Do people still read white papers?

Do people still read white papers? That was a question someone posed to me recently.

They argued that senior decision-makers in end-user organisations don't have time to read whitepapers. Meanwhile IT managers and tech specialists don't read them because they already have a reasonably good grip on what's going on in their technology space.

I'm not sure I entirely agree. Absolutely, any IT vendor who believes traditional whitepapers are read by senior decision-makers in end-user companies is surely dreaming.

But I know plenty of people in IT departments who will gobble up information from a whitepaper if it's on a subject they're interested in but don't know a lot about.

(What do I mean by traditional whitepapers? Well, that's probably a topic for another post - not least because I have to go out now. Hope everyone in the UK is having a great bank holiday!)

Forecasting: An Insider Explains

Rahme, one of the team here, has just written an article on forecasting.  She used to be an analyst with InfoTrends / CAP Ventures and forecasting was her job.  

This piece gives some interesting insights into the processes and challenges involved so I wanted to share it with you.  

If you'd like a PDF version, please let me know – analyst.insight AT gmail DOT com

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Forecasting - An Insider Explains

The forecast
Producing market forecasts is a key part of the job for many industry analysts.  They use their specialist market knowledge to predict growth or decline in segments of the market they track.

Regardless of the scepticism that is sometimes heard in the IT industry over the accuracy and value of analyst forecasting, in many cases these statistics are the only market information that a company has on which to base its product development and sales forecasts.

Without the availability of good data, the analyst will find it more difficult (perhaps impossible) to produce an accurate forecast.  An inaccurate forecast doesn’t benefit anyone as companies make investment decisions on a misunderstanding of the market they operate in.

Types of forecast
The length of a forecast period can vary depending on the technology being tracked but is generally five years.

The most common type of forecast is on sales or shipments.  This type of forecast will look at how many products and services in each segment of the market have been sold historically and, looking forward, estimates how many are estimated to be sold over a specific period.

For example, many printer manufacturers are interested in understanding how the entire printer market is developing.  This means that analysts covering the printer industry need to provide forecasts for both inkjet and toner technology printers.  They segment these technologies by the printing speed of the products and the environment in which they are used.  In addition, this is segmented by the printers’ ability to print colour and black-and-white.  And you must not forget the growing trend for multi-function devices (printers including copier technology and vice-versa).  All of a sudden, providing forecasts for the printer industry seems more complicated than it did.  (It’s not just the printer market.  Analysts tracking other products or services will face similar challenges).

Things aren’t easy for the printer manufacturer either though.  There are a variety of analyst firms that track the market and each has a different way in which they segment the market.  They are often similar but not always.  This is the most common reason for finding discrepancies between one forecast and another.

Analysts are aware of the difficulties that companies can face when trying to understand whether differences in forecasts are based on fundamentally different views of how the market will develop or whether it’s simply because of segmentation.  If that’s the case, get in touch with the analyst.  They should be happy to explain how they segment the market and help you understand the differences.

Forecast quality
The quality of the forecast will be determined by a number of factors:

-    statistical rigidity  of the forecasting model

-    the accuracy and depth of the numbers used in the model (accurate numbers dating back over several years provide a firm foundation)

-    the degree to which the forecast can be related to specific business decisions (is it being used for decisions that it was designed to support?)

-    the length of time that the forecast looks ahead (the further out a forecast goes, the less accurate it becomes).

Why you should help the analysts with their forecasts
There are two main advantages of helping analysts with their forecasts:

First, the analysts appreciate the help so it aids the development of positive relationships

Second, the better the numbers used in the forecasting model, the more accurate the forecast estimate and analysis of the market and major trends.

What do analysts require to produce a forecast?
Analysts have developed some sophisticated forecasting models based on techniques relevant to the sector being tracked.  There are a number of considerations that usually influence the choice of forecasting technique.

These include:

·   The availability of hard data

Hard data is historical data that a quantitative model will be based on to produce a forecast.

There are different types of data that a manufacturer can provide an analyst with.  Generally, anything is better than nothing!

Some companies only provide a total number, for example total sales of all their products and services for Western Europe.  This is regarded as top-level data.  The analyst will then have to break this data down by segment using their own market knowledge and expertise.

Other companies will split this top-level data themselves, as they understand it helps ensure that the analyst has a more accurate statistical base from which to produce the forecast.

Some manufacturers won’t provide numbers but will give feedback and guidance on estimates that the analysts submit (eg xx% above or below the actual number).  This is less helpful but at least allows the analyst to check whether their own estimates are relatively accurate.

·   The level of accuracy required

Generally, the greater the level of accuracy required, the greater the need for sophisticated models.

Analysts are aware that even the most accurate forecasts will never be 100% spot-on but in order to get as close as possible, analysts will sometimes conduct interviews with the distribution channels, such as resellers, systems integrators, etc.

These interviews help double-check the accuracy of the numbers provided by the manufacturers.  

In addition to this, the interviews help the analysts check the assumptions they have made in the forecast are as accurate as they can be.

Every forecast model includes assumptions but it is vital that the analysts have explanations for all the assumptions they make in the forecast.  Any forecast that doesn’t include details on the assumptions made should be treated with suspicion.

Why producing a forecast takes so long
Analysts normally have a process plan in place six months to a year before the publication of a forecast.

This is because producing a sophisticated forecast means planning ahead.

It takes an incredible amount of time and effort to develop or update a model, to gather and segment the data, to run the model and test it is producing the type of results which were expected.

Without planning, analysts are not able to get their forecasts complete on time.  They must plan their work in stages and ensure that they anticipate set backs in the process.

Some set-backs are minor, like a company providing data a day or so late.  Others are more serious.  Major set backs include companies agreeing to provide data and then not doing so, interviews with people in the industry not going ahead on time, technical issues with the data collection etc.

Given that some analyst firms produce forecasts on a quarterly basis (eg on fast moving sectors such as mobile telecoms), it becomes apparent how much planning is required and how much resource forecasting takes.

How do analysts collect the data and information for a forecast?
It varies from sector to sector but in the printing industry, for example, analysts contact companies on a quarterly basis to collect sales or shipment data.

The data is generally sent to the analysts in the IT manufacturers’ own format (usually Excel) but some companies request a data sheet from the analyst firm that they can fill in and return.

Several manufacturers have someone in the European head office whose job includes contacting all their European subsidiaries to collect the data and provide it to the analysts.  This is generally someone who works in the market intelligence or product marketing division.

Other companies ask the analyst to contact each subsidiary themselves to collect this data.  This can be a huge job, depending on the number of companies being tracked.

Analyst relations managers are now becoming a lot more involved with the data collection process for analyst forecast.  So are some AR agencies.

It’s also worth remembering that a common problem for analysts is suddenly finding that the person responsible for collecting the data has moved on and their colleagues are either unaware that this job needs to be done or don’t understand the relationship with the analyst and refuse to help.

And if companies don’t help by providing data, the analyst will seek to get this information from alternative sources and make a prediction of market size and trends based on that information.  

A couple of things to end with
In most analyst firms that produce them, the forecast is king.  Analysts come under immense pressure to get them completed on time.  Until the forecast is done, they are not encouraged (sometimes, not even allowed) to work on additional projects.  This means other reports and consultancy projects you could be interested in won’t be available.

A late forecast is less useful to corporate and product planning departments.

Without a certain level of detail, an analyst is simply unable to truly understand the changes or influences impacting the market they track.  In turn, their advice will be less informed and therefore less helpful.