Three Innovative Ideas from the Pitney Bowes Analytics Summit
At the 2017 Pitney Bowes Analytics Summit, analysts and business leaders came together to share their insights and perspectives on how they turn their data into strategy. Momentum was excited to be one of the presenters at this year’s summit!
We’ll be posting our presentation soon, but in the meantime we’d like to share a few other new and exciting applications of technology and analytics explored by other experts at the Summit. If embraced, these ideas have the power to transform the financial industry and give adopters an edge over their competition.
Unprecedented Visibility into Consumer Behavior
Bill McKeogh (Principal Consultant, Pitney Bowes), Jason Hills (CRO, ARM Insight)
Visualize one of your branch locations. Imagine what you could do in terms of strategy if you knew the spending preferences of people walking through the neighborhood, what businesses they visit, and the general path they take as they travel from one destination to the next. If people visit a coffee shop across the street after shopping at a store next door, could you capture some of that traffic by partnering with a coffee shop in your branch location or building a community area?
There is a goldmine of insightful data available from an unlikely source: credit card transactions. Credit card companies provide access to anonymized data that can paint an incredibly detailed picture of the habits of consumers in your market. You can extract unique customer segments such as lifestyle types, or learn which businesses are visited by the same consumers and in which order. This information is a powerful tool that can guide your branching strategy, marketing strategy, product offerings, and even cross-selling goals at a neighborhood level.
The Evolving Role of the Store in an Omni-Channel World
Curt Newsome (Market Planner, REI), Bill McKeogh (Principal Consultant, Pitney Bowes)
One of the biggest challenges facing financial institutions today is aligning their delivery channels into an omni-channel strategy. It’s a topic we discuss extensively at Momentum. How can you create a consistent, seamless, and engaging experience throughout your online, mobile, and physical presences?
REI has some big ideas in this area, and Curt Newson, their Market Planner, shared a few at the Summit. One that caught our eye was bringing online reviews into the physical retail environment.
Customer feedback is a great asset for building trust and confidence in your products and brand, and not just in the retail industry. But traditionally, this advantage has remained exclusively online. REI and Target are changing this by showcasing online reviews in store through signage, screens, and even mobile apps that interact with store displays to engage customers on a higher level.
Can you think of a way that your financial institution could pull digital engagement and customer feedback into physical branches?
Driving Business Decisions Through Deep Learning and Data
Arthur Berrill (Head of Location Intelligence, RBC), Hal Hopson (Managing Director, Pitney Bowes), Kristin Rahn (Director, Product Management, Pitney Bowes)
There’s a lot to unpack when discussing data driven strategy. Nearly every organization is implementing some form of Business Intelligence with tools like Tableu or Power BI, which empower you to visualize big data and use simple machine learning algorithms to predict future trends. Advanced custom analytics such as Pitney Bowes’ solutions have also been delivering incredible results.
But over the past few years, advancements in the development of graphics processors for computer gaming and increased data gathering efforts have fueled an explosion in the machine learning subfield of deep learning. Machine learning generally involves selecting useful features from a data set and fitting a specific mathematical algorithm to that data by feeding it information and adjusting its variables based on feedback. Deep learning, on the other hand, is a process that enables computers to self-select features and make up unique algorithms by training a very large network of relatively simple “neurons” over hundreds of thousands or millions of data points until the network reaches a point where all the variables in those neurons work together to output a desired result. Much like the neural networks in the human brain that inspired the process, it is very difficult and often impossible for humans to look inside these trained deep learning networks to understand exactly how they “think.”
You may wonder what computer gaming has to do with this. Your main computer processor (CPU) is very good at tackling a few complicated tasks at once. But a graphics processor (GPU) calculates a large number of small mathematical operations at the same time in parallel, which allows it to tackle these giant networks of simple neurons much more efficiently. Thanks to the widespread popularity of high definition graphics in computer games, this powerful hardware that was a science fiction fantasy when deep learning was first developed in the 1950s (and even ten years ago!) is now readily available at a low price in any electronics store.
As your data strategy matures and you gather more and more useful data, deep learning will become a powerful asset to every level of your organization. It will be able to assist nearly every process, from informing big picture strategies like branch planning to snap decisions like flagging transactions as fraudulent. Andrew Ng, a leading deep learning researcher, compared deep learning today to mobile development ten years ago. Many financial institutions were hesitant to invest in mobile platforms at first, creating an opportunity for those who actively invested in technology and talent to dominate the market while other banks played catch-up.
We see firsthand how advancements in technology and data analysis are driving changes at the very core of the financial industry. There is a fundamental shift happening in the way consumers interact with financial institutions, and it goes beyond mobile apps. But the same technology driving this shift also empowers financial institutions to better understand and accommodate their member and customer needs.
Bob Saunders, a cofounder of Momentum, presented alongside one of Pitney Bowes’ top analysts, Steve Rymers, on turning data analysis into tangible branching strategy and design decisions. We will be posting the presentation, “The Branch Channel in the Digital Age,” soon. To learn more, read our in-depth interview with Steve Rymers here.