Just came across this post from Vincent Granville on AnalyticBridge. Quite interesting and informative. Am sharing it here, along with a few additions of my own.
- The model is more accurate than the data: you try to kill a fly with a nuclear weapon.
- You spent one month designing a perfect solution when a 95% accurate solution can be designed in one day. You focus on 1% of the business revenue, lack vision / lack the big picture.
- Poor communication. Not listening to the client, not requesting the proper data. Providing too much data to decision makers rather than 4 bullets with actionable information. Failure to leverage external data sources. Not using the right metrics. Problems with gathering requirements. Poor graphics or graphics that is too complicated.
- Failure to remove or aggregate conclusions that do not have statistical significance. Or repeating many times the same statistical tests, thus negatively impacting the confidence levels.
- Sloppy modeling or design of experiments, or sampling issues. One large bucket of data has good statistical significance, but all the data in the bucket in question is from one old client with inaccurate statistics. Or you use two databases to join sales and revenue, but the join is messy, or sales and revenue data do not overlap because of a different latency.
- Lack of maintenance. The data flow is highly dynamic and patterns change over time, but the model was tested 1 year ago on a data set that has significantly evolved. Model is never revisited, or parameters / blacklists are not updated with the right frequency.
- Changes in definition (e.g. include international users in the definition of a user, or remove filtered users) resulting in metrics that lack consistency, making vertical comparisons (trending, for a same client) or horizontal comparisons (comparing multiple clients at a same time) impossible.
- Blending data from multiple sources without proper standardizations: using (non-normalized) conversion rates instead of (normalized) odds of conversion.
- Poor cross validation. Cross validation should not be about randomly splitting the training set into a number of subsets, but rather comparing before (training) with after (test). Or comparing 50 training clients with 50 different test clients, rather than 5000 training observations from100 clients with another 5000 test observations from the same 100 clients. Eliminate features with statistical significance but lack of robustness when comparing 2 time periods.
- Improper use of statistical packages. Don't feed a decision tree software with a raw metric such as IP address: it just does not make sense. Instead provide a smart binned metric such as type of IP address (corporate proxy, bot, anonymous proxy, edu proxy, static IP, IP from ISP, etc.)
- Wrong assumptions. Working with dependent independent variables and not handling the problem. Violations of the Gaussian model, multimodality ignored. External factor explains the variations in response, not your independent variables. When doing A/B testing, ignoring important changes made to the website during the A/B testing time period.
- Lack of good sense. Analytics is a science AND an art, and the best solutions require sophisticated craftsmanship (the stuff you will never learn at school), but might usually be implemented pretty fast: elegant/efficient simplicity vs. inefficient complicated solutions.
My additions:
- What is your problem?
Without a real business problem, modeling or data mining will just give you numbers. I have come across people, on both the delivery and client side, who comes up with this often repeated line “I’ve got this data, tell me what you can do?”
My answer to that – “I will give you the probability that your customer will attrite based on the last 2 digits of her transaction ID. Now, tell me what are you going to do to make her stay with your business?”
Start with a REAL business problem.
- What are you gonna do about it?
So if your customers are leaving in alarming numbers, don’t just say that you want an attrition/churn model. Think on how you would like to use the model results. Are you thinking of a retention campaign? Are you to going to reduce churn by focusing on ALL the customers most likely to leave? Or are you going to focus on a specific subset of these customers (based on their profitability, for example)? How soon can you launch a campaign? How frequently will you be targeting these customers?
Have a CLEAR idea on what you are going to do with the model results.
- What have you got?
Don’t expect a wonderful earth-shattering surprise from all modeling projects. Model results or performances are based on many factors, with data quality as the most important one, in almost all the cases. If your database is full of @#$%, remember one thing. Garbage in, Garbage out. Period.
- Modeling is not going to give you a nice easy-to-read chart on how to run businesses.
- Technique is not everything.
A complex technique like (Artificial) Neural Networks doesn’t guarantee a prize winning model. Selecting a technique depends on many factors, with the most important ones being data types, data quality and the business requirement.
- Educate yourself
It’s never too late to learn. For people on the delivery side, modeling is not about the T-test and Regression alone. For people on the client side, know what Analytics or Data Mining can do, and CANNOT do. Know when, where and how to relate the model results with your business.
Friday, January 1, 2010
Tuesday, December 1, 2009
Sunday, August 30, 2009
Means and Proportions with two populations
Statistical inference about means and proportions with two populations seems to be one of the most commonly used applications in the field of analytics - comparing campaign response rates between 2 groups of customers, pre and post campaign sales, membership renewal rates, etc.
Call it chance or whatever, but whenever these kind of tasks came up I hear people talking about the t-tests only. No issues as long as you want to compare means or when your target variable is a continuous value. But how or why do people talk about the t-test when they want to compare ratios or proportions? Whatever happened to the Chi-Square tests or the Z-test for difference in proportions?
I did a bit of research on the net, a bit of calculation using pen and paper [very good exercise for the brain in this age of calculators and spreadsheets :-) ], read a very good article by Gerard E. Dallal, and I found the answers.
Going back to our introductory class in statistics, let’s check out the formulae for the t-tests.
1. Assuming that the population variances are equal,
T = (X1 – X2)/sqrt (Sp2(1/n1 + 1/n2) ..........Equation 1
where
X1, X2 = means of sample 1 and 2
n1, n2 = size of sample 1 and 2
Sp2 = pooled variance = [((n1-1)S12+(n2-1)S22)/(n1+n2-2)]
2. Assuming that the population variances are not equal,
T = (X1 – X2)/sqrt(S12/n1 + S22/n2) ..........Equation 2
We have also been taught that the test statistic Z is used to determine the difference between two population proportions based on the difference between the two sample proportions.
And the formula for the Z statistic is given by
Z = (P1 – P2)/ sqrt(P(1-P)(1/n1 + 1/n2)) ..........Equation 3
where
P1, P2 = proportions of success (or target category) in samples 1 and 2
S1, S2 = variances for samples 1 and 2
n1, n2 = size of samples 1 and 2
P = pooled estimate of the sample proportion of successes =(X1 + X2)/(n1 + n2)
X1, X2 = number of successes (or target category) in samples 1 and 2
The test statistic Z (equation 3) is equivalent to the chi- square goodness-of-fit test, also called a test of homogeneity of proportions.
But how different is the proportions from means? The proportion having the desired outcome is the number of individuals/observations with the outcome divided by total number of individuals/observations. Suppose we create a variable that equals 1 if the subject has the outcome and 0 if not. The proportion of individuals/observations with the outcome is the mean of this variable because the sum of these 0s and 1s is the number of individuals/observations with the outcome.
Let's suppose there are m 1s and (n-m) 0s among the n observations. Then, XMean (=P) =m/n and is equal to (1-m/n) for m observations and 0-m/n for (n-m) observations. When these results are combined, the final result is
∑(Xi – XMean)2 = m(1-m/n)2 + (n – m) (0 – m/n)2
= m(1 – 2m/n + m2/n2) + (n – m) m2/n2
= m – 2(m2/n2) + (m3/n2) + (m2/n) – (m3/n2)
= m – (m2/n)
= m(1-m/n)
= nP(1-P)
So, variance = ∑(Xi – XMean)2/n = P(1-P)
Substituting this in the equation 3 (for Z statistic), we get
(P1 – P2)/ sqrt(Variance/n1 + Variance/n2)), which is not so different from equation 2 (the formula for the "equal variances not assumed" version of t test).
As long as the sample size is relatively large, the distributional assumptions are met, and the response is binomial – the t test and the z test will give p-values that are very close to one another.
And in the case where we have only two categories, the z test and the chi-square test turn out to be exactly equivalent, though the chi-square is by nature a two-tailed test. The chi-square distribution for 1 df is just the square of the z distribution.
The various tests and their assumptions as listed in Wikipedia are given below:
1. Two-sample pooled t-test, equal variances
(Normal populations or n1 + n2 > 40) and independent observations and σ1 = σ2 and (σ1 and σ2 unknown)
2. Two-sample unpooled t-test, unequal variances
(Normal populations or n1 + n2 > 40) and independent observations and σ1 ≠ σ2 and (σ1 and σ2 unknown)
3. Two-proportion z-test, equal variances
n1 p1 > 5 and n1(1 − p1) > 5 and n2 p2 > 5 and n2(1 − p2) > 5 and independent observations
4. Two-proportion z-test, unequal variances
n1 p1 > 5 and n1(1 − p1) > 5 and n2 p2 > 5 and n2(1 − p2) > 5 and independent observations
Call it chance or whatever, but whenever these kind of tasks came up I hear people talking about the t-tests only. No issues as long as you want to compare means or when your target variable is a continuous value. But how or why do people talk about the t-test when they want to compare ratios or proportions? Whatever happened to the Chi-Square tests or the Z-test for difference in proportions?
I did a bit of research on the net, a bit of calculation using pen and paper [very good exercise for the brain in this age of calculators and spreadsheets :-) ], read a very good article by Gerard E. Dallal, and I found the answers.
Going back to our introductory class in statistics, let’s check out the formulae for the t-tests.
1. Assuming that the population variances are equal,
T = (X1 – X2)/sqrt (Sp2(1/n1 + 1/n2) ..........Equation 1
where
X1, X2 = means of sample 1 and 2
n1, n2 = size of sample 1 and 2
Sp2 = pooled variance = [((n1-1)S12+(n2-1)S22)/(n1+n2-2)]
2. Assuming that the population variances are not equal,
T = (X1 – X2)/sqrt(S12/n1 + S22/n2) ..........Equation 2
We have also been taught that the test statistic Z is used to determine the difference between two population proportions based on the difference between the two sample proportions.
And the formula for the Z statistic is given by
Z = (P1 – P2)/ sqrt(P(1-P)(1/n1 + 1/n2)) ..........Equation 3
where
P1, P2 = proportions of success (or target category) in samples 1 and 2
S1, S2 = variances for samples 1 and 2
n1, n2 = size of samples 1 and 2
P = pooled estimate of the sample proportion of successes =(X1 + X2)/(n1 + n2)
X1, X2 = number of successes (or target category) in samples 1 and 2
The test statistic Z (equation 3) is equivalent to the chi- square goodness-of-fit test, also called a test of homogeneity of proportions.
But how different is the proportions from means? The proportion having the desired outcome is the number of individuals/observations with the outcome divided by total number of individuals/observations. Suppose we create a variable that equals 1 if the subject has the outcome and 0 if not. The proportion of individuals/observations with the outcome is the mean of this variable because the sum of these 0s and 1s is the number of individuals/observations with the outcome.
Let's suppose there are m 1s and (n-m) 0s among the n observations. Then, XMean (=P) =m/n and is equal to (1-m/n) for m observations and 0-m/n for (n-m) observations. When these results are combined, the final result is
∑(Xi – XMean)2 = m(1-m/n)2 + (n – m) (0 – m/n)2
= m(1 – 2m/n + m2/n2) + (n – m) m2/n2
= m – 2(m2/n2) + (m3/n2) + (m2/n) – (m3/n2)
= m – (m2/n)
= m(1-m/n)
= nP(1-P)
So, variance = ∑(Xi – XMean)2/n = P(1-P)
Substituting this in the equation 3 (for Z statistic), we get
(P1 – P2)/ sqrt(Variance/n1 + Variance/n2)), which is not so different from equation 2 (the formula for the "equal variances not assumed" version of t test).
As long as the sample size is relatively large, the distributional assumptions are met, and the response is binomial – the t test and the z test will give p-values that are very close to one another.
And in the case where we have only two categories, the z test and the chi-square test turn out to be exactly equivalent, though the chi-square is by nature a two-tailed test. The chi-square distribution for 1 df is just the square of the z distribution.
The various tests and their assumptions as listed in Wikipedia are given below:
1. Two-sample pooled t-test, equal variances
(Normal populations or n1 + n2 > 40) and independent observations and σ1 = σ2 and (σ1 and σ2 unknown)
2. Two-sample unpooled t-test, unequal variances
(Normal populations or n1 + n2 > 40) and independent observations and σ1 ≠ σ2 and (σ1 and σ2 unknown)
3. Two-proportion z-test, equal variances
n1 p1 > 5 and n1(1 − p1) > 5 and n2 p2 > 5 and n2(1 − p2) > 5 and independent observations
4. Two-proportion z-test, unequal variances
n1 p1 > 5 and n1(1 − p1) > 5 and n2 p2 > 5 and n2(1 − p2) > 5 and independent observations
Sunday, May 31, 2009
Analytics: Reality and the Growing Interest
This is a guest post by Bhupendra Khanal, CEO of InRev Systems.
InRev Systems is a Bangalore based Decision Management Company, which works on Data Based Information Systems. Their interest areas are Marketing Services, Web Information, MIS Reporting, Social Media Services and Economic Research. Bhupendra also maintains a personal blog at Business Analytics.
Introduction
Huge amount of data is collected by any business houses today. There are also certain data collection agencies who have information like economic variables, demographic variables, police fraud list, loan default list, telephone and electricity bill payment history etc. All these data if analyzed, tend to separate people into various similar groups. These groups can be fraudulent group, defaulter group, risk averse and risk takers group, high income and low income groups, etc.
Based on this information, many business decisions can be made in a better and rational way. Analytics leverages almost the same concept but with assumptions:
· The behavior of people do not change with time
· People with similar profile behave similarly
Predictive Modeling and Segmentation are the major components of Analytics. The profile and the behavior of a set of people are taken, and a relation is found out. This same relation is used for building future profiles and predicting the behavior of people having the same profiles. This is commonly done using advanced statistical techniques like Regression Modeling (Linear, Logistic, and Poisson etc) and Neural Networks.
The Business Analytics Services Market comprises solutions for storing, analyzing, modeling, and delivering information in support of decision-making and reporting processes.
Analytics, regardless of its complexity, serve the same purpose – to assist in improving or standardizing decisions at all levels of an organization.
Size and Type of Market
The size of the Analytics market globally is estimated around $25 billion today. It is increasing very fast, doubling almost every five years for the last few decades, with $19 billion dollars in 2006. It is expected to grow to $31 billion by the end of 2011 (source - IDC 2007).
There are many areas for the implementation of Analytics. The most common Analytics practices are Risk Management, Marketing Analytics, Web Analytics, and Fraud Prediction etc. These functions are handled by different organizations in different ways, with most companies maintaining a fine balance with the in-house team, outsourcing partner and consulting project vendors. Such variation makes calculation of market size very difficult.
Risk Management is one of the largest component of the Analytics Industry today, and it is the pioneer component too. The market is huge in the US and Europe; Asia Pacific is coming up fast and it is yet to get full swing in China and South Asia, including Nepal.
Web Analytics (analytics of Web Data) has picked up very fast and it is increasing by more than 20% for the last few years, thanks to the revolution led by Amazon, Google and Yahoo. Web Analytics market is a late entrant but have already passed the one billion mark.
The biggest of all is the Marketing Analytics (MA) and Strategy Science component. This is really huge owing to the effort put by companies like Dunhumby, Axiom and others. MA is critical as it tends to compete directly with Marketing Research Firms and Strategy Consulting firms in the type of work it does. This makes it difficult to calculate the market size but it is worth billions of dollars.
Big Players in the Area
The size of the Analytics market is huge today but the industry is fragmented. None of the core Analytics and Decision Management Company has ever touch a billion dollar revenue mark.
Fair Isaac is the pioneer and the largest Decision Management Company with revenue of around 800 million dollars. The other core DM and Analytics companies are quite smaller. This has happened due to the aggressive moves by IT Services Companies and Information Bureaus to acquire Analytics companies.
Experian, Transunion and Equifax are three major bureaus in the US while there are others too - Innovis, Axiom, Teletrack, Lexis Nexis etc. Each of these bureaus has analytics services as their offerings. Apart from these the BI majors like SAS, SPSS, Salford Systems etc. offer Analytics Services.
The Indian Analytics Market is small but growing. This can be justified by the more than double salary hike rates in Analytics compared to the Software domain. The outsourcing shops and India-focused companies both have mushroomed in the last five years in India while the problem remains in getting proper talent and retaining them.
Major Banks like ICICI and HDFC have their strong in-house analytics unit while SBI has partnered with GE Money for Analytics support for their Cards portfolio. The smaller banks are yet to start.
Other majors in non banking industries are also using Analytics through Outsourcing or Consulting. Airtel and Reliance are leading the way for the use of Analytics in India in Telecom.
Scope on how it can grow: Indian Context
The Analytics Industry is growing fast. It has the scope of forming a separate process across industries like HR, Operations, and IT System. IT is now in the early stage of process metamorphosis, where each process start through consulting, grow through in-house establishments and finally settle down as outsourcing to third parties.
The future growth depends on the approach of major players and consolidation of the industry. All this will make it a high value and high growth industry, where players can provide high quality products and services while maintaining their profitability.
Another big challenge is the supply of quality man-power and training. Today India neither has good institutes training people on Analytics nor has it got an Infosys for Analytics (who can employ and train huge number of freshers). Even the number of good Statistics and Mathematics Institutes in India is less.
Amidst all these challenges, India is positioned fairly well in the world today, and it will be interesting to see if it can become a Knowledge Process and Analytics hub in the days ahead.
InRev Systems is a Bangalore based Decision Management Company, which works on Data Based Information Systems. Their interest areas are Marketing Services, Web Information, MIS Reporting, Social Media Services and Economic Research. Bhupendra also maintains a personal blog at Business Analytics.
Introduction
Huge amount of data is collected by any business houses today. There are also certain data collection agencies who have information like economic variables, demographic variables, police fraud list, loan default list, telephone and electricity bill payment history etc. All these data if analyzed, tend to separate people into various similar groups. These groups can be fraudulent group, defaulter group, risk averse and risk takers group, high income and low income groups, etc.
Based on this information, many business decisions can be made in a better and rational way. Analytics leverages almost the same concept but with assumptions:
· The behavior of people do not change with time
· People with similar profile behave similarly
Predictive Modeling and Segmentation are the major components of Analytics. The profile and the behavior of a set of people are taken, and a relation is found out. This same relation is used for building future profiles and predicting the behavior of people having the same profiles. This is commonly done using advanced statistical techniques like Regression Modeling (Linear, Logistic, and Poisson etc) and Neural Networks.
The Business Analytics Services Market comprises solutions for storing, analyzing, modeling, and delivering information in support of decision-making and reporting processes.
Analytics, regardless of its complexity, serve the same purpose – to assist in improving or standardizing decisions at all levels of an organization.
Size and Type of Market
The size of the Analytics market globally is estimated around $25 billion today. It is increasing very fast, doubling almost every five years for the last few decades, with $19 billion dollars in 2006. It is expected to grow to $31 billion by the end of 2011 (source - IDC 2007).
There are many areas for the implementation of Analytics. The most common Analytics practices are Risk Management, Marketing Analytics, Web Analytics, and Fraud Prediction etc. These functions are handled by different organizations in different ways, with most companies maintaining a fine balance with the in-house team, outsourcing partner and consulting project vendors. Such variation makes calculation of market size very difficult.
Risk Management is one of the largest component of the Analytics Industry today, and it is the pioneer component too. The market is huge in the US and Europe; Asia Pacific is coming up fast and it is yet to get full swing in China and South Asia, including Nepal.
Web Analytics (analytics of Web Data) has picked up very fast and it is increasing by more than 20% for the last few years, thanks to the revolution led by Amazon, Google and Yahoo. Web Analytics market is a late entrant but have already passed the one billion mark.
The biggest of all is the Marketing Analytics (MA) and Strategy Science component. This is really huge owing to the effort put by companies like Dunhumby, Axiom and others. MA is critical as it tends to compete directly with Marketing Research Firms and Strategy Consulting firms in the type of work it does. This makes it difficult to calculate the market size but it is worth billions of dollars.
Big Players in the Area
The size of the Analytics market is huge today but the industry is fragmented. None of the core Analytics and Decision Management Company has ever touch a billion dollar revenue mark.
Fair Isaac is the pioneer and the largest Decision Management Company with revenue of around 800 million dollars. The other core DM and Analytics companies are quite smaller. This has happened due to the aggressive moves by IT Services Companies and Information Bureaus to acquire Analytics companies.
Experian, Transunion and Equifax are three major bureaus in the US while there are others too - Innovis, Axiom, Teletrack, Lexis Nexis etc. Each of these bureaus has analytics services as their offerings. Apart from these the BI majors like SAS, SPSS, Salford Systems etc. offer Analytics Services.
The Indian Analytics Market is small but growing. This can be justified by the more than double salary hike rates in Analytics compared to the Software domain. The outsourcing shops and India-focused companies both have mushroomed in the last five years in India while the problem remains in getting proper talent and retaining them.
Major Banks like ICICI and HDFC have their strong in-house analytics unit while SBI has partnered with GE Money for Analytics support for their Cards portfolio. The smaller banks are yet to start.
Other majors in non banking industries are also using Analytics through Outsourcing or Consulting. Airtel and Reliance are leading the way for the use of Analytics in India in Telecom.
Scope on how it can grow: Indian Context
The Analytics Industry is growing fast. It has the scope of forming a separate process across industries like HR, Operations, and IT System. IT is now in the early stage of process metamorphosis, where each process start through consulting, grow through in-house establishments and finally settle down as outsourcing to third parties.
The future growth depends on the approach of major players and consolidation of the industry. All this will make it a high value and high growth industry, where players can provide high quality products and services while maintaining their profitability.
Another big challenge is the supply of quality man-power and training. Today India neither has good institutes training people on Analytics nor has it got an Infosys for Analytics (who can employ and train huge number of freshers). Even the number of good Statistics and Mathematics Institutes in India is less.
Amidst all these challenges, India is positioned fairly well in the world today, and it will be interesting to see if it can become a Knowledge Process and Analytics hub in the days ahead.
Monday, May 18, 2009
A Tale Of Two Banks and One Telecom Service Provider
I have bank accounts at ICICI & Citibank (India). I also use credit cards of these two companies. Let’s first talk about their Mobile Banking services. ICICI has the following options:
My account getting credited above
My account getting debited above
Salary credited to my account #
Cheque deposited in my account bounced
Account Balance above
Account Balance below
Debit Card Purchases above
Messages for the above can be received through SMS, Email or both.
Citibank calls it Alerts, and they offer the following options:
Withdrawal balance by account
Withdrawal balance by account
Time deposit maturity advice
Cheque Status
Cheque Bounce Alert
Time Deposit Redemption Notice
Cheque dishonor
These messages can be received through SMS, Email or Both depending on the alert type. Also, for some of the alerts, message frequencies can be chosen as Daily, Weekly, or Monthly.
At first glance, it seems that Citibank offers more options but a closer look will reveal ICICI has done more research and come up with a better offering. The Citibank alerts are based on how frequently you want them while ICICI’s alerts are based on particular (defined by the customer) credited and debited amounts. ICICI’s options make more sense as I don’t want alerts daily or weekly, but only when I have made a transaction.
And because of the lack of options, I continue to receive daily alerts on my Citibank account balance irrespective of the fact that the balance remains the same or I haven’t done any transaction for a month. Also, the system at Citibank looks like a typical CRM system while the one at ICICI looks more like a BI system.
Now, let’s discuss their Credit Cards and their Customer Analytics.
I have been using an ICICI Gold credit card for almost 3 years now. I used it to pay all my bills – electricity bill, mobile bill, internet bill, shopping bills….and I paid all dues in time. I applied for and got the Citibank Gold card about a year after I got my ICICI card. I used the Citibank Gold card for 2-3 purchases, paid all the dues in time, and Citibank increased my credit limit every time.
Encouraged by their response (I got very nice emails from their customer service) and actions (increase of credit limit), I started using the Citibank Gold card more frequently. In a few months’ time, I got a free-for-life Citibank Platinum card with all these attractive features and benefits. I even got an invitation to join a wine club though I’m more of a rum and whiskey guy. Don’t blame them though; getting your hands on such kind of consumer lifestyle/preferences data will be next to impossible in India.
I have now almost forgotten the ICICI Gold card; I use it very rarely these days. The credit limit given to me 3 years back still remains the same. And I have never received a single email or communication from ICICI. I also know that ICICI bank outsources its Customer Analytics to an Analytics Service Provider in Mumbai. So where is the up sell analytics? Doesn’t their data show the fact that I am “almost” leaving now? That again reveals the fact that customer spending information is not at all analyzed and they are not doing much about customer churn either.
On a different note, I am a post-paid mobile customer of India’s largest telecom service provider, Airtel. Every month, whenever my bill is generated I receive about 6 SMSs from Airtel within the next 5-6 days. The messages can be summarized as:
1. Your bill has been generated…
2. You can view your bill at your online account…
3. Your bill has been emailed to abc@gmail.com and the password to open it is… and if you haven’t received it… (this is actually 2 SMSs because of the length of the message)
4. Your bill amount is XXX…
5. Your bill amount is XXX and the last date of payment is…
For the last 3 years or so, I have been paying all my bills before the due date. Once I got so irritated that I emailed their customer service, and their reply? These are server generated messages and we can’t do anything about it. I got more irritated and asked my email to be forwarded to Airtel’s CRM, Business Intelligence, Analytics or whatever the team there like to call themselves!
I got just one SMS alert when my next month’s bill was generated. But it was back to square one from the 2nd month onwards.
My question is which “smart” manager came up with the idea that 6 SMSs should be sent to all their post-paid customers every month? Why doesn’t one SMS saying “Your bill amount of XXX has been generated and the due date is ABC” suffice? Has anyone at Airtel calculated the cost of sending 5-6 SMSs to all their post-paid customers, every month? Shouldn’t the last SMS be sent only to those customers who have a habit of making late payments? Do they send the second SMS to those customers who don’t have online accounts too? And why can’t these alerts be customized based on a customer’s usage and payment behavior?
I can give more examples of Indian companies in the retail, entertainment, and services sector that are not doing nothing or very little about all the customer data they have, inspite of mentioning or advertising that they use BI & Analytics. So how mature is the Business Intelligence, and CRM Analytics setup at Indian companies? And how skilled or knowledgeable are the senior people associated with it?
My account getting credited above
My account getting debited above
Salary credited to my account #
Cheque deposited in my account bounced
Account Balance above
Account Balance below
Debit Card Purchases above
Messages for the above can be received through SMS, Email or both.
Citibank calls it Alerts, and they offer the following options:
Withdrawal balance by account
Withdrawal balance by account
Time deposit maturity advice
Cheque Status
Cheque Bounce Alert
Time Deposit Redemption Notice
Cheque dishonor
These messages can be received through SMS, Email or Both depending on the alert type. Also, for some of the alerts, message frequencies can be chosen as Daily, Weekly, or Monthly.
At first glance, it seems that Citibank offers more options but a closer look will reveal ICICI has done more research and come up with a better offering. The Citibank alerts are based on how frequently you want them while ICICI’s alerts are based on particular (defined by the customer) credited and debited amounts. ICICI’s options make more sense as I don’t want alerts daily or weekly, but only when I have made a transaction.
And because of the lack of options, I continue to receive daily alerts on my Citibank account balance irrespective of the fact that the balance remains the same or I haven’t done any transaction for a month. Also, the system at Citibank looks like a typical CRM system while the one at ICICI looks more like a BI system.
Now, let’s discuss their Credit Cards and their Customer Analytics.
I have been using an ICICI Gold credit card for almost 3 years now. I used it to pay all my bills – electricity bill, mobile bill, internet bill, shopping bills….and I paid all dues in time. I applied for and got the Citibank Gold card about a year after I got my ICICI card. I used the Citibank Gold card for 2-3 purchases, paid all the dues in time, and Citibank increased my credit limit every time.
Encouraged by their response (I got very nice emails from their customer service) and actions (increase of credit limit), I started using the Citibank Gold card more frequently. In a few months’ time, I got a free-for-life Citibank Platinum card with all these attractive features and benefits. I even got an invitation to join a wine club though I’m more of a rum and whiskey guy. Don’t blame them though; getting your hands on such kind of consumer lifestyle/preferences data will be next to impossible in India.
I have now almost forgotten the ICICI Gold card; I use it very rarely these days. The credit limit given to me 3 years back still remains the same. And I have never received a single email or communication from ICICI. I also know that ICICI bank outsources its Customer Analytics to an Analytics Service Provider in Mumbai. So where is the up sell analytics? Doesn’t their data show the fact that I am “almost” leaving now? That again reveals the fact that customer spending information is not at all analyzed and they are not doing much about customer churn either.
On a different note, I am a post-paid mobile customer of India’s largest telecom service provider, Airtel. Every month, whenever my bill is generated I receive about 6 SMSs from Airtel within the next 5-6 days. The messages can be summarized as:
1. Your bill has been generated…
2. You can view your bill at your online account…
3. Your bill has been emailed to abc@gmail.com and the password to open it is… and if you haven’t received it… (this is actually 2 SMSs because of the length of the message)
4. Your bill amount is XXX…
5. Your bill amount is XXX and the last date of payment is…
For the last 3 years or so, I have been paying all my bills before the due date. Once I got so irritated that I emailed their customer service, and their reply? These are server generated messages and we can’t do anything about it. I got more irritated and asked my email to be forwarded to Airtel’s CRM, Business Intelligence, Analytics or whatever the team there like to call themselves!
I got just one SMS alert when my next month’s bill was generated. But it was back to square one from the 2nd month onwards.
My question is which “smart” manager came up with the idea that 6 SMSs should be sent to all their post-paid customers every month? Why doesn’t one SMS saying “Your bill amount of XXX has been generated and the due date is ABC” suffice? Has anyone at Airtel calculated the cost of sending 5-6 SMSs to all their post-paid customers, every month? Shouldn’t the last SMS be sent only to those customers who have a habit of making late payments? Do they send the second SMS to those customers who don’t have online accounts too? And why can’t these alerts be customized based on a customer’s usage and payment behavior?
I can give more examples of Indian companies in the retail, entertainment, and services sector that are not doing nothing or very little about all the customer data they have, inspite of mentioning or advertising that they use BI & Analytics. So how mature is the Business Intelligence, and CRM Analytics setup at Indian companies? And how skilled or knowledgeable are the senior people associated with it?
Tuesday, April 28, 2009
Workforce Analytics
When I was actively working in the Marketing Research domain, I designed and programmed a lot of surveys on employee satisfaction/morale/happiness for US companies. That was around 2004, I guess a lot has changed then.
I came across this article on Workforce Analytics by Becca Goren on the SAS website. It sounds very promising and it seems to be THE RIGHT THING TO DO. I have summarized the article and edited it a bit for my blog.
------
Most organizations today do not track who is critical, who will likely leave, or why they will leave, so there’s no opportunity to develop effective strategies to retain critical employees.
Workforce analytics is the missing link in today’s business strategy. It is imperative for organizations to know how to attract, grow and retain these employees, as well as sustain the already seasoned professionals that bring depth and value to the organization.
Everyone across an organization can play a role:
• Business managers need to identify pending skill gaps and a pipeline for tomorrow’s leaders.
• Finance managers need to determine costs related to vacancies, overtime, outsourcing, recruitment and loss of critical skills, and then model strategies to address these issues.
• HR needs to spot trends and develop strategies to support changing workforce demands while partnering with business and finance managers to determine the best organizational structure/restructuring to address change.
Five ways to optimize the organization through its work force
1. Align work force with business goals:
• Forecast the amount and types of talent required to execute business strategy.
• Gain full information needed to make decisions for tomorrow.
• Manage the work force to drive the organization to meet its goals.
• Identify specific talent gaps.
2. Address workforce demands at every stage of the talent life cycle:
• Acquisition: Match the right employee with the right skills at the right time at the right cost.
• Growth: Develop skills for today’s star performers and tomorrow’s leaders.
• Retention: Proactively respond to changing workforce demographics and trends.
3. Identify and mitigate risks:
• Analyze the past and look forward to spot trends in key factors related to voluntary termination, absences and other sources of risk.
• Determine the impacts of organizational change on employee performance.
• Predict where vacancies and leadership needs are likely to occur.
• Understand workforce supply-and-demand patterns, and create strategies with additional labor sources to meet that demand.
4. Plan for business change, such as mergers, acquisitions and downsizing:
• Model what-if scenarios of potential effects across divisions and geographies.
• Make strategic decisions to reduce the risk of losing good employees and keeping redundant or underperforming ones.
5. Synchronize financial and operational workforce strategies:
• Expand background for each employee to look beyond salaries and general workforce costs for a more granular understanding: absences, overtimes, training costs, headcount, salaries and other compensation.
• Develop a defensible position on how costs drive value for the organization.
But my biggest question is how many organizations actually put these into practice?
I came across this article on Workforce Analytics by Becca Goren on the SAS website. It sounds very promising and it seems to be THE RIGHT THING TO DO. I have summarized the article and edited it a bit for my blog.
------
Most organizations today do not track who is critical, who will likely leave, or why they will leave, so there’s no opportunity to develop effective strategies to retain critical employees.
Workforce analytics is the missing link in today’s business strategy. It is imperative for organizations to know how to attract, grow and retain these employees, as well as sustain the already seasoned professionals that bring depth and value to the organization.
Everyone across an organization can play a role:
• Business managers need to identify pending skill gaps and a pipeline for tomorrow’s leaders.
• Finance managers need to determine costs related to vacancies, overtime, outsourcing, recruitment and loss of critical skills, and then model strategies to address these issues.
• HR needs to spot trends and develop strategies to support changing workforce demands while partnering with business and finance managers to determine the best organizational structure/restructuring to address change.
Five ways to optimize the organization through its work force
1. Align work force with business goals:
• Forecast the amount and types of talent required to execute business strategy.
• Gain full information needed to make decisions for tomorrow.
• Manage the work force to drive the organization to meet its goals.
• Identify specific talent gaps.
2. Address workforce demands at every stage of the talent life cycle:
• Acquisition: Match the right employee with the right skills at the right time at the right cost.
• Growth: Develop skills for today’s star performers and tomorrow’s leaders.
• Retention: Proactively respond to changing workforce demographics and trends.
3. Identify and mitigate risks:
• Analyze the past and look forward to spot trends in key factors related to voluntary termination, absences and other sources of risk.
• Determine the impacts of organizational change on employee performance.
• Predict where vacancies and leadership needs are likely to occur.
• Understand workforce supply-and-demand patterns, and create strategies with additional labor sources to meet that demand.
4. Plan for business change, such as mergers, acquisitions and downsizing:
• Model what-if scenarios of potential effects across divisions and geographies.
• Make strategic decisions to reduce the risk of losing good employees and keeping redundant or underperforming ones.
5. Synchronize financial and operational workforce strategies:
• Expand background for each employee to look beyond salaries and general workforce costs for a more granular understanding: absences, overtimes, training costs, headcount, salaries and other compensation.
• Develop a defensible position on how costs drive value for the organization.
But my biggest question is how many organizations actually put these into practice?
Sunday, April 5, 2009
Subscribe to:
Posts (Atom)
Posts
