Beyond Common Sense

2006-10-30T23:46:00.000-08:00

Analytics and Attrition in BPO Industry - 1

The problem

Do I really need to state the fact that the biggest problem facing the BPO industry is attrition?
Estimates vary but the ballpark figure for the loss on attrition is 1.5 times the annual salary of the attritor - Training, Overheads, etc.

So what are the ways out?

An overzealous (I think that the application of Maslow's Hierarchy was obvious and typical of BSchool forced fillers ) but nevertheless award-winning essay by Shradha Prakash & Rahul Chowdhury http://www.coolavenues.com/know/hr/s_1.php is a good summary of the ongoing efforts in the industry.
These include -
Short term -

Job rotation
More recreation
Incentives - Performance based

Long term -

...the industry needs to work with the government to introduce courses at a school and college level, which are in line with the requirements of the ITES-BPO industry.
Bilateral agreements between companies
A Common Database
Tier II and Tier III cities
...sponsoring employees on post-graduate programs...

Refer to http://zinnov.com/blog/index.php?m=200601 also for a crisp summary.

In the range of solutions that I have come across, there is one I would especially like to mention here - Exercising! http://in.news.yahoo.com/061015/211/68i9y.html

Instead of confidence that the article seeks to portray, I find more of blind panic.

I think the BPO industry has to first wake up to the fact that attrition is here to stay. Today's generation has too many options and no matter how much you blow the balloon, most ITES jobs would be low in the value chain.
And this IS NOT a shame... this is pure economics - cold, precise and impersonal. People will grow out of their first BPO jobs.

The only credible solution, I feel, is to hire older, more settled people. But given the nascence of the industry, I think there might be productivity and flexibility issues. Also, once the economy matures a bit more, it will be harder to find older people, other than housewives, willing to settle for these jobs.

So the only reasonable solution is to accept the fact of attrition and with this realization, MANAGE and PREPARE for attrition rather than trying to eradicate it.

Analytics will go a long way to help us with that.

2006-10-16T03:11:00.000-07:00

How to plan for Data Mining projects

http://www.dmreview.com/portals/portalarticle.cfm?articleId=1038094&topicId=230255

As someone who has suffered through the ambiguities of mis-scoped data mining intensive projects, I found the following article to be fairly useful.

Eric King is bold enough to identify that DM projects, especially the first engagements, are an evolving optimization problem and, hence, expectations should be inherently leveled to never expect a "final answer" nor anticipate a single pass.

A doomed project is typified by the following features where the buyers-

Collect product literature from data mining tool vendors at industry events or as advertised in journals.
Invite vendors whose retail price of their flagship product fits within available discretionary budgets to visit on site.
Gain a free education in data mining through subjective presentations at the vendor's expense (too many are anxious to chase any sales bait, qualified or otherwise).
Purchase a data mining tool from the vendor who presented last.
Throw some data at the tool and await magical results.
Stare at the numbers or even visualizations thereof, wondering why an angelic chorus did not accompany the results.
Without knowing whether the results are useless or phenomenal, data mining is dismissed as hyped and/or pie-in-the-sky technology.

His first recommendation is simple enoguh -

Hire independent expertise in both the organizational/business problem being addressed and data mining and ensure some sort of symbiosis or a third-part liason, if need be. Bundling the task into one person might run risks. An in-house business manager might reject some very significant results on the basis of their seemingly contradicting his experience (...it is actually preferable not to have the industry's strongest domain expert who also happens to do some data mining. While the consultant may appear impressive at the outset, too much industry expertise can introduce subjectivity and preconceived notions that may skew the way models are developed and interpreted.). A pure data manager might rush to analyze the data, instead of focusing first on amassing a comprehensive understanding and assessment of the client's business model and all available resources.

To highlight the first point, I once wasted 2 months in convincing a client that what he conceived as a "premium" brand, actually had a very high price elasticity. That is,the lower the price, the more the sales. People won't buy it at whatever price the seller puts it at. This obstinacy essentially derived from the client's biases priorto the modeling.
As for the second case, anybody who has worked in any analytics team would tell you that the biggest problem in directing a team of statisicians is to instill in them the fact that the essence of the problem at hand is business and not mathematics for its own sake.

2. The paper suggests using a DMPA - DM Project Assessment to evolve a flexible framework of strategy. the output is usually a situational assessment regarding -

Data Certification: A topical survey of the structure and nature of the data to support predictive analytics.
Existing Resources: Additional tools may be recommended to support or replace existing products. Are the skills available in house to support the modeling process after deployment? What other technologies or methods have been used in the past? Are previous performance benchmarks available?
Stakeholder Objectives: Are the questions to which executives seek answers aligned with the resources amassed in the findings? Are there desired and/or required performance levels? Are the benchmarks realistic from the consultant's experience?
Functional Managers: There are many situations in which companies are either unable or unwilling to take the actions recommended by the model. (In the words of Jack Nicholson in A Few Good Men, it should be determined in advance if "You can't handle the truth!")
Constraints: Are there hard boundaries that must be identified and built into the decision process - either before or after the model's implementation? Because virtually all data mining methods present a tradeoff between accuracy and explainability, a point on the scale should be defined. What are tolerable levels of false positives or negatives from the model?
User Buy-in: If they won't adopt it, why build it? How may the system be designed to encourage dedicated use?
IT Support: While usually not a deal-killer, IT is typically far more willing to support the model's function when they are included in the strategy and are invited to become data mining advocates. If IT is going to support another project that requires data access, it helps if they can also appreciate the high-level vision and benefits to the organization.

I think that the most important decision to come out of a pilot is the readiness of the organization for DM in terms of its data, people and processes. This fact, if identified earlier, can save a lot of seat, money and blame game.

The value of the DMPA is all in the strategy, not the tactics.

Again the need for flexibility is stressed. The recommendations report from the DMPA will produce an overarching project plan. Early stages may be firmly priced. However, later stages may only be estimated because it cannot be known in advance what information will be derived from the data and how it should be leveraged.

In all, I think that a small pilot on some sample data can also solve the same problem. The added advantage would be that this would also yield an inkling on what the final results would look like. Hence, a strategy may be evolved from the issues faced in running the pilot and an idea of the "truth" might save the client emptional and monetary expenses in the future if he decides that the results, however robust, are not something he can work on right now. Maybe due to internal resistance, no control over the top factors identifed, whatever.

I think, that cleints have to understand DM is a consultancy project. It can just offer a diagnosis or suggestion. Implementation is the onus of the client.

Hence, like any consultancy, DM involves buy-ins from the stakeholders and all-round honesty.

2006-08-17T01:21:00.000-07:00

I am trying to define data-mining.
This is not as trivial as it sounds. The word is perhaps as misunderstood as relativity.
I have a friend who builds models on financial reports posted on Excel sheel sheets and calls it data-mining. Another friend googles to find the best deal on a camcorder and calls it, you guessed it, data-mining.
Since every fact that you come across is data and every digging around you do for better understanding of facts is mining, data-mining is many a times used for any activity that involves linking data to understand a pattern. Which pretty much encompasses everything we do. Intelligence functions by seeking and learning patterns in the sea of data it is surrounded with and in that sense data-mining is the necessary condition for any intelligence.

But data-mining is much more rigorous than that. Just as education cannot be reduced to knowing the alphabets and the ability to write a letter (that's literacy), the same is the case with data-mining.

Thearling defines data-mining as extraction of hidden predictive information from large databases. There is still ambiguity here what we mean by large databases but we have a start.

Let us examine the implications of this definition in detail.

Large Databases
The very term database gives us an indication that data-mining works on a repository of data and not on adhoc sprinkling of figures and data from here and there pasted on an excel sheet. In fact, it would be safe to say, all good data-mining is based on good data warehouses which in turn implies the most important aspects of the data in data-mining - extensivity and rigor.

Predictive

This does not necessarily imply only forecasting. Whenever we are building a model on past data , the goal is always to build an actionable prediction on behavior or outcome. And, hence, validation is as important a milestone as the modeling itself.

Hidden

Eric A King in (http://www.dmreview.com/portals/portalarticle.cfm?articleId=1038094&topicId=230255) qualifies Data Mining as exploring previously unknown interrelationships and recurrences across seemingly unrelated attributes in order to predict actions, behaviors and outcomes. He thus differentiates the same from OLAP reporting.
He further qualifies it as "...we are looking at prediction derived from information hidden within large volumes of data rather than retrospection drawn from an OLAP or SQL query."