Appendix: Price-Benefit Analysis
The author has described a method of assessing price according to the specific conditions of demand, a technique economists name "hedonic price regression." This analysis looks at the qualities of the product in comparison to market conditions and determine the degree to which they influence the customer's willingness to purchase a specific product.
Since it is a critical tool, the author means to examine it in further detail - but since it is technical and a bit oblique to the main thrust of the book, he has relegated it to an appendix.
Overview: Mapping The Market
The author refers to the price-benefit map, originally presented in his HBR article of November, 2007, which plots the prices and benefits against X and Y axes to imply four quadrants (high price high benefit, high price low benefit, low price low benefit, and low price high benefit) against which competitive offerings can be plotted to identify clusters. Dreating such a map involves three steps:
First, frame your analysis by considering which benefit you mean to measure and what offerings you mea to include. It is important to cast a wide net in order to have a comprehensive view, and especially to consider all challengers so you are not blindsided by small firms or unusual offerings. You should also consider whether it would be useful to limit your analysis to a specific geographic region or market segment. (EN: It might also be useful to bubble-plot - that is, to represent each firm with a dot whose size represents its market share so that all are not considered equal.)
Second, consider the price and primary benefit. The price may be as simple as the asking price, or it could involve other factors, such as the cost of ownership over a longer period of time, the other costs to the customer such as cost of acquisition, adjusting price to location or other factors, etc. Regarding benefit, it is important to consider from the perspective of the customer, not the industry or firm, and to rely on data other than the opinions of product managers to assess the degree to which any given product satisfies the need. It may be useful to present benefit as an aggregated score, and to have separate charts for each benefit identified.
Third, interpret the data by plotting positions to find strategic groups and trends, then determine what the results imply. Consider the location of the various firms in the final plot. Draw a "price line" that best fits the point of the map to determine how much customers are willing to pay for a higher level of the primary benefit. Finally, consider what the map implies about the groups of customers, not merely groups of companies or products.
The analysis can be repeated over several points in time to recognize changes in the marketplace that may constitute trends, and imply the way in which the industry might change in future.
Framing Your Analysis
You must determine the specific question to answer or issues to investigate as a first step. If analysis is done without a specific purpose or definition of the market to be studied, it can be a complete waste of time and effort. It is also worthwhile to consider the actions your firm is willing to consider: there is little e point in conducting an analysis if no action will be taken as a result.
That said, having a lack of clarity can sometimes be beneficial in casting a broader net, as a pure act of discovery without specific expectations. If you take too narrow a view of the market, you will be more likely to be blindsided by new entrants, unusual products, or substitutes. There is a balance to be struck.
Getting the market definition right, and specifically defined, is also important: general data is less meaningful that the information about the geographic and demographic group you presently serve or the group you wish to better serve.
It might also be worthwhile to analyze the market your competitor seeks to win, particularly when you are seeking to serve the same or similar needs, which will give you a more complete view of the market than just the customers your presently serve.
Where sufficient data can be collected, the definition of the market can be scaled to specific product-customer segments. You may begin with a broad analysis of the automotive market, then break it down it by vehicle type, then by demographic segments.
The Price-Benefit Map
The way in which customers consider price requires a bit of consideration, as pricing is a nebulous notion (EN: I would say it has been "made nebulous" by firms that wish to manipulate the customer into considering or disregard certain elements of the total cost of ownership in a way that is favorable to their offering.) For different products, customers may consider:
- The seller's asking price of the item
- The total price paid to the seller, including taxes, delivery, and other charges
- The cost of acquisition, including things such as travel to a store and transporting the item to its place of use
- The cost of ownership, including costs associated to the use of the item, such as fuel costs for a vehicle or electricity cost of an appliance
- The long-term cost of ownership, including the need to repurchase the item repeatedly over a longer period of time.
The author also mentions that an accurate estimation of cost also takes account of factors such as inflation (which increases price over time) and temporary situations (a shortage of raw materials or an increase in the price of fuel may is typically passed along to customers). And the cost of a product must also be considered in relation to the income or budget of the customer. An item regarded as cheap to am affluent customer may be regarded as expensive to a low-income customer.
The same may be true of the benefit of a given product: there is no universal agreement among customers on what the primary benefit of a given product happens to be (though sellers generally wish it to be so) or what qualities and features are most important.
The author suggests looking into the correlation between the various benefits, qualities, and features of a product and running an analysis to determine their correlation (R-shared) against the price, on the assumption that people are willing to pay a higher price based on the degree to which a product best embodies the benefits/qualities they most value. This which is most highly correlated is likely the most important to customers.
(EN: This seems reasonable, but I have the sense the logic may be a bit circuitous, and two key considerations are overlooked: first, the qualities that are critically important but are taken for granted; and second, qualities that the customer does not presently value, but might value or should value.)
It's also important to look to the customers' use of a product rather than the qualities intrinsic to the item itself. Even a simple-seeming commodity may have a broad array of qualities customers consider important. Given the example of coal, some are interested in the thermal output, but may also consider the amount of ash (waste removal) or sulfur (environmental concerns) to be important.
Still others may value a secondary quality such as reliable delivery from a producer. When all providers of a given good satisfy the primary need, it may be taken for granted, and customers then look to these secondary benefits as the factor that drive their decision.
It is mentioned that the list of benefits and features to consider should include those determined by market research - asking the customer what they value rather than letting company and industry insiders indicate what they want customers to value ... but since the analysis is mathematical and based on correlation to actual purchasing behavior, the results may not be severely impacted.
However, focus groups are notoriously inaccurate in determining customer behavior: what they say is not always an accurate reflection on what they do, and the behavior of the "average" customer may be a mathematically derived assumption that does not reflect the actual behavior of the majority of customers.
Data about product benefits is readily available from many sources, some for free (the Internet and government statistics), others for a cost (research firms and industry associations). Specific mention is made of Consumer Reports magazine, which offers decades worth of data for a broad range of products.
But no source is perfect, and the analysis is often based on the assumptions of the qualities that customers value, often taken directly from insiders. The actual data that drives the assumptions is very often hidden or unavailable, and is quite often suspicious - as much research is rigged to produce a conclusion that will sway consumer opinion rather than accurately measure whatever it happens to truly be.
It's also mentioned that there is an incestuous relationship between public research and public opinion. Going back to Consumer Reports magazine, an article is written to determine what customers value in a product - but customers who read the magazine are influenced by the results of the study and are often convinced that the factors that were used in the analysis are more important than the ones they might have considered had they not been aware of the research.
Because the number of benefits that a customer might consider is so numerous, a common practice is to determine the correlation among sets of benefits that appeal to specific consumer groups. For example, a customer who seeks a vehicle with greater seating capacity also seeks to have more cargo space and greater safety, whereas another who looks to vehicle performance might not find any of these features to be desirable.
While this is often intuitive (the kind of person who wants a sports car does not care about passenger space, cargo capacity, or safety), these should be derived from statistical correlation rather than mere assumptions. There are instances in which factors that seem to go hand-in-hand are not correlated, and others where factors that seem unrelated are highly correlated.
On the age of information: it's generally accepted without question that the latest data is the most accurate and reliable - but that is not necessarily so. Most consumers do not pay attention to the industry and do not keep up to date on the most recent product developments, and their buying decisions may be driven by an article or advertisement they read several years in the past.
Interpreting The Data
Proper interpretation of price-benefit mapping requires the map to be drawn properly, with knowledge of the context of the customer and the nuances in competition. It is very easy to misinterpret, and misinterpretation of the data can lead to disastrously wrong decisions.
In the simplest sense, a price-benefit map is developed by plotting the price and benefit on a scatterplot. The benefit may be the primary benefit of the product, or a bundle of correlated benefits, and there will likely be separate maps for separate benefits. The result is generally that clusters of data points become evident, and it generally follows a diagonal line (the "expected price line" for a product that delivers the benefit in question), showing that customers are willing to pay higher prices for more benefits.
Where the scatterplot is diffuse, it may be worth considering whether the benefits under analysis are too broad or too granular - but if correlation has been found between price and a given benefit, the plot should not be diffuse. In some instances, such diffusion represents the conflict among competing benefits and a customer's perception of price, the variances in which were discussed previously.
In addition to providing a general sense of the market, price-benefit mapping can be used to understand the way in which a given product is considered (where it falls on the line) and how products compare to one another (the relative position of one to another). This technique simply uses ellipses or polygons to identify where customer perception is clustered, with attention to how they are positioned and, especially, to where they overlap.
Isolating the data relative to a specific product, company, or brand can reduce the complexity of the map, but any simplification will also eliminate some of the richness of the data, which can lead to false conclusions. However, being overly general can create diffusion.
On the expected price line: it generally has a positive slope (you get what you pay for) but is not a perfectly straight line. The author notes that, in his experience, it tends to be bowed downward at the extremes, reflecting the general perception that a very low price suggests a very bad product and a very high price means that the price has exceeded the benefits.
Weighting products according to volume of sales is also worthwhile - which should present small sellers from distorting the line for the rest of the industry. (EN: My suggestion would be altering the size of the 'dots" rather than altering the curve of the line to get a better perception of where the market is most profitable - or possibly doing a separate analysis of economic factors, price and quantity, and considering the two in conjunction.)
In general, the line indicates how individual products are considered by consumers to be low price/quality, basic, midrange, premium, and super-premium. But generally, any position that falls below the line is considered to be a good deal, and one that falls above the line is considered overpriced.
It is also valuable to consider the way that the different points move over time to track movements in the marketplace, which is essential to keeping abreast of both the changes in customer values and the effects of your rivals' strategic initiatives. For example, you may see a major competitor moving into areas you presently serve, or a new rival may pop up in your demesne.
The author lists a number of other questions that may be answered by a price-benefit map:
- What opportunities appear based on the competitive intensity of various value propositions?
- What trajectory and speed are your rivals moving, and what new opportunities and threats does that motion create?
- Does the market appear to be commoditizing - gravitating toward the same point (or points) on the map?
- Are competitors to your major products attempting to push into your territory, assume a position below your price, or assume a position above your quality?
- Where are the apparent weaknesses of your product line or brand positioning?
- How is the market's preference for price-benefit balance changing? Where do you expect the price line to move in the future?
- Does it appear that a secondary benefit is increasing in importance, such that it may become a primary benefit in the market?
- What would the future of your market look like based on a what-if scenario? What to do if your what-if scenario turns out to be right?
- Can you proactively influence the map in order to move customers toward the positions you presently occupy, rather than passively seeking to change your position to suit present preferences?
The author concedes that elasticity can be a critical factor in considering gaps in the price-benefit map. The upper end of price and quality may be completely unoccupied, but unprofitable to serve if there is insufficient demand for a product at a give price.
It's also critical not merely to consider the map as an end unto itself: when you notice a situation or a trend, it's important to consider causality. Conditions don't change on their own, but are caused by some factor. It may be an environmental factor (a slowing economy causes customers to compromise quality to save money) or it may be the actions of a specific firm (an advertising campaign creates an appetite for higher quality or a lower price). This knowledge is essential for taking effective action.
In a general sense, there are two directions in which the expected price line can tilt over time: it can become flatter or steeper. To take it a bit further, the author lists some factors to consider:
- Does elasticity of demand have an effect? While the expected price line attempts to account for quality (instead of quantity), it remains true that more cost-conscious customers will reject a certain level of quality if it requires a much higher price.
- Is the overall capacity of the market being met? The more scarce goods are, the less options the customers are able to demand.
- Is the overall size of the market increasing or decreasing? If customers are moving out, it may be to substitute goods that should be brought into the equation.
- What external factors might impact the line? If income goes up or down, or credit becomes more tighter or looser, how will this affect the expected price line?
- How is technology and competition changing the expected price line? Rapid product improvement and strong competition are also factors that create additional price-value options and shift the value of existing ones.
- What defines the "ceiling" of the level of quality a customer is willing to pay to obtain and the "floor" of the level of quality a customer is willing to forego to save on price?
- Where are existing segments merging or being subdivided? In particular, look to price-benefit propositions that are between existing clusters to determine if they have gravity to attract from both ends.
As a final note, it is important to be constantly aware of conventional wisdom and unstated assumptions that may guide you to interpret the data in the traditional ways: the point of research is to discover the unknown, not merely to confirm the known.
Plan Your Offensive
Once the analysis has been done, the next step is to evaluate possible actions you can take - whether proactively seeking to improve your position or reactively seeking to mitigate or evade a competitive threat.
The author speaks in purely competitive terms: understanding the strengths and weakness of your present position as well as the positions of your rivals. Generally, there are voids on the map where a different price-benefit balance can attract customers who are presently buying products that are not a match - either paying too much for quality they don't need or accepting too little quality for the price they are willing to pay.
As such, presenting the market with an offer that fills a void can avoid direct competition, but at the same time cause customers to gravitate toward positions they accept, for lack of alternatives, toward a price-benefit balance they would find more acceptable. Where your aim is to "attack" a competitor, exploiting their vulnerabilities provides a better chance of success; and sometimes you can attack on a second front to take advantage of a competitor who is already under attach and is moving to defend itself.
Another analysis to consider, when looking at the movements over time, is whether your own firm is taking the initiative or merely being reactive to changes in the marketplace. In general, a firm is in stronger position if it is acting in pursuit of opportunities rather than merely fleeing threats, and being a leader is preferable to being a laggard. But at the same time, there are serious risks to being entrepreneurial, and the cost of blazing a new trail is more than merely following in someone else's footsteps.
Ultimately, the decisions you make must be based on the nature of your market and the capabilities of your own firm. You likely cannot fight all competitors on all fronts, but must pick your battles wisely and fight them efficiently.
Getting The Most Out Of Your Price-Benefit Analysis
While the price-benefit analyses here make the process seem scientific and quantitative, there's also an art to interpreting the marketplace: there's a great deal of complexity to the numbers, with many factors interacting, and intuition and common sense are often necessary to get out of the weeds and see the situation clearly.
The author compares them to any other kind of map: they provide a great deal of information about the terrain, but in order to make the journey successfully, a map only shows the lay of the land. It does not set a destination, or choose the best route to get there. It takes vision and leadership to put the information to good use.
And while the author is fond of analysis, he does concede that analysts can get carried away, and end up doing "analysis for the sake of analysis" and burying themselves in the complexity of the statistics, which ends up obscuring more than it clarifies.
Much of this can be prevented at the onsite: by framing and scoping your task carefully, and considering what kind of information is necessary to make a decision rather than engaging in the pointlessness of pure research. In the end, the point of doing analysis at all is to inform decisions and lead to action, not to create uncertainty and demoralize with doubt.