The Analytics of Everything
Retail success hinges on the ability for retail executives to infuse analytic insight into the decision making process.
At the Retail and Consumer Goods Analytics Summit (RCAS) 2017, held in Chicago April 26-28, headline speakers from Kimberly-Clark, Walmart and NBCUniversal drilled this business reality home with real-world examples from the field and advice for those far along the analytics maturity ladder as well as those just starting out.
In its fourth year RCAS really hit its stride. We had our most impressive speaker lineup to date and a packed meeting room filled with the who’s who in analytics in both retail and consumer goods.
The event kicked off with a masterful keynote presentation by Suja Chandrasekaran, CIO, Kimberly-Clark Corporation. The former SVP and global chief technology officer & chief data officer at Walmart gave a tour-de-force recap of the current state of analytics across both retail and consumer goods.
“As an industry we are behind in analytics,” she said. “And we can’t be behind. It is not rocket science we can master it.”
The key to the mastery of next-gen analytics according to Chandrasekaran is artificial intelligence and machine learning that can provide true predictive insight into shopper behavior and demand. “Traditional analytics lose value over time,” she said. “While AI based systems gain value.”
Following Chandrasekaran’s opening keynote, David Ahuja, senior director, Walmart Technology took to the stage and followed the theme of taking analytics capabilities to the next level. He spoke of the evolution of analytics from a misunderstood discipline to the backbone of every major organization today.
Analytics started with the buzz of big data and IT execs spent a lot of time explaining its use to business leaders. Analytics 2.0 ushered in the age of the data scientist. “We all hired a bunch of people with the title of data scientist and told them to go do data science,” he said. “They were all over the company doing great work but it was not very organized.”
As we enter what he dubs Analytics 3.0 the digital enterprise is not about technology but providing value for the customer and retailers need to adjust their strategy and processes to meet this new reality.
“Our biggest problem is data,” he said. “Not creating data but working with it. If you are not paying attention to governance, on how you make decision models, you will need to do it. If you do not get the data right you cannot do the analytics effectively.”
The effective use of analytic tools was the cornerstone of David Dittmann’s, director, business intelligence & analytics services, Procter & Gamble, presentation on predictive and prescriptive analytics. He discussed P&G’s long history with analytics, from its humble beginnings in 1963 to its market-leading position today.
He discussed the importance of getting business leaders’ buy-in to build truly dynamic analytic capabilities. “Leadership doesn’t view models as sexy,” he said. “You need to come up with a way to engage business in the models. There must be a place for senior leadership to come in and engage.”
The key to enterprise level success Dittmann believes is the analysts’ ability to be highly fluent in the business. They cannot be a separate entity, but valuable members of the decision-making process. To earn this seat at the table, however, data scientists must be able or create unexpected insights that can be leveraged to make critical business decisions.
The innovative use of analytics firepower was a key theme of Cameron Davies’, senior vice president, analytics, NBCUniversal, presentation. He discussed Everett Rogers five traits of successful innovations and provided an analytics spin to the author’s seminal work, and provided real-world examples from NBCUniversal.
The five traits are create a relative advantage; compatibility; simple and easy to use; easy to trial; and observable result.
Create a relative advantage. There are four types of superiority: create an economic benefit, social prestige, pain relief (Uber), and immediate reward (tinder) he said. “We created an analytics dashboard so people can get what they need and the team doesn’t have to spend 70% of their time sending out reports. Build from the business problem down, not the tech up.”
Compatibility. The degree to which an idea aligns with the targeted users’ current worldviews, habits, and activities.
Simple and easy to use. How well users can easily learn and adapt a concept’s key functions.
Easy to trial. How well an innovation can be sampled, tested, or trialed at low risk. “You need to have centralized teams that can help people get started,” he said. “You need to be able to spin up a low cost proof of concept. Fail fast and cheap, adjust, then try again.”
Observable results. How the benefits of the concept can be easily measure or observed. “Success breeds success and failure breeds contempt,” he said. “Profitable projects rarely have trouble finding funding.”
This is just a small taste of the magic that was up on the stage at RCAS this year. If you are involved in the field of analytics or simply need a better understanding of the discipline to better perform your job, attendance at RCAS is a must. Be sure to join us at the end of April next year when RIS and CGT Magazine will once again join forces to put on RCAS 2018. As the event and the field grows this is the place to learn from and network with the best and brightest in the industry.