How not to sell Analytics

10.We have the best modelers in the world
9. And they don't clean data; they only build the best models
8. Not sure if your Marketing guys can appreciate what we do
7. We typically don't work with your IT folks
6. Do you maintain robust no-mail Controls?
5. We need atleast 50,000 observations
4. Our Model documentation is the best; typically 186 pages long
3. How are your demographic variables bucketed?
2. Our last response model had a 82% lift over random in the top 3 deciles
1. Unfortunately two of the most significant model variables were not available at implementation

The challenge I see is that most analytics-focused consultants get lost in technicals; failing to understand that in almost 100% of the cases, understanding the context, communicating the real-life impact of analytics to the business and ensuring that outcomes can be robustly implemented are much more important that getting that incremental 2% lift in model performance or using a slew of alternative statistical techniques. And that businesses rarely have the benefit of perfect information and foresight that most analytics-focused folks typically assume.

Role of Analytics in a Developing Market

Analytics started making its first appearance in highly developed markets where growth and therefore profitability became a challenge for businesses. To maintain profitability, these businesses had to fine tune their strategies to ensure all spends were done with minimal waste and every opportunity that existed was unearthed and tapped into effectively. Because of the wealth of data available and advancement in the tools and techniques managers soon realised that decisions could be made based on quantified information rather than “feel” or “gut”, thereby reducing the risk of decision making. Analytics has evolved so much that it has become an integral part of decision making.

However when it comes to developing markets it is a completely different scenario. First of all, most of the developing markets have seen unprecedented growth. So the biggest challenge is of managing growth, which (everyone thought) automatically implies profitability. So naturally the whole focus was on execution and fulfilling the demand.

But when the boom times waned and people started doing finer analysis, couple of realities started coming out. First was that even though there was huge growth in customer base, a closer look revealed that the newer customers were not as profitable as the existing ones, and in some cases not profitable at all. So, from a long term perspective there was no guarantee that profitability will be maintained as the business grows. Also people were misled by the growth into believing that the new customers were bringing in the profits, while in reality it was coming from continued engagement with existing customers. Worse, businesses soon realised that some of the customers acquired were not sticky making the whole growth non-profitable and unsustainable, so much so that businesses are now talking about actively encouraging attrition among non-profitable customers acquired mindlessly!

All these have lead to sectors like Banking and Telecom, which have rich data, to leverage analytics to do business more efficiently. The bigger advantage, they realise is, that this helps in gearing up the whole system towards efficiency which is critical to maintaining profitability during tough times. Probably other businesses can take a leaf from that and introspect on how analytics can help them to manage cost and grow profitably. After all even in boom times a little bit of extra profits won’t hurt.

'Advanced Analytics' a top 10 Strategic Technology for 2010: Gartner

Interesting! Gartner seems to be a bit late. But looking at it from a holistic perspective, businesses are slowly realizing the relevance of Advanced Analytics. With many stars aligned - fast data warehouses, robust BI systems, powerful analytic tools that don't need specialized talent, and a global economic slowdown - its a great time to leverage the power of advanced analytics to make an impact - both from a maximizing revenue and a minimizing cost perspective. 'Advanced Analytics' here refers to tools like predictive modeling, optimization, simulations, etc.

A point of note is IBM's recent acquisition of SPSS and Redpill Solutions. With this, they have an end-end solution offering.

Could we have avoided the 2008 financial crisis?

Since the early 90's, banks - Consumer, Corporate and Investment - have become very savvy in adopting and implementing sound risk management practices. In fact, if you were to ask the Chief Risk Officers of (almost) all banks in 2006, you would have gotten statements like - 'We have the best global risk management practices', 'Our risk management dept is staffed by 54 PhD's', 'we have the best consultants advising us', etc, etc

So where did (almost) everyone go wrong????

I see a few root causes:
- Musical chairs: No one wanted to be left behind. As Chuck Prince famously said in July07: "As long as the music is playing, you’ve got to get up and dance. We're still dancing".  Although this statement applied to leveraged buyouts; it had implications across lending; including consumer finance.
- Complexity: Risk management got too complex.  In fact, the moment you have 54 PhD's managing your risk, no one knows whats going on.  Simplicity brings Clarity. (disclaimer: I sport a PhD myself)
- Not enough business sense: Everyone had the best mathematical models.  What was missing was expert judgement that would have allowed us convert them into contextual information and allowed us to stress test our assumptions and these models.
- Pass the pillow: A connected global market meant that we could 'diversify' risk; essentially meaning that the loans a bank made could be broken up into smaller pieces and sold (along with their risk) to someone else in some other country.  So everyone had incentives to underwrite but few had incentives to ensure that they were underwriting profitably.

In the end, banking is not too complex.  You borrow money at low interest rates; and you lend at higher rates.  The idea is that you make money on the interest rate spread while ensuring that your debtors pay you back.  Some companies are doing this all too well.  Check out Annaly (they have managed to run their business quite well; the crisis notwithstanding.  So far.......).

Sounds simple; doesn't it ????

Webinar on Predictive Analytics

I will be hosting a webinar Dec 8 2009 at 10am pst.  

The webinar is a part of a series that is being hosted by many leading influencers in the area of Decision management;  and managed by James Taylor.

Brief description: Predictive models,  aka scorecards, have been extensively used across industries in driving tangible benefits - both to the top line and bottom line.  This session covers the critical success factors and an analytical framework that will help get you there.  Real-life examples will also be provided.

Please register if you are interested;  or email me for a copy of the presentation. 

Data Visualization Anyone??

Data visualization seems to always be a hot topic. A number of large players (SAP, IBM, Oracle, etc) have taken advantage of Sr. Management's need to have the ability to "visualize" data at all times by creating (expensive and clunky) packages like BO, Cognos, Hyperion, etc. But at best these packages collate data and create a MIS (with fancy 3D charts, colors, funnels, etc) in real time. Their data visualization capabilities are at best non-existent.

I am looking for tools that will help me in real data visualization - essentially transforming the massive rows and columns of data into visuals from which I can draw meaningful conclusions quickly and simply.

A few tools do this well - Tableau Software - seems to be one. And Microsoft Excel can by itself be a potent tool; but in the right hands (need to know VBA, macros, complex functions, etc). For a good tutorial on this, please see Jorge Camoes' Charts.

Any other tools out there???


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