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

We provide here a summary of selected talks along with the key takeaways.
Here are highlights from Day 1 (Wednesday, May 21, 2014):

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.

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.

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)

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
Related: