Last week I geeked out at the SAS conference Analytics Experience 2016 in Las Vegas. It was a heady mix of business and technical sessions on how to take advantage of the power of analytics.
Of course there are innumerable applications for analytics, from fraud detection to supply chain optimization to garden variety business intelligence. For this post I’ll focus mainly on customer-related applications, and some of the more advanced SAS capabilities discussed at the conference.
New Open Platform
Let’s start with what SAS announced: SAS® Viya™ — billed as a new open, “cloud-ready” analytics platform. The idea is to bring together the SAS portfolio, built over some 40 years, into one platform that can be used by data scientists and other analytics professionals; business users and IT management; and senior management.
SAS is also working harder to be more “open” — in the sense of being interoperable with other enterprise systems. This is hugely important in the age of cloud-based systems and infrastructure services like Amazon Web Services and Microsoft Azure. If you can’t access a key functionality via a cloud-based API, it might as well not exist. While SAS is not yet offering transaction-based pricing, it wouldn’t shock me if they end up there in the next few years.
I also wanted to learn more about some relatively new buzzwords (for me, at least): “machine learning” and “cognitive computing.”
SAS defines machine learning this way:
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
Analytics expert Bob Hayes of Business Over Broadway largely agrees: “Machine learning is a set of techniques that allow computers to make dynamic, data-driven decisions without explicit human input.”
Makes sense, but to me machine learning seems like a new term for “self-learning” which is not exactly a new idea.
Cognitive computing is about providing more of a human-like interface to machine learning and other analytics. To me, it seems like a combination of natural language processing (NLP) and advanced analytics. Karl Rexer of Rexer Analytics credits IBM with popularizing the term, and says: “In general it seems like the term Cognitive Computing is being used more like Artificial Intelligence.”
Buzzwords aside, what’s important here is that analytics is getting more powerful (machine learning helps) and easier to put to use (via NLP and the Cloud).
Analytics and the Customer Experience
I’ll shift gears now to the business side, and discuss what I learned about how analytics can improve the customer experience.
Dudley Gwaltney, Manager of Analytical Modeling at SunTrust Bank, sees a number of opportunities to put analytics to work. While the number of SunTrust branches is declining, they are still a critical part of the customer experience. Activity on digital properties can provide some clues as to customer goals, so the bank can provide more targeted services and offers online or offline.
Trae Clevenger, EVP Analytics and Chief Strategy Officer at service provider Ansira, said his firm worked with Panera to use analytics to identify wait time as a customer point. The fix: a combination of digital (mobile app) and physical (kiosk) innovations.
Finally, I had a great chat with Adele Sweetwood, SAS Senior VP Global Marketing and author of a new book The Analytical Marketer. Adele has many years of experience transforming the SAS marketing organization to become more data-driven in how it makes decisions. In short, it’s not something that happens overnight. You’ll need to invest in education/training, implement new metrics related to desired outcomes (e.g. pipeline impact) instead of activities (lead volume), and foster more cross-function collaboration with sales, service, and IT.
Analytics and Competitive Advantage
Does analytics drive a competitive advantage? I’d say it’s a competitive necessity for big companies to optimize their resources and make better decisions. Advances in power and usability are bringing analytics within reach to smaller businesses that may not have in-house data scientists.
My research on customer-centric practices finds analytics one of the 5 habits of industry leaders. In other words, they are more proficient at these activities (each broken down into 5 practices):
- Listen—Understand What Customers Value; Act on Their Feedback
- Think—Make Smart, Fact-Based Decisions
- Empower—Give Employees Resources and Authority to Serve Customers
- Create—Produce New Value for Customers and the Company
- Delight—Exceed Expectations; Be Remarkable!
So while analytics proficiency (“Think”) is a trait of leadership firms, it not the only thing they do well. To me, analytics is a wonderful tool to help listen to customers (via Voice of Customer and their behaviors) to optimize the customer experience. Do that well, and the company and its customers should both see value.
Disclosure: This post is part of my independent coverage of technology industry developments. No endorsement is implied for any companies mentioned in this post. SAS invited me to attend the conference, provided a free pass, and paid travel expenses.