Business Analytics Innovation Summit 2014 Chicago: Day 1 Highlights
Tags: Business Analytics, Conference, Credit Risk, Customer Experience, IE Group, Predictive Analytics
Highlights from the presentations by Business Analytics leaders from Bank of America, Northern Trust, AOL and Liberty Mutual on day 1 of Business Analytics Innovation Summit 2014 in Chicago.
The Business Analytics Innovation Summit (May 21 & 22, 2014) organized by the Innovation Enterprise at Chicago, IL covered major challenges and opportunities being observed by Analytics leaders across industries. Executives at the forefront of analytics shared their innovative approaches, providing insight into how they have gained valuable information from raw data. Executives discussed the challenges faced within some of the world leading organizations and provided case study examples of how they are pushing the boundaries of analytics.
We provide here a summary of selected talks along with the key takeaways.
Here are highlights from Day 1 (Wednesday, May 21, 2014):
Andy Curtis, Senior Vice President, Business Analytics , Northern Trust talked about the increasing trend of client experience projects, in his talk titled "Leveraging Analytics to Drive Client Satisfaction". Many companies are embarking on client experience projects to improve client satisfaction, loyalty, and most importantly overall relationship profitability. He recommended an Analytic Framework comprising the following six phases in an iterative fashion: Knowledge (develop 360 degree client view), Assess (assess gaps in current client experience), Link (link plans to fix gaps with strategic goals), Test (pilot concepts), Launch (roll-out winners), and Monitor (continuous optimization).
To develop a holistic understanding of clients, prioritize client data with highest impact (such as demographics or share of wallet) and layer additional data over time. Next, organize clients into homogeneous groups. Profile key behaviors and attitudes, and use this information to map client journey. It is very important to balance customer goals with business goals through an appropriate business model. Any major investment should be preceded with pilot ideas to hone in on the target and gain learning without major costs. Finally, leverage Big Data to monitor client sentiment and improve the Analytics framework through feedback loops.
Minakshi Srivastava, Vice President, US Card Acquistion Risk/Reward Strategies, Bank of America gave an interesting talk on "Application of Predictive Analytics in Credit Risk Optimization". Credit Risk Analysis is integral to every step in the credit lifecycle process, from prospect and customer segmentation, through origination scorecards, to the design and execution of account management and collection strategies, whether for mortgages, loans, credit cards, and other consumer finance vehicles.
Credit Analytics can be defined as identifying and mitigating risk associated with financing credit product to customers. Risk team quantify risk, monitor and report risk of prospect or customer by development of risk monitoring tool, scorecard and models. Credit Analytics plays a great role in taking decision on customer’s associated risk for pricing as well credit exposure decision associated with it. Risk team uses predictive analytics for quantitative analysis, forecasting and strategy development. Risk analysis comprises of several different aspects (listed in the order of increasing complexity as well as business value):
- Reporting(What happened?)
- Analysis(Why did it happen?)
- Monitoring(What is happening now?)
- Prediction(What might happen?)
- Simulation(What will likely happen?)
Banks rely on several Credit Risk models based on the customer’s demographic, bureau and past payment information. Effective management of credit risk throughout the credit life cycle allows institutions to optimize their capital investments, reserve for future loss, (baseline and expected), and maximize shareholder value. Credit Analytics plays a critical role in monitoring and predicting future risk within various baseline and stressed macroeconomic scenarios, and from changing customer behavior.
Matthew Sharp, Senior Manager, Consumer Analytics , AOL shared insightful observations from a recent research conducted by AOL in his talk "Moving Beyond Conventional Wisdom: Measuring Digital KPIs that Matter". AOL, along with other partners, cataloged over 22,000 purchases across 20 product categories and examined shopping behaviors and attitudes of over 5,000 online users. One of the most interesting observations is that insights consistently run counter to conventional wisdom. The implications of this research challenge traditional means of customer acquisition and demonstrate the importance of aligning marketing KPIs with true consumer behavior.
Given the immense change in our digital lifestyle, it is worth asking: does the metaphor of a "journey" still make sense today? So, the consumer analytics & research team at AOL decided to test the commonly-held conventional wisdom about a customer's journey to purchase. They observed that advertisers still underestimate the importance of Digital in brand-building. He summarized the research findings into the following three implications:
- A strong brand is more important than ever (Brand plays a big role in user-initiated product information requests)
- The traditional purchase funnel is no longer relevant (Online & Email have greater impact than TV)
- Digital touch points aren't always what they appear (Analytics are required to correctly understand the situation)
Edward Kwartler, Director, Advanced Analytics, Liberty Mutual delivered a great talk on "Text Mining Social Media for Competitive Intelligence". Social Media is a great source of input data for competitive intelligence given the tremendous magnitude of information-sharing over social media from people across all demographics. However, social media also has a lot of noise, making it harder to exploit the signal. This social information is being used for making decisions in a wide variety of situations ranging from customers purchase decisions being influenced by social reviews to marketers mining social data for consumer insights.
Before starting to think about mining social data, we need to identify where the relevant conversations are happening, so that we can focus and save ourselves from getting lost in the wilderness of social media. Social data can be collected using technology such as Xpath, APIs, JSON (Java Script Object Notation), Web Scraping, or other third-party software/services. Next, he mentioned that despite huge potential text analytics has still not found mainstream adoption. He described the text mining process as comprising of four steps: Discover, Gather, Evaluate, and Inform. He explained the benefits of text mining social data through various real-life examples including one which used Glassdoor text data to identify the key differences in employee experience across two major insurance companies.
The key challenges of text mining social data are organizing the data, extracting insights that add business value and distinguishing signal from noise. In conclusion, he noted that the social media competitive intelligence has several major advantages over legacy competitive challenges, such as truly customer-centric, real-time feedback and unbiased insights.
Day 2 highlights