Forecasting New Media: Use Techniques, Track Records, and Make Right Prediction

From the start, the media is used to be presented as graphs or time series, and require definition TV sets (HDTV) in 2012 or the cumulative penetration of HDTVs in households the earlier six years. When the broadcast is firstly based on voice, phone invented by Alexander Graham Bell was introduced to be the carrier for broadcasting service. And while the forecast not only used for voice and business calls, the techniques used on it extends to different transistors — videotex, which is for transmitted data over telephone lines for display on computers and, in its early days, television sets for person to person messaging.

At the same time, the other techniques seems play different roles in the industry.

The radio is for broadcasting speeches to the public , and mobile phone acts as the carrier material for emerging workers. And when it has needs to display DVD movies , the high definition television is developed to display DVD movies.

But one fact comes that forecast will have track mistakes, and we need to figure out the reasons under those poor tracks.

The root of poor records might come from the fast changing speed of new media products, and the error happened in comparing similar products . Also , there exists a time interval between the appearance of product and the time it comes to the market.

When studies the mathematical models, we can figure out that the unexpected variables will affect the prediction of track record due to the following aspects.

First is the risk of entirely overlooking new products to be succeed in near future. Second is about the upgrading of manufacture. If a manufactured is updated, we not only need to forecast the demand for a new service, but also need to study the demand changes for the new product.

Under all these background, track record mistakes stem from the following reasons.

To begin with , sometimes journalists and purchasers need to find reports to do track report, but the report cannot sometimes give journalists full scrutiny about assumptions and methodology.

In addition, some true track records might work for single client in industry by company. So they remain as confidential and unpublished to the public. When it comes to multi-users, the privacy problem happened in the same way.

Then, some track record has underestimated and overestimated problems.

The elements which would be underestimated or overestimated includes: technical change and demand for forecasts.

Forecasts for similar products often err in assumptions about which features of the competitive products will have appeal or how marketplace conditions will influence adoption.

And there might be other reasons for the failure of track recording, which comes from the appliances such a mobile phones and satellites.

And when it comes to the real life, all these enter into market place become disappointments. The failure of broadcasting a new product might come from the fact that the estimate of probability that new product will attain a specified level or demand by a certain date is less complex than most forecasts.

If I want to test one new media product, I will predict it in four steps.

The first step is historical review. If a company has introduced similar new media products into similar markets in the past, these histories can often be good predictors of future outcomes. And else, the histories of similar new products introduced by competitors or other companies can also serve as historical guidelines to help derive a new product sales forecast.

A second method of forecasting new product success is the test market. The new product is developed and introduced into one or more test markets. We need to target the different types of consumer groups, both local and International market, and adoption of media in those places.After analyzing these variables, I will also pay attention to the changing of markets in different time periods.

The third step is making before-after retail simulation. A sample of target-audience consumers is presented with simulations showing the in-store retail environment and a realistic display of all the major media adoptions in the category. The consumer is asked to choose or “purchase” service from those new media as they normally would, or as they might on their next 10 purchases. The new product is missing from the simulation during this “before” measurement. Then the consumer is exposed to the new product concept and/or advertising that conveys the new product concept. Later, the consumer sees the exact same simulated display (except now it contains the new product) and is asked to make the same choices or purchase decisions as before. So, we have market shares for all brands in the category before the new product is introduced, and the same data after the new brand is introduced.

The forth step for forecasting new product sales is the “normative” approach. A database of historical norms for new products is assembled (trial rates, repeat purchase rates, purchase cycles, and so on) by product category. A mathematical model sits atop the normative database and includes the marketing plan variables that might cause a new brand to perform above, at, or below the norms.

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.