What are the market opportunities for a product or service?  Especially when it is a new product unlike anything else, some companies find themselves resorting to "guesses" or  "wishful extrapolations" based on not so relevant historical pointers.  But it is possible to do a lot better than that.  While it seems that there is never enough data, we can make good use of the data available by combining it with a reasonable theoretical framework.  Because there is logic to the way customers decide to spend money, there is much to be learned from other markets even if the products or services differ from what is currently under consideration.   However, the parallels may not be directly evident and it may take some number crunching to get the desired insight.  The objective of this page is to show conceptually how data from other business areas can become useful in evaluating new products.

Most products offer the customer with solutions to a few fundamental needs.  For example, most jackets offer the people wearing them warmth and/or a fashion statement.  In this discussion these solutions are called value drivers for the product.  A value driver, as defined here, has two properties:

• It relates to the few basic functions or tasks accomplished by a product (or service), not the many features a product may offer.  Each of the individual product features may, however, contribute to one or more solutions offered by the product.

• It originates when a customer has a need AND a product has a solution to that need. Evidently the same product, may therefore provide different groups of customers with different value drivers.  Totally useless products and totally unsatisfied needs have no value drivers - and no market.

Conceptually the profit or loss we can expect is (assuming no time delay between expenses and income):

Profit = Price * Volume - Cost

Price: How much can we expect a customer to be willing to pay?  A customer will not pay more than the value they expect to receive from the value drivers offered by the product.  While it can be hard to quantify we can look at what they currently pay to solve the basic problems that the product offers a solution to or how much the product will save them in terms of money and time.  A problem for the customer is that the value comes at a later time than the time of purchase and the buyer cannot be sure that the product actually will deliver the value they were hoping for.  Maybe the customer did not need it after all, or maybe the product does not work.  Like we discount stocks according to how risky they are customers tend to discount the value of a product according to the estimated risk.  Manufactures have been eager to reduce this risk by offering warranties, money back guarantees and customer support.  Existing products have quite a range of risks and risk mitigating offerings, and from that we can learn how much risk affects the customer's willingness to pay.  Had the customer no alternatives they may be willing to pay an amount equal to the value discounted for risk, however, competitors are often eager to capture part of the market by offering the customer alternatives.  The more alternatives the customer has the less market power the seller has.  Again, from existing markets we can learn  what the importance of market power is and what drives it up or down.  We can now write a conceptual equation for the price:

Price = h * Market Power * Value / Risk

the factor h is a pricing decision factor reflecting that the price charged may be less than what the customer would have been willing to pay. 

The next big question is how much can we expect to sell?  Going back to the concept of value drivers we can use them to define the axis in an utilityscape, in which we can plot products and sales volumes. For example we can use the two value drivers of most jackets: warmth and fashion to define an x-y space.

Warmth is relatively easy to quantify, like different types of building materials are rated using R-values.  Fashion is harder to quantify, and you cannot necessarily say one style is more fashionable than another.  However, we can cluster jackets according to styles, and arrange these styles along the x-axis in the diagram.  We can also arrange the styles so, in general, the more fashionable they are, the more to the right they fall in the diagram.   Based on existing sales we can now map where customers aggregate.  Will that be the same next year? fashion will be changing so the styles will too, however, the fraction of the customers seeking very fashionable clothing rather than non-fashionable clothing will only change slowly from year to year, and the diagram can therefore be useful for the year to come. The diagram will tell us the total sales volume in each "segment" of this space defined by the value drivers, one of the segments could for example be define as very fashionable and only medium warm.  Because of the way the value drivers are defined here they are closely tied to markets created by bringing needs and solutions together.  The segments relate therefore more directly to sales than, lets say, segmentation based on customer- and/or product characteristics alone.

Now we want to introduce a new product, and there are, lets say, already 10 other products in that "segment".  Based on historical data we can estimate the market share each of these products will receive, based on advertisement spending, brand equity, distribution channels, relative pricing and so on.  There are many  methods for estimating sales in well established markets, but what if the market is changing?   If we add a new value driver to address an unsolved customer problem, then we can still estimate how customers may distribute themselves in this new space based on how many prospective customers have this problem, historical adoption rates for other products, etc. Typically we will see a new product gaining traction in one corner of the space, and then gradually spread to other parts - and when we understand the space well we can predict where it will start and how it will evolve.  Most new products do not create any new value drivers, but they are the first product in an empty segment in a space already defined.  Sometimes these situations can be dealt with by enlarging the segments to include the new product, in other situations simulations of how the customers migrate within the space can help us estimate how sales volumes will change over time.  It can even be possible to show the effect on the sales of existing products, when a new product with different characteristics is introduced.       

The cost can normally be estimated fairly accurately using conventional methods.  We thus have all the estimates for the variables in the profit-loss equation.  And it can all be supported by patterns observed in existing markets.

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Note: not all authors define the terms uniformly and some of the definitions used here differ from what is most commonly seen.   These changes have been done in order to achieve an internally consistent and coherent set of concepts that it is relatively easy to quantify based on historical data.