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jerryking : customer_profiling   7

Using 'remarkable' source of data, startup builds rich customer profiles - The Globe and Mail
Ivor Tossell

Special to The Globe and Mail

Published Monday, Jan. 06 2014

RetailGenius, a product from a Toronto startup called Viasense, promises to algorithmically generate customer profiles based on a remarkable source of data: Anonymous location data that’s collected by big mobile carriers, from the passive pings that every single cellphone sends out as it goes through the day.

The data that RetailGenius uses is anonymized – it doesn’t have any way of knowing whose cellphone belongs to who; it simply has a gigantic plot of where thousands of cellphones were at any given time.

“We create a unique identifier between those signals, and we can see those signals move throughout the city,” says Mossab Basir, RetailGenius’ founder. “We can see those changes in your location but we never really know who it is.”

What the product does next is intriguing: Based on some 50 million pieces of location data a day, RetailGenius crunches the numbers to make inferences from where each cellphone spends its time, and generates customer profiles by the thousands.

For instance, if a given cellphone spends the hours between 7 p.m. and 6 a.m. in a single area, it’s a good bet that its owner lives there. If that cellphone spends its working hours downtown five days a week, its owner is probably a daily commuter. And if it visits a given retail store once a week, a picture of its owner’s habits living and shopping habits starts to emerge.

By lumping these inferred profiles together, RetailGenius can give retailers a picture of who walks through their doors. For instance: What are the top 50 postal codes that are represented in their customers? What kind of volumes of customers are arriving at the store? How long do they stay?
data  start_ups  customer_insights  customer_profiling  RetailGenius  location_based_services  massive_data_sets  data_marketplaces  algorithms  Viasense  metadata  postal_codes  inferences  information_sources  anonymized  shopping_habits 
january 2014 by jerryking
From Harvard Yard To Vegas Strip Article
10.07.02 | Forbes.com - Magazine | Carol Potash.

Through branding, cross-casino marketing, loyalty cards, and technology, CEO Gary Loveman has made Harrah's Entertainment, the most diversified of the big four gaming companies, a model of effective customer feedback. In an industry accustomed to relying on intuition, Harrah's has built a database of 25 million customers that drills down through all its activities. Digital profiles are based not on observed behavior of what customers have spent but on analysis of what they are capable of spending. The technology includes built-in marketing interventions designed to close the gap between actual and potential spending. In this new world of computer-generated predictions, the customers are willing participants. Harrah's may be the best example of this kind of ongoing feedback system that could be applied to theme parks, ski resorts, cruise lines, retailers, and subscription businesses such as AOL and satellite TV.
predictive_modeling  Las_Vegas  databases  theme_parks  gaming  CEOs  Harrah's  casinos  yield_management  data_mining  customer_profiling  loyalty_management  customer_feedback  variance_analysis  leisure  branding  Gary_Loveman  marketing  observations 
july 2012 by jerryking
Bottom-Feeding from Blockbuster Businesses
March 2003 | Harvard Business Review | David Rosenblum, Doug Tomlinson, and Larry Scott.

Unprofitable customers are the pariahs of the business world. Marketing experts encourage companies to analyze the economics of their customer portfolios and ruthlessly weed out buyer segments that don’t generate attractive returns. Loyalty experts stress the necessity of aiming retention programs at the “good” customers—the profitable ones, that is—and encouraging the “bad” ones to buy from competitors. And customer-relationship-management software provides ever more sophisticated means for identifying poorly performing customers and culling them from the ranks.

On the surface, the movement to banish unprofitable customers seems eminently reasonable—what company, after all, can afford to waste precious resources courting and serving customers that don’t provide any payback? But writing off a customer relationship simply because it is momentarily unprofitable is at best rash and at worst counterproductive. Customers are scarce, and every one should be approached as a potential asset. Executives shouldn’t be asking themselves, How can we shun unprofitable customers? They need to ask, How can we make money from the customers that everyone else is shunning?

When you look at apparently unattractive segments through this lens, you often see what others are blind to: opportunities to serve those segments in ways that fundamentally change customer economics.
HBR  business_models  underserved  Bottom_of_the_Pyramid  blockbusters  overlooked_opportunities  customer_segmentation  customer_profiling 
june 2012 by jerryking
Shunned Profiling Technology on the Verge of Comeback - WSJ.com
NOVEMBER 24, 2010 | | By STEVE STECKLOW and PAUL SONNE.
Shunned Profiling Technology on the Verge of Comeback. Two U.S.
companies, Kindsight Inc. and Phorm Inc., are pitching deep packet
inspection services as a way for Internet service providers to claim a
share of the lucrative online ad market.
privacy  ISPs  online_advertising  customer_profiling 
november 2010 by jerryking
Seeking to Build a Better Consumer Profile - WSJ.com
MARCH 15, 2010 | Wall Street Journal | by EMILY STEEL.
Exploring Ways to Build a Better Consumer Profile
Nielsen, Digital-Marketing Firm eXelate Form Alliance to Merge Online
and Offline Data in Bid to Improve Ad Targeting. Digital-marketing
companies are rapidly moving to blend information about consumers'
Web-surfing behavior with reams of other personal data available
offline, seeking to make it easier for online advertisers to reach their
target audiences.
Advertisers say the push could enhance their ability to target ads at
specific types of consumers, but it is drawing scrutiny from Congress,
federal regulators and privacy watchdogs, who are already concerned
about the use of Web-surfing data.
Nielsen  consumer_research  privacy  data_driven  personalization  person  EXelate  Acxiom  advertising  marketing  customer_profiling 
march 2010 by jerryking

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