut instincts. Companies ask QA to listen for things they think are important (e.g., saying the company’s name at the beginning of a call for brand association or saying the customer’s name multiple times to make the customer feel the interaction is personalized). But these assumptions, regardless of how well-intentioned, have rarely been tested with data. This is why most companies constantly update their QA scorecards—without a compass to show them where to go, they resort to guessing.

    New technologies today allow companies to combine the best of human intelligence with the best of artificial intelligence to deepen a company’s knowledge of what actually drives customer outcomes. Put differently, the promise of AI isn’t just about automation, it’s about understanding.

    Armed with machine learning and data science techniques, leading companies are seizing upon this opportunity to finally overhaul call center QA so that it delivers what it was originally intended for: higher quality customer interactions.

    Putting AI to use – real-world examples

    One large telecommunications provider, for example, had long used their call center QA team to assess whether their reps demonstrated appropriate acknowledgement when customers expressed frustration—i.e, “I’m sorry you’re having this problem” and “I know how frustrating this must be for you.” Using AI to understand how unstructured voice data impacted known outcomes (like CSAT, NPS and Customer Effort Score), they came to learn that this sort of acknowledgment—which the company had always assumed drove positive customer outcomes—actually made customers more frustrated, not less. The frustration level, in fact, was on par with what customers experience when they’re transferred to another department. Now, the company teaches its reps to resist the urge to acknowledge and apologize and instead get on to solving the problem at hand.

    A provider of home services that we work with used AI to figure out what objection-handling techniques lead to higher sales conversion rates. The company had long assumed that when customers balk about the price of their services, the best approach was to explain to the customer that the company offered some of the lowest rates in the business and, if push came to shove, to offer a small discount. But, when the company used machine learning to study this technique across thousands of sales calls, they found that this technique wasn’t remotely correlated with higher sales conversion. Instead, reiterating the company’s money-back guarantee ended up being much more highly correlated with conversion (and much cheaper to offer than a discount).

    This company also used AI to understand—at a very specific level—how their reps should demonstrate “advocacy” in customer interactions. While advocacy had been on the call center QA “checklist” for many years, they never really knew how best to demonstrate advocacy in different customer situations. A large-scale analysis using machine learning demonstrated that in sales interactions, reps are best served by using language that demonstrates control, confidence and authority (e.g., “Here’s what I recommend” or “This is the option I would pick”). In fact, the data analysis suggested that such approaches backfire in issue-resolution situations. In those sorts of calls, reps are far better off proposing an option but hinting that there are other options if the first one doesn’t pan out (e.g., “I’ve got some ideas for how to fix this…let’s try this first”).

    Finally, we worked with one large insurer to apply AI to their voice data in order to identify–among more than 250 categories of rep behaviors and interaction dynamics–which ones actually drive one of the key outcome metrics the company is focused on, Customer Effort Score. In the end, we identified 14 statistically significant drivers (ten behaviors that eroded CES and four that improved it). Most impactful among the behaviors that eroded CES was when reps used language that indicated they were “powerless to help” a customer to resolve a specific issue. The company is now focused on training and coaching their reps on how to avoid these phrases and instead use language that demonstrates advocacy and empowerment. And, importantly, their call center QA team now knows what critical behaviors and techniques to be listening for in calls.

    Fix first, then automate

    It is true that advances in AI, machine learning and natural language processing finally afford companies with the opportunity to automate call center QA, which represents a step-function change in efficiency for companies. But, the greater opportunity is to dramatically improve the effectiveness of QA by finally identifying the language techniques that actually drive quality. Armed with this insight, companies will then be well-positioned to automate their processes and finally capture the scale benefits of scientific and data-driven quality assurance.

    Our strong advice to service leaders is this—“fix first, then automate,” not the other way around.

  • The Omnichannel Experience (Part 2): What Matters to Customers...and What Doesn't
    The Omnichannel Experience (Part 2): What Matters to Customers...and What Doesn't
    Dec 08, 2016
    Matt Dixon...
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    The Omnichannel Experience (Part 2): What Matters to Customers...and What Doesn't
    Dec 08, 2016
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    In my last post, we discussed the promise of omnichannel –integrating data streams and queues across all channels to ensure a seamless service experience and unified view of the customer. Omnichannel seems to hold a lot of promise, but it clearly comes at a (steep) cost. At the end of that first piece, I left you with the question, “is it worth the time and money to do it?”

    So, here I want to share some results of CEB’s recent research on the omnichannel benefits customers care about – and those they’re less likely to reward you for.

    Most of our members tell us that their budgets are tight and they're not in the position to spend the millions (in some cases, tens of millions) necessary to implement a comprehensive omnichannel solution. Instead, what they really want to know is which specific elements of omnichannel will deliver the highest returns for their business. Put differently, what will generate the most customer value while also helping to reduce operating expenses? To figure this out, we scoured all of the major omnichannel vendors’ websites to create a list of the claimed features and benefits that their solutions deliver. We boiled this list down to 30 categories – everything from channel integration (the ability to deliver a seamless experience to customers as they switch from one channel to the next) to customer knowledge and recognition (the ability to deliver a 360-degree view of the customer).

    We then surveyed more than 2,000 customers to understand which of these attributes deliver the lowest-effort experience. It's important to note that we didn't just ask customers their preference on features and benefits. Instead, we tested the impact these benefits had on actual experiences customers had with companies. In the end, we were able to discern what, if any, impact they had on the customer's experience. What we found was pretty surprising and not at all what most companies would expect.

    Probably the biggest surprise was that the benefit most often touted by vendors – the ability to seamlessly migrate a customer from one channel to another (e.g., from a chat session online, to the phone) – doesn't actually deliver that much impact on effort reduction. In fact, it reduced customer effort by only 5.3%. To be fair, it does help, but at what cost? After all, this level of functionality is one of the most technically complicated (and therefore expensive) omnichannel benefits to deliver.

    Another benefit that delivered only nominal returns was “customer knowledge and recognition” – the ability for a company to integrate customer data and channels to instantly recognize who they're talking to and deliver to an agent a 360-degree view of the customer. Again, it delivers some benefit – 5.4% according to our research – but certainly not the value expected from a multi-million dollar omnichannel solution.

    It turns out, the two things that customers do care about are "service transparency" and "service proactivity." Meaning, customers want the companies they deal with to inform them of the steps taken and the timeframe expected to resolve their issue (transparency) and to alert them of updates/issues related to their request (proactivity). These two benefits/features help to reduce customer effort by 58.1% and 15.3% respectively.

    What can explain this discrepancy--the fact that channel integration and customer knowledge don't seem to matter much, but transparency and proactivity matter a lot? In our view, it all boils down to something we call "customer uncertainty." When customers leave a service interaction uncertain as to what will happen next and whether or not their issue is actually being resolved, they worry. That worry leads to additional customer effort in the form of another call/email/tweet/etc. to the company just to double-check everything. It’s an additional cost for the company and extra effort for the customer.

    To reduce customer uncertainty, leading companies do three specific things:

    1. Instill confidence by providing customers with information on next steps;
    2. Demonstrate progress by making the resolution process – and timing – more transparent, and;
    3. Increase access to information customers need when they need it.

    A good example is Delta Electricity, who provides customers with positive identification of the technician that will be servicing their home and then sends text message updates with the estimated time of arrival – helping to set customer expectations up front and reduce uncertainty along the way.

    To be clear, an omnichannel solution certainly can help companies provide greater levels of transparency to customers and deliver higher levels of proactive service. But, technology isn't the only way to deliver these benefits. Companies can also train and coach frontline staff, or update existing web content to accomplish the same thing.

    The reality is that most customer service reps have enough knowledge about the issues they're handling that they could deliver greater transparency into the resolution process, but handle-time restrictions often preclude them from doing so (even when it's clear that doing so would help reduce customer uncertainty).

    The same is true when it comes to proactive service. The average company can leverage their existing technology, tools and frontline staff to deliver more proactive service than they do currently. It’s about thinking one step ahead of the customer and helping them avoid frustrating, high-effort experiences down the road.

    In an era of “more” (more information, more channels, more options, etc.), a little extra transparency and proactivity will take you a long way in driving more customer loyalty. And, the best part is that it doesn’t have to cost you an arm and a leg to do it.

     

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