Archive for the ‘Analytics’ Category

Sportswriter say: Advanced analytics can save the Redskins … oh, really

December 3, 2014

We’re working through predictive analytics in class these days.

So, my eyes are open for articles on the subject.

Predictive analytics.

You know, the stuff that Moneyball got rolling in baseball … and Target popularized by identifying pregnant women before the women knew they were expecting.

Let’s set the stage.

The Washington Redskins have been having (another) rough season.

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Veteran sportswriter Tony Kornheiser says advanced analytics could save the Redskins…

(more…)

Want a job? Then learn to crunch nums …

September 26, 2014

McKinsey recently published a report “Big Data – The Next Frontier” that concludes:

The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to  make decisions based on their findings.

image

Crunch those nums …

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Summer Read: The Numerati

May 27, 2014

The Numerati, Stephen Baker, Haughton Mifflin

Ken’s Take: I read it so you don’t have to.

I was really excited when the TV show “Numbers” launched.  Being a quant guy, I thought the concept of solving crimes by using math analysis had a ring to it.  Disappointment set in (for me) when they started focusing on the characters and their relationships instead of the numbers.  Oh well.

Math overwhelming man

I was equally as excited about the prospects for The Numerati … and about equally as disappointed.  Nice topic, but way too superficial.

The central premise of the book is good: prolific data accumulation (including mucho private data), integration of massive data sets, high speed data access and processing, sophisticated statistical models and data mining algorithms, and an increasing number of uses and users … is making all facets of life more and more numbers based.

* * * * *

Specifically, Baker provides some anecdotal examples of numbers-in-use:


 

  • In the financial markets: credit scoring by bankers and credit card companies started the snowball rolling …
  • In the workplace: some companies are already trying to derive behavioral profiles of employees that can provide insight re: how to motivate them, which teams to assign them to, and how to build a leveragable database of employee skills and interests.
  • In the store: some retailers are combining market research behavioral, and financial information to more closely target products and promotions ..  think of it as loyalty carding on steroids with a dose of customer profitability management.
  • In politics: the Chicago machine controlled precincts, the Bushies went after “values” segments and swing voters, and the Obama folks micro-targeted and “rolled up” using social marketing methods (e.g. Facebook, Tweeter).
  • On the blogs: some companies routinely scour the population of blogs to find references to their products that can be consolidated into a real time view of how the products are being perceived.
  • In the war on terror: neural data networks are processing a constant stream of information and electronic communications, hoping to spot behavior patterns that might provide an early warning of potential terrorist activity …  think “Patriot Act”
  • In the doctor’s office: electronic medical records appear to be gaining traction, providing docs with real time access, distributive capability (i.e. sending the info to other docs), and “evidence-based” analysis of best practices.  The looming questions: scalability and privacy.
  • In the heart: mate-finding sites (e.g. E-Harmony) are getting increasingly sophisticated – using behavioral and deep-psyche info and concepts to make the perfect matches.

Bottom line: For businesses, quant analytics used to provide a competitive edge.  Now, they are required just to compete.

For individuals, kiss privacy good-by and expect to be increasingly targeted with customized products and promotions.

“These statistical tools are going to be quietly assuming more and more power in our lives.  We might as well grab the controls and use them for our own interests.”

* * * * *

Want a job? Then learn to crunch nums …

April 11, 2014

McKinsey recently published a report “Big Data – The Next Frontier” that concludes:

The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to  make decisions based on their findings.

image

Crunch those nums …

>> Latest Posts

Want a job? Then learn to crunch nums …

September 18, 2013

McKinsey recently published a report “Big Data – The Next Frontier” that concludes:

The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to  make decisions based on their findings.

image

Crunch those nums …

>> Latest Posts

Nums: A world of battling algorithms

February 26, 2013

I’ve been getting back into behavioral economics and predictive analytics.

Led me back to a cool 15 minute TED Talk.

Tech entrepreneur Kevin Slavin tells how algorithms have reached across industries and into every day life.

A couple of lines caught my attention:

  • There are more than 2,000 physicists working on Wall Street developing operational algorithms
  • Massive scale speed trading is dependent on millisecond read & respond rates …
  • So, firms are physically literally locating right next to internet routing hubs to cut transmission times
  • And, of course, there isn’t time for human intervention and control
  • “We may be building whole worlds we don’t really understand, and can’t control.”

Worth listening to this pitch … a very engaging geek who may be onto something big.

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The election’s “Rosetta Stone” … really!

November 3, 2012

The polls have been bouncing all over the place, and pundits are broadly whining that the reason is difference in “turnout models”.

That is, how many more (or less) Democrats will show up to vote for Obama.

To understand the issue, I framed a related – but inverted — analysis by asking the question: by how much does Dem turnout (in swing states particularly) have to exceed GOP turnout for Obama to win?

The answer: Dem turnout has to be more than about 4 percentage points higher than GOP turnout for Obama to win.

Here’s my summary chart … below it are the assumptions and analytical logic.

From the chart: if Dem turnout is about 8 percentage points more than GOP turnout (as it was in 2008), Obama wins by about 4%;  if Dem turnout is less than 4 percentage points greater than the GOP’s, Obama loses.

It’s as simple as that … especially on a swing state by swing state basis.

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= = = = =
Assumptions & Analysis

While there has been a lot of bounce in the numbers, a couple of things appear to be pretty stable.

First, both Romney and Obama capture over 90% of their party’s votes.

Second, independents are generally about 1/3 of the total voting base … and, independents seem to be breaking towards Romney 55% to 45%.

In a nutshell, that means that Obama has to overcome a 3.3% Romney vote advantage with over-performance in Dem turnout.

  • 10 percentage point independent vote differential times 1/3 of the voting population equals 3.3%

Let’s run through a couple of examples:

1) Assume that the turnout is evenly split among Dems, GOP, and independents; that Obama & Romney each get 95% of their party’s votes; and that independents vote Romney 55% to 45%.

Under these assumptions, Dems have no turnout advantage (because that’s what we assumed) … and Obama loses by 3.3%.

image

= = = = =

2) Same assumptions as example #1, except assume that the Dem turnout is 8 percentage points greater than the GOP’s … roughly comparable to 2008 voting patterns.

Under these assumptions,  Obama wins by almost 4%.

image

= = = = =

3) Same assumptions as example #1, except assume that the Dem turnout is 3.7 percentage points less than the GOP’s.

Under these assumptions, the race is tied … we’ve found the sweet spot … if the Dems turnout advantage is more than 3.7 percentage points, Obama wins; less than that and he loses.

image

= = = = =
Final Notes

1) It’s simply math magic that the relationship works out to be linear … as displayed on the summary chart.

2) If you don’t like my assumptions, plug in your own … my conclusion: the numbers are pretty robust to changes in the assumptions

3) Nobody seems to be predicting Dem turnout comparable to 2008 … In fact, some are predicting that GOP will have a turnout advantage,

4) You haven’t seen an analysis like this anywhere else, right?  Only in the Homa Files …

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Behavioral analytics … bad when Target does it … OK for political campaigns?

September 19, 2012

A couple of months ago Target got some bad press when it was revealed that the company was mining customers’ purchase histories to slot them into behavioral groups susceptible to tailored promotional pitches.

For example, Target identified purchases that mothers-to-be made early in their pregnancies – sometimes before they even knew they were pregnant.  Think bigger jeans, skin care lotions.

Many folks railed that it was an example of big brother invasion of privacy.

Well, guess what?

Political campaigns are using the same methods that Target was using

The modern science of politics is increasingly based on principles from behavioral psychology and data analytics.

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Campaigns today mine large data bases with mathematical algorithms that slot folks into categories and provide the basis for how people should be approached (or ignored).

According to the WSJ:

Perhaps the most valuable data in modern campaigns comes from statistical “microtargeting” models—the political world’s version of credit scores.

Campaigns gather thousands of data points on voters, culled from what they put on their registration forms, what they have told canvassers who have visited their homes in the past, and information on their buying and lifestyle habits collected by commercial data warehouses.

The campaigns then run algorithms trawling for patterns linking those demographic characteristics to the political attitudes measured in their polling.

Financial institutions run such statistical models to generate predictions about whether a given individual will demonstrate a certain behavior, like paying a bill on time or defaulting on a loan.

Campaigns do the same, only they are interested in predicting political behavior.

So it’s typical now to generate individual scores, presented as a percentage likelihood, that a voter will cast a ballot, support one party or the other, be pro-choice or antiabortion, or respond to a request to volunteer.

These scores now stick to voters as indelibly as credit scores.

And just as a bank officer won’t sign off on a loan without requesting one, a field director for a campaign won’t send a volunteer to a voter’s door without knowing the relevant number.

BTW: It’s Team Obama that’s doing most of this stuff.

Bad for Target … but OK for Obama.

Hmmm

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WSJ source: “The Victory Lab: The Secret Science of Winning Campaigns” by Sasha Issenberg

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Are Mac users easy pickings?

July 19, 2012

Punch line: Online retailers are using sophisticated analytics and web tracking methods to tailor their offerings… and to get folks to pay higher prices.

To get  the lowest prices: (1) Use a PC (not  Mac or iPad), (20 don’t sign on from a ritzy location, (3) pass thru a price-shopping site on your way to the destination site, (4) ask to see items sorted by price — from low to high, (5) check out at least one cheap item — maybe even put in your cart — then delete it later

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Excerpted from WSJ

Retailers are becoming bigger users of so-called predictive analytics, crunching reams of data to guess the future shopping habits of customers.

The goal is to tailor offerings to people believed to have the highest “lifetime value” to the retailer.

Online, seemingly innocuous information is available to predict shoppers’ tastes and spending habits.

For example, The average household income for adult owners of Mac computers is $98,560, compared with $74,452 for a PC owner.

Drilling down, Orbitz  has found that people who use Apple spend as much as 30% more a night on hotels, so the online travel agency is starting to show them different, and sometimes costlier, travel options than Windows visitors see.

More specifically …

  • Mac users on average spend $20 to $30 more a night on hotels than their PC counterparts, a significant margin given the site’s average nightly hotel booking is around $100
  • Mac users are 40% more likely to book a four- or five-star hotel than PC users,
  • When Mac and PC users book the same hotel, Mac users tend to stay in more expensive rooms.

Other factors that have influence over results include

  • A user’s location (e.g. geo-targeting high wealth zip codes)
  • A shoppers history on the site (e.g. purchases at list price or at discounts).
  • The referring site (e.g. Kayak delivers price-sensitive shoppers to travel sites)takes those properties into account.

Caveat emptor !

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Want a job? Then learn to crunch nums …

October 5, 2011

McKinsey recently published a report “Big Data – The Next Frontier” that concludes:

The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to  make decisions based on their findings.

image

Crunch those nums …

>> Latest Posts

Social media and the dearth of analytic skills among business leaders …

May 5, 2011

TakeAway:  IBM thinks so.  That is why, IBM recently announced a new product, Cognos Consumer Insight, to analyze social media data to see how a firm and its products are fairing among consumers. 

IBM has also partnered with the Yale School of Management to address the gap of data analytical skills among business leaders …

* * * * *

Excerpted from Gigaom, “IBM targets the future of social media analytics,” April 28, 2011

IBM announced a new product, Cognos Consumer Insight, to help customers perform sentiment analysis of social media data and a new program with the Yale School of Management’s Center for Customer Insight to train students in advanced data analysis skills.

With businesses increasingly using social media as a way of connecting with customers, and with an industry-wide need for analytics skills, both the product and project are well-timed …

According to Deepak Advani, IBM’s VP of predictive analytics, there’s a lot of value in performing text analytics on data derived from Twitter, Facebook and other social forums to determine how companies or their products are faring among consumers.

Cognos lets customers view sentiment levels over time to determine how efforts are working, and skilled analysts can augment their Cognos Consumer Insight usage with IBM’s SPSS product to bring predictive analytics into the mix.

The partnership with Yale is designed to address the current dearth of analytic skills among business leaders … Advani explained that within many organizations they’re not using analytics at the point of decision or across all their business processes. Advani says partnerships like those with Yale will help instill the thought process of using mathematical algorithms instead of gut feeling…

We’ve been talking about the need for advanced analytics capabilities for a while now — highlighted by the high demand for data scientists — but the need spans all levels of business users … U.S. Bureau of Labor has said analytics jobs will increase 24 percent over the next eight years.

Edit by KJM

Predictive analysis: the way of the future

September 23, 2010

TakeAway: The tools available to marketers to help determine the best course of action for specific customers are becoming more powerful. 

While the Excel spreadsheet is the typical tool of choice to help make decisions, IBM has a comprehensive suite of tools that focuses on predictive analysis. 

By better understanding individual customer’s needs, these tools can help deliver more targeted promotions that result in an increased ROI.

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Excerpted from Brandchannel, “How Marketers Use Predictive Analysis to Target Their Branding Efforts,” by Barry Silverstein, August 5, 2010

Brand marketers know they must leverage the power of social media, but they face a dilemma: How do they truly measure social media’s effectiveness? This leads to an even larger issue: Given the use of multiple media in an integrated way, how do marketers understand what media combination is really working?

To answer those questions, more brand marketers are turning to predictive analysis

Brandchannel had the … opportunity to discuss the topic with Dr. Michael Haydock, global analytics leader for IBM’s Business Analytics & Optimization Practice. …

Interestingly … even chief marketing officers responsible for budgets of $1 billion or more typically use only spreadsheets to analyze their expenditures. That’s fine for looking at past programs and what’s happening now – but it gets far more difficult when these executives must make smarter decisions; specifically, determining how to make their marketing investment work harder going forward.

That takes analysis, and a lot of it. Haydock’s … approach is to break a client’s customer file into clusters based on what he calls “feature vectors” … These … are used to describe customer behavior and predict what customers might do next. … each customer is scored for his or her propensity to buy

When it comes to social media … most brand marketing organizations still view social media and traditional media separately, … predictive analysis can draw a connection between the two. For example, if a customer sees a television ad and then uses a smartphone to visit a website, it’s important to look at that customer contact cycle and understand the ROI of the total program.

A particularly intriguing application of predictive analysis is something IBM calls the “Next Best Action Program.” Haydock says the program offers marketers the ability to analyze the customer touches made through any channel and then establish a relevant “conversation”

In essence, the program literally determines the next best action to take with every customer on an individualized basis. …

Ultimately, the value of predictive analysis to marketers is getting an intimate understanding of each customer’s needs, and delivering more relevant promotions that are better targeted. The end result: a superior ROI on the company’s marketing investment.

 

 

 

Edit by DMG

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Full Article
http://www.brandchannel.com/features_effect.asp?pf_id=510

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Don’t interview for jobs on rainy days !

January 11, 2010

Dr. Don Redelmeier examined University of Toronto medical school admission interview reports from 2004-2009.

After correlating the interview scores with each day’s weather archives, he determined that candidates who interviewed on rainy or snowy days received interviewer ratings that were significantly lower than those of candidates who were lucky enough to visit on a sunny day, a difference that could not be explained by demographic factors or by grades and test scores.

In fact, the impact of the bad weather on applicants was the equivalent of a 10% lower score on the MCAT, easily enough to influence acceptance and rejection in many cases.

Some other Redelmeier findings …

* * * * *

The belief that arthritis pain is related to the weather is just another example of perception trumping reality.

After following 18 arthritis patients for a year, Dr. Don found no relationship between weather and pain.

It’s just that people tend to notice the weather more when their joints are aching, and that humans like to find patterns and explanations (particularly for unpleasant phenomena), even when none exist.

* * * * *

Mortality among patients admitted to hospitals on weekends is higher than weekdays.

Dr. Redelmeier found that seriously ill patients admitted to hospitals on weekends were significantly more likely to die.

This study catalyzed important discussions about weekend staffing patterns in hospitals around the world.

* * * * *

Academy award winners live longer than runner-ups.

Dr. Redelmeier, noting that most research examining the correlation between social status and health focused on society’s lower rungs, decided to use the Academy Awards to examine the relationship among the glitterati.

This study found that Academy Award winners live an average of 4 years longer than runner-ups, an astounding survival advantage.

The full mechanism of the apparent survival benefit among successful actors and actresses is not known. Untangling the explanations is complicated because some stars also engage in superstitious and deleterious behaviors.

* * * * *

Medical school class presidents die early

Comparing medical school class presidents to a control group comprised of those who appeared alphabetically before or after the president in the medical school class photo , the class presidents died about 2.5 years earlier.

“The type of medical professional who sacrifices themselves for this type of professional prestige may also be the type who fails to look after their health or is otherwise prone to early mortality.”

* * * * *

Full article:
http://community.the-hospitalist.org/blogs/wachters_world/archive/2009/12/21/rainy-day-interviews-oscar-winners-mortality-and-a-randomized-trial-of-niceness-in-the-er-the-extraordinary-mind-of-don-redelmeier.aspx

What’s happening? Check out Google Domestic Trends

September 14, 2009

New to me is an interesting tool from Google.

Google Domestic Trends track Google search traffic across specific sectors of the economy. Changes in the search volume of a given sector on google.com may provide unique economic insight.
http://www.google.com/finance/domestic_trends

You can access individual trend indexes.  (Full list below)

For example, the Google Auto Buyers Index tracks queries related to “car, blue book, toyota, kelly book”.
http://www.google.com/finance?q=GOOGLEINDEX_US:AUTOBY

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The Google Real Estate Index tracks queries related to “real estate, mortgage, rent, apartments”.
http://www.google.com/finance?q=GOOGLEINDEX_US:RLEST

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The Google Unemployment Index tracks queries related to “unemployment, social, social security, unemployment benefits”. 
http://www.google.com/finance?q=GOOGLEINDEX_US:UNEMPL

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Full list of Goggle Domestic Trends

image image

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Ken’s Take: Google Domestic Trends is a blunt tool that will make hardcore statisticians puke.  Nonetheless, an interesting — and, I bet, directionally accurate set indicators.

* * * * *

Thanks to MSB MBA alum John Tags for the heads-up

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Summer Read: The Numerati

August 3, 2009

The Numerati, Stephen Baker, Haughton Mifflin, 2008

Ken’s Take: I read it so you don’t have to.

I was really excited when the TV show “Numbers” launched.  Being a quant guy, I thought the concept of solving crimes by using math analysis had a ring to it.  Disappointment set in (for me) when they started focusing on the characters and their relationships instead of the numbers.  Oh well.

I was equally as excited about the prospects for The Numerati … and about equally as disappointed.  Nice topic, but way too superficial.

The central premise of the book is good: prolific data accumulation (including mucho private data), integration of massive data sets, high speed data access and processing, sophisticated statistical models and data mining algorithms, and an increasing number of uses and users … is making all facets of life more and more numbers based.

* * * * *

Specifically, Baker provides some anecdotal examples of numbers-in-use:

  • In the financial markets: credit scoring by bankers and credit card companies started the snowball rolling …
  • In the workplace: some companies are already trying to derive behavioral profiles of employees that can provide insight re: how to motivate them, which teams to assign them to, and how to build a leveragable database of employee skills and interests.
  • In the store: some retailers are combining market research behavioral, and financial information to more closely target products and promotions ..  think of it as loyalty carding on steroids with a dose of customer profitability management.
  • In politics: the Chicago machine controlled precincts, the Bushies went after “values” segments and swing voters, and the Obama folks micro-targeted and “rolled up” using social marketing methods (e.g. Facebook, Tweeter). 
  • On the blogs: some companies routinely scour the population of blogs to find references to their products that can be consolidated into a real time view of how the products are being perceived.
  • In the war on terror: neural data networks are processing a constant stream of information and electronic communications, hoping to spot behavior patterns that might provide an early warning of potential terrorist activity …  think “Patriot Act”
  • In the doctor’s office: electronic medical records appear to be gaining traction, providing docs with real time access, distributive capability (i.e. sending the info to other docs), and “evidence-based” analysis of best practices.  The looming questions: scalability and privacy.
  • In the heart: mate-finding sites (e.g. E-Harmony) are getting increasingly sophisticated – using behavioral and deep-psyche info and concepts to make the perfect matches.

Bottom line: For businesses, quant analytics used to provide a competitive edge.  Now, they are required just to compete. 

For individuals, kiss privacy good-by and expect to be increasingly targeted with customized products and promotions.

“These statistical tools are going to be quietly assuming more and more power in our lives.  We might as well grab the controls and use them for our own interests.”

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Manage your marketing ROI … or else.

January 29, 2009

Excerpted from Brandweek, “CMOs Pressured To Show ROI” By Kenneth Hein, Dec 8, 2008

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Some CMOs are feeling awfully paranoid these days. With good reason, a number of recent studies show that marketers’ spending choices are coming under far greater examination as the economic vise tightens. In fact, 89% of marketers said they are under more intense scrutiny than ever before…The greatest pressure being applied is the demand to show return on investment. However, many are struggling to do so, finding the ROI process complex…

Yet, most recognize a need for improvement maximizing dollars spent. 67% believe that they are not realizing the full revenue potential of customers…

So how are marketers adapting? 64% of CMO Council respondents said they were evaluating all areas of marketing spend to increase yield and accountability…“In a constrained economy you’ve got to focus monetizing existing customer relationships. It requires analytics and better use of customer data. [However], in many cases marketers struggle to integrate and leverage data.”

64% of respondents said better segmentation, profiling and targeting strategies were the top ways they were trying to better engage core audiences…

Despite years of conversation about ROI, the tactic of actually measuring marketing investments is still in its infancy…Among the reasons marketers have been slow to adopt ROI tactics: problems with data and integrity (47%), lack of technology (41%) and resource dedication (39%)…

“The mandate is to do more with less…Part of that is using new strategies and techniques to make sure money isn’t left on the table.”

Edit by SAC

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Marketer’s have long argued that marketing costs and results are difficult to measure, making ROI nearly impossible to quantify.  With the current economic situation the pressure for CMO’s to show results is not likely to go away anytime soon.

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Full Article:
http://www.brandweek.com/bw/content_display/news-and-features/direct/e3ib7f2dc11ebcfe13a1a67c9e3add4f502?imw=Y

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How much of a discount? Depends on how much you're worth.

January 22, 2009

Excerpted from WSJ “Marketers Reach Out to Loyal Customers” by Emily Steel, November 26, 2008

* * * * *

With the critical holiday-sales season at hand, there’s a new character joining Santa and his elves on the advertising circuit: the analytics geek…Marketers…are mining their customer databases and reaching out to loyal consumers with targeted ads, instead of relying on the traditional yuletide blitz.

Rather than create one TV commercial or send out a single, shotgun email promotion, uneasy retailers…are tapping statistical models and other technologies to send specific consumers promotions based on what is potentially on their shopping lists…

Persuading a satisfied customer to return is cheaper than attracting a new one…in the struggle to do more with less, that concept is becoming even more important. Acquiring a new customer costs about five to seven times as much as maintaining a profitable relationship with an existing customer…

Sears and Ogilvy have developed a system to identify the categories of merchandise Sears customers have purchased in the past and to measure the chance that they will buy those sorts of items again this season. That helps Sears determine the type of emails and point-of-sale offers to aim at individual customers. When customers buy an item online, Sears confirms the purchase with an email including a promotion tied to that product. A person who buys a new appliance at Sears.com might get an email offering a deal on the store’s extended-warranty program.

Sears is even offering customers differing discounts based on its predictions about the value those customers will bring to the company in the long term.

Companies have long tracked the habits of their consumers, but they have been overwhelmed by the reams of data they collect. Only fairly recently has the technology become sophisticated enough to allow marketers to link all the data points together — and work effectively with their advertising partners to leverage that data in ad campaigns…

Even if marketers get closer to predicting what’s on consumers’ wish lists, it’s going to be a tough sell, with people strapped for cash. Growth in e-commerce sales has already slowed significantly this year…

Edit by SAC

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It is clear that marketers can benefit from targeting customers based on Customer Lifetime Value (CLTV).    This is especially important for retailers facing a challenging economic situation with trimmed advertising budgets and customers who are cutting their spending.  The retailers that take advantage of the technology available to more accurately calculate CLTV and then target the more profitable customers have a better chance at a profitable holiday season. 

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Full Article:
http://online.wsj.com/article/SB122766322705958805.html?mod=article-outset-box

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The Numbers Don’t Lie: Competing on Analytics

January 20, 2009

Excerpted from Knowledge@Emory, “The Value and Benefits of Competing on Analytics”, November 13, 2008

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A lot of companies collect data. But it’s the ability to analyze and strategically act on that data that matters, notes Thomas Davenport, the author of the best-seller Competing on Analytics: The Science of Winning.. Most companies use data in a supporting role—not as the strategic weapon it can be.

At a time when competing companies offer similar products and have access to the same technologies analytical customer-facing processes are some of the only areas where businesses can differentiate themselves. “Analytical competitors” mine their data for every sliver of information it offers and then utilize that information in strategic ways. Firms like Harrah’s, Marriott  and Google that use analytical tools in a strategic manner are raising the bar in their industries when it comes to things like customer service, supply chain management and marketing.

* * * * *

Companies are taking their metrics—marketing metrics, sales metrics and service metrics—and using them, among other things, to segment, cross-sell, forecast, target and study drivers of satisfaction. Rather than looking at what happened, companies are able to predict more precisely what will happen and to better understand how and why it happened. Decisions are based on facts, not hunches or incomplete information.

Historically there’s been as much “art” in advertising and marketing as there has been “science.” Davenport argues that science is more likely to be correct than “art,” and adds that science enables a company to experiment on a small scale before committing a slew of resources to a huge marketing campaign or program.

* * * * *

Example: Harrah’s

In order to attract and retain customers, Harrah’s uses analytical tools not only to identify profitable customers, but to pinpoint its customers most likely to be wooed by competitors. Through well-designed marketing campaigns—created around information the analytical tools delivered—Harrah’s marketing methods can be strategically aimed at keeping those customers.

Analytical tools allowed the entertainment company to create a well-defined profile of its best customer, developing a beefed-up customer relationship management system anchored by a centralized data warehouse filled with pertinent information about how Harrah’s customers interact with the company. And given this information, Harrah’s marketing efforts are tailored to attract its different constituencies.

For instance, Harrah’s customers who live within driving distance of a casino receive different offers than those who do not. Frequent visitors to the casino in New Orleans are likely to receive different offers than frequent customers in Las Vegas or St. Louis. Harrah’s only targets customers likely to respond to such offers and it has more than 80 different segments for each marketing campaign.

One of the reasons Harrah’s has been so successful, notes Davenport, is because they focused on “one thing early” when it came to analytics. In Harrah’s case that “one thing” was customer loyalty. “In terms of marketing, companies need to think about target areas and about what they’re trying to accomplish,” adds Davenport. According to information available on Harrah’s website, nearly 50 percent of the company’s revenue is driven by marketing and the company’s analytical efforts have helped boost the company’s bottom line.

* * * * *

In studying companies that use analytics successfully, Davenport crafted what he calls “The Ladder of Analytical Marketing Targets”—essentially a best practice guide. First up, build a centralized customer database so that the company can get a comprehensive idea about its customers. The company can then treat “different customers differently” and respond appropriately to a customer’s activity. Companies can keep track of who got what and better manage their marketing campaigns—to the point of personalizing them. Via predictive modeling, companies can answer with much more certainty what customers are likely to do next given what they’ve already done. As a result, companies can make real time, customized offers that make sense to their customers.

Edit by DAF

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Full article:
http://knowledge.emory.edu/article.cfm?articleid=1193#

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