Knowledge Services

The US stock markets were in a celebratory mode yesterday as the Q3 2009 GDP grew by 3.5% in real terms, better than what the economists were expecting.

 

First, the details. As per the US Bureau of Economic Analysis (BEA), the GDP grew by 3.5% after contracting for four consecutive quarters. This, in fact, has been the longest and deepest recession in the post-war period.

Real final sales rose 2.5%. The contribution of private inventories’ to the GDP growth turned positive at 0.94%. Real personal consumption expenditure (PCE) grew by 3.4% (as against a decrease of 0.9% in the previous quarter) as the "cash for clunkers" programme boosted durable goods consumption (22.3% versus a decline of 5.6% earlier). Fact is, motor vehicles and parts added 1.0% to GDP growth. Not surprisingly, private investment also moved to positive territory (11.5%) as the residential investment rebounded (up 23.4%) although there was a smaller contraction in non-residential investment (-2.5%). Government expenditure slowed to 2.3% due to the decline in state government spending. However, with import (growth of 16.4%) picking, there was a negative contribution of net exports to the GDP growth to the extent of 0.53%.

While the growth rate does look impressive, question is, is it actually so? Not really. Firstly, there is a technical aspect to this growth. The change in real private inventories added 0.94 percentage point to the third-quarter change in real GDP after subtracting 1.42 percentage points from the second-quarter change.  Fact is, private businesses decreased inventories $130.8 billion in the third quarter, following decreases of $160.2 billion in the second quarter and $113.9 billion in the first. While inventories are still being drawn down, the pace of draw down has clearly diminished. Hence the second derivate of inventories (i.e change in the change in inventory) has turned positive. Going forward, inventory built up will slowdown because I fear contraction of consumer spending.

Most of the analysts who has commented on the Q3 GDP release have talked about the positive effect of the ‘cash for clunkers’ scheme(which ended in August) in pushing up the PCE for the quarter. According to some estimates, increased auto sales, directly attributable to the above programme, was to the tune of 700,000. But the bigger issue is, sales plummeted the very next month in September after the programme was withdrawn.

The rebound of residential investment has also a lot to do with the incentive offered by the government to first time home buyers. A credit of USD 8,000 is indeed a big deal. And this programme expires in November. In case the programme is not extended any further, then one can expect residential investments to drop off. In fact, even before the scheme ended, new home sales dropped a tad in September.

 

End of the day, it is the US consumers and their ability to open up their wallet that will drive and sentiment as well as the economy. And, this does not look good. During the third quarter, there has been a surprising drop in disposable personal income (DPI) to the extent of USD 20 billion. A closer look at the data reveals an interesting aspect. While, given the increasing level of unemployment, the wages and salary component has expectedly declined (and has been declining), what has been holding up the number in the previous quarter was rising transfers (unemployment benefits) and lower tax incidences (tax breaks given by the government). Each of these components faltered in this quarter. 

Let’s first take unemployment benefits. In the US, an unemployed person is entitled to unemployment benefit for a period of 26 weeks only. Thereafter, they cease to get the benefit. As mentioned earlier, this has been the longest and deepest recession during the post-war period. As a result, people have remained unemployed for much longer period of time.

As per the data, the average duration of unemployment touched 26.2 weeks in September 2009, highest ever recorded. As a result, a large number of people have been falling off the statistics of those people who are eligible for unemployment benefits. With the decline in Unemployment rate slowing down, the number of people in the 15 to 26 week bracket (i.e those who would be eligible for the unemployment benefit) were down by 510,000 during Q3, while more than a million people became ineligible for the allowance having failed to find a job for more than 26 weeks. Not surprisingly, transfer income received during this quarter was lower by USD 9 billion. Additionally, the tax breaks that the government was giving to the residents in the previous quarters were withdrawn during Q3, resulting in higher incidence of personal tax. During Q3, personal tax paid out rose by about USD 5 bn. Not surprisingly, the PDI was down substantially.

As mentioned earlier, despite the PDI being lower, PCE rose mainly because of the various stimulus package that was available. Going forward, these packages would no longer be available. Additionally, as the unemployment rate slowly inches up and more and more people end up using the 26 week window for claiming the unemployment benefit, what to talk of disallowance of the tax breaks, the PDI is likely to move south. In addition, as we all know, the recovery that would be visible in the near future would be jobless.

Additionally, as I have discussed several times earlier, the US consumers are still highly indebted.

Indebted consumers, increasing job loss and withdrawal of stimulus are a potent combination of disaster waiting to hit the US economy. To me, this is a highly technical recovery which is not going to be sustainable in the near future. In fact, the consumer spending number released today gives an inkling of what’s coming, as it shrank 0.5% in September, the largest drop in nine months.

Posted by Kunal Kumar Kundu at 2:16 PM | Permalink | Comments (1) | TrackBacks (0)

As expected, the RBI kept the interest rates unchanged during the Monetary Policy review announced on Tuesday. At this point in time, the RBI is faced with two divergent concerns – growth and inflation.

Currently the growth concern correctly held the centre stage. As a result they decided to keep the interest rate unchanged. The feeling naturally is that, any tightening of the monetary stance can impact the nascent growth that is visible now.  However, RBI has indicated that it cannot continue to be oblivious to inflationary concerns. Hence, while the interest rate remains unchanged for the time, the central bank is giving enough indication that the excess liquidity that is currently in the system needs to be sucked out. To this end, they have hiked the CRR SLR (My sincere thanks to Pankaj for bringing this typo to my notce) requirement from 24% to 25% and they have indicated that RBI would look at exit strategy at the earliest. RBI is clear in their view that the excess liquidity that is in the system will lead to higher inflation. Which is why, they increased their expected inflation to 6.5% in March as compared to their expectation of 5%, which was given out a few months back.

I, however, beg to differ on this text bookish interpretation of inflation by RBI. For one, the rising inflation has a lot to do with the base effect. Also, if one looks at the corporate performance data during the second quarter, it is clear again that most of the companies recorded a much higher margin while the sales have virtually stagnated. The higher margins are due to lower input costs, lower interest costs and of course lower wage bills. If input costs are lower, then clearly the pressure on inflation comes from some other avenue. And it is clear that it is the rising food prices which are driving the inflation. And, I do not believe that monetary policy has much impact on food prices. Liquidity has very little to do with food price movement. The problem is much deeper that RBI would want us to believe. The malaise afflicting this sector is beyond the control of RBI. For one, the current food price inflation has a lot to do with monsoon failure and, to my knowledge; domestic monetary policy cannot impact the will of god.

On the other hand, despite the excess liquidity situation, credit growth to the real economy leaves room for desire. Credit growth so far this financial year continued to remain below RBI’s estimated growth. In fact, the non food credit growth, after having shown some semblance of pull back in July, fell again in August, recording the lowest annual growth in the current fiscal year. In fact, the central bank has actually reduced their target credit growth from 20% to 18%. With domestic demand not rising much (as is reflected in generally stagnant corporate revenue and continuously lower non-oil import) and lower credit growth, the economy would be hard pressed to cross the 6% growth mark during this financial year.
The stock markets withdrew subsequently because they are expecting RBI to increase the interest rates going forward. I feel they are mistaken. I am not sure that RBI will walk that path, since this can threaten the recovery.

The focus might continue to be to suck out liquidity so as to prevent the excess liquidity from getting into speculative mode which can lead to asset price inflation not supported by underlying economic fundamental. In fact, globally a lot of economies are experiencing such asset price inflation.

The only concern currently is, while it is important to prevent the excess liquidity to get into a speculative mode, RBI should not be too hawkish and suck out liquidity so much that the economy starts to hobble. For this, mere focus on inflation to decide on the monetary stance would mean that RBI might be barking up the wrong tree.

Other measures reported:

The RBI has asked banks to ensure that their total provisioning coverage ratio is not less than 70% and imposed a timeframe of September 2010 to achieve this target. The coverage ratio is a measure of the bank’s ability to absorb potential losses for non-performing assets (NPAs) and is arrived at by calculating the loan loss reserve balance with the total non-performing loans. Clearly with the recent global turmoil fresh in mind, the RBI does not want to take much chance. While nobody can fault RBI for trying to being cautious with the experience, one hopes that the central bank would be flexible in their approach and take steps to ensure that availability of liquidity to the real economy and at reasonable rate. Fact is, increasing provisioning requirement will lead to increased borrowing cost and the banks might be forced to pass the same to the borrowers, which can be debilitating for economic recovery.
Most banks will thus have to significantly raise the coverage ratio, which is quite low for some banks. This has the potential to impact the bank’s profitability and this is reflected in the beating that many bank stocks took today in the market.

The central bank also increased the provisioning requirement for advances to the commercial real estate sector classified as ‘standard assets’ from the present level of 0.40% to 1%, a move that makes lending to the sector tougher. This definitely is a move in the right direction. Earlier, the central bank has been too lenient on the realty companies, which allowed them to survive the difficult situation most of them have passed through. Fact is, this is not a well regulated sector and the real estate companies have generally had a free run at the cost of buyers, especially when the going was good. But when their rogue business practices put them in a soup during the market meltdown, they sought for and got more than warranted level of support from the central bank. As a result, the real estate companies felt no need to change the way they do their business which leads to creation of bubble. This move by RBI would help build cushion against likely non-performing assets.

Posted by Kunal Kumar Kundu at 1:21 PM | Permalink | Comments (4) | TrackBacks (0)

For analytics to really kick-in for any organization, managers need to be able to strategize based on the data analysis done. More often than not, the means to the end is defeated as managers are swamped doing data analysis leaving little or no time for the strategizing part.

The analysis for why this problem originated has been done too! Organizations typically have a pyramid structure for their resources…broad base of junior employees, small middle management and still smaller senior management. In any Knowledge Services division in the US, due to paucity of requisite skill set, junior and middle management get promoted faster leading to a huge rocket booster strap in the middle. The problem is this booster strap weighs the division down as the roles change but the job profile remains the same… analyzing data.

The KPO analytics outsourcing wave has been built on the premise that by adding skill set relevant resources largely at junior levels to do the data analysis piece, the rocket can be stabilized and lift off can be achieved.  

 

Posted by Anand Madhavan at 11:30 AM | Permalink | Comments (0) | TrackBacks (0)

Historically, the HR world has been relying on a lot of qualitative data for taking many decisions which have an effect on the organization at multiple levels. The sparse usage of quantitative data could have many reasons of which non-availability of data, lack of one-view of the employee (data), non-availability of an integrated data system to enable various data types to talk to each other or simply the inability or unwillingness of the HR professional to use hard data to take decisions related to human beings are some of the oft repeated reasons.

In the current economic downturn many of the business enabling functions (including HR) have seen drastic cut in head counts to save the head counts in some other part of the organization which in the decision makers eyes is a revenue generating business unit. Most of the time the HR is called upon to address many crunch situations by the ops manager. However, when the going is great and everything appears to be smooth, managers tend to forget the HR.

When it comes to the question of ‘Where does the HR fit at the business high tables already occupied by the Finance experts, the marketing wizards and the sales stars?’ most of the time the average HR professional tries hard to quantify the business impact it has on the day to day running of the business or the effect it has on the business bottom line with little success.

HR analytics could help the HR leader to equip herself with high business impact data points which could be important for all the CXO level executives in the organization. One case in point is attrition modeling. By modeling the attrition behavior of employees and successfully implementing the same in the organization, the HR leaders could help the organizations bring down the attrition rates and have a very positive impact on the operating profit of the organization. To support this view we can discuss an examples.

Example - A company in the BPO industry

Average no of months spent in the training room before certified as a process associate : 2 months

Average no of months before joining operations: 1 month

Attrition rate : 35%

Total wage bill for the BPO company: USD 120 Million

Other expenses: USD 40 Million

Revenue: USD 210 Million

Operating Profit = Revenue - All expenses= USD 50 million

Bench/Attrition cost=((training duration + bench duration)/12)*% attrition*wage bill= USD 10.5 Million

Lets assume that by using various interventions enabled by the attrition model the organization brings down the attrition by 5%

Bench/Attrition cost after interventions=((training duration + bench duration)/12)*% attrition*wage bill= USD 9 Million

There is a saving of USD 1.5 million which would take the operating profit up by 3%. Here we are not including the cost of recruitment/induction/on boarding etc.

Any CEO/CFO would certainly like to see the 3% increase in operating profit - recession or no recession

Posted by Suvendu Tripathy at 9:18 AM | Permalink | Comments (1) | TrackBacks (0)

Thousands of years back man went to work every day to fetch food for the family by hunting in the jungles. Much later man went on to do farming to produce enough food for the family. From Hunting to Farming it probably took thousands of years. There was no concept of efficiency or productivity. Also there was no wage for that matter. One hunted in the jungles, if lucky got some food for the day. if not then tried another day. Similarly,  the farmer labored in the field throughout the year to produce the food he wanted. There was no connection between the produce and the time and effort put into the process. No one ever asked a farmer how much time it took to produce a bag of wheat.

Much later, when the industrial age happened, efforts, time and the output got measured and that is how the Job happened to the mankind. With due course of time various types of Industries came into being and the Job went through various stages of evolution and got classified as Blue collar, White collar etc.... However, the motive behind doing a job remained the same. People worked ... and still work for money, for realization of self-worth, self-confidence, respect etc.. besides many other reasons which could vary from person to person.

Along the evolution of job, the way wage is determined for the various jobs has also evolved. In the factories the wage is in some way proportional to the time one puts in on the assembly line. Wages for salesmen depend on the no of sales/value of sales they closed. In the service sector, the wage is dependent on not only what one delivered, but also how, when and where one delivered the same.

Today, the services industry contributes majorly to the GDPs of economies across the world and the competitors in each of the services industry have  differentiated their services on the basis of who delivers the service and the corresponding wages for doing the same job by different persons vary considerably from each other If one considers the time, effort, sweat and tears that go into acquiring a particular skill set to deliver a service in a certain way that would be the differentiator in the market, one could very well imagine that one day very soon the job and the corresponding wage for most of the services would go back to the pre-industrialization era when no one asked how much time, effort, sweat, tears, seeds  etc.. went into producing that one bag of wheat.....

Posted by Suvendu Tripathy at 10:23 AM | Permalink | Comments (2) | TrackBacks (0)

Traditionally PE firms have turned to off shoring in the context of bringing greater efficiencies to operating their portfolio companies. Rarely have PE firms themselves leveraged the off shoring benefits to their own business model.
There are tremendous benefits that PE firms can derive from off shoring especially of high end work in the research and finance area. In our experience the investment research area is a very good starting point.. This is often performed by the analysts/associates in the PE firm. 
Our experience of engaging with a PE firm shows that an analyst/associate spends roughly 40% of time in offline activities relating to research (often viewed as noncore). Following is a typical break down
a.     Identifying company leads (5%)
b.     Screening and evaluating investment opportunities (20%)
c.     Industry research (10%)
d.     Developing best practices in origination (5%)
However given there are constant pressures on deal related activities and the fact that within the firm this is viewed , as lower priority, associates and analysts within a PE firm find it difficult to maintain level of quality and consistency in these activities.
By off shoring this activity the PE firm can derive several benefits such as consistent quality due to the deeper domain expertise, better knowledge management and transparency in the amount of time spent to get this work done.

Posted by Bharat Kidambi at 6:28 AM | Permalink | Comments (0) | TrackBacks (0)

A lot has been said & written about sustainable competitive advantage that could be gained by organizations by leveraging analytics. While large organizations might have the wherewithal to put in place a robust data & analytic infrastructure, attract talent to leverage their data & analytic stack, most small & medium enterprises would be lucky to have just clean data.

Also, commercial feasibility of implementing analytic tools and / or recruiting dedicated analysts may be a problem. So can't these businesses leverage the power analytics: to increase new customer additions, target specific customer segments to improve marketing effectiveness & increase revenue / profitability per customer, predict demand, reduce stock-outs/surpluses?

If you are a business manager wanting to do better on the above metrics, think you have the relevant data, but not the right tools or skills at your command, what would you do?

Among options like hiring a consultant, how about if you could put out your business question & relevant data securely over the web to a reliable partner who could analyze the data, deliver insights, and provide specific recommendations on improving business metrics?

To put it shortly, how about if you had a choice to consume "Analytics as a Service"? How would you value it? Do you think this is possible?

Posted by Santhanakrishnan R at 3:30 PM | Permalink | Comments (1) | TrackBacks (0)

Analytics, though a (relatively) recently coined term, is said & understood to mean so many different things.

As often meant by IT Managers, Analytics is what Business Intelligence (BI) tools deliver as drillable outputs. To functional Managers, Analytics seem to mean going much beyond.

So, what's your take on "What is Analytics?", and what comes to your mind when you hear "Leveraging Analytics"?

Data..? BI & Analytic tools/platforms? Analytic processes? Analysts?...

All ears...

Posted by Santhanakrishnan R at 3:16 PM | Permalink | Comments (3) | TrackBacks (0)

As the cloud computing model gains ground, businesses of niche product vendors are at significant risk, unless they do something drastically different. What is it that they can do differently? Leveraging outsourcing of complex processes provides a different world of growth options and revenue models to the IT product vendors. It enables them to leverage their client base to open up a huge potential revenue stream of processes being executed on their products. Services route can be leveraged to provide functionalities to clients virtually on demand and bake the high count functionalities into the product evolution roadmap. Thereafter, boundaries of how much can be automated can be tested. This approach also puts to rest the age old criteria of product selection in terms of change management for process changes as per the product workflow in the client’s organization. Thereafter, further offering the product itself on the cloud completely changes the game. The product can now be offered at a minimal cost thus opening up a huge untapped market of small and medium scale enterprises. It also enables reduction in the sales and marketing costs as rather than demos, prospects can be offered test usage of the product for a short period of time, thus increasing their comfort factor.

Posted by Rahul Shah at 7:48 AM | Permalink | Comments (0) | TrackBacks (0)

Business Research happens in corporates in multiple pockets. Vendor management groups do vendor and credit research, sales teams do prospect and client related credit research, risk group performs credit research and CXO’s & Business heads need competitive analysis, trend analysis, industry analysis etc. 

Unlike financial firms where research units are ring-fenced, in non-financial corporations, research need exists in a finger and toes fashion – multiple people including senior management conduct research and analysis for a short fraction of their time. This poses multiple challenges. It exists as a significant hidden cost and worse still, it is a fixed cost. There is significant turn-around time implications of not getting a piece of research in time but given the nature of the current model, turn-around time does not get the right focus, thus significantly impacting business decisions. Research requirements are not uniform across the year and have seasonal peaks during the budgeting & planning cycles or at quarter-ends. The process is not standardized or metricised and the output is neither templatised nor stored and indexed from the knowledge management perspective.

Even after recognizing these imperfections and the significant research effort within organizations, centralization of this effort in the form of a shared service is difficult from the perspective of change management given the finger and toes nature of the activity.

Creating an on-demand model for research can enable firms to provide a platform for slow transition of the in-house research activities. Transition will be slow and need based. Need will be high during the peaks. Soft management nudge can be provided by reducing staffing budgets or by putting KPI’s and targets for the on-demand services, though significant value will need to be demonstrated by the delivery model in terms of quality, timeliness, productivity and knowledge management of the research. The workflow needs to be simple so that the whole model is extremely simple to use. Reporting around use of the on-demand model to the senior management would be a significant benefit and in our view, even the research requests to the internal teams should be logged in order to gather metrics.

By adopting the on-demand model, firstly, the firms would be able to understand the research effort within their organizations. They would be surprised by the magnitude of this effort, that’s for sure. In the medium term, fixed costs can be converted into variable costs, Significant productivity advantages can be achieved due to consolidation of the teams, standardization, knowledge management and templatization. Finally, this can be a model that can enable firms to embrace creating shared services in knowledge processes like research, analytics and any other complex pieces of work within the organization – that have required huge internal management efforts in terms of change management and have been difficult to define.

 

Posted by Rahul Shah at 7:43 AM | Permalink | Comments (0) | TrackBacks (0)

Traditional route to automating contract management is to go for enterprise CLM (Contract lifecycle management) applications. This leads to multiple challenges. Firstly, it involves a high upfront cost in terms of CLM implementation, customization and user training. Secondly, the time to market for these type of implementations is long as clients go through a long process of product selection, business case presentations, customization and implementation. Thirdly, the existing contracts take quite some time after the implementation to get uploaded. This limits the CLM’s capabilities to provide insights and analytics to the management till such time that all contracts are uploaded.

A CLM on a SaaS model offers an attractive option. There is minimal upfront cost for adoption. This enables CLM champions within firms to face lower resistance in terms of funding and proving a business case. My view is that the SaaS model can enable the CLM demand to explode since there are numerous organizations that are sitting on the fence in terms of adoption given the cost of the traditional model. Further, this model enables organizations to pay minimal charges based on the transactions they do going forward. So, if the platform is not good, the adoption will be less and hence the organization is not stuck with a bad investment and can pay based on the usage.

Bundling Legal contract services can significantly make the adoption of a SaaS based CLM easier. Firstly, for functionalities that are missing in the platform, legal services can provide a bridge through manual effort. E.g. if there is a report that clients want but is not baked into the application currently, that report can be provided manually. Secondly, it enables organizations to leverage the bandwidth with the service provider to abstract and upload the existing contracts, thus providing complete analytics and insights into contracts from day one. Thirdly, it enables advantages associated with outsourcing in terms of cost arbitrage, scalability, turn-around time improvement and templatization.

Posted by Rahul Shah at 5:29 AM | Permalink | Comments (0) | TrackBacks (0)