tag:blogger.com,1999:blog-57067199958429282372024-03-08T12:29:18.445+05:30anuradha@NumbersSpeakLetting numbers do the talkingAnuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.comBlogger20125tag:blogger.com,1999:blog-5706719995842928237.post-34224803444006247872010-04-12T22:41:00.016+05:302010-04-16T22:28:22.956+05:30Botanical art and data analysis-Huh?I've written a lot of times about making business analysis more science than art. Is there then any aspect of analysis that I think needs more artistic vision? Yes, the entire process itself.<br />Birgitta Volz is an unusual botanical artist who works in auroville, India. She makes bark prints(by coloring the tree bark with organic natural color and making direct impressions on paper from it), wood prints and plant prints. When I first laid eyes on her <a href="http://www.birgittavolz.de/">work</a>, the analyst in me stood in awe. The beauty of the art lay in it's clean, clear ink capture of the plant and the end result clearly brought out the thought, process, work, story and final result.<br />When I look at an analysis what I usually see is a crisp story sometimes well put together and at other times all over the place. What is usually missing is the creative aspect and something that I 'm always looking for. It must make me feel!<br />What is the art in the science then you may ask? Here is my version-<br /><br />1. Has the doer really visualised the details of her work in her minds eye? It could be the story, the charts, the main points, the 'how to tell it' or the statistical work.<br /><br />2. Has she followed a process(some times structured sometimes abstract) to arrive at the end?<br /><br />3. Has she worked smart and hard? The fleshing out of the story with the right numbers and analysis is the key.<br /><br />4. Does the overall analysis flow well? Is there attention to detail?<br /><br />5. Has she stuck with the smaller picture and detail or the larger picture and less detail or both?<br /><br />6. Does the final product reflect all that has gone in? Does it go where it intended to at the start?<br />Unfortunately some of the best analysis or numerical work is missing the above. Most of them leave me cold not because the work is not done but because 'the something extra' is missing. That something extra is not the 'art of creative story telling' rather the passion of number crunching to bring out a good story.<br />For all those of you not yet convinced that data analysts should learn about 'art' of their work from painters; here are some examples of the art I call science. They tell the story of plants.<br /><a href="http://www.chameliramachandran.com/gallery">Chameli Ramachandran</a><br /><a href="http://huntbot.andrew.cmu.edu/ASBA/ASBA-MembersGallery.html">ASBA(The American Society Of Botanical Artists) member gallery</a><br /><br />Wow!Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-36857177815720781122010-04-12T22:17:00.003+05:302010-04-12T22:21:09.959+05:30Back to the number writingFor all of you who have been wondering what <span id="SPELLING_ERROR_0" class="blsp-spelling-corrected">happened</span>, it's a long story...<br />I will be posting regularly from now on so keep reading.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com1tag:blogger.com,1999:blog-5706719995842928237.post-26355594425999552572009-06-17T23:06:00.022+05:302009-06-18T00:00:19.705+05:30To do SEM or Not to do SEM, that is the question?<div align="justify">I've been avoiding writing this because I'm not sure if I can present a balanced case. The intense heat of the last couple of months in Delhi and Pondicherry (well over 40 degrees C) has not helped either as every time I got down to writing a draft, I felt drained and irritable. Finally, I've decided to take the bull by the horns and put it out there.</div><div align="justify"><br />I like Structural equation modeling (SEM) for it's ability to model the rhythm, flow and intricacies of relationships in real data BUT (and here's the hard part) I would not recommend that it be used unless a strict set of conditions are applied and met for its use.<br /></div><p align="justify">For the uninitiated, here is what structural equation models do-</p><ul><li><div align="justify">They are a step up from regression models and allow you to incorporate multiple independent and dependent variables in the analysis.</div></li><li><div align="justify">The dependent and independent variables may be latent constructs formed by observed variables.</div></li><li><div align="justify">They allow for testing of specified relationships among the observed and latent variables in a kind of testing of hypothetical theoretical frameworks.<br /></div></li></ul><div align="justify">The problem is that I have rarely seen well done SEM with market research data. In fact unlike regression, botched up SEM can get really ugly. This is surprising because SEM has been around for a while now and there is enough available literature on its use in the industry along with issues that practitioners need to be aware of and deal with while constructing these models. Thus to many a client I would simply say-if you are going with SEM, make sure you have addressed all the issues outlined below else, go with the simple factor and regression.</div><div align="justify"><br />Here is why SEM completely falls apart in the hands of unskilled (even somewhat skilled) or ‘software trigger happy’ practitioners:</div><ol><li><div align="justify">Use of SEM in situations where the measurement structure underlying a set of items is not well established and there is no sound theoretical framework available for possible patterns of relationships among constructs </div></li><li><div align="justify">Having too many single indicator constructs</div></li><li><div align="justify">Items loading on more than one construct </div></li><li><div align="justify">Low sample sizes relative to number of parameters to be estimated </div></li><li><div align="justify">Lack of addressing issues such as outliers or normality of variables</div></li><li><div align="justify">Use of too few measures for assessment of fit of model or use of measures of fit that do not address sample size biases</div></li><li><div align="justify">Building models that are too complex</div></li><li><div align="justify">Lack of use of measures of reliability to assess the quality of construct measurement</div></li><li><div align="justify">Little attention given to variance fit measures in the structural model</div></li><li><div align="justify">Lack of specifications of alternate nested models and testing of the same</div></li><li><div align="justify">Using modification indices and residual analysis too liberally to re-specify the model </div></li><li><div align="justify">No cross validation of model<br /></div></li></ol><div align="justify">Of all the above, the main reason why SEM fails on market research data is the first point. Some studies like satisfaction and engagement research are easier to work with due to existence of stronger theoretical frameworks and measurement of constructs by cohesive indicators. Others like brand analysis sometimes rely heavily on use of SEM as an exploratory tool for ‘confirming’ model structure.</div><div align="justify"><br /></div><div align="justify">Thus what is the way out one may ask? My point of view is-unless the first condition of having compact indicators that measure constructs and existence of a sound theoretical framework for latent constructs is met, proceed slowly and with care. In this case, SEM should be used taking into consideration all the issues stated above of which the most important is the cross validation of the model.</div><div align="justify"><br /></div><div align="justify">To sum up, try and use the technique in a more confirmatory way for a-priori specification of hypothesized models. What you may get if SEM is used without prudence is a model structure so unique that it may not be real.</div>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-50555753575421914832009-06-09T16:54:00.026+05:302009-06-10T23:40:22.969+05:30When numbers just don't add up<p align="justify">I read about the row over Nielsen’s low American Idol ratings between Fox Network and Nielsen last month with some amusement. This came hot on the heels of my sister's comment that networks really need to deepen their understanding of what's going on with the ratings of shows because of so much riding on them. She works with a large network and is responsible for a couple of big reality show on the same; she voiced the same angst as Fox’s CEO Tony Vinciquerra. The frustration echoed by the networks is something that I have heard time and time again usually when the ‘unexplainable’ happens. I’ve also been privy to the war of methodology between two TV rating providers in the Indian market (they finally merged through acquisitions).<br /><br />Personally, having been on the agency’s end for years now, I must take Nielsen’s side for no better reason than to talk about how 'glitches' in analysis are looked at by agencies and why this may just be a case of bad communication on both sides along with a product recalibration issue.<br /><br />Analytical solutions provided by agencies in the form of products or customized research are usually well accepted by their clients till something goes wrong. The chaos that ensues is more in the case of established research and products vs. new customized solutions. Usually agencies are the first ones to catch the ‘glitch’ and after the dreaded news is broken to the client, many a nights are spent arriving at an internal explanation and fixing it or accounting for it.<br /><br />The ‘what went wrong’ analysis usually centers on three areas at the agencies end:<br /></p><ol><li><div align="justify">Quality control(human error is the first thing that is checked out)</div></li><li><div align="justify">‘The non normal event’ that could have caused the error</div></li><li><div align="justify">Methodology-usually sample design and representation</div></li></ol><p align="justify">While the first two are the easier items to find and fix or explain, it’s the third that is usually more troubling to discover and to correct.<br /><br />On the Nielsen rating issue with Fox, the first thing the agency would probably do is to check if the reporting of Idol ratings were correct. For this they would look at the generation of the report itself or may sample various breaks to re-tally results. </p><p align="justify">If this does not throw up anything, they would then search for other events that could explain what happened. For this, they would use information already available through other studies or look at issues in the past which pointed to the problem. In Nielsen’s case, my conjecture is that one of the hypotheses generated would have been the noncompliance in homes i.e. people using their meters incorrectly. This insight that Nielsen stated was generated from another research would form one of the many hypotheses that the agency would explore. I’m not sure if this was the only reason for the low ratings communicated to the networks, but my feeling is that it was the one picked up and blown apart. Nielsen, on their part is right to contend that the 8% discrepancy they talked about is not a number that can trickle down to the network show rating since it was garnered in an absolutely different study done for different reasons. But I doubt the networks are listening.<br /><br />With so much on the line in terms of money, the way that the client sees it is that ‘he better have answers or someone will pay’. In all fairness, if I were the client I would feel the same way i.e. enraged, puzzled and frustrated. The problem is that ‘fixing the issue fast’ may not be as easy or appropriate unless there is a real causal link established between the households that are not in compliance and the low ratings. To do this the agency will have to test this hypothesis out in a comprehensive manner and then arrive at the x% number for non compliance or under reporting and then link it to the fact of low ratings across some disputed and non disputed shows. Not a simple task. </p><p align="justify">This brings me to the third point and one which most agencies dread, questioning their design if all else fails. Since established research and products go through a thinking and creation phase, casting doubts on or revamping design is not the preferred approach with most outages. The need to re-look at methodology only arises if the issue raised is not resolved and if the agency fundamentally comes to believe that a change in methodology would benefit clients. Methodological issues especially those of sample selection, type of sampling, weighting, margin of error reporting and power calculations in studies especially large ones like TV ratings still leave room for improvement and require another blog entry. Nielsen may very well be in the process of thus investigating and learning from the first two possible reasons for the data error.<br /><br />That the Fox CEO feels things are very unclear are feelings that a lot of clients echo when issues arise with research results. Some pointers that may help clients manage these situations better for the future are:<br /></p><ul><li><div align="justify">Understand the various elements of the design of the study especially sampling, the type and representative aspect and weighting. Ask the agency for a pros and cons analysis for the design in place or proposed.</div></li><li><div align="justify">Evaluate the margin of error in reporting not just at an overall level but by necessary segments and what it means at a ground level.</div></li><li><div align="justify">Invest in a statistician/(s) or another analytics agency at your end (if you don’t have one already). Their job should be to slice and dice the numbers, give you more insights and raise pertinent questions while working with the agency.</div></li><li><div align="justify">Calibrate results got from the agency with other findings internal or external. This is harder in a monopolistic situation but past data and parallel studies should guide you.</div></li><li><div align="justify">When data issues arise, work with the agency to fix them, once they are fixed test the new situation. Play devil’s advocate; don’t rest after things stabilize. Try and get the agency to establish a cause and effect analysis for the data blip controlling for other factors.</div></li><li><div align="justify">Question, Question, Question-it keeps the agency on its toes and helps preempt disasters.</div></li><li><div align="justify">Ask for the process of quality control employed by the agency in it's data collection and reporting. Review it, check for loopholes and facilitate correction.</div></li><li><div align="justify">Be kind (unless the agency is a repeat offender). Recognize that in the data analytics game, agencies usually try and give you the best that they’ve got. Data outages are also frustrating and traumatic for them.</div></li></ul>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-11321486450472920442009-04-13T23:28:00.045+05:302009-04-14T21:43:02.317+05:30Poor randomized testing-why a rose by any other name does not smell as sweet?<div align="justify">While rigorous testing of new ideas, offerings and approaches is the order of the day at companies like Capital One(my hero), Amazon, Google, Netflix, some retailers, direct marketers and pharmaceutical companies; at many others important decisions are still based on 'gut feel' and 'wrong evidence'. In spite of the availability of software, capability and adequate research in the area of randomized testing, most companies still continue to flounder when it comes to executing a test.<br /></div><div align="justify"></div><br /><div align="justify">The two main reasons why I have seen testing break down(in spite of good intentions and an adequate hypothesis) are-</div><br /><ol><li><div align="justify">Lack of rigor in the design</div></li><br /><li><div align="justify">Execution of a half-hearted test to show evidence</div></li></ol><br /><p align="justify">The lack of rigor in the test design creeps in in many ways:<br /></p><ul><li><div align="justify">Small sample sizes(not adequate to yield statistically valid results)-Clients usually quote costs as an issue for the same, however a large margin of error in the results make the test a no go right from the start. This applies to not just the overall sample sizes but also sample sizes for the breakouts at which data needs to be analyzed and reported.</div></li><br /><li><div align="justify">Inadequate matching of test to control groups-Not enough analysis and matching is done of the test and control groups which should be almost comparable. Thus results from the analysis cannot be pegged to the new stimulus due to confounding factors present. The rush to start the experiment is another reason for this lack of fit between test and control.</div></li><br /><li><div align="justify">Wrong number of cells in the design-While complex designs, usually factorial exist that reduce the cells needed without compromising reads on the data, simple less adequate designs continue to be used. While I like the idea of simple models being able to explain complex phenomenon, that should not be a deterrent to the use of more complex models for complex real world scenarios.</div></li><br /><li><div align="justify">A too short testing period-In a rush to complete the test and convey results, clients don't give the test the adequate time it needs to generate stable metrics(especially if those metrics have a high variance). </div></li></ul><p align="justify">Since most marketers recognize the need for a 'test-learn-roll out' approach, the second reason why randomized tests fail is harder to understand. There seems to exist a 'need to test' to show evidence of 'having tested' and the results from such tests are couched in scientific jargon with liberal extrapolations. Initiative roll out decisions are made on the basis of these tests with numerous rationalizations, for example:</p><ul><li><div align="justify">The results pan out for some regions, they will thus work at a national level</div></li><li><div align="justify">The results are positive even though margin of errors are large, with a big enough sample things will be fine</div></li></ul><p align="justify">Here is my advice for marketers -</p><p align="justify">DON'T TEST if a new approach cannot be tested(for whatever reasons some of them valid). Use a think tank of key executives to do a swot analysis and go with the final call on the same.</p><p align="justify">DON'T TEST if you don't want to test due to a lack of belief in testing or a disinclination to test with rigor. Roll out the new product without testing and be ready to explain to the boss if the initiative fails. Something that looks and feels like a test is not a test.</p><p align="justify">BUT...</p><p align="justify">DO TEST if you-</p><ol><li><div align="justify">Want to find out what really works and put your hypothesis under a rigorous scanner.</div></li><li><div align="justify">Want to optimize the money you put behind a new product or idea before pushing it to customers(who may be unwilling to accept it).</div></li><li><div align="justify">Want to learn and apply and not make the same mistakes twice.</div></li></ol><p align="justify"></p><p align="justify"></p><p align="justify"></p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-74172105490320707102009-04-11T22:25:00.045+05:302009-04-23T01:30:41.751+05:30Trends: Recommendations-Tell me what else I should buy and do it well<p align="justify">Here are three scenarios that address the power of recommendations and how they can work for consumers and marketers-<br /><br />Scene 1: I log in to Amazon and search for the book 'Predictably Irrational', their recommendation algorithm tells me the other books that customers who bought this book have also bought i.e. 'Sway', 'Outliers', 'The Drunkard's Walk', 'The Black Swan', 'The Wisdom of Crowds' and many more. Sometimes the recommendations are interesting enough for me to look through them and I end up buying more books than I budgeted for.<br /><br />Scene 2: I enter Debenhams the UK department store with my son in tow for a quick buy to wear at an anniversary lunch. I am in a huge rush thus getting it right quickly is the key. I show the shop assistant the style I am looking for and she promptly picks up three of the same kind and hands them to me. While I try them on, she comes back to give me some more tops that match my style. She tells me what a deal I would be getting on them-Betty Jackson designs at 70% reduction, that's a steal! Well you guessed it, I buy three tops and a pair of shoes and walk out happy and satisfied after thanking her personally.<br /><br /><br />Scene 3: I call Nirulas for a home delivery order and ask for my favorite item on the menu, their hot chocolate fudge (HCF's for short). For those new to this homegrown north Indian brand-they have the best hot chocolate fudge in the world. Well, before I can say 'some extra nuts and chocolate please' the order taker tells me if I were to add some extra nuts and chocolate, they would charge me Rs 17 extra for each. While the consumer in me is chagrined at having to cough up money for something I got for free for years, the data analyst in me realizes someone's been analyzing the orders and pricing better.<br /></p><p align="justify">Recommendations make sense to us because they help us sift through piles of information and focus quickly on what will maximize our buying experience i.e. finding relevant, new and interesting things. However for them to work, the underlying assumptions must hold:<br /></p><ol><li><div align="justify">They must come from a deemed 'trusted' source whose judgment we value</div></li><p align="justify"><br /></p><li><div align="justify">They must hit our sweet spot in terms of experience</div></li><p align="justify"><br /></p><li><div align="justify">They must be consistent and thus build trust</div></li></ol><p align="justify"><br /></p><p align="justify">How does this translate at ground level, with data on purchases being recorded both offline and online-very soon I envisage walking into a store(physical or online) to be told not just what I should buy based on my taste but what else I should be looking at. While a lot of e-commerce websites offer this personalized shopping experience via crude and sophisticated variants of recommendation algorithms to users, recommendations generated to fit individual customer preferences still have a long way to go.<br /></p><p align="justify">Consumers inundated with loads of choice want good subsets of that choice but within the context of 'what they like or would like'. Marketers would like to offer the consumer products that have a higher probability of being bought. Looking at past historical purchase data or user rating of items attempts to marry the need of the customer with that of the marketer. The problem lies in how to read and interpret what the customer is looking for? My experience has been that the answer is tied to satisfaction and loyalty. If the customer comes back for more and increases his burn rate over time, then what you are recommending is working-if not, then there is scope for improvement in the recommendation algo. Testing what recommendations worked may help in this process in fine-tuning what did not work. Analyzing customers who picked up recommended items vs. those that did not for a particular purchased product may also lend insights into what may be going on.</p><p align="justify"><br /></p><p align="justify">An interesting article by Anand V. Bodapati titled "Recommendation Systems with Purchase Data" in the Journal of Marketing Research Vol XLV Feb 2008, talks about why recommendation decisions should be based not on historical purchase probabilities but on the elasticity of purchase probabilities to the action taken on the recommendation.</p><p align="justify"><br /></p><p align="justify">How would I rank the suggestions given by the three companies based on my experience with them and would I go back for more?<br /></p><ol><li><div align="justify">Debenhams: Bang on. I got what I wanted at a good price and looked at a right variety of relevant alternatives before making my choice(remember time was an issue). Would definitely go back.</div></li><p align="justify"><br /></p><li><div align="justify">Amazon: It's a hit or a miss and the list of suggestions is very long and not always worth the browse. They could do better but it's not bad. Would definitely go back.</div></li><p align="justify"><br /></p><li><div align="justify">Nirulas: While I appreciate that someone recognized that the 'extras' needed to be paid for, I would like some suggestions like 'try our Jamoca almond fudge' or the 'Mango Tango is to die for'. They could do much much better. Would definitely go back(It's a monopolistic situation-no other brand comes close on the HCF).<br /></div></li></ol><p align="justify"><br /></p><div align="justify"></div><p align="justify"><br /><br /></p><div align="justify"></div>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-49577780478925687782009-03-31T00:48:00.056+05:302009-04-09T02:12:49.061+05:30Quantifying creativity in developing and evaluating package design<p align="justify">I'm a product and graphic design buff, and as I sat drooling over Phaidon's wonderful book Area_2 on upcoming graphic designers, I wondered if quantitative research could really pick out winners in this creative field? I am no expert but I can tell instantly if I like what I see or don't like what I see in a visual image. If I am undecided, I need to process the visual and then understand it before I take a call.<br /><br />Yes, things work a lot differently when product packages are on the shelf and consumers are filtering the visual among others with heaps of information in their heads (brand affinity, the time they have, size of package they buy, advertising awareness, frequency of buying and a lot more). But is it so hard to pick a winner quantitatively when it comes to package design or do companies simply rely more on non quantitative or flawed quantitative approaches to choose a winner?<br /><br />I am a loyal Tropicana consumer and I thought the change in package design for the brand smacked of 'not listening to consumers and not quantifying their voice in research', else why would a design change that drastic (it makes the brand look ordinary) make it through research? If consumers called, e-mailed and telephoned to express their feelings about the new design, where were these consumers when the design was tested? A well done online test (among other tests) with the right samples of loyalists and other segments would have saved PepsiCo a lot of grief.<br /><br /><br />What could have gone wrong in the research? Some hypotheses I generated about the consumers in the study:<br /></p><span></span><span><ul><li><div align="justify">They were the wrong sample(it can happen)</div></li><li><div align="justify">They were not enough in size and voice</div></li><li><div align="justify">They gave wrong answers</div></li><li><div align="justify">They favored the new design but had a violent reaction later when they saw it on the shelf and wanted the old packaging back(blame it on the recession)</div></li><li><div align="justify">They were misinformed or did not understand the research</div></li><li><div align="justify">They were not taken seriously about something as creative as packaging design</div></li><li><div align="justify">They could not evaluate the new design clearly since it was a radical change from the original</div></li><li><div align="justify">...</div></li></ul><p align="justify"></span>A combination of qualitative and rigorous quantitative research (and I prefer quantitative for all but initial research) can pack a punch when it comes to developing and evaluating package design. Here is how to get it right:</p><ol><li><div align="justify">Set goals and objectives for the new design using qualitative research.</div></li><li><div align="justify">Communicate the objectives and vision for the new design clearly to package designers.</div></li><li><div align="justify">Evaluate the initial rough designs through online testing. Identify the best four or five.</div></li><li><div align="justify">Fine tune the best designs through quantitative research.</div></li><li><div align="justify">Quantitatively test the best designs via various simulated tests (online or offline) to identify the winner.</div></li><li><div align="justify">Go ahead with the winner design only if it emerges as a clear winner with respect to the control (keeping in mind the status quo bias in marketing research).</div></li></ol><p align="justify">Online package design research tools are helping marketers evaluate and quantify how consumers will react to the creative aspects of the design. <a href="http://www.packagedesignmag.com/issues/2008.11/research.shtml">Package Design Magazine</a> talks about three of these solutions.<br /></p><p align="justify">Pure quantitative analysis of a creative process like package design is still viewed with skepticism among marketers. However, using the numbers to aid in the creative process helps companies avoid big mistakes and let's designers work and create within a framework that echoes the consumer's needs and wants.<br /></p><p align="justify">One loyal customer is happy Pepsi scrapped the new Tropicana package and bought back the old.</p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-27325943336790152132009-03-31T00:47:00.024+05:302009-04-23T00:48:22.066+05:30Why frequentist statistical approches still win in analysis of market research data?<div align="justify">I recently read part two of Ray Kent's article 'Rethinking Data Analysis-Some alternatives to frequentist approaches' in the latest issue of the International Journal of Market Research(Vol. 51 Issue 2). The article makes a case for looking at alternatives such as bayesian statistics, configurational and fuzzy set analysis, association rules in data mining, neural network analysis, chaos theory and the theory of the tipping point when data does not meet the requirements of frequentist approaches. </div><div align="justify"><br />My point of view on the article is :</div><ol><li><div align="justify">As someone who works in this field, it is annoying to be constantly told about limitations of frequentist methods that I am aware of.</div></li><li><div align="justify">The reasons for lack of adoption of newer more appropriate techniques in market research are more basic than researcher knowledge(or lack of in this case), challenges in presenting results or client adoption.</div></li></ol><p align="justify">Here are some reasons why a lot of market researchers continue to rely primarily on frequentist approaches:<br /></p><ol><li><div align="justify">Most researchers are not statisticians thus find it hard to understand and apply complex newer techniques. In fact most market research companies don't have an adequate number of statisticians on board.</div></li><li><div align="justify">Companies need to put money, research and time behind these techniques in order to sell them to clients(we see this trend among data analysis software companies like SAS, Sawtooth software, Latent Gold etc). Without this, it is difficult for lone researchers to push newer ways of analysing data to clients.</div></li><li><div align="justify">Researchers prefer to be 'shown' and not 'told' how these new techniques are applicable to their industry. A lot more collaboration is needed among academicians and practicing researchers to apply these techniques to relevant data in order to see the merits. Trying to replicate results of published articles in real time still falls under 'exploratory research not paid for by the client'.</div></li><li><div align="justify">While it makes sense to argue for an approach that looks at data using a variety of techniques, in reality researchers are pushed for time and looking at various alternatives is very hard.<br /></div></li></ol><p align="justify">It feels good to get that off my chest...</p><br /><p></p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com2tag:blogger.com,1999:blog-5706719995842928237.post-76766402034248484672009-03-18T12:16:00.020+05:302009-03-19T01:05:34.032+05:30Breakthrough Ideas for 2009: Should you get together a global analytics team?I read Thomas Davenport and Bala Iyer's article 'Should you outsource your brain?' in the Harvard Business Review 'Breakthrough ideas for 2009' with mixed feelings. The title of the piece is provocative and fails to address the real issue. It talks about how companies are now also outsourcing their decision making analytics along with other less cerebral jobs to countries like China, India and Eastern Europe. Towards the end of the piece they mention a shortage of talent, deep domain expertise at the vendor's end and project structure of the off shoring engagement as some reasons for outsourcing important decisions to third parties.<br /><br />Here is my problem with this piece(apart from the title). The issue I believe is not about whether companies should outsource their brains(decision making based on analytical insights would be a more appropriate term) but about how these companies can gain competitive advantage through analytics by leveraging a global talent pool of professionals. That this global talent pool happens to reside primarily in India, China and Eastern Europe is incidental.<br /><br />Shortage or a lack of talent in analytics exists across the world not just the US. In India, for example while there are a larger number of people with statistics degrees, they still lack skills that allow them to apply the same to real life business problems. Statistics and analytical thinking are hard to teach and the teaching methods available at universities across the world do not match the requirement of the industry. It is then left up to individual organisations and passionate practitioners to train and mentor new professionals in this field or wait for them to learn by experience. In the short term however, this training and mentoring does not figure as a top priority in most companies. Thus as a result everyone is left fighting over the same small talent pool and no one wins.<br /><br />Analytics required to take strategic decisions involves the working together of people with different skill sets, for example-the marketing manager, key people in marketing and other areas in charge of various customer portfolios and an analytical team of people adept at data management, statistical analysis and insight generation. For companies that are comfortable with analytical decision making the main challenge lies in the recruitment and retention of analytical talent. I know of instances where six months to a year are kept aside to hire middle level talent for advanced analytical jobs. Under these circumstances what should these companies do?<br /><br />Graduate programs in statistics and mathematics in the US are flush with students from China, India and Eastern Europe. Most of these graduates go on to find jobs in programming, risk management, database management and bio-statistics. Ironically, the pharmaceutical industry in the US has managed to leverage these global talent pools much better than any other. The rest of us could learn from their experience.<br /><br />Since analysis of data, technical model building and statistics are necessary to make decisions in analytically savvy companies, pulling in professionals from across the world to aid in the process is the smart way to go. What this does is give these companies the firepower they need to make sound decisions rather than those based on the past or driven by hasty analysis or the gut.<br /><br />The trend for 2009 is not that businesses are outsourcing their thinking but that they are recognising that adding talent from across the globe to their rolls helps them get ahead in the game.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-2034713595535724032009-03-17T21:53:00.041+05:302009-03-19T00:30:05.136+05:30Analytical harakiri-Ignoring latent class modelsMy black books are out and here are some notes that were jotted down on projects;<br /><ul><li>Scenario 1: You're working on an analysis to study drivers of purchase intent and you keep feeling that different sets of of customers may have different drivers. The client however, has not asked for segmenting the <span class="blsp-spelling-corrected" id="SPELLING_ERROR_0">customer base</span>.</li><li>Scenario 2: Your data is a mix of continuous, ordinal and nominal variables, yet you continue to try and segment your customers by using some distance metrics. You wonder why two customers are slotted in different segments though there is not much difference in their average spend or other variables?</li><li>Scenario 3: You know the major themes that your client's brand stands for but are unable to fully break them out from the data into meaningful, actionable sets and subsets for understanding or positioning purposes.</li></ul><p>The reason why researchers and analysts continue to grapple with these issues in market research data is because they are not getting the most from their analysis. One reason I love statisticians in social science is because they are the 'early adopters' for most new and emerging techniques in statistics. They quickly work on and distill new learning's in the area to their everyday problems and thus boldly go where the rest of us are timid or lazy to venture.</p>How do we get more from our analyses in the areas of regression, choice, factor and segmentation problems? Latent class or finite mixture models are the answer. These set of models differ from the traditional models in their structure due to the inclusion of one or more discrete latent(unobserved) variables into the model relationship along with the observed. Thus categories/classes of these unobserved variables are interpreted as latent segments.<br /><br />Some key advantages of latent class models are:<br /><br /><ol><li>They are less affected by data not conforming to modeling assumptions(linear relationship, normal distribution, homogeneity)</li><li>They work with mixed scale(continuous, ordinal, nominal, count) type variables in the same analysis</li><li>They are able to simultaneously do two analyses together i.e. segment and predict thus eliminating the need for two steps in an analysis</li></ol><br />In the area of segmentation, these models bring in a model based approach and an ability to accommodate categorical and continuous data and predictive and descriptive segmentation under a common modeling framework. This leads to far superior and insightful results in testing and estimation of market size and structure and profile of market segments. The probability based segment prediction criteria provides a more realistic picture of market reality since consumers can belong to more than one segment at a time. Some areas that latent class segmentation models should be used to 'do more' with the data are classical segmentation problems including descriptive ones, global segmentation and studying change in segments over time.<br /><br />When working with factor analyses, latent class factor models are better able to make composites out of variables because they handle non-continuous data in a more elegant way. Plots and perceptual maps generated from the analysis score over the traditional technique because the factor scores have probabilistic interpretations. The categorical nature of the composites allows a more holistic extraction of themes and sub themes and helps in developing a more precise brand positioning strategy. Use of fewer variables to form factors is an added plus. Applying latent class factor models to attribute data(with ordinal and categorical scales) is an absolutely delightful exercise once the results are compared to traditional factor analysis. The new models provide a more accurate and vivid picture of the brand/product.<br /><br />Very often analysts pass off the low predictive power of a regression model to a 'lack of all the right explanatory variables' and tell clients that the model may have been better if they had more data. What they don't check is that the same model may not hold for all customers. Latent class regression modeling allows for a simultaneous segmentation and regression of the data thus unearthing latent segments that may have different regression equations and estimates. This makes the estimation more precise and gives clients a more informative way to look at a drivers analysis. Applications of latent class models in regression lie in the area of conjoint analysis, customer satisfaction studies, purchase intent drivers or any traditional regression model that benefits from explanation of unobserved variance in the data.<br /><br />Latent class models thus represent powerful improvements in model building, prediction and insight generation over traditional approaches to segmentation, factor and regression analysis. They truly allow the data to talk much more and analysts need to take them mainstream by learning to use them and unleashing them on projects which can benefit from the same.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-65313791292304263052009-02-24T11:28:00.044+05:302009-02-25T03:23:40.700+05:30Predicting the success of Slumdog Millionnaire and other moviesThe dust from the Oscar award ceremony has settled and my fellow countrymen continue to go about their businesses with big smiles on their faces over the oscar wins for AR Rahman, Resul Pookutty and the film. A question that I keep thinking about is whether analytics could have predicted the success for Slumdog?<br /><br />Movie box office revenue prediction is coming of age and Hollywood is beginning to recognise that it may need the help of number crunchers to utilise it's funds better by getting behind films with higher chances of success. I remember reading in detail the article by Ramesh Sharda a few years ago, where he deployed a neural net model to predict the box office receipts of movies before their theatrical release. Risk and money involved for investors in the movie business is very high, coupled with the fact that a large portion of a movie's total revenue comes from the first few weeks after release. Interest in the ability of statistical and mathematical models to predict this revenue not only for funding purposes but for better distribution and marketing strategy has been growing in the last few years.<br /><br />There are some companies and individuals who have cracked the code using a variety of variables. While there are still many expert sceptics out there, making predictions that do significantly better than chance present a win-win solution for both the developer of these algorithms and the investor and studio.<br /><ul><br /><li>Can it be done? YES, YES and YES! With more and more people throwing their weight behind the science of the subject, prediction algorithms in this space continue to get better and better.</li><br /><li>Is it easy? NO! That's where the frustration and challenge among data crunchers lies.<br /></li></ul><p>What's my recipe to get this right? In my experience(yes I've had the pleasure of taking a shot at this exciting problem) employ the 'layered accuracy approach'.<br /></p><ol><li>Decide which part of the problem you want to tackle-pre release or post release prediction(first few weeks) or both.</li><br /><li>Identify the structure of your base model(this is the model that will provide you with the benchmark predictive power and understanding of the revenue aspect of the movies). Try a model structure that is easy to execute and interpret and fits the data well. Make decisions about quantitative vs. behavioral models, point estimates vs. classification into revenue groups, segment models or all movie population models. .</li><br /><li>Use tried and tested variables relevant to the model being built-star power, no. of screens, genre, MPAA rating, time of release, competition at time of release, critics ratings, sequel etc. I recommend that you breakdown any variable that is still too dense-for example create your own version of the traditional genre variable as it usually does not add much in it's present form.</li><br /><li>Use other not so mainstream variables-plot, positive buzz on internet forums and the Hollywood blacklist for starters. This is your creative space, use it to construct variables that you believe can add more punch to the model. </li><br /><li>Build the model and examine predictive accuracy and insights. Rank order the insight variables. If something does not make sense explore it again.</li><br /><li>Validate the model to see that it stands up tall.</li><br /><li>Try another model structure and see if you get better results(it's all about accuracy Watson, even a little more lift counts when we are talking millions of dollars).</li><br /><li>Get a movie fanatic data cruncher to do all the above for you(I promise the predictive accuracy will dramatically improve).</li><br /><li>Explore other non-conventional ways to better your prediction accuracy. A big area now is prediction markets.</li></ol><br /><p>As science makes the business of revenue prediction in movies and other entertainment areas much easier, the issue becomes less about whether we could have predicted the success of Slumdog Millionaire and more about if we want to. <a href="http://www.newyorker.com/archive/2006/10/16/061016fa_fact6?currentPage=1">Malcolm Gladwell </a>presents this case so eloquently in his absolute must-read piece in The New Yorker.<a href="http://www.newyorker.com/archive/2006/10/16/061016fa_fact6?currentPage=1"></p></a>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com3tag:blogger.com,1999:blog-5706719995842928237.post-42829473376589485722009-02-19T17:03:00.037+05:302009-04-06T06:08:34.536+05:30The Practical Statistician-A Toolkit<p>I have had the pleasure of working with a lot of statisticians, mathematicians, data miners and econometricians (let's call them PEMD-persons extracting meaning from data, for ease) in my career. An observation I have often made is that while all of them know the tools of their trade, only a few eventually go on to become excellent practitioners or as I call them 'practical statisticians' in the industry. What is it that these experts have that gets them far ahead in their trade? A toolkit that helps them survive the real world journey. Here is the list of items in that toolkit:<br /><br /><br />Item #1: Pen and notebook (a thick one)-they carry this around at all times even to bed. This helps them make copious notes when others are talking and think aloud when they are structuring their thoughts, attacking problems and analyzing outputs. They guard this notebook zealously and get visibly upset if it ever gets lost or misplaced. They recognize that in order to streamline loads of work, manage their time well, analyze the problem fully and present the output lucidly without going insane they must structure their thoughts. Written matter is the key.<br /><br />Item #2: Three books for reference and speed reading skills-one is usually about the software they are using, the other two are the best applied texts on most used techniques in their field and new emerging areas(which no one else has a clue about). They read many more research articles than other people (and yes they usually do that during their breaks or in their leisure time). If they don’t understand an article the first time round, they absolutely have to read it again and again till they do.<br /><br />Item #3: Data dirty fingers-they execute projects no matter how high they rank in the corporate hierarchy. They recognize that leading from the front means ability to do the work at the back end especially when all hell breaks lose.<br /><br />Item #4: Non-technical speak-they are able to communicate their ideas and statistical methods to a wide audience without using statistical jargon.<br /><br />Item #5: Graphs-they like to graph data and get a sense of numbers visually. This ability to look at both numbers and graphs helps them get a finer sense of the data and what they don’t know and must find out from it.<br /><br />Item #6: A good dose of imagination, critical thinking and skepticism-they function like detectives and for them most business problems present cases to be cracked. After the project starts they devote all effort in cracking the case oblivious to everything and everyone else.<br /><br />Item #7: Mentoring and training calendar-unless they pass on their wisdom and how they put the problem, method and experience together, they know they will continue to do the same work over and over again.<br /><br />Item #8: A broad view of their role -they define their role rather than let client's, coworkers and organizations peg them. They like their roles to be larger and ‘more whole’ not constrained by their degree and specialization.<br /><br />Item #9: Practical adequate solutions-while striving for the best solution, they recognize that they may need to deliver less optimum solutions based on project constraints and client readiness.</p><p><br />Item #10: A passion for statistics-especially it's applications in different fields, and an understanding of what it can and cannot do.</p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-18660823664347403912009-02-18T23:12:00.011+05:302009-02-19T00:09:37.336+05:30Trends: R vs. SAS-What's really at the heart of the matter?Okay, I promised myself that I would not jump into this debate and I bit my tongue and fingers like a thousand times last week. Go ahead and shoot me I'm only human.<br /><p>Here goes...</p>Methinks this R vs. SAS debate is less about the merits and demerits of the two software and more about the David vs. Goliath(or Hare vs. Tortoise) effect. David, in this case also provides strong competition in a slightly monopolistic market situation.<br /><br /><br /><br />I have worked with SAS and I don't have a strong opinion against it(except for it's really bad graphics). I am new to R and I like it(yes, there will be some pet peeves as time goes by). I have also used most other competing software in this space(SPSS, Minitab, Stata, Matlab etc).<br /><br /><br />So what's the issue you may ask? Well, no matter what anyone says(or posts), I believe one of the main reasons that R is generating such a lot of press(and don't get me wrong-it has strong merits) is the fact that with all it's merits it is also FREE! Whether we like to admit it or not, it bothers us that we have to pay for using SAS when R which is as good(if not better in some areas) is available for zero cost. Would the same debate be as heated if R did not deliver? I doubt it.<br /><br />Add to this the point that R comes in as the 'underdog' that most of us like to see win and you get a better idea of why there is so much angst all around on this issue.<br /><br />Enough said.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-56903456293798714912009-02-17T01:23:00.013+05:302009-04-05T22:06:18.499+05:30Parlez vous Statistics?As I sat debating the issue about whether we should have an informal case study 'test' for statisticians who want to work or intern with us, I read Andrew Gelman's blog article on a new course in statistical communication that he would like to teach sometime. It brought home to me the fact that if I went ahead with this test we would not have any new hires at all, since most would flunk out.<br /><br />Why oh why do we not teach statistical communication at most universities or even at jobs? The lack of this skill has made proponents and users of the subject in the industry unable to communicate in the same language.<br /><br />So what are some of the skills I would test for statisticians? Here is my list :<br /><ol><li>Translating a business problem to an analytical and statistical problem </li><li>Writing a proposal(or at least the proposed analytical solution part of the proposal)</li><li>Creating a process/flow chart of the analytical solution </li><li>Graphical presentation of data(raw, cleaned and analysed or modeled)</li><li>Summarizing and communicating statistical results in both technical and non-technical ways(depending upon the audience). This would also include documentation of the project and an executive summary of findings</li><li>Ability to write simple and elegant computer code and read the same(irrespective of software and writer differences)</li><li>Collaborative work effort with other colleagues(programmers, consultants, academicians etc)</li><li>Knowledge of statistical pitfalls</li><li>Other communication skills(e-mails, blogs, discussions, knowledge sharing etc)</li><li>Ability to read, understand and summarise research papers(good knowledge of work in relevant focus areas)</li></ol>More organisations need to get involved with universities to encourage teaching of these skills at an early level to practitioners who plan to join the industry. Statisticians on the other hand need to move out of their comfort zone and ensure that they become adept at communicating their language to a wider audience. Once the above skills have been mastered along with a sound knowledge of statistics, we may finally be viewed as the geeks with the sexy job(as Hal Varian-Google's chief economist points out in an interview with the <a href="http://www.mckinseyquarterly.com/Strategy/Innovation/Hal_Varian_on_how_the_Web_challenges_managers_2286">McKinsey Quarterly</a>).Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com3tag:blogger.com,1999:blog-5706719995842928237.post-17861296248483050282009-01-28T11:33:00.023+05:302009-02-01T00:40:24.657+05:30Plan a coming out party for Outliers-Part 1I picked up Malcolm Gladwell's book on Outliers because it got me thinking about an issue I encounter in all my analyses. I remembered the case quoted by statisticians as reason for not leaving out outliers. For those who don't already know the story, here it is:<br /><br /><p><em>In 1985 three researchers-Farman, Gardinar and Shanklin were extremely puzzled by data gathered by the British Antarctic Survey showing that ozone levels for Antarctica had dropped 10% below normal January levels. The reason for the puzzlement was because the Nimbus 7 satellite, which had sophisticated instruments aboard for recording ozone levels, hadn't recorded similarly low ozone concentrations. </em></p><br /><p><em>When they examined the data from the satellite they realised that the satellite in fact had been recording these low concentrations levels and had been doing so for many years. Because the ozone concentrations recorded by the satellite were so low, they were being treated as outliers by a computer program and left out of the analysis.</em></p><br /><p><em>The Nimbus 7 satellite had in fact been gathering evidence of low ozone levels since 1976. Due to the outliers being discarded without being examined, the damage to the atmosphere caused by CFC's went undetected and untreated for up to nine years(this account is disputed by NASA researchers who say that they had flags in place for low values and did notice the low ozone values and subsequently presented thier paper but the Farman trio's paper on the same beat them to it).</em></p><p>What is the moral of the story? To take a deeper look at outliers in your data because they usually tell a unique story if you are really willing to listen.</p><p>Why should you be looking for outliers-you may ask, here's why:</p><ol><li>Erroneous results in reporting, dashboards and executive summaries as these are comprised mainly of 'mean/average' numbers. Statistical tests and analysis may be negatively affected.</li><li>The outlier may be the story of interest in your data i.e. the high value accounts, the seasonal spenders, the defaulters etc.</li><li>The understanding that an outlier may yield about the data gathering process. I remember years ago being asked to cross check data when the results showed that median age of women at the birth of their last child was mid forties for certain eastern Indian states. This result was completely off from the national average(which was lower) and the client suspected a data issue at the agencies end. On enquiry, we learnt that women in these states were losing teenage children(that they had when they were much younger) to terrorism and drugs and thus were having more children in later years. In this case the explanation held else we would have had to investigate why the error occurred in the data collection. </li></ol><p>Once you find outliers in the data, what do you do ? Before anything else-report them! It does not matter if they are few in number, if you understand why they occurred or if you plan to leave them out for whatever good reason. In a lot of analysis, I see a disturbing trend of suppressing or 'fixing' outliers without understanding them or reporting them. </p><p>My suggestion thus is to have a discussion on outliers in your data before deciding what you will do with them. I will talk about addressing outliers in part 2 of this piece, but for now here are some things to mull over-</p><ul><li>Can tracking financial performance of companies and individuals and identifying outliers help curb scams of the Satyam and Maddoff type? Markopolos and mathematician DiBartolomeo warned regulators for years that Madoff could not be consistently generating higher than market profits unless he was running a ponzi scheme. I am sure some people out there also looked through Satyam's records and had misgivings but kept quiet.</li></ul><p></p><ul><li>Last year Republican representative Mark Souder proposed that baseball players whose on-field statistics suddenly improved should be tested more often for performance-enhancing substances. The thought is to measure actual player performance against projected performance and history based on a typical career path and identify outlier performance or sharp deviance. Maybe undertaking this analysis for track athletes may give sharper results.</li></ul><p>What does all this have to do with Gladwell's book on outliers? Nothing really, except a reiteration to take a fresh and deep look at outliers before tossing them away or standardising them. As for the book, it was alright(not earth shattering), the reason behind the Korean airline crashes made for the best reading.</p><p></p><p></p><p></p><p></p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com1tag:blogger.com,1999:blog-5706719995842928237.post-85115569041252043312009-01-07T11:05:00.013+05:302009-01-15T17:30:52.826+05:30Book Review: Super Crunchers-The fight between experts, gut and dataI enjoyed reading Ian Ayres book. Let me say that again and right - I really enjoyed reading Ian Ayres book. For those who have not <span class="blsp-spelling-corrected" id="SPELLING_ERROR_0">already</span> read the book-it details how data driven number crunching algorithms work better than expert predictions and gut feelings and how super crunchers(read-statistically literate and number crunching savvy) individuals will have an edge in decision making in the future.<br /><br />I liked the book because it lucidly illustrates trends that I have seen in the last decade-a better adoption of predictive models among businesses, more data generation and storage, an <span class="blsp-spelling-corrected" id="SPELLING_ERROR_1">industry wide</span> need for talented number crunchers and the conflict when data driven approaches come face to face with the resident expert or the manager who swears by his gut.<br /><br /><div>The case studies are <span class="blsp-spelling-corrected" id="SPELLING_ERROR_2">very </span>interesting and apt-it was amusing to read about the prediction of a vintage by an algorithm(I must pick up some wine based on the prediction soon). I could empathise with the story about a fellow economists frustration at waiting to get the final odds number on the <span class="blsp-spelling-corrected" id="SPELLING_ERROR_3">Downs</span> syndrome screening for his unborn child, and the inability of the technicians to apply the <span class="blsp-spelling-corrected" id="SPELLING_ERROR_4">Bayes</span> theorem(I've been there). As Ayres points out, I agree neural nets have a long way to go before they replace other mainstream techniques and it's not just due to the <span class="blsp-spelling-corrected" id="SPELLING_ERROR_5">over fitting</span> problem. Randomised trials still need to become mainstream among most marketers.</div><br /><br />What really makes the book stand out is that data crunchers like me along with millions others 'get it'. I build predictive models that are elegant and simple and able to help clients make better decisions about <span class="blsp-spelling-corrected" id="SPELLING_ERROR_6">their</span> businesses. We constantly face sceptics about how predictive models can fare better than the resident experts knowledge of his market or brand or business. We sometimes pitch to client's who tell us <span class="blsp-spelling-corrected" id="SPELLING_ERROR_7">their</span> business problems cannot be put in an equation(it makes me squirm because I have a personal data project on which aims at predicting market prices for <span class="blsp-spelling-corrected" id="SPELLING_ERROR_8">Indian</span> contemporary art). After years in statistics, it's still difficult to help people understand standard deviation or 2SD.<br /><br />Do I agree with the book's central premise-yes I do. In a data driven world, let numbers do the talking-stand aside experts and intuition.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-9018967507988005132008-12-05T13:02:00.024+05:302009-02-18T22:50:31.500+05:30Trends: Going the way of R and other open source softwareMy colleague <span class="blsp-spelling-error" id="SPELLING_ERROR_0">Girish</span> recently mailed me a <a href="http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?_r=1&emc=eta1">New York Times</a> business computing article about how data analysts have taken to R as the open source programming language.<br /><br />The article took me back in time to 1996, when I was a graduate student in the US. Fellow statisticians were raving about R as the new generation data crunching language and something that was going to give other data packages a run for <span class="blsp-spelling-corrected" id="SPELLING_ERROR_2">their</span> money. We were at that point doing our statistical number crunching on student licenses of <span class="blsp-spelling-error" id="SPELLING_ERROR_3">SAS</span>. While intrigued with the whole issue, I was too busy learning applied statistics and <span class="blsp-spelling-error" id="SPELLING_ERROR_4">SAS</span> and just getting through grad school semesters.<br /><br />Now years later it is with a feeling of <span class="blsp-spelling-error" id="SPELLING_ERROR_5">deja</span> <span class="blsp-spelling-error" id="SPELLING_ERROR_6">vu</span> that I read the article because today I am much closer to embracing R as 'the' crunching language for myself and our business.<br /><br />We've done the testing and it's won hands down every time;<br /><br /><br /><ul><br /><li>Ease of use</li><br /><br /><li>Readily available code modules(learning from others is a key here-we techies love to outsmart each other)</li><br /><br /><li>Wonderful graphics</li><br /><br /><li>Excellent data manipulation</li><br /><br /><li>No fees</li><br /><br /><li>Ability to customise</li><br /><br /><li>Lots more...</li></ul><br /><br /><p>While competitors are quick to dismiss it, R works because it has created a democratic community of statisticians and others who like to see number crunching become easier and more visual. The fact that it is open source provides the added kick to be able to create customised modules that the community can use. It blends programming and statistical skills together more elegantly than I have ever seen. The fact that it has a fan following among my tribe is therefore not surprising.</p><p>Thus, is R and other open source software the way to go-absolutely! The reasons are many but let me rank order them based on how we took the leap-</p><ol><li>Stacks up and beats competition on most data crunching modules.</li><li>Easy to use.</li><li>Collaborative value model: the conviction that a collective community can create better thought and tools than a competitive one.</li><li>Better service: less <span class="blsp-spelling-corrected" id="SPELLING_ERROR_7">downtime</span>, quicker error resolution and a <span class="blsp-spelling-corrected" id="SPELLING_ERROR_8">help desk</span> of people dedicated to fixing issues.</li><li>Excellent customisation options: The ability to create what you want for your business and put it out there.</li><li>Cutting edge graphics.</li><li>The geek factor-the thrill of creating, bettering and showing off to other like minded individuals cannot be underestimated.</li><li>Lower technology cost: while this is great, believe me this is not the main reason that businesses use open source.</li></ol><p></p><br /><p></p><br /><p></p><br /><p></p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com1tag:blogger.com,1999:blog-5706719995842928237.post-83179950936283898232008-12-01T20:39:00.001+05:302008-12-05T23:41:42.795+05:30Segmentation-making it more science than art<p>The reason for delay in posting this has been because I've been toying with <span class="blsp-spelling-corrected" id="SPELLING_ERROR_0">whether</span> I should write on segmentation or not. So much has been written on this subject that it makes me a little hesitant about revisiting this space. </p><br /><p>What got me to finally pen this was the title of a paper at an upcoming conference that said 'How statistics get in the way of actionable segmentation'. I <span class="blsp-spelling-corrected" id="SPELLING_ERROR_1">don't</span> know what the presenters have to say (must source a copy after the conference) but the title made me laugh. The two words that stuck in my head were 'statistics' and 'actionable segmentation' and <span class="blsp-spelling-corrected" id="SPELLING_ERROR_2">whether</span> the twain will ever meet.</p><br /><p>I have undertaken enumerable segmentation projects in my career, some simple, others complex, yet others that go nowhere. All of them in the end have the same things in common:<br /></p><ol><li>Too many bases variables </li><br /><li>Over reliance on cluster analysis as the primary tool for segmentation </li><br /><li>Use of subjective judgement to evaluate results of the cluster solution </li><br /><li>Lack of reliability and validity tests on the solution</li><br /><li>Recreation of the scientific solution into a more 'creative and arty' one</li></ol><br /><p>What the above means for managers who implement segmentation solutions is that they could be formulating strategy and targeting segments that are unstable and unreal. There exists a body of research that calls for a deeper look at the statistics and data that go into cluster analysis and segmentation(I will be happy to provide the references). </p><p>The real issue continues to be an inability of both analysts and practitioners to put together a common <span class="blsp-spelling-corrected" id="SPELLING_ERROR_3">road map</span> for segmentation that takes into account statistical robustness of the technique along with creation of actionable segments that can be <span class="blsp-spelling-corrected" id="SPELLING_ERROR_4">targeted</span> through <span class="blsp-spelling-corrected" id="SPELLING_ERROR_5">focused</span> marketing programs. In my experience, the science of segmentation gets lost in the art.</p><p>Here are the five key things that analysts and practitioners must do to create better, robust and more scientific segments-</p><ol><li>Choose bases variables for segmentation that tie in with the end goal of segmentation and keep <span class="blsp-spelling-corrected" id="SPELLING_ERROR_0">their</span> number not more than 8-10. Build a set of good profiling variables that tie into the bases variables(there is no restriction in number here).</li><li>Explore other tools for segmentation(sometimes simple business rules work just as well). Latent class analysis offers excellent alternatives for both survey and crm data and is still a highly underused technique. Try two techniques, if possible and compare and contrast results.</li><li>Use a variety of statistical parameters to evaluate a solution instead of relying on one or two or on subjectivity. Decide which metrics you want to look at before the study. For example-<span class="blsp-spelling-error" id="SPELLING_ERROR_7"><span class="blsp-spelling-error" id="SPELLING_ERROR_2">dendograms</span></span>, change rate, <span class="blsp-spelling-error" id="SPELLING_ERROR_8"><span class="blsp-spelling-error" id="SPELLING_ERROR_3">psuedo</span></span> <span class="blsp-spelling-error" id="SPELLING_ERROR_9"><span class="blsp-spelling-error" id="SPELLING_ERROR_4">Rsq</span></span>, <span class="blsp-spelling-error" id="SPELLING_ERROR_10"><span class="blsp-spelling-error" id="SPELLING_ERROR_5">hotelling's</span></span> <span class="blsp-spelling-error" id="SPELLING_ERROR_11"><span class="blsp-spelling-error" id="SPELLING_ERROR_6">Tsq</span></span> can be some metrics for evaluating no. of clusters in a cluster solution. The BIC, p-value, <span class="blsp-spelling-corrected" id="SPELLING_ERROR_12">parsimony</span>(no. of parameters) and the bootstrap p-value can be the parameters to nail number of segments in a latent class segmentation. Reliance on 'many' statistics vs. 'few' should be the mantra. </li><li>Test reliability through hold out samples and validity through looking at profiling variables and how they differentiate the solution. The holdout sample results must match those of the developmental sample in terms of the number of segments and profiles. Most of the picked profiling variables must adequately differentiate the final segment solution. If there is an issue with reliability and validity-the solution may have a problem. Going back and reworking the same is the best way out.</li><li><span class="blsp-spelling-corrected" id="SPELLING_ERROR_7">Don't</span> use the art of segmentation to sidestep the science for a solution, use it if you will to add to the same. </li></ol><p></p>Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com3tag:blogger.com,1999:blog-5706719995842928237.post-45184188260934621572008-11-29T22:16:00.007+05:302011-05-31T13:22:45.210+05:30About Anuradha@Numbersspeak<a href="mailto:Anuradha@Numbersspeak">Anuradha@Numbersspeak</a> is a weekly blog dedicated to statistics and analytics and their use in the real business world, run by New Delhi based statistician Anuradha Sharma.<br /><br />Started in November 2008, it features views on statistics and it's applications along with reviews of articles, books, products and software on the analytics industry. In addition, <a href="mailto:Anuradha@Numbersspeak">Anuradha@Numbersspeak</a> has a separate section dedicated to covering emerging techniques and trends in the industry.<br /><br /><a href="mailto:Anuradha@Numbersspeak">Anuradha@Numbersspeak</a> contributor, Anuradha Sharma has a unique angle on the industry bringing in a blend of hard technical rigor with deep business insight. She has worked as a market researcher at ORG-MARG, India's premier market research company and as a risk analyst at GE-SBI. In addition, Anuradha has served as the technical think tank and Chief Analytics Officer for Marketics, an analytics startup that was acquired in 2007 by WNS, a leading global knowledge outsourcing provider. At Marketics she led the thought and vision in both the CRM and Consumer analytics space.Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0tag:blogger.com,1999:blog-5706719995842928237.post-16590043754834664772008-11-29T01:10:00.000+05:302008-11-29T21:49:17.100+05:30Leveraging the power of data analyticsI have long wanted to share my passion for data crunching and analysis and hypothesize about why some people do it better than others inspite of sophisticated software available to all for the same.<br /><br />Is there a method to the madness or is this more art than science or more science than art?<br /><br />I am a statistician and I analyse data using sophisticated approaches. Most people either look at me with awe or thier eyes glaze over at this work description. Alas, the work conversation does not go further than that. At my first research job when I stated I do modeling-they actually thought it was of the ramp variety! While most of my friends have long conversations about thier jobs, it's usually hard to find people who display the same enthusiasm about mine.<br /><br />I love what I do and there are a lot like me slaving away at thier desks who get a kick out of making sense of reams of data. It's exciting, nervewracking, fun, hard, exhausting and so much more. A good analysis is like a poem that you finally understand after reading it scores of times(and thinking you got it).<br /><br />Well, enough said for now-I need to dig out the old scrapbook on everything I wanted to rant about each time I finished a project but did not do because the next project started. More later on that...Anuradhahttp://www.blogger.com/profile/12544972829455596956noreply@blogger.com0