Analysis of both cost and WTP is needed to really understand whether or not a firm has a competitive advantage which is defined as a wider gap between cost and willingness to pay than competitors.
Customer willingness to pay(WTP) is estimating how much a given customer would be willing to pay for a particular product or service.
Demand estimation is predicting the overall size of the market or segment which a company chooses to serve.
Willingness to Pay (WTP)
Different customers will have a different willingness to pay for a firm’s product which would place them in a different market segment. We should start from understanding the relative valuations of each segment.
Understanding WTP is also valuable for more tactical reasons such as pricing and new product design. The basic approach to calculate WTP is attribute valuation.
There are a number of techniques that can be used to calculate a customer’s willingness to pay for a product and even to value a particular attribute or feature of that product. The most obvious is market research that simply asks customers, how much would you pay for this product? However, more accurate and sophisticated techniques such as revealed preference (valuable for products already on the market) where analysis of the actual consumer purchases can reveal their willingness to pay and discrete choice(valuable for new products) which is useful for identifying the value of individual attributes of a product or combinations of features that have not yet been offered for sale in the market.
Data on actual customer purchase behaviour is a natural starting point for analyzing WTP if there are limited customer segments. The analysis of such actual purchase data can reveal the underlying preferences of customers. Simple regression can be used to infer customer preferences from the purchase data. The weakness of this approach is that it identifies the average market value of each attribute rather than a segment value of the attribute.
The best method to determine customer’s WTP is discrete choice analysis and the principle underlying this approach is based either on actual purchase data or by asking the customer her preference across alternatives that contain different bundles of attributes. While customers cannot directly articulate the value they attribute to any characteristic, everyone can say whether they prefer package A to package B. Something similar to regression analysis of a sufficiently large number of these discrete choices can then identify a customer’s implicit valuation of each characteristic called conjoint analysis.
Every customer is unlikely to have the same WTP for a particular product. Ideally, one would survey every possible customer and construct a graph of WTP against volume, however, in practice we are often only able to do so for aggregates of customer segments. The intent is to construct a market demand curve that shows how many customers will buy at any given price (i.e. their WTP) and to calculate from this the market price elasticity.
Price Electricity = Δq/Δp at a point (q,p)
Any firm needs to have a good sense of the potential size of the market it serves. In order to plan capacity expansions, to know whether it is gaining or losing market share, the firm needs to develop an accurate estimate of market size. There are a number of sources available which can provide historic data on market size. However, the strategically important numbers are usually not the past but future market sizes. The obvious place to start forecasting is with past market size. Extrapolation of the recent market growth rate is in many cases not a bad estimate of future demand however these sorts of analyses tend to provide accurate forecasts in relatively stable and mature markets when underlying circumstances are not changing radically. The more difficult question is how to predict demand for new products or in uncertain and changing markets when projections of the past are unlikely to be useful indicators.
Substitution and Diffusion
One useful approach is based on the assumption that a new or improved product must be substituting for an existing offering. In the beginning stage of a new product, only a few bleeding trend setters purchase the product. Gradually the product receives wider acceptance and the rate of adoption accelerates until most customers who will ultimately end up using the product have switched at which point the rate of adoption slows.
The formula for the rate of adoption is log [s/(1-s)]
where s = share of current users (a) as a % of maximum penetration (b)
When plotted against time, this adoption becomes a straight line with slope equal to the rate of growth of the product. This can be used to predict future demand from historic data.
Another approach to determine market size for new products is market research. You can interview potential customers in order to understand the likelihood of their purchasing your new offering and at what price. Whatever methodology is used to predict future market size — extrapolation of past trends, substitution or market research, it is always useful to triangulate your results against other relevant benchmarks as a common sense check of your projection.
Give it a try and let me know how it works for you.
#versioning #pricing #productmanagement #productmanager #b2b #product #collaboration #businessvalue #businessimpact #customervalue #customerexperience #engineering #design #marketing #adobe #microsoft #atlassian #oracle #servicenow #alphabet #intuit
Read Similar Stories: