How AI Unlocks Customer Data for Long-term Profitability
Author’s note: Before we get into discussions of how customer data can be unlocked with AI (and how it can be used by AI for more scalable and better liked by the customer experiences), I just wanted to let you know that this post, although standalone, is also the final part of a 3-part blog series. The first post is a call to organizations to prioritize customer value projects over projects that primarily deliver shareholder value. It is not necessary to read these posts in order, particularly if you are looking to understand how to make better use of your data, but if you would like to read it first, you can find that post here. The second post is a discussion of what and how value is created for customers, and is also a standalone post. But, if you’d like to read it before diving deep into data, you can find that post here.
Now, with those caveats aside, let’s dig into data!
And, we’re going to start our discussion on unlocking customer data using AI with a story about how I used customer data to actually build a customer-centric AI-based chatbot.
So, back in 2016, I was working with a global software company who decided to shift to a direct-to-consumer, software-as-a-service model, and they wanted to know how to do that without alienating their resellers. So, we were hired to do the product research and go-to-market strategy. Within that research, I found that 80% of their customer service calls came from a single end customer need: Getting a product activation code.
They didn’t need help with the product. They didn’t need to understand account information, invoicing, tech support—nothing. Because all of that was managed by their resellers—but, if they moved B2C, those kinds of calls would come in, and they’d have to staff up to support them.
So, I recommended using a chatbot to address the activation code need because their customers didn’t really want or need to talk to a person at that point. They just needed an activation code to prove the product they were trying to use was licensed to them. So, use a chatbot.
I gave them the business case that in doing so they could take the almost $1M they were spending annually on people who 80% of the time just answered the phones to give someone a string of letters and numbers. And, I told them to use that money to start staffing up the tech and customer support they would need as they shifted from a B2B to a B2C SaaS-based software company. This chatbot was an improvement to the current customer experience:
In how the customers could get the information they need when they need it—which upon delivery was easy, simple, available on desktop and mobile devices—and, on a 24x7 basis.
In what the customers will want and need as the company changed how they sold their products and how their products were consumed.
But, if you’ve read this far, it’s because you’d like to know how companies could do better. That starts with customer data, so let’s get into that now.
Understanding Your Customer is Achievable through Unlocking the Value of Your Customer Data
No matter where you are in your business & technology maturity, you have some version of this:
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What’s important to realize is that across all of these teams, tools, and the very rich structured and unstructured—qualitative and quantitative data—is the ability to understand your customer.
But, the problem is that it’s too difficult and too costly to access, aggregate, synthesize, understand, utilize, and most importantly to share across the enterprise, in an actionable way, what your customers are thinking, saying, doing, and feeling.
It takes so many resources, so many tools, teams, and TIME to understand what your customers want, what they need, how they’re thinking, using, not using, where they’re engaging, where they’re not, and most importantly—what to do about it.
Companies have tried through business intelligence, datawarehouses, and data lakes, through CRMs, CDPs, through analytics packages from the web to their products, service interactions, service calls, NPS and more. Because we keep tacking on more single-serve applications purchased and maintained by one group, but used by another group, and the full utility and value it could give to the enterprise is understood by few, if any.
But, if you focus
on the customer data piece,
it can be your greatest, untapped intellectual property.
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This is all information your company has the ability to utilize across the enterprise at a point when you can intersect and positively influence a buyer’s decision to buy, proactively address a customer’s need for support, influence a customer’s willingness to buy more from you or willingness to advocate for you—this is ALL information you have and can use.
You just have to unlock it. And, that is where AI comes in.
Because of the surge in AI developments over the past few years—from new developments in conversational AI to startups created through investment in AI—to better understanding of how machine learning has been used over the past several years, AI provides the ability to unlock ALL this data and surface it to the individual employee who has an opportunity to influence, support, and sell to your customer—not just on a single transaction basis. But, on a continued, additive, relationship-building basis over the entirety of the customer lifecycle.
And, AI product companies, today, have the ability to do it in a meaningful, scalable, and financially accessible ways across these specific use cases:
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But, it takes an investment in a strategic, customer-centric approach to AI. This is not short-term value delivery which is why so many companies haven’t seen the returns they expected from AI investments. Because they invested in using AI for cost-cutting, shareholder-focused projects.
The investments into AI must be from a customer-centric perspective—in the way and in the places and moments that matter to your customer.
Use AI to make it easier, faster, more intuitive, and more proactive versus reactive for them to derive REAL VALUE—more than what they expected from buying from you, and you will see growth in sales, organic growth, NPS/CSAT, product utilization, sentiment, stickiness, and advocacy.
Bottom line: Companies, stop using AI as a cost-cutting measure.
And, start focusing it on how you’ll use AI to grow marketshare over the next 5 years. Because, if you don’t, it only takes one savvy, customer-centric competitor to put you out of business.
So, in summary:
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Then, you’ll waste less time, money, and resources arguing over how to grow revenue and profit. Instead, you can actually DO IT.
So, before companies are ready to jump into an AI initiative, they really need to rethink how they how they operate, evaluate, and act to put the customer at the center of every discussion, priority, and investment. Or, these, and other products and projects are going to have trouble proving longterm value.
Now, to recap:
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And, thus concludes my 3-part series of how companies can use AI for longterm gain, but only if they stop prioritizing short-term shareholder value.
This concludes my TED talk. Thank you. :)