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Cognitive Computing

The Customer Insights’ Imperative for CMOs

June 7, 2018 by Lisa Minneci Leave a Comment

News & Events

“The most critical capability of the CMO is to have a profound, deep understanding of customers and their needs and know how to engage and serve them.  This of course involves knowledge of data and analytics. – Jamie Moldafsky, CMO, Wells Fargo, in “Redefining the CMO,” Deloitte Review, issue 22.

According to a recent study by the CMO Council and Deloitte, “CMOs have been increasingly asked to elevate their activities from brand and marketing plan management to acting as an enterprise-wide revenue driver that taps into the hearts and minds of their customers.”  Based on this research, Deloitte makes three recommendations on where CMOs should start.  One of these recommendations is to “relentlessly pursue customer expertise.”

The imperative is clear: today’s CMO is no longer just the brand steward, but also the customer steward.  He or she should be able to steer marketing strategies and activities that, as the report mentions, “engage customers with messaging that better speaks to their needs and values, establishing an ongoing relationship rather than a transactional one.”

The question then, is how the CMO becomes the customer steward.  Part of the equation involves traditional marketing tools and techniques such as personas, NPS, and focus groups in order to fully understand the customer (or customers) and how to meet their needs.  The other part – as mentioned by Moldafsky in her quote – is having a knowledge of data and analytics.  Today’s CMO must be able to analyze meaningful customer data in order to “lead the customer-centric charge.”

Unfortunately for many CMOs that presents a technical challenge, and for some, one that simply does not fit within their marketing budget.  The technical challenge is how to build an enterprise-class customer analytics platform that enables you to ask vital questions of your data.  And yes, it’s almost always build – even if you buy a software package, most require a significant amount of customization and development in order to make it integrate with your existing systems, deliver the answers to your business questions, and is user-friendly.  On top of that, you may also need a data scientist to build out the questions and structure the data so that those questions can be easily answered.  All of this takes time – many months – and money.  Lots of money.

You might be a CMO with a fairly unlimited technology budget. But chances are you do not have unlimited time. The average CMO has a tenure of just 4.1 years, which is half the average tenure for a CEO, and less than that of a CFO, CIO, or CHRO.

How do you, the ambitious CMO who understands the opportunity to be the customer steward and the challenges presented with a traditional approach to a customer analytics platform, resolve this inherent conflict?  The answer is simple: you seek out technology that is built for marketers, but robust enough to deliver the insights you need. One that works with your data, but that does not involve months of development efforts.

This approach puts you on the path spelled out in the Deloitte report: “CMOs wishing to transform their role can take advantage of their unique position to elevate themselves as the customer expert with stakeholders across the enterprise.”

Read more blog posts on customer insights and analyzing customer data:

Pull a Rabbit out of your Hat – or at Least Pull Insight from Your Data

Revelwood Named IBM Customer Analytics Partner of the Year

Home » Cognitive Computing

Filed Under: News & Events Tagged With: Advanced Analytics, Analytics, Cognitive Computing, Customer Analytics, Data Science

What the Heck is Cognitive Computing?

September 26, 2016 by Justin Croft Leave a Comment

News and events

I’m often asked to define cognitive computing. Honestly, it’s not always easy to define. So first, let’s step back and take a look at the overall analytics landscape. Within analytics, you have descriptive, predictive, and prescriptive analytics. Descriptive analytics summarizes what happened. Predictive analytics studies recent and historical data and enables analysts to make predictions about what is likely to happen. Prescriptive analytics defines a set of actions users should take based on predictions.

Cognitive computing takes all of this much further. It’s an intelligent solution that helps people—or more specifically, knowledge workers—make better decisions. What’s really exciting about cognitive computing is that it learns as it goes. And it’s learning from your unique data, your specific business drivers and scenarios. So ultimately, the answers cognitive computing delivers to you are truly unique to you.

Think of it this way… Software, even customized software, is fairly formulaic. If this, then that. Or think of it as a decision tree. Or a hierarchy. The foundation of the software you are using is the same foundation your competitor is using. The data, of course is different, but at the end of the day, how different are the results?

Cognitive computing starts with that same foundation, but adapts as it learns. It adapts to your data. There is no one right answer, just the right answer for your situation. For example, IBM Customer Insight for Banking is used by both large national banks and regional banks. The questions they ask may be the same, but because their customers, their business goals, their marketing campaigns, their demographics, and many other variables are all different, the answers will be different. It is as different as purchasing a suit off the rack is as from purchasing a bespoke suit, custom tailored just for your measurements, your style, your taste, and your budget. Not just hemmed, or let out, or taken in here or there.

Of course, this definition really just skims the surface of cognitive computing. The magic of it, if you will, is the sheer power of it—Watson can perform cognitive computing against extremely large data sets. Whether it’s IBM Watson Health, or our first introduction to Watson years ago, when it competed on Jeopardy!, Watson can quickly sort through volumes of data that humans simply never could.

When I look at cognitive computing in this context, I like to pose this question: if your knowledge workers had the power of cognitive computing today—the power to quickly sort through untold volumes of data and find the right data to make the best possible decision—what could that mean to your business?

If the idea of cognitive computing intrigues you, consider attending the upcoming IBM World of Watson conference, which focuses on cognitive computing in action today, and where it is going tomorrow.

Read more blog posts on cognitive computing and AI:

Embracing Cognitive Computing

How will Artificial Intelligence Impact your Industry?

Pull a Rabbit out of a Hat — or At Least Insight out of Your Data

Home » Cognitive Computing

Filed Under: News & Events Tagged With: Advanced Analytics, Analytics, Cognitive Computing, Data Science, Predictive Analytics

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