Microfiche, Metadata and Models: The Promise and Problems of Channel Checks

Developing an effective financial model requires reasonable assumptions for values and growth rates, with channel checks often providing useful insight.? Channel checks involve gathering observations about demand and supply conditions from suppliers and distributors.?The challenge of channel-checking is knowing how to design, execute and draw useful inferences from unstructured or anecdotal data. This process might involve extrapolating growth rates and values based on observations that are broadly directional and sometimes qualitative in nature.?

In the early days of my #financialmodeling career, I was asked to conduct an in-depth study of advertised retail prices in the soft drink industry and determine the severity of price competition.?In those pre-internet days, the best way to observe retail prices was to review advertising circulars in daily newspapers. The annex of the New York Public Library housed a large collection of regional newspapers archived on microfiche. Reviewing those ads provided some insight into the extent of beverage price competition but it was broadly directional.?

In the 1990s, with internet-based tools at my disposal, I analyzed bookings and pricing trends in the cruise industry by using a web-based platform to distribute questionnaires to hundreds of travel agents. However, a limited survey-response sample size elevated my risk of drawing misleading inferences.

Weakness in cruise bookings observed by a travel agent in a particular survey region might be due to weakening industry demand – or it might simply reflect the business challenges in a particular geographic market, its local economic conditions or the rising or falling business fortunes of other agents. As a result, the insights gleaned from those studies were also broadly directional. I had enough context to determine that cruise fares were weakening in certain markets, and at certain points in the distribution channel. However, those conclusions were insufficient to gauge the extent to which industry-wide retail yields were softening. Because US travel distribution is fragmented, it was difficult to draw meaningful insights about overall industry pricing conditions.

Visiting stores and attending industry trade shows in order to gauge buyers’ interest in products or consumers’ reaction to retail product displays can be a useful discipline. Those channel checks may help experienced industry analysts gather valuable market intelligence. However, channel-checkers must be discerning enough to look beyond the hype associated with an attractively priced, hot new retail product and consider how margins might be affected by rising input costs, or whether channel observations are a poor revenue indicator because the product’s category is very small.

Modern channel-checking tools offer promise because they aggregate large data sets to mitigate sample-size risk and other biases. Some, like Bloomberg Second Measure , provide US consumer transaction analytics and insights, based on “billions of credit card and debit card purchases, using a subset of millions of consumers from a consumer panel that includes 20+ million members,” according to Bloomberg Professional Services . Tracked metrics include the observed number, average value and per-customer level of customer transactions.

Even sophisticated channel checks, relying on alternative data sets or large samples of aggregated data, might not be granular enough to provide a sound basis for challenging consensus-based assumptions about growth and margins. It’s likely that no single analytical tool will be sufficient to replace the judgement of analysts who assimilate insights from multiple channels and industry sources to develop an informed alternative to consensus estimates.?

Analysts spend considerable time determining the values or growth rates of volume and price drivers for company revenue. The Bloomberg Interactive Calculator relies on brokerage analysts’ average estimates to generate default growth rates and values that populate those drivers.? The Calculator also gives users an option to enter their own driver assumptions. These flexible drivers are powerful modeling inputs that enable analysts to evaluate their own assumptions about company volumes, prices and margins and compare those to consensus.?

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