The Research Playbook

Archive for the ‘Market Forecasting’ Category

Written in collaboration with Dr. Ralph Finos

In an earlier post, we talked briefly about near-term and long-term forecasting – Why Do We Forecast?

Near-term Forecasting and Planning
In near-term planning we’re trying to be as accurate as possible about the outcome – regardless of how we get there.  The premium is on prediction vs. explanation. 

Problems like:

“What are my sales for established products likely to be in the next 6 months?”

Or demand pull considerations in the supply chain:

“How many toilet seat covers is Walmart going to need from me in the next 2 weeks?”

This type of forecast enables short-term resource deployment and benchmarks to facilitate near-term decision making “now decisions.”  Recent history is a key factor because where you have recently been is likely to be highly correlated with where you will be in the near future. Methods that project from recent tracking data are the most useful.  Moving averages and cyclical factors (i.e., seasonality) are important.  Theories of market behavior are generally less important (unless you’re forecasting emerging products or markets.

Near-term planning is also vital for long-term planning.  If you’re not predicting the near-term effectively, then assumptions in your long-term forecast might be incorrect.  Near-term forecasting can serve as a benchmark/hypothesis test for a long-term forecast.

Long-term Forecasting and Planning
In most industries, confident forecasting can typically be accurate for 6-18 months.  It should draw on the recent past, but must focus on prediction related to likely customer behavior (i.e., adoption behavior) and market context (i.e., substitute technology, competition, economic factors).  As opposed to near-term forecasting, the forecaster must create a theory or set of assumptions about market adoption in the context of value as well as market context (i.e., other products and services).

For example, technology markets are subject to evolution and possible revolution in technology itself that can redraw the landscape of a market – or create entirely new ones.  The technology market forecast confidence horizon doesn’t typically go much beyond 12-18 months. Obviously, the more inclusive the market segment being forecasted (i.e. all application software), the more stable and amenable to forecasting a market will be.

However, the interesting forecasting problems (new products, new markets, new technologies – and their interplay) often involve narrow segments as entry points and complex factors that govern adoption, making prediction a challenge.  Therefore, any forecast going out 2 years or more requires solid theories of adoption as well as solid metrics that are correlated with adoption allowing the forecaster to create predictive models that lave a longer half-life and are robust, yet flexible.

Useful (i.e., good) forecasts must be:

1. Robust enough to enable repeat calibration of core market drivers
2. Flexible enough to adapt to new market realities and drivers

These conditions are easier to state than to accomplish. Any forecaster who has created and then validated his or her forecasts against actual events can testify to the challenges.

Written in collaboration with Dr. Ralph Finos

We forecast in order to discern what is most likely to happen in the future, which enables us to do something about it.  While the future is impossible to control (and very hard to predict), good forecasting allows us to see into dark corners. We gain insight into what could happen next. This insight allows users of the forecast to take action and influence the vision of the future offered by the forecaster.

Market Forecasters answer questions like:

• What are the likely sales in my market over the next 6 months?
• What are the likely sales in my market over the next 5 years?
• What is the growth potential in segment X of my market vs. segment Y in 5 years
• What is the potential to create a market where there are no products yet?
• What is the growth potential of my market if the basic product features and functions are different from what’s offered today?

Depending on the purpose, the forecaster can be in the business of prediction or explanation or both.  Consistently getting the answer right (regardless of the “how”) is a great benefit.  Understanding why the result occurred, gives you power to influence the future – which is a greater benefit. Be sure you are clear about which one you’re doing.

For the purpose of clarity, we’ll call the focus on prediction “near term forecasting” and the focus on prediction and explanation combined “long-term forecasting”.  “Near” and “long” are relative terms – consumer products may have short life cycles in real-time (perhaps the duration of the December holiday season). This type of product is certainly a candidate for a near term forecast.  On the other hand, the life cycle of a large-scale technology product such as data center sized storage products, are likely to require a long term forecast.

The answer to this question might appear to be obvious. However, the reasons companies invest in market sizing and the approach they use or purchase will vary dramatically based on what they (you) are trying to do, the requirements for precision, and  the tolerance for ambiguity in the room.

Market sizing is (and this is true of many areas of market research) a combination of science and art. Many analyst firms provide market sizing services to companies as a cornerstone of the syndicated research programs they offer. Companies interested in understanding (typically from a supply side analysis) the size of a market, use these services to determine market share and to plan future business strategy.

Gathering sales information from at least all the major players in a market is a typical approach to sizing. Further detail by market segment and geography is also typically part of the effort to size a market. The greater the detail the easier it is to find errors in the estimations.

Ultimately, companies use market sizing to estimate the position they hold in the market place. The estimates of market share become increasing accurate as firms compare the data for current size with the historical information they have collected. An analyst firm with five or more years in the business can pinpoint the size of a market with remarkable accuracy – assuming they are using a solid methodology, but that’s a topic for another time.

Written in Collaboration with Dr. Ralph Finos

This is a short collection of quotes related [in some cases loosely] to market forecasting. Forecasting is a difficult and challenging endeavor. It can be a dirt job, but someone has to do it!

We’ll kick it off with a quote from Mr. Churchill, he is always to the point.

The Basic Challenge
The future is just one damn thing after another. – Winston Churchill

Prediction is very difficult.  Especially when it’s about the future – Anonymous

Our business is prophecy and if prophecy were certain, there would not be much credit in prophesying – Max Radin

Never mind the noise in the market, pay attention to the price of the fish – Bahamian saying

Forecasting Tools and Processes
Anything worth doing is worth doing poorly – GK Chesterton

If we knew what we were doing, it wouldn’t be called “Research” [or in our case forecasting!] – Albert Einstein

Sources and Data Quality
Every tool carries with it the spirit by which it has been created –  Heisenberg

Torture the data long enough and it will confess – Anonymous

Staying on Top of Your Forecast
None of us really understands what’s going on with these numbers. –  David Allen Stockman, Director US OMB, on the U.S. Budget, 1981

Every model, no matter how detailed or how well conceived, designed, and implemented, is a vastly simplified representation of the world, with all the intricacies we experience on a day-to-day basis. – Alan Greenspan

Be Open to Novel Outcomes
We do not know what the future will bring, except that it will be different from any future we could predict –  John Maynard Keynes

Normal science often suppresses fundamental novelties because they are necessarily subversive to its basic commitment. -  Anonymous

We can’t solve problems by using the same kind of thinking we used when we created them – Albert Einstein

We make progress in economic theory one academic funeral at a time.

While it is easy to poke fun at ourselves as we endeavor to create useful forecasts, the forecaster’s job is a difficult one. Fortunately, there are best practices and a body of work we can draw upon.

Companies stress the use of different types of market research depending on economic conditions. When businesses are struggling in an atmosphere of unemployment and budget cuts, they act differently than they do when the economy is booming.

During growth years, companies look for market opportunities often relying on syndicated research to provide guidance and direction. When budgets are tight and the research must help navigate very choppy seas, the research dollar is more likely spent on some form of consulting to address a specific concern the company is facing.

As we recover from hard economic conditions the need for market data and an analyst’s view of the market, which never disappeared, will become more important. The continuing need for this form of guidance was simply subsumed by the more immediate issues companies were grappling with as they tried to counteract very specific market conditions. Companies focus on point solutions rather than on broader market views when navigating through the uncertain waters of a recession or transformational change within an industry.

Market Sizing

Syndicated research is a wonderful business model for the thousands of research and analyst firms that employee this one-to-many approach. It provides a great service to companies interested in understanding (typically from supply-side analysis) the size of a market. While sizing a market can be challenging it is typically possible to arrive at a good estimate.

Market Forecasting

Buyers also want the analyst firm’s opinion of market growth or decline typically over a 3-5 year period in the form of a Compound Annual Growth Rate (CAGR). Forecasting a market is much trickier than sizing a market.

How the research analyst determines the growth rate, i.e., what assumptions he or she applies and how much the rate is simply a linear extension based on actual rates from past years are questions that should be addressed in the forecast methodology. However, these and other details are not always available. The reasons for less than a full disclosure can be vague and often revolve around the issue of some proprietary approach, which some firms claim they cannot make public.

Be wary of these proprietary claims, especially if you are a client and have paid for the privilege of obtaining the forecast. If you cannot evaluate the validity of the forecast, you may not want to place too much at risk based on its lack, or potential lack, of varsity. Caveat emptor – buyer be ware!

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“The most powerful force in the Universe is compound interest”- Albert Einstein

It is likely that Einstein would agree that this powerful force also applies to compound growth rates.

In the world of marketing and market research, sizing and forecasting a market, usually with estimates of varying degrees of accuracy is a part of life. Here are some basics on how to calculate a compound annual growth rate (CAGR) that might come in handy.

The simple form of a compound annual growth rate is calculated by taking the n^th root of the total percentage growth rate, where n is the number of years in the period (typically 3-5) being considered.

The calculation is written as follows:

CAGR = (Ending Value / Beginning Value) ^ (1 / # of years) -1

In this calculation, which is relatively easy to set up in a spreadsheet, the ending value is the expected size of the market for the last year of the compound period (e.g., 5 years). The beginning value is the (actual or estimated) size of the market for the starting year in the period (often the year prior to when you are publishing the CAGR,  for which hard data rather than estimates exist), and the number of years is the period for the CAGR calculation.

An example will help:

Assume a market is estimated at $500,000 and is expected to grow to $1 million in five years:

($1,000,000 / $500,000) ^ (1 / 5) -1 = 0.14869

Breaking it down the calculation is:

($1,000,000 / $500,000) = 2

(1/5) = 0.2

(2^0.2)-1 = 0.14869 or 14.87%

Therefore, the CAGR for a five-year period in this example is equal to 14.87%, representing the smoothed annualized growth of the market estimated over this time horizon. The year-by-year growth may fluctuate with some years predicted to achieve higher growth than other years. In fact, it could conceivably include negative growth years. However, in this example the overall five-year results are expected to be positive.

You can apply this approach to any market and develop a spreadsheet to explore how plausible  your estimates are for each year in a forecast using different assumptions. Have fun!

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Facebook’s success is nearly unparalleled and marketing, as well as marketing research, professionals are scrambling to find the best ways to take full advantage of this social media phenomena. However, it is this very zeal to immediately and aggressively leverage Facebook that could be its undoing.   

Crowds Flock to Facebook

As we rush in to take advantage of opportunities presented by an audience of 400 million people we could be destroying the goose before she has a chance to lay any golden eggs. The question is will these 400 million (and growing) Facebook users accept the potential promotional onslaught that Fan Pages could unleash or will they reel against this wave of marketing and research into their lives and “unFan” in droves leaving Facebook an empty shell?  

Abusive Marketing Could Cause Unfanning

It’s hard to bet against a company like Facebook, their track record is so impressive, yet I wonder if it isn’t time to be looking beyond this and other social media networks that may have peeked without us knowing it.  

The shame of it is social media has so much potential, but it could be wasted by greedy and aggressive forces in the marketplace. We have so often ruined our own opportunities. Take the fishing industry, which fished the Grand Banks into exhaustion, the greed on Wall Street that people believe caused our current recession, or the over use of natural resources, a list of cases too numerous to include in a short blog post.  

Social media networks are a resource too. Will we plunder it or will we use it wisely and help to sustain its value for the long-term?  

Please share your thoughts on this  issue. Add your comment below, and please email a friend about this blog. Thanks! 

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Yesterday I had the privilege of attending the IDC Directions 2010 full day conference, which I believe is the longest running conference in the IT industry. Approximately 1,000 attendees were present at the Sheraton Boston Hotel. There were four major sessions in the morning, special interest groups during lunch, three sessions each with 8 Tracks in the afternoon and a closing CIO panel discussion, which alone was worth the trip.

International Data Corporation (IDC) continues to provide leadership in several areas of market research for the IT industry. John Gantz in his usual witty and insightful manner set the tone in the morning with a talk that several other presenters referenced throughout the day.

In addition to the market sizing and forecasting data that IDC is well known for by customers and competitors alike, there were numerous sessions where customer data was presented helping to provide new insights and give direction for capitalizing on the economic recovery, which was the theme of the Directions conference this year.

I am not impressed easily, but yesterday I was impressed by the caliber of the IDC team. My only regret was that I could only attend a fraction of all the sessions offered.

One important take away is that while prospects for the future may not look wonderful today, do not let appearances fool you. Do not sit on the sidelines. The pendulum is swinging! There are signs around the global that indicate strongly the world is poised to build a smarter economy than we have ever imagined before. The warning is to move ahead and invest now or risk being left behind!

NOTE: This theme did not spring from idle hopefulness or marketing hype, the IDC team backed it up with research and analysis!

Please send out a tweet about this blog. And, sign up to be on our email list. Thanks!

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It appears that Market Forecasts are on everyone’s mind these days. A sample of over N=250 purchasers of market research put Market Forecasts on the top of their shopping list this year. Interestingly, customer satisfaction ratings for Market Forecasts were not achieving the same level of enthusiasm as the demand for forecasts. This misalignment appears to be clear market opportunity.

Those of you who have been in the business of sizing and/or forecasting markets or purchasing this type of analysis know there are numerous trap doors you can fall through. As Keynes once said, “We do not know what the future will bring, except that it will be different from any future we could predict” (John Maynard Keynes)

The problems related to executing a good quality sizing and forecasting project are complex and typically require a modeling expert who collaborates closely with a domain expert to develop a sound forecast. If only one or the other type of professional tries to develop a forecast (solo) the results are often less than satisfactory. But, I digress.

This story focuses on one set of the results from a recent study, The Market Research Customer and Prospect Study conducted 3^rd quarter 2009. The intention of the study was to shed light on the direction customers want to see market research firms take now and in the coming year (2010). Evidence is strong that market forecasts are among the most desirable deliverables (Table 1).

Here is question that we asked:

Please rate the value you receive from each of the following types of deliverables. On a scale where 1 = Extremely Low Value and 7 = Extremely High Value.

Ironically, there is a lack of satisfaction with available forecasts and perhaps Box and Draper can help us understand why. They wrote, “Remember that all [forecasting] models are wrong; the practical question is how wrong do they have to be to not be useful?” (Box, George E. P.; Norman R. Draper, 1987, Empirical Model-Building and Response Surfaces. Wiley)

Twenty items were rated on satisfaction. The question asked was:

Please indicate your general level of satisfaction with products and services from MR firms you personally do business with on the following dimensions.  Please use the scale below, where 1 = Extremely Dissatisfied and 7 = Extremely Satisfied.

The item with the highest satisfaction rating was Quality of quantitative research with a mean of 5.7 and the overall satisfaction score was 5.5.  Market Forecasts received a score of 5.2 and   Syndicated services ranked last with a rating on 4.8.  Market research consumers are talking the question is, who is listening?

Please email this blog to a friend or colleague visit again soon we have more forecasting methods coming soon.  Thanks!

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