Posted July 5, 2015on:
This is a Harvard Business Review (HBR) post written by Ric Merrifield. Here is the link to the original article.
Just as it’s hard to remember what life was like before the iPhone, it can be hard to remember business before there was CRM software — back when you still had to explain that it stood for “customer relationship management.” Today, CRM pervades the way many companies track and measure how they interact with other organizations, across many departments: marketing, sales, customer service, support, and others. CRM made it possible to determine precisely who responded to a specific marketing campaign and then who became a paying customer, which customer called the most for support, and so on. It gave companies some overall measure of revenue compared with marketing spend — something described in this 2007 article in The New York Times.
But now that Big Data and the Internet of Things have come along, we can go beyond the transaction to every little detail of the customer’s actual experience. You can know when customers enter your store, how long they are there, what products they look at, and for how long. When they buy something, you can know how long that item had been on the shelf and whether that shelf is in an area of things that usually sell fast or slowly. And then you can view that data by shoppers’ age, gender, average spend, brand loyalty, and so on. Today, this sort of thing is possible not just for online experiences; it’s possible for physical experiences as well — and not just retail shopping. This vivid view of the end-to-end experiences is rapidly changing the way people think about, measure, and manage their customer relationships.
Consider Waze, a wildly popular crowdsourced driving application that people use for real-time traffic information, warnings about hidden police, and turn-by-turn guidance about how to get around congestion. It only works because users give up their location information (on their mobile device) in exchange for information that will enhance their own experiences. So each mobile device pushes real time information into the main information hub, which processes all of the data and then pushes personalized messages back to every machine that is connected. It works incredibly well.
Another offering that taps the power of the Internet of Things and Big Data (and in whose development I was directly involved) is Disney’s MyMagic+. Disney customers (aka “guests”) wear a MagicBand bracelet that allows Disney to know where they are at all times. Guests use an application called My Disney Experience to plan all of their bookable and non-bookable activities: dining, rides, attending parades, and so on. Then, Disney can use the tracking information and send them personalized messages via their smartphones about things like where they might find a cold drink if they are ahead of schedule, what they might skip if they are running behind, and, if they are heading toward a congested area, a better route to take.
In addition, guests can use the band to get into their hotel rooms in their resort as well as the park (by tapping a Mickey Mouse icon instead of going through a turnstile). This has greatly increased the rate at which guests can enter the park.
Customers can also tap the MagicBand to pay for things, and scanners read it during rides. This means that after you finish a roller-coaster ride, you no longer need to go and find your picture and write down some 10-digit number if you want to buy a copy; all of those images show up on your My Disney Experience page so you can buy them at any time.
With every passing day there are more examples of Internet of Things adoption. And with every passing adoption, what people will accept (giving up things like their location information) and what they expect (“If Waze can help me with my driving, why can’t my grocery store tell me the fastest way to get through my grocery list?”) both change. As this intersection of what people will accept and what they expect evolves, the kinds of experiences that can be captured in the form of new big data evolves with it.
Now you can have visibility into everything. Not only can you tell that Customer A (who has a shopping app) went into a Lord & Taylor store to buy an expensive pair of shoes (which you could know with CRM). In addition, you can know how long they were in the store, where they walked, and whether they lingered or went straight to the shoe department and bought the shoes. Then, you can compare that visit to every visit to that store that Customer A has had (since getting the app), and you can at least infer what is most valuable to her. If she is always a get-in, get-out kind of shopper, speed of service may be her #1 thing. If she spends a great deal of time shopping, maybe price or product selection is her thing. If she buys a lot at the store, maybe she wants some form of recognition for her loyalty (whether it’s points or just a “welcome back”). If you compare online experiences with in-store experiences and weekend vs. weekday behaviors, your picture of the customer becomes three-dimensional very fast.
As exciting as it can be to talk about this and to see that it is happening right now in broad daylight, talking about how to assess customer experiences and how to engage customers differently when they have this information gets complicated quickly. The important thing is to acknowledge that the measurements of yesterday may need an overhaul, and to understand where your customers are on the acceptance-expectation path so you can try to stay with, if not get ahead of, them. An increasingly common method for getting a handle on this is documenting the customer (and employee) experience journeys. What that means is examining high level areas such as:
- Discover. How do they discover that they could have this experience?
- Plan/Enroll. Do they need to sign up or enroll to have the experience?
- Arrive. When do they begin to have the experience?
- Engage. What are they doing while they are having the experience? What is their top priority (e.g., speed of service, best price, product selection)?
- Complete. How do they choose to end their experience (e.g., when they are ready to leave that Lord & Taylor store)? Not all experiences start and end with entering a physical location, but that is a common, simple, and familiar experience boundary.
- Reflect. After the experience, do they visit social-media sites to comment on their experience, do they look at a loyalty-program site to ensure they got the points or credits that they expected?
Once the experience steps have been documented for the experience that occurred today, be specific about how you will make it different in the future because of the additional information that you have. Where will you and won’t you use that additional information? Will you greet them by name when they enter your store? Will you offer them what they had last time? Will you instruct your staff to leave them alone so they can feel anonymous? Many things are possible with all of this new experience information. Using it in a way that is aligned with your brand, improves the customer experience, and makes things easier for your staff are the things to prioritize and try.
Your plan probably won’t be perfect, and you may need to make some new hires, or get some outside help from people who have done these Big Data and Internet of Things projects. But whatever you do, don’t underestimate the impact of acting on something seemingly minute. The right incremental changes can truly transform the way people experience your organization. So even if something seems trivial, try it and see how it goes. You might find that you had underestimated just how powerful a small change can be.
This is an excellent article written by Be Money Aware Blog on the basics of GDP interpretation and the mechanics behind that number. Here is the link to the original article.
Overview of GDP
GDP, or Gross Domestic Product, is the value of all goods and services produced in the economy over a period of time, normally a year. GDP is considered to be yardstick of measuring the functioning of the economy. GDP is usually expressed as a comparison to the previous GDP value in percentage. That is calledGDP Growth Rate. Usually yearly or quarterly GDP growth rates are used. To account for inflation two GDP figures are released one based on current prices called as Nominal GDP and other one based on base-year prices called as Real GDP. GDP can be calculated in many ways such as income approach, production approach, expenditure approach.
Who releases GDP numbers?
In India GDP is calculated by Central Statistical Office (CSO) which is part of Ministry of Statistics and Programme Implementation. CSO introduced the quarterly estimates of GDP in 1999 as part of the requirements under the Special Data Dissemination Standard of the IMF. In India, the quarterly GDP releases with a delay of approximately two months with respect to the end of the reference period. For Instance- the data for Fourth Quarter(Jan-Mar) 2011-12 were published on 31st May 2012. MOSPI releases the Advance Release Calendar which is updated on 1st of every month. The Advanced Release Calendar for Year 2012-13 is shown below.
Central Statistical Office or CSO, a part of part of Ministry of Statistics and Programme Implementation,is responsible for coordination of statistical activities in India, and evolving and maintaining statistical standards. Its activities include compilation of National Accounts; conduct of Annual Survey of Industries and Economic Censuses, compilation of Index of Industrial Production, as well as Consumer Price Indices. It also deals with various social statistics, training, international cooperation, Industrial Classification etc.
Understanding GDP figures of India
Market Prices and Base Year Prices
Every time any GDP number is released, 2 different numbers for GDP are released, one for the base year prices which currently is 2004-05(Real GDP) , which are inflation adjusted prices and one for the current prices (Nominal GDP) or market price. Base Year keeps on changing. Change of base year from 1999-2000 to 2004-05 happened in n 29.1.2010 The reason for periodically changing the base year is to take into account the structural changes which have been take place in the economy and to depict a true picture of the economy through macro aggregates like GDP, consumption expenditure, capital formation etc. MOSPI:Brochure on the New Series of National Accounts, Base Year 2004-05 explains the change of base year to 2004-05.
Production and Expenditure Approach
As we know GDP can be calculated in many ways such as income approach, production approach, expenditure approach. In India the two ways used are Production and Expenditure Approach. Ref:Onemint:How GDP is calculated (Dec 2011)
- Production Approach: It breaks down the economy into different sectors such as agriculture, forestry, fishing, mining, manufacturing, electricity etc and then computes the value that has been added in each sector. This is reported as ESTIMATES OF GDP BY ECONOMIC ACTIVITY
- Expenditure Approach: is calculated by adding what everyone has spent. A common equation for GDP calculation is:
GDP = Private or Consumer Consumption(C) + Government(G) + Investment (I)+ Net Exports(NX)
This is reported as ESTIMATES OF EXPENDITURES ON GDP
Quarterly, Cumulative Data
Along with the Data for each quarter the data is also reported for
- Quarterly data of previous quarters of the same year: say if report if for Q3 or third Quarter Oct-Dec 2011-12, GDP numbers for Q1 (Apr-Jun), Q2 (Jun-Sep) of 2011-12 are also reported, as shown in the first figure GDP calculation using production approach given below.
- Quarterly data of previous quarters of the two earlier years: say if report if for Q3 or third Quarter Oct-Dec 2011-12, GDP numbers for Q1 (Apr-Jun), Q2 (Jun-Sep) of 2009-10, 2010-11 are also reported, as shown in the first figure GDP calculation using production approach given below.
- Cumulative data of earlier quarters for this year: say if report if for Q4 or fourth Quarter Jan-Mar 2011-12, GDP numbers for entire year are reported as in figure 3 GDP using production approach showing annual(cumulative) data. Q2 report will have half-yearly data from Apr-Sep, Q3 report will have data from Apr-Dec.
- Cumulative data of earlier quarters for previous year: say if report if for Q4 or fourth Quarter Jan-Mar 2011-12, GDP numbers for year 2009-10, 2010-11 are also reported as in figure 3 GDP using production approach showing annual(cumulative) data
- GDP calculation using production approach
Interpreting GDP numbers
The information per-se is not in numbers but what story these numbers tell. NDTV: 10 things that numbers say (Jun 2012), Economic Times:Has GDP growth rate bottomed out?(Jun 2012) Hindustan Times:Anatomy of Slow Down (Jun 2012) details out the interpretations. The interpretation includes:
Understanding the quarterly numbers and the trends as shown in figure below:
Comparing GDP numbers with earlier numbers and finding out the trend
Understanding the what is happening to economy and why
Looking for solutions
These images are from the HindustanTimes: Anatomy of SlowDown (pdf Jun 2012) and Economic Times (Jun 2012)
Understanding the living conditions
As onemint:A few thoughts on the GDP Numbers says breakup of GDP by economic activity shows
- India went from being primarily an Agriculture economy to a Services economy, and missed the Industrial phase in between
- A 19% agricultural share when close to three quarter of Indians live on agriculture is totally off balance.
- Disparity that exists between Indians today.
- We have one of the fastest growing economies, but we also have an unbelievably high number of people below the poverty line.
- Developing a manufacturing sector is a must to bring millions of people out of poverty, and bridge this huge gap.
Understanding the Growth of Indian Economy
Wiki:Economic History of India covers how Indian Economy has been over the years. Quoting from it:
India has followed central planning for most of its independent history, which have included extensive public ownership, regulation, red tape, and trade barriers. After the 1991 economic crisis, the central government launched economic liberalization. India has turned towards a more capitalist system and has emerged as one of the fastest growing large economies of the world
After independence from 1950′s to 1980′s India had growth rate around 3.5% called as Nehruvian Socialist rate of growth or Hindu rate of growth
Economic liberalization in India in the 1990s led to large changes in the economy.
The graph shows GDP per capita of South Asian economies and South Korea as a percent of the American GDP per capita. Compare India (orange) with South Korea (yellow). Both started from about the same income level in 1950.
Questioning GDP data
Top policy makers, economists, rating agencies have questioned the robustness of the India’s GDP Estimates. Quoting from Times of India:Crisil questions GDP data (Jul 2011)
“Large revisions in GDP estimates in recent years have led to questions on the robustness of these estimates,” said Roopa Kudva, MD & CEO, Crisil. “As the most important economic indicator, GDP estimates influence policy making at the highest level, and hence the need for accurate estimation,” she added. Crisil study finds that the current method of computing GDP underestimates the size and growth of the Indian economy. The Crisil study comes on the heels of RBI governor D Subbarao questioning the series of revisions for a number of crucial data heads that are taken into consideration for major policy decisions, including the key policy rates by the central bank that decide the interest rate in the economy.
Dedication:This post is dedicated to Manshu of onemint.com who explained the concept of GDP’s in very simple terms to his readers and also provided the links to the reports. That helped a non-financial person like me to understand about GDP and even write an article on it.
This article covered Understanding Gross Domestic Product or GDP numbers of India. Who releases the GDP data? What numbers are released? How it is calculated? Interpreting the GDP numbers. We look forward to hearing from you!