Profit impact of marketing strategy
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The profit impact of market strategy (PIMS) database "yields solid evidence in support of both common sense and counter-intuitive principles for gaining and sustaining competitive advantage": Tom Peters and Nancy Austin. It was developed with the intention of providing empirical evidence of which business strategies lead to success, across different industries. Data from the study is used to craft strategies in strategic management and marketing strategy. The study identified several strategic variables that typically influence profitability. Some of the most important strategic variables studied were market share, product quality, investment intensity, and service quality, (all of which were found to be highly correlated with profitability).
According to Lancaster, Massingham and Ashford (Essentials of Marketing, 4th edition, McGraw Hill), PIMS seeks to address three basic questions:
- What is the typical profit rate for each type of business?
- Given current strategies in a company, what are the future operating results likely to be?
- What strategies are likely to help improve future operating results?
Dibb, Simkin, Pride and Ferrell (Marketing Concepts and Strategies, 4th European edition, Houghton Mifflin) cite six principal areas of information that PIMS holds on each business:
- characteristics of the business environment
- competitive position of the business
- structure of the production process
- how the budget is allocated
- strategic movement
- operating results.
Brief history of PIMS
The PIMS project was started by Sidney Schoeffler working at General Electric in the 1960s, managed by the Marketing Science Institute in the early 1970s, and has been administered by the American Strategic Planning Institute since 1975.
It was initiated by senior managers at GE who wanted to know why some of their business units were more profitable than the others. With the help of Sidney Schoeffler they set up a research project in which each of their strategic business units reported their performance on dozens of variables. This was then expanded to outside companies in the early 1970s.
The initial survey, between 1970 and 1983, involved 2,600 strategic business units (SBU), from 200 companies. Today 12,500 observations exist for 4162 SBU's; PIMS is managed by PIMS Associates in London. Each SBU give information on the market within which they operated, the products they had brought to market and the efficacy of the strategies they had implemented.
The PIMS project analysed the data they had gathered to identify the options, problems, resources and opportunities faced by each SBU. Based on the spread of each business across different industries, it was hoped that the data could be drawn upon to provide other business, in the same industry, with empirical evidence of which strategies lead to increased profitability. The database continues to be updated and drawn upon by academics and companies today.
Conclusions drawn by PIMS
The original PIMS data survey led the PIMS project to identify 37 variables which account for the majority of business success. Two leading marketing texts differ slightly on which variables are the most important, with Dibb, Simkin, Pride and Ferrell (p676) identifying:
- a strong market position
- high quality of product
- lower costs
- lower requirement for capital investment
and Lanacaster, Massingham and Ashford (p535) citing:
- market share
- investment intensity
- market growth
- life cycle stage
- marketing expense to sales ratio.
While many of these seem obvious, PIMS has the advantage of providing empirical data that define quantitative relationships and back what some may consider to be common-sense.
Participation in the PIMS study: cost and benefits
PIMS evaluated businesses' market position and suggest possible strategies, based on the data gathered from participating companies. Businesses wishing to use the service provide detailed information, including details of their:
- competitors and market
- balance sheet
- assumptions about future sales.
In return, PIMS provides four reports, described by Lancaster, Massingham and Ashford as:
1. A 'Par' report - showing the ROI and cash flows that are 'normal' for this type of business, given its market, competition, technology, and cost structure.
2. A 'Strategy Analysis' report, which computes the predicted consequences of each of several alternative strategic actions, judged by information in similar businesses making similar moves, from a similar starting-point and in a similar business environment.
3. A 'Report on Look-Alikes' (ROLA), which aimed at predicting the best combination of strategies for that particular company, by analyzing strategically similar business more closely.
4. An 'Optimum Strategy' report, which is aimed at predicting the best combination of strategies for that particular company, again based on the experiences of other businesses in 'similar' circumstances.
A critique of PIMS
Clearly, it could be argued that a database operating on information gathered in the period 1970 - 1983 is outdated. However data continues to be collected from participating companies and PIMS argues that it provides a unique source of time-series data, the conclusions from which have proven to be very stable over time.
It has also been suggested that PIMS is too heavily biased towards traditional, metal-bashing industries, such as car manufacturing; perhaps not surprising, considering the era in which the majority of the surveys were carried out. In reality, as of 2006, the 3,800+ businesses contained within the database includes data from the consumer, industrial and service sectors.
It is also heavily weighted towards large companies, at the expense of small entrepreneurial firms. This resulted from the data collection method used. Generally only larger firms are prepared to pay the consulting fee, provide the survey data, and in return have access to the database in which they can compare their business with other large businesses or SBUs. Mintzberg (1998) claims that because the database is dominated by large established firms, it is more suitable as a technique for assessing the state of "being there rather than getting there". (page 99) This criticism is very important because if one is trying to get "average" results across industries to give us the "laws of the marketplace", a dubious enterprise as it is, the sampling strategy is important if one wants to obtain results that are representative.
The most important criticism leveled at PIMS is the fact that causation implies correlation but correlation does not imply causation. One of the most important "findings" of the PIMS program was to find a statistically significant relationship between profitability and market share (see Buzzell and Gale (1987)). The empirical work conducted by PIMS suggested that high market share yielded high profitability, but this correlation cannot be considered a "true" causal relationship because of the fact that correlation does not imply causation. In the multivariate correlation analysis, high market share was associated with high profits, but high profits could have been associated with high market share, or a third factor common to both could have caused the correlation. Many analysts believe that it is possible to use a statistical causality test to determine causation, but if the whole problem is that correlation is insufficient to determine causation in the first place, then how can using another correlation, which is what is used in the tests, determine causation.
Another important criticism of PIMS is that it does not take into account heterogeneity in the data set. The presumption of PIMS analysis is that the same "laws of the marketplace" apply to all industries. However, the statistical assumptions employed in the econometric analysis make the assumption that all cross-sectional observations come from one statistical distribution that is the same for all cross-sectional observations. This tends to be the Achilles heel of virtually all cross-sectional analyses. If this homogeneous assumption is false, then cross-sectional observations are being drawn from different populations. While one can use estimation techniques such as fixed-effects to control for different population means, co-variances can also differ across populations (meaning behavior differs across populations) and the only way one can control for this aspect is to run regressions on each population separately. This means that the "laws of the marketplace" differ across populations, directly contradicting one of the main presumptions of using the PIMS data base for analysis.
Tellis and Golder (1996) claim that PIMS defines markets too narrowly. Respondents described their market very narrowly to give the appearance of high market share. They believe that this self reporting bias makes the conclusions suspect. They are also concerned that no defunct companies were included, leading to "survivor bias".
- Buzzell, R. and Gale, B. (1987) The PIMS Principles: Linking Strategy to Performance, Free Press, New York, 1987.
- Mintzberg, H. Ahlstrand, B. and Lampel, J. (1998) Strategy Safari: A guided tour through the wilds of strategic management, The Free Press, New York, 1998.
- Schoeffler, S. Buzzell, R. and Heany, D. (1974) Impact of Strategic Planning on Profit Performance, Harvard Business Review, March–April, 1974.
- Tellis, G. and Golder, P. (1996) First to Market, First to Fail: The Real causes of enduring market leadership, Sloan Management Review, vol. 37, no. 2, 1996.
- Ceccarelli, P. and Roberts, K. (2002) I nuovi principi PIMS: la gestione dell'impatto sul profitto, Sperling & Kupfer, Milano, 2002.