In today’s business world with big talk about Big Data, more and more companies are looking into data science and advanced analytics to improve customer relationships, increase marketing impact, and enhance products and services. It’s an exciting new development as data science offers an unprecedented opportunity for businesses gain insights to operate more efficiently and optimize customer experience. But how can companies drive the most effective and profitable results from their data-focused efforts? Incorporating a Chief Data Officer (CDO) into the organization is a smart approach to help ensure actionable insights make it to the top for better executive decisions.
How to make the most of your new CDO
Giving Data a Seat in the Boardroom, a recent article by 1–to–1 Magazine highlighted the importance of capturing key insights from oceans of data and putting it to work effectively. As the article notes, many companies are challenged by having departmental silos that result in losing valuable time and resources that could otherwise be used more efficiently. Beyond the Arc’s CEO, Steven Ramirez offered a solution to bridge those silos and synchronize lines of business to work toward a common goal: “You need a person who has visibility into data [that resides in different divisions] who can ask the right questions and share the insights to inform future decisions.” It’s important to distinguish the CDO role as more than simply an IT function. As Ramirez notes, “[The CDO] is a data scientist, an interrogator of data,” – not simply a gatekeeper. The CDO should be responsible for understanding (and championing) ways to effectively leverage data for business improvements – and a key factor is having a holistic implementation that aligns analytics strategies and insights across all business units for more impactful, data-driven decision making.
Bringing the customer into the boardroom
Having a data-focused role in a company’s C-suite can increase an organization’s potential to differentiate their brand. Ramirez notes it’s an important opportunity to “bring the customer into the boardroom,” as the CDO can deliver insights that essentially put the customer face to face with the executive team. Those insights can help lines of business make meaningful improvements that matter most to customers, address unmet needs, and inform ways to exceed expectations. Strategies such as using social media text analytics to uncover emerging issues, or predictive analytics to boost marketing ROI are a just few ways businesses can truly unlock the power of their data.
To learn more about raising the level of your data science efforts, read the full article from 1–to–1 Magazine >
Sallie Mae learns a hard lesson in the importance of data analytics. If they had been paying attention, they could have seen all their customer experience problems coming.
In August 2013, Richard Cordray, head of the CFPB, announced that the agency was turning its gaze toward student loans. So it was no surprise when the CFPB’s student loans ombudsman Rohit Chopra posted an article on scoring student loan servicers to the agency’s website. It may have been a surprise to Sallie Mae, however, when he called the company “the worst in borrower, school, and federal personnel satisfaction.” Although the CFPB did not announce any monetary penalty, the findings will affect the company’s bottom line since it will be allotted “the fewest new loans to service in the upcoming school year.”
And the fines have been coming. The company itself has warned investors that it “expects the Federal Deposit Insurance Corp. to publicly accuse it of violating several federal laws regarding its private student loans. The Department of Education has said that it, too, was probing the company to ensure borrowers with taxpayer-backed loans were not harmed.”
UPDATE: In January 2014, we reported on the fines imposed on Sallie Mae >
The most frustrating aspect of this for Sallie Mae must be the fact that this should not have been a surprise. In fact, it need not have happened at all. If the company had been monitoring the customer experience to identify emerging pain points before they were escalated to the CFPB, it wouldn’t be in this situation and its customers would be much happier.
In August 2013, Beyond the Arc posted an article to its blog warning Sallie Mae that their student loans practices would draw the CFPB and other regulators’ watchful eyes. Beyond the Arc regularly mines the CFPB’s consumer complaint database to help identify customer experience pain points so banks and credit unions can take action before the CFPB does. In Sallie Mae’s case, the data showed there was clearly a problem that needed to be addressed. Here’s a brief recap of the analysis:
A focus on student loans
Richard Cordray did not mince words when he announced the agency’s focus on student loans last month. “It’s a big priority for us,” he said, adding that borrowers “come to our complaint line and they come by the thousands.”
That’s no exaggeration. As of August 12, 2013, the public CFPB complaint database contained 4,427 student loan complaints about private lenders. According to a mid-year report on student loans recently released by the CFPB, outstanding student loan debt is approaching $1.2 trillion. At $165 billion, private student loan debt accounts for 14% of that total. The report goes on to cite the “disproportionate use (of private student loans) by high-debt borrowers. For borrowers graduating around the time of the financial crisis with over $40,000 in student debt, 81% used private loans.”
Cordray openly expressed interest in this subset of the population: “Frankly, those are the people we should care about as much as anyone in our society… They are young people with some amount of promise and talent and they just lack the means.”
Given the evident importance of private student loans to the CFPB, it’s important that financial institutions understand the complaint data the agency uses when making regulatory and investigative decisions.
Let the data speak
To determine which lenders should potentially be concerned, we took a look at which types of student loan complaints are currently resulting in monetary relief to the customer. Out of all 4,427 student loan complaints, 258 or 6%, resulted in monetary relief. As you’ll see in the chart below, the most prevalent issue resulting in monetary relief was “Repaying your loan.” This was the most frequent issue for student loans overall.
Next, we looked into which financial services companies had the most complaints about loan repayment that resulted in monetary relief. As you’ll see in the graph below, Sallie Mae generated the most activity, accounting for 46% of all student loan complaints (the blue bar). More importantly, it represented a disproportionate percentage of complaints about loan repayment that resulted in monetary relief (the red bar). 76% of these complaints were about Sallie Mae. This should have been a red flag to the company, as it mostly likely was to regulators at the CFPB.
Further examination revealed that Discover® is also facing the same issue, albeit on a much smaller scale.
Be proactive not reactive to avoid regulatory risk
Our analysis indicated that Sallie Mae had an opportunity to improve their loan repayment process to avoid regulatory risk. As the company had access to specific complaints, they could have tried to determine the root cause of these complaints and fix the problems. They still have that opportunity, but their bottom line has already experienced a direct impact.
Other companies can learn from Sallie Mae’s example and take action well before customers lodge complaints with the CFPB. Banks and credit unions now have access to millions of customer feedback data points from multiple channels and sources. A typical bank captures not only customer profile and transactional data, but also customer emails, survey data, banker notes, escalations, and complaints. When you factor in publicly available data sources such as social media and the CFPB database, you begin to gain a very clear view of the customer experience.
The keys to avoiding the trap Sallie Mae fell into are analysis and action. Just capturing the data itself does not make it useful. You need to have established analytical and reporting processes in place in order to identify emerging pain points. Even more importantly, you must have business practices in place for taking swift and decisive action. The analytics team should be integrated with the lines of business to alert them so they can make changes. Then, the results of these changes need to be measured through continued monitoring so you can demonstrate success or improve the solution if necessary.
Like this post? Take it with you – download the PDF >
Check out Beyond the Arc’s free on-demand webinar with tips on how to leverage the CFPB complaint database to improve your customer experience >
Want to gain insights to drive greater market share, increase customer retention, and reduce risk? Predictive analytics can get you there – learn about it in our free hands-on IBM SPSS Modeler Workshop on March 5, 2014.
Take advantage of this opportunity to get:
- Experience using the intuitive SPSS software, including expert nodes for experienced analysts and automated modeling feature for novices.
- Step-by-step guidance for predictive modeling techniques that you can apply to your own data to create immediate value.
- Insights on how SPSS incorporates structured and unstructured data from many sources so you can focus on analysis instead of prep work.
||March 5, 2014
10:00 am – 2:00 pm
||IBM Innovation Center
1001 E Hilsdale, Suite 400
Foster City, CA
|Beyond the Arc
- Arik Killion, IBM Technical Specialist – Predictive Analytics
- Matt Hosman, IBM Business Analytics Specialist
- Matthew Slovitt, IBM SPSS Specialist
|10:00 – 10:30 am
||Intro to Predictive Analytics and SPSS Modeler
|10:30 – 12:00 pm
|12:00 – 12:30 pm
|12:30 – 2:00 pm
||Applied Learning and Case Studies
Space is limited, so please register today >
Event: Sentiment Analysis Symposium
Workshop – “Customer Insight Analytics”
When: March 5, 2014, 1:30 – 5:00pm
Where: New York, NY
Please join us in New York on March 5 at the Sentiment Analysis Symposium for the “Customer Insight Analytics Workshop.”
The afternoon workshop will offer a thorough, practical look at how business analysts, managers, and executives can leverage Big Data to compete more effectively to meet rapidly evolving consumer demands. You’ll learn how to get the most value from unstructured data with:
During the workshop, Steven will examine a data-science case study and highlight best practices of some of the most successful Voice of the Customer programs. This is event is your opportunity to learn a straightforward methodology you can deploy immediately.
Key takeaways – learn how to:
- Benchmark your customer experience, marketing, and social insights efforts
- Accelerate your program and deliver greater value to your internal business partners
- Prioritize your data acquisition and data management efforts
- Identify themost effective analyses to perform to gain relevant, actionable insights
If you’ve been challenged with how to derive real, demonstrable ROI, and would like to learn more, join us for the Customer Insight Analytics workshop. Register online today >
After the workshop, continue to learn from other leaders in the industry, including Amazon, American Express, Huffington Post, IBM and more at the symposium on March 6. To learn more, please visit http://www.sentimentsymposium.com/workshops.html.
Event: Predictive Analytics World, Chicago 2014
Presentation: “Open Your Eyes and Ears: Leveraging Predictive Analytics and Alternative Data Sources to Improve the Customer Experience
Speaker: Steven Ramirez, CEO, Beyond the Arc, Inc.
Date: Tuesday, June 17, 3:05 – 3:25 p.m.
Register online >
In today’s competitive market, the name of the game is customer experience, and to win, companies need to meet and exceed consumer expectations. That starts with really understanding what customers want and what drives repeat business –and fixing any pain points that get in the way. To get there, predictive analytics can power your business strategy by delivering actionable insights for making smarter decisions.
What are customers saying about your brand? How can you provide an optimal customer experience? To answer these kinds of questions, your analytics program should take advantage of the abundance of publically available customer feedback. Consumers now rely on public platforms to share positive and negative experiences about companies, which means social media networks and resources such as the Consumer Financial Protection Bureau (CFPB) complaint database may be critical to your success.
The 2014 Predictive Analytics World in Chicago event is the ideal venue to learn how to leverage advanced analytics to build more profitable relationships and improve customer experience. In his session at PAW, Steven Ramirez will highlight case studies from Beyond the Arc’s analysis of open data from the CFPB, Capital One, Citibank, and Bank of America to show you how to:
- Analyze social media and customer feedback to identify and address potential issues before they become problems
- Simplify how you evaluate customer insights
- Respond to necessary service changes to prevent loss of business
Don’t miss it — Register online today >
Bruce Johnson, Chief Data Scientist
Nick Baldocchi, Fantasy Football League Champion
For many years, the NFL has tried to bolster ticket sales to undersold games by blacking them out on television. But as that option may soon be off the table, many NFL teams have been considering a new ticket pricing model. This week, the Detroit Lions became the first NFL team to adopt variable ticket pricing for the 2014 season.
In the current approach to pricing single game tickets taken by most teams, the same seat has the same price for each game of the season, including the preseason, regardless of the opponent. With variable ticket pricing, the same seat may be priced differently for different games, depending on the forecasted demand for that game. Contests that are expected to sell out would command a premium, while preseason games and games against a lackluster opponent would be priced less to account for the lower interest in the game.
Detroit announced that each game will now be assigned a level of demand from 1 to 3. Level 1 will consist of lower-demand and preseason games. Level 3 will include the traditional Thanksgiving Day game and other high-demand primetime games like Monday Night Football.
This new approach to pricing can help teams boost ticket sales, but may be too reliant on the day and date of a game to determine fans’ interest. How can teams accurately forecast demand to make sure they don’t leave anything on the table?
Push it over the goal line with predictive analytics
To answer these questions, Beyond the Arc recently evaluated which factors might influence ticket prices for an NFL team. We approached the problem as we would any business research question, applying our data science methodology to design an approach to the problem. The steps of the process include:
- Business understanding
- Data understanding
- Data preparation
During the business understanding phase, we explored key theories about which factors might influence the willingness of fans to pay more for specific games. Some of these variables included:
- Temperature and weather conditions on game day
- Win-Loss record of the visiting team
- Returning home team players on visiting team (e.g., Peyton returning to Indianapolis)
- Playoff contention
- Week number in the season
- Day of the week
- Rivalries between teams
- Excitement factor of the teams and players
- Social media commentary and sentiment
The data draft
To test our hypotheses, Beyond the Arc obtained publicly available attendance and sales data for a particular NFL stadium and augmented that data with information from other public sources. Regional rivalries, simple win-loss standings, and weather and temperature at game time were easy to gather from Wikipedia. However, we needed a way to quantify “excitement,” so we looked at:
- The number of ProBowl players on each team
- The ESPN weekly team rankings
- The Las Vegas betting lines
- Information from various Fantasy Football research sites
The ProBowl players are a matter of historical record and easy to find on Pro-Football_reference.com. However, if the NFL sticks with its new method of selecting ProBowl players who are chosen draft-style by two former NFL players (this year by Deion Sanders and Jerry Rice), then this factor will not play a role in determining ticket prices as there is no reliable way to predict which players will be chosen prior to draft day.
The other information on team excitement is more subjective, so we sought different kinds of crowd-sourced data in order to get a balanced perspective.
The ESPN rankings are developed by sports experts. The Vegas Insider data claims to be the best handicapper of sports events in the world. Whether or not that’s true, we can leverage their opinion for this research. Finally, we found team ranking and player information from Fantasy Football sites like NFL.com and fftoolbox.com. Fantasy Football data is both peer-reviewed and self-correcting, two attributes we look for in a reliable dataset.
Defense wins championships, winning sells tickets!
So as not to give away the recipe for a variable ticket pricing system, we won’t reveal our exact findings in this article. However, we did find that weather was not a key factor as we had anticipated. Rather, competitiveness throughout the season stacked up as a key determiner of continued fan interest and willingness to buy tickets.
So, if struggling teams want to pack the stadium for late season games, they may want to consider incentives or discounts that lower the effective cost of each seat. A “frequent fan” program that offers discounts and rewards based on the number of games attended could drive loyalty and keep seats warm on cold December Sundays. Data science insights could help to identify the right tailored offer to make to each individual member of the loyalty program.
Like this post? Take it with you – Download the PDF >
Sallie Mae, the leading student loan provider, recently announced it expects to spend $70 million to address issues raised by government and regulatory agencies about its lending practices. Based on our ongoing analysis of the Consumer Financial Protection Bureau (CFPB) complaint database, this expense came as no surprise to Beyond the Arc. Sallie Mae could have avoided the costly penalty if they had been paying closer attention to their customer experience.
In August 2013, we published an article about the results of our analysis of student loans complaints in the CFPB database, which indicated Sallie Mae was in trouble. The biggest issue – over 65% of complaints– focused on loan repayment. We found that Sallie Mae had a disproportionate number of complaints about loan repayment that resulted in monetary relief – over 70% of them. Discover was second with just over 12%.
Back in August, we pointed out that Sallie Mae had an opportunity to identify the root cause of customer complaints and improve their repayment processes before the CFPB took action. Analysis and customer experience improvements might have prevented further investigation and spared the lender millions of dollars in fines.
Other financial services providers can learn from Sallie Mae’s costly mistake.
To see the types of insights companies can derive from the CFPB database, check out our recent analysis of student loan complaints incorporating 2012 census population data to determine which states have the highest number of complaints per capita and why.