Rank #1: EP18: Location and Timing Based Marketing Analytics
All day long, mobile devices are being bombarded with shopping offers. Marketing analytics routinely explores which offers are likely to be accepted and which will be ignored. But does it make a difference if that sales pitch arrives when shoppers are sit
Oct 02 2017
Rank #2: EP9: Designing Data Warehouse for Business Intelligence
Imagine two restaurant chains (one pizza and the other ice cream) that recently had to fully redesign their data warehouse. One of them had more than 30,000 codes for coupons—and there were really only about 21 different coupons. Instead of changing the coding for existing coupons, they kept creating new ones, which were worded SLIGHTLY different. In the end, it made effectiveness and analysis worthless and it cost them thousands of person hours. This is a case where business intelligence wasn’t intelligent at all.
Jul 24 2017
Rank #3: EP13: Sales Data Analysis for Sales and Marketing Operations
Nowhere is crunching numbers more revered than in a sales team, with all manner of compensation tied to deals closed and revenue booked. But, bizarrely, sales analytics today often tracks the wrong elements and thereby fuels wrong conclusions. Kevin Styers, the director of sales operations at ShopKeep, has spent years figuring out old sales
Aug 21 2017
Rank #4: EP3: How Farmer's Insurance Rebranded using Data Analytics
There is little in corporate marketing that is more emotional …. more right brain …. than choosing a new logo. It’s the subliminal messaging from the colors, the shape and everything else that matters. How do executives make that kind of choice without being overly swayed by their own emotions?
Mike Linton, the chief marketing officer at the Farmers Insurance Group, thinks he has the answer and it was to let analytics make much of the decision. Well, analytics layered on top of customer surveys and literally hundreds of logo variations. Linton used to be the CMO at eBay and Best Buy—as well as a brand manager at Procter & Gamble. Mike joins us today to talk about how good software is at taking the emotions out of an emotional decision.
Jun 05 2017
Rank #5: EP15: Data Preparation and Cleansing - Why the Boring Stuff is Important with Barry Devlin
Interview with Barry Devlin, recognized as the Father of the Data Warehouse. In this podcast we discuss the role of data preparation, data cleansing, and the boring mundane stuff and why the boring stuff matters. A lot.
In just about every business analytics project, there are the fun parts and the drudgery parts. Most data scientists want to plunge in with the algorithms and the complex modeling, while rushing through mundane production tasks, such as data cleanliness and data governance. In short, they want to jump to the exploratory areas while blowing past production issues. But, like almost everything else in business, there’s a cost for avoiding the boring details. Barry Devlin, author and consultant with his 9Sight Consulting firm, has been leading in data warehouse work since 1985. Devlin argues that these corner-cutters are increasing how long projects take to get done and their financial cost.
Sep 12 2017
Rank #6: EP26: Banking Analytics at Bank of America
Predicting cashflow is certainly common enough, but what about predicting the costs ASSOCIATED with cashflow? What are the specific financial impacts of moving from check to ACH in terms of speed and costs? Should you offer a discount to encourage preferred payment methods? And, if so, how MUCH of a discount can you justify? Also, what are the costs of matching invoices and fixing invoice typos? There clearly IS a cost, but have you ever calculated precisely what it IS? One person who HAS is Rodney Gardner, head of global receivables at Bank of America Merrill Lynch. Rodney, how many companies are blissfully UNAWARE of these costs?
Oct 23 2017
Rank #7: EP4: Untangling 4 Electronic Medical Record Systems in 1 Hospital
Today’s typical hospital lives on its paperwork—even as an increasing percentage of that work no longer has anything to do with actual paper. But those electronic patient files—called either EHR for electronic health records or EMR for electronic medical records—bring them their own problems. For the Madison Memorial Hospital in Idaho, the problems come in the form of incompatible systems and difficult data exchanges. At that one hospital, there are at least five different EHR systems—and, no, they don’t talk with each other very well. That means a plethora of headaches for Troy Christensen, the hospital’s Chief Financial Officer. The top headaches? Duplicate bills and invoices that don’t match the contracted amounts.
Jun 12 2017
Rank #8: EP12: Business Meeting Data Analytics with Teem, Workplace Analytics Platform
How efficiently is your time used in meetings? If you needed to make decisions on the number of conference rooms you need make on an analysis of scheduled meetings, how accurate would it be? For architectural firms, this is crucial. Are rooms being created that aren’t needed? Think about calendar listings. When the company shuts down a project that held team meetings every Wednesday morning, does anyone bother to delete that meeting?
Dal Adamson is a product manager at Teem and he has been trying to fix the meeting management nightmare so companies what they do and don’t. Exactly how much time do we waste in meeting that don't help the company? Listen in to hear the answer.
Aug 14 2017
Rank #9: EP7: Facebook Targets Emotions for Advertising
A leaked document showed that Facebook targeted audience members that felt "overwhelmed", "stressed", "anxious", "nervous", "stupid", "silly", "useless", and "failure" with relevant ads that fit their emotional profile. We talk with Ron Guymon, Chief Data Scientist at Numetric about the technical aspects of data science like this (no moral discussions or judgment, just technical).
Jul 10 2017
Rank #10: EP5: Improving Patient Satisfaction, RFID Patient Data Analytics
Senior management at the cardiovascular center at the University of Utah had a problem. They knew that patients were unhappy with the service, but they didn’t have enough objective pieces of data to fix it. In an ambitious hospital experiment, they RFID tagged every single person involved. That meant tagging every nurse, doctor, orderly and clerical staff—and, yes, every single patient. These were active RFID tags so the software knew who was standing near who and for how long. They were where they were standing. It gave an objective picture of how many people were waiting for an X-Ray. It found how long injured patients had to wait and how long physicians spent with each patient. It counted how many seconds after a patient alarm sounds and when help arrives. And who arrived last.
Steven Tew, the cardiovascular center’s administrator, is running the experiment, tracking 175 patients a day and more than 50 employees. And doing it all with 1,000 active RFID tags, costing about $20 each. Steven joins us today to explore this intriguing analytics goal.
Jun 19 2017