Archive for December, 2010

Insight Central Resumes January 3, 2011

December 22, 2010

Insight Central will resume posting on Monday, January 3, 2011.  Analysights thanks you for checking in with us each day for the best practices in forecasting, modeling, analytics, and marketing research.  We wish you and your families a very Merry Christmas and a Happy, Healthy and Prosperous New Year!

Advertisements

Are Mail Surveys Useless?

December 21, 2010

These days, most surveys are delivered online. Researchers – especially with the proliferation of consumer panels – can now program a survey, administer it to a relevant sample and get results within a few days, for a relatively low cost per complete. This is a far cry from the day when most surveys were conducted by mail. Mail surveys often needed to be planned out well in advance, had to be kept in the field for several weeks – if not a few months, – required incentives and reminders, and often generated low response rates. Needless to say, mail surveys were also quite costly and could not be changed once in the field.

Most marketing research professionals don’t even consider conducting a survey by mail anymore; most now view mail surveys as obsolete. While I certainly favor online and social media surveys more than mail surveys, I caution not to dismiss mail surveys out of hand. They still have some relevance and, depending on the business objective, may be a better choice than the popular online survey methods.

There are several reasons why you might still consider doing a survey by mail:

  1. Some people still aren’t online. What if you need to survey elderly persons? Or low-income households? Many persons in these groups do not have Internet access, so they cannot be reached online. Assuming they’re not homeless, virtually all of them live at a physical address with a mailbox.

     

  2. Advance permission is often needed to send e-mail surveys. Because of the Controlling the Assault of Non-Solicited Pornography and Marketing (CAN-SPAM) Act of 2003, marketers cannot send promotional e-mail to prospects without permission. While a survey is not promotional, consumers upset about receiving an unsolicited survey might still report it as SPAM, getting you into trouble. This is why most e-mail surveys make use of pre-recruited panels. Mail surveys don’t require such permission.

     

  3. Mailing lists can be obtained to conduct surveys. E-mail address lists cannot be sold. Quite often, you can rent mailing lists to send out surveys.

     

  4. Mail surveys these days may get a better-than-expected response rate. Response rates likely won’t be double-digit, but since few mail surveys are sent these days, those few that are have a better chance of catching the respondent’s attention. And since the respondent isn’t being bombarded with mail surveys, he or she may be more inclined to answer.

     

  5. Mail surveys offer greater perception of anonymity and confidentiality – and hence more truthful responses – than online surveys. Since surveys are administered online, it’s easy to tell who didn’t respond. When you send a respondent a reminder e-mail, the respondent knows his or her lack of response is known. This may lead him or her to feel that the answers he/she gives are also traceable back to him/her. As a result, he or she may be less-inclined to respond truthfully, let alone respond. Although tracking mechanisms have been placed on mail surveys, they’re not as easily discernable as they are for online surveys.

While online surveys appear to be the preferred survey method, there are still times when mail surveys are the better means of data collection. Sometimes, survey projects need to be multimodal in order to achieve a representative sample. Just because online surveys are faster and cheaper than mail surveys, you must consider the value of the insights each mode promises to bring to your business objective.

Insight Central Resumes Week of January 3, 2011!

In observance of the Christmas and New Years, Insight Central will resume the week of January 3, 2011.  We here at Analysights wish you and your family and friends a very Merry Christmas, and a Happy, Healthy, and Prosperous New Year! 

*************************

Be Sure to Follow us on Facebook and Twitter !

Thanks to all of you, Analysights now has more than 200 fans on Facebook … and we’d love more! If you like Forecast Friday – or any of our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like forecasting and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you and they will be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

New Indicators to Predict the Economy?

December 20, 2010

In the December 9 Forecast Friday post, I discussed the use of economic indicators when predicting a time series. In that post, I mentioned that when you use leading indicators to predict how your business is going, you should be sure that it is representative of your industry and that the indicator adequately leads your industry. Investment professionals, it seems, have been doing that for some time now. An article in the January 2011 issue of SmartMoney, “Finding the Market’s Buried Treasures,” talked about different statistics investment pros have been using to predict the way the economy will go.

The various agencies of government put out several economic reports each week, month, quarter, and year. Some are better known than others. Savvy investment pros have learned how to mine these different reports for lesser known, but more reliable for making predictions. The SmartMoney article mentioned that some pros are using Commercial and Industrial Loans data from the Federal Reserve, rather than the Fed’s quarterly bank-officer survey to predict bank lending activity; number of hours worked is supplanting number of people hired; the Job Openings and Labor Turnover Survey (JOLTS) is being used in place of the unemployment rate; and disposable personal income is being substituted for wage growth.

The metrics being supplanted aren’t useless or wrong. Rather, they are being replaced because they aren’t as good or relevant a predictor to the decision-maker’s business as they once were. In fact, depending on the nature of your business, the better known metrics may be more useful than the lesser known. You must truly understand your business before you can decide which statistics are the best indicators of activity for your industry.

*************************

Be Sure to Follow us on Facebook and Twitter !

Thanks to all of you, Analysights now has over 200 fans on Facebook … and we’d love more! If you like Forecast Friday – or any of our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like forecasting and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you and they will be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

Forecast Friday Topic: Calendar Effects in Forecasting

December 16, 2010

(Thirty-third in a series)

It is a common practice to compare a particular point in time to its equivalent one or two years ago. Companies often report their earnings and revenues for the first quarter of this year with respect to the first quarter of last year to see if there’s been any improvement or deterioration since then. Retailers want to know if December 2010 sales were higher or lower than December 2009 and even December 2008 sales. Sometimes, businesses want to see how sales compared for October, November, and December. While these approaches seem straightforward, the way the calendar falls can create misleading comparisons and faulty forecasts.

Every four years, February has 29 days instead of the usual 28. That extra day can cause problems in forecasting February sales. In some years, Easter falls in April, and other years March. This can cause forecasting nightmares for confectioners, greeting cards manufacturers, and retailers alike. In some years, a given month might have five Fridays and/or Saturdays, and just four in other years. If your business’ sales are much higher on the weekend, these can generate significant forecast error.

Adjusting for Month Length

Some months have as many as 31 days, others 30, while February 28 or 29. Because the variation in the calendar can cause variation in the time series, it is necessary to make adjustments. If you do not adjust for variation in the length of the month, the effects can show up as a seasonal effect, which may not cause serious forecast errors, but will certainly make it difficult to interpret any seasonal patterns. You can easily adjust for month length:

Where Wt is the weighted value of your dependent variable for that month. Hence, if you had sales of $100,000 in February and $110,000 in March, you would first start with the numerator. There’s 365.25 days in a (non-leap) year. Divide that by 12. That means the numerator will be 30.44. Divide that by the number of days in each of those months to get adjustment factors for each month. So, for February, you’d divide 30.44 by 28 and get an adjustment factor of 1.09; for March, you would divide by 31 and get an adjustment factor of .98. Then you would multiply those factors by their respective months. Hence, your weighted sales for February would be $109,000, and for March approximately $108,000. Although sales appear to be higher in March than in February, once you adjust for month length, you find that the two months actually were about the same in terms of volume.

Adjusting for Trading Days

As described earlier, months can have four or five occurrences of the same day. As a result, a month may have more trading days in one year than they do in the next. This can cause problems in retail sales and banking. If a month has five Sundays in it, and Sunday is a non-trading day (as is the case in banking) you must account for it. Unlike month-length adjustments, where differences in length from one month to the next are obvious, trading day adjustments aren’t always precise, as their variance is not as predictable.

In the simplest cases, your approach can be similar to that of the formula above, only you’re dividing the number of trading days in an average month by the number of trading days in a given month. However, that can be misleading.

Many analysts also rely on other approaches to adjust for trading days in regression analysis: seasonal dummy variables (which we discussed earlier this year); creating independent variables that denote the number of times each day of the week occurred in that month; and a dummy variable for Easter (having a value of 1 in either March or April, depending on when it fell, and 0 in the non-Easter month).

Adjusting for calendar and trading day effects is crucial to effective forecasting and discernment of seasonal patterns.

Forecast Friday Resumes January 6, 2011

Forecast Friday will not be published on December 23 and December 30, in observance of Christmas and New Year’s, but will resume on January 6, 2011. When we resume on that day, we will begin a six-week miniseries on autoregressive integrated moving average (ARIMA) models in forecasting. This six-week series will round out all of our discussions on quantitative forecasting techniques, after which we will begin discussing judgmental forecasts for five weeks, followed by a four week capstone tying together everything we’ve discussed. There’s much to look forward to in the New Year.

*************************

Be Sure to Follow us on Facebook and Twitter !

Thanks to all of you, Analysights now has over 200 fans on Facebook … and we’d love more! If you like Forecast Friday – or any of our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like forecasting and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you and they will be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

Forecast Friday Topic: Leading Indicators and Surveys of Expectations

December 9, 2010

(Thirty-second in a series)

Most of the forecasting methods we have discussed so far deal with generating forecasts for a steady-state scenario. Yet the nature of the business cycle is such that there are long periods of growth, long periods of declines, and periods of plateau. Many managers and planners would love to know how to spot the moment when things are about to change for better or worse. Spotting these turning points can be difficult given standard forecasting procedures; yet being able to identify when business activity is going to enter a prolonged period of expansion or a protracted decline can greatly enhance managerial and organizational planning. Two of the most common ways managers anticipate turning points in a time series include leading economic indicators and surveys of expectations. This post discusses both.

Leading Economic Indicators

Nobody has a crystal ball. Yet, some time series exhibit patterns that foreshadow economic activity to come. Quite often, when activity turns positive in one time series, months later it triggers an appropriate response in the broader economy. When movements in a time series seem to anticipate coming economic activity, the time series is said to be a leading economic indicator. When a time series moves in tandem with economic activity, the time series is said to be a coincident economic
indicator; and when movements within a particular time series trails economic activity, the time series is said to be a lagging indicator. Economic indicators are nothing new. The ancient Phoenicians, whose empire was built on trading, often used the number of ships arriving in port as an indicator of trading and economic activity.

Economic indicators can be procyclic – that is they increase as economic activity increases and decrease when economic activity decreases; or countercyclic – meaning they decline when the economy is improving or increase when the economy is declining; or they can be acyclic, having little or no correlation at all with the broader economy. Acyclic indicators are rare, and usually are relegated to subsectors of the economy, to which they are either procyclic or countercyclic.

Since 1961, the U.S. Department of Commerce has published the Survey of Current Business, which details monthly changes in leading indicators. The Conference Board publishes a composite index of 10 leading economic indicators, whose activity suggests changes in economic activity six to nine months into the future. Those 10 components include (reprinted from Investopedia.com):

  1. the average weekly hours worked by manufacturing workers;
  2. the average number of initial applications for unemployment insurance;
  3. the amount of manufacturers’ new orders for consumer goods and materials;
  4. the speed of delivery of new merchandise to vendors from suppliers;
  5. the amount of new orders for capital goods unrelated to defense;
  6. the amount of new building permits for residential buildings;
  7. the S&P 500 stock index;
  8. the inflation-adjusted monetary supply (M2);
  9. the spread between long and short interest rates; and
  10. consumer sentiment

 

These indicators are used to measure changes in the broader economy. Each industry or organization may have its own indicators of business activity. For your business, the choice of the time series(‘) to use as leading indicators and the weight they receive depend on several factors, including:

  1. How well it tends to lead activity in your firm and industry;
  2. How easy the time series is to measure accurately;
  3. How well it conforms to the business cycle;
  4. The time series’ overall performance, not just turning points;
  5. Smoothness – no random blips that give misleading economic cues; and
  6. Availability of data.

Over time, the use of specific indicators, and their significance in forecasting do in fact change. You need to keep an eye on how well the indicators you select continue to foreshadow business activity in your industry.

Surveys of Expectations

Sometimes time series are not available for economic indicators. Changes in technology social structure may not be readily picked up in the existing time series. Other times, consumer sentiment isn’t totally represented in the economic indicators. As a result, surveys are used to measure business optimism, or expectations of the future. Economists and business leaders are often surveyed for their opinions. Sometimes, it’s helpful to know if business leaders anticipate spending more money on equipment purchases in the coming year; whether they plan to hire or lay off workers; or whether they intend to expand. While what respondents to these surveys say and what they really do can be quite different, overall, the surveys can provide some direction as to which way the economy is heading.

Next Forecast Friday Topic: Calendar Effects in Forecasting

Easter can fall in March or April; every four years, February has an extra day; in some years, months have four weekends; others years, five. These nuances can generate huge forecast errors. Next week’s Forecast Friday post discusses these calendar effects in forecasting and what you can do to adjust for them.

*************************

Be Sure to Follow us on Facebook and Twitter !

Thanks to all of you, Analysights now has nearly 200 fans on Facebook … and we’d love more! If you like Forecast Friday – or any of our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like forecasting and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you and they will be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!