Professional advice for entrepreneurs and business managers in the context of Europe's recovery from the financial crises. Marketing notes, stories and videos.

Price Elasticity

The concept of elasticity is used to measure the degree of market response to price changes (price sensitivity).

  • Elasticity is a measure of demand responsiveness.
  • Can be computed for a range of prices and quantities.

Definition of elasticity:

Percentage change in sales volume divided by the percentage change in sales. [Read more…]

Pricing Strategy

Pricing is extremely impactful. For many products and for many consumers price is a key feature. Price changes have immediate impact and prices are in many cases very visible. Competitors can react quickly to price changes. Price is the only element in marketing mix that has a direct impact on revenues.

R = P x Q

How to increase price without being noticed?

  • Increasing the size of packaging while decreasing the size of its contents.

[Read more…]

Conjoint Analysis

Conjoint Analysis is an analytic technique used in marketing that helps managers to determine the relative importance consumers attach to salient product attributes or the utilities the consumers attach to the levels of product or service attributes.  Conjoint procedures attempt to assign values to the levels of each attribute so that the resulting utilities attached to the stimuli match as closely as possible. Like MDS, Conjoint Analysis relies on respondents subjective evaluations. [Read more…]

Marketing Analytics – why decisions based on analytic analysis are better?

People overestimate the probabilities of events and often become overconfident. The quality of the information available to them from external sources, such as media, real life experiences as they occur, or heuristic may give a false impression of the events. The problem with overconfidence, especially in the business environment, is that it often leads to the boiled frog syndrome. Bias is a cognitive phenomenon and has its roots in psychology (availability heuristic). Analytical methods for data processing and decision making are used to remove bias from the process of information assessment. [Read more…]

Facebook 2020: $100 Billion Superstar or Failure

This essay investigates the likelihood of Facebook keeping its superstar status by 2020. Using a Political, Economic, Social, Technological and Legal (PESTL) analytical structure supported by a brief examination of the valuation methods used by Facebook for the Initial Public Offering (IPO) as well as a Five Forces assessment the question will be broken fully into elemental parts to reach a conclusion. The key issues discussed in this report are the effects of the highly speculative valuation of the company before and during its IPO regarding its future; the political forces responsible for on-line privacy, data exchange controls and breach of confidence laws; barriers to entry to China and the world’s general current economic situation. Lastly the motives and cultural differences of Facebook users, opportunities shaped by the 4G mobile technology, the ability of users to influence Facebook strategic management, the entry of a new market player, the bargaining power of advertisers, threat of substitute and the rivalry between Facebook and Google are also discussed. [Read more…]

Regression Analysis – predicting the future

Let’s start with the definition of regression: Regression is a prediction equation that relates the dependent (response) variable (Y) to one or more independent (predictor) variables (X1, X2).

In marketing, the regression analysis is used to predict how the relationship between two variables, such as advertising and sales, can develop over time. Business managers can draw the regression line with data (cases) derived from historical sales data available to them.

The purpose of regression analysis is to describe, predict and control the relationship between at least two variables. The basic principle is to minimise the distance between the actual data and the perditions of the regression line. Regression analysis is used for variations in market share, sales and brand preference and this is normally done using variables such as advertising, price, distribution and quality.

  •  Regression analysis is used:
  •  To predict the values of the dependent variable
  • To determine the independent variables
  • To explain significant variation in the dependent variable and whether a relationship between variables exists
  • To measure strength of the relationship
  • To determine structure or form of the relationship


An online t-shirt sales company invested in Google AdWords advertising:

  • £1000 in January
  • £1000 in February
  • £1000 in March

Their sales grew steadily in this period:

  • £5000 in January
  • £5500 in February
  • £6000 in March

The managers can predict by looking at the regression line that with current level of advertising spent (£1000 per month) the sales in April will be £6500. This obviously would be the case if all other things remain equal but in reality they never do. The sales managers should use the prediction data from the regression analysis as an additional managerial tool but should not exclusively rely on it. The level of sales can be affected by elements other than the level of advertising. This includes, but is not limited to, factors such as weather conditions or the central bank’s increase or decrease of base interest rates. Regression analysis is concerned with the nature and degree of association between variables but does not assume causality (does not explain why there is relationship between variables). Other good examples of how regression analysis can be used to test marketing relevant hypothesis are: Can variation in demand be explained in terms of variation in fuel prices? Are consumers’ perceptions of quality determined by their perceptions in price? For a simple tutorial about the regression analysis for beginners please view the video below:

Regression analysis consists of number of statistics used to determine its accuracy and usefulness for certain purpose. Some of those statistics and methods are clearly explained by the statistics experts in the videos listed below. It is recommended that you read the text first and then watch the corresponding video:

  • Product Moment Correlation (r) is a statistic summarising the strength of association between two metric variables (for example: X and Y). It is used to determine whether a linear (straight line) relationship exists between X and Y. It indicates the degree to which the variation in one variable (X) is related to the variation in another variable (Y) (also known as Pearson or Simple Correlation, Bivariate Correlation or Correlation Coefficient). Covariance is a systematic relationship between two variables in which a change in one implies a corresponding change in the other (COV x Y).  The correlation coefficient between two variables will be the same regardless of their units of measurement. If r = 0.93 (a value close to 1.0) it means that one variable is strongly associated with the other. It does not matter which variable is considered dependent and which independent (X with Y) or (Y with X). The ‘r’ is designed to measure the strength of linear relationship, thus r= 0 does not suggest that there is no relationship between X and Y as there could be a non-linear relationship between the two.

  • Residuals – the difference between the observed value of Y and the value predicted by the regression equation.

  • Partial Correlation Coefficient – measures the association between the variables after adjusting for the effect of one or more additional variables. For example: how strongly related are sales to advertising expenditure when the effect of price is controlled?
  • Part Correlation Coefficient – is a measure of the correlation between Y and X when the linear effects of the other independent variables have been removed from X but not from Y.
  • Non-metric Correlation – a correlation measure for two non-metric variables that rely on rankings to compute the correlations.
  • Scatter Diagram – is a plot of values of two variables for all the cases of observation. The dependent variable on the vertical axis and the independent variable on the horizontal axis. If one variable increases so does the other – there is a linear relationship between X and Y. Scattergram
Image sourced from Flat World Knowledge
  • Least Squares Procedure – is a technique for fitting straight line to a scattergram by minimising the vertical distances of all the points from the line. The best fitting line is a regression line. The vertical distance from the point to the line is the error (e). read more
  • Significance Testing – significance of the linear relationship between X and Y may be tested by examining two hypothesis:
    • There is no linear relationship between X and Y
    • There is a relationship (positive or negative) between X and Y

The strength and significance of association is measured by the coefficient of determination r-square (r2). Significance Testing involves testing the significance of the overall regression equation as well as specific partial regression coefficients.

Multiple Regression

Multiple Regression is extremely relevant to business analysis. Itinvolves single dependent variable such as sales and two or more independent variables such as employee remuneration, number of staff, level of advertising, online marketing spend. For example: can variation in sales be explained in terms of variation in advertising expenditures, prices and level of distribution? It is possible to consider additional independent variables to answer the question raised. Statistics relevant to multiple regression are: adjusted r-square (r2) – coefficient of multiple determination is adjusted for the number of independent variables and the sample size to account for diminishing returns. To get more insight into multiple regression and understand how other statistics such as significance testing influence the usefulness of the analysis please watch the video below:

Multicollinearity – a state of high inter-correlation among independent variables. When multi-collinearity is present, special care is required in assessing the importance of independent variables. Here, once again, it is recommended that you watch the video below.


  • Tuk, M., 2012. Regression Analysis, Marketing Analytics. Imperial College London, unpublished.
  • Malhotra, K. N. and Birks, F.D., 2000. Marketing Research. An applied approach. European Edition. London: Pearson

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Cluster Analysis – a market segmentation procedure

Cluster Analysis in marketing is a process of grouping consumers of similar psychometric, demographic, geographic or socio-economic attributes into groups called clusters. The primary objective of cluster analysis is to classify objects into homogenous groups based on the set of variables considered. Marketers can use cluster analysis to segment the market and more effectively target the selected segments with relevant to them marketing campaigns. Cluster analysis examines an entire set of interdependent relationships and makes no distinction between dependent and independent variables. Independent relationships between the whole set of variables are examined. Cluster analysis is mainly used for:

  • Market segmentation
  • Examination of buying behaviour on a collective rather than individual basis.
  • Brands in the same cluster usually compete more fiercely with each other. A brand can use cluster analysis for strategic positioning and to identify threats and opportunities on the market.
  • With a set of homogeneous geographic clusters marketers can test their strategy on one cluster and if the strategy proves successful it can be expanded to all other clusters of similar characteristics.
  • Cluster analysis can be used as a general data reduction tool to manage individual observations.

Simple example:

When optimising Google AdWords for our international shipping business we used cluster analysis as a campaign targeting tool. We wanted to reduce the cost of our Google advertising by putting all the large cities in the UK into two homogeneous clusters; the more and the less profitable one. The variables we used for the clustering procedure are:

  • Number of paid clicks
  • Number of conversions per click (CPC)

We identified from the cluster analysis that there are profitable and non-profitable groups of UK cities for our Google AdWords advertising. Birmingham, Glasgow and Manchester receive high number of clicks but relatively low number of conversions. On the other hand; Liverpool, Edinburgh, Sheffield and London receive higher number of conversions relative to the number of clicks. With this simple clustering procedure we know which geographic areas in the UK should be excluded from our AdWords campaign. The budget consumed by the unprofitable cities can now be allocated to the more profitable ones.

Cluster Analysis


Nowadays cluster analysis is done using SPSS or MS Excel software but in order to understand this procedure properly one should know the mathematical logic behind it. For a simple demonstration of how cluster analysis can be done manually please watch this video:

Statistics associated with cluster analysis:

  • Agglomeration schedule gives information on cases being combined at each stage of clustering.
  • Cluster centroid is the mean value of all the variables or all the cases in particular cluster.
  • Cluster membership indicates the cluster to which each case belongs.
  • Dendrogram is a tree graph for displaying clustering results.
  • Distances between cluster centres indicate how separate individual pairs of clusters are.
  •  The process of conducting Cluster Analysis

Formulating the problem is the most important part of the clustering procedure. Selecting one irrelevant variable may distort the clustering solution. Once you define the problem and select the right set of variables you now must select a distance between clusters or similarity measure. The most commonly used measure of similarity is the Euclidean Distance or its square. There are other methods also available and these are used for comparing the results and checking their validity.

Clustering procedure can be hierarchical where clustering is characterised by the development of a hierarchy or treelike structure. Agglomerative clustering starts with each object in a separate cluster and clusters are formed by grouping objects into bigger and bigger clusters. Divisive clustering on the other hand starts with all the objects grouped into a single cluster and clusters are then divided or split until each object is in a separate cluster. K-means clustering is a non-hierarchical clustering and is a procedure which first assigns or determines a cluster centre and then groups all the objects within a pre-specified threshold value together working out from the centre. Deciding on the number of clusters is usually based on theoretical or practical considerations. In hierarchical clustering the distances at which clusters are combined can be used as criteria. In non-hierarchical clustering the ratio of the total within group variance to between group variance can be plotted against the number of clusters.

Interpreting and profiling the clusters involves examining the cluster centroids. The centroids represent the mean values of the objects contained in the cluster on each of the variables. The centroids can be assigned with a name or label. To assess reliability and validity one has to perform cluster analysis on the same data using different distance measures and compare the results to determine stability of solutions. Splitting the data randomly into halves and performing clustering separately on each half and comparing cluster centroids across two sub-samples is one of my favourite ways. In hierarchical clustering the solution may depend on the order of cases in the dataset. To achieve the best results make multiple runs using different order of cases until the solution stabilises.


  • Tuk, M., 2012. Cluster Analysis, Marketing Analytics. Imperial College London, unpublished.
  • Malhotra, K. N. and Birks, F.D., 2000. Marketing Research. An applied approach. European Edition. London: Pearson

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Choice Illusion – getting the prices right

How do the number of purchase options affect our buying process? According to the economic theory, more choice, due to preference matching, always has a positive effect on a buyer. Psychologically however, there is a choice conflict and choice overload. Too much is demotivating. The experiment in which I participated at Imperial College London provided evidence to support the idea that rather than facilitating our decision making process, having more products to chose from actively leads to choice fatigue.

One of the most fundamental techniques for display and pricing is known as the Attraction Effect. Products subject to sale become more or less attractive in the context of other superior or inferior products. Making a choice between two similar objects is hard.

Two cameras to choose from

The choice is easier to make when there is another product added to the choice list. The middle option is usually the winner.

Three cameras to choose from

By adding an inferior product to the list of choices available to the buyer we can facilitate the choice making process. The consumer perceives the products originally listed on the choice list as superior and finds choosing the right product easier. Junk options make the alternatives look good.


Compromise Effect – a set of three choices can be stretched upwards if the entire set is compared to another similar set with a salient end option.

Compromise Effect


 Formula for Choice Illusion:


Downward Stretch

1st Set:  A                    B                    C

                              1st choice      2nd choice

2nd Set:                       E                     F                   G

                                                                                Upward Stretch

  • Maciejovsky, B., 2012. Choice Illusion, Consumer Behaviour. Imperial College London, unpublished.

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Ebbinghaus Illusion: controlling the perceived value

The Ebbinghaus Illusion is frequently used in TV and printed media advertising. DFS, a UK furniture store, was told by the Advertising Standards Authority (ASA) to withdraw their advertisement from TV broadcast because of the false impression it gave to the viewers about the true size of a sofa. DFS, using green screen technology, manipulated the size of actors superimposed into the advertising video which made the sofas placed next to the actors look larger than they really were (BBC 2008). Interventions by ASA on the grounds of an illegitimate use of Ebbinghaus Illusion in advertising are rare. Generally, advertisers will get away with similarly blatant manipulation of product size for marketing purposes.

Which orange dot is larger?


The use of the Ebbinghaus Illusion is not limited to marketing only. The value of money is subject to it too. The perceived value of money is determined by the comparison between the subject (money) and memory (anchor) or context in which a valuation takes place. Marketers can change the perceived value of money by influencing the context in which the valuation takes place. For example: A pair of jeans priced at £75 may seem expensive if sold in suburban areas of London. The same pair of jeans might seem cheap if found in shops in London’s Kensington.

Intertemporal Choice

Intertemporal pricing is the right pricing strategy for goods and services of high monetary value. This is due to people’s diminished ability to judge the true cost to them of purchase if payments for the purchase are spread across longer period of time. By multiplying the number of payments for the product over time, companies usually benefit from people’s reduced aptitude to compare the price they pay with alternatives. The notion of Intertemporal Choice has its roots in the Affect and Cognition branch of Psychology. Human minds are basically structured on two systems:

  • Basic – spontaneous and rapid (animals and people)
  • Rational – cognitive (only people)

So how does it work?

If we are already engaged in a cognitive process (thinking) at one moment in time, our ability to engage another cognitive process dedicated to another task is diminished. In a circumstance where we have to make two important decisions at once, our mind, in order to process those two decisions, will refer one to the basic system (spontaneous and rapid) and as a result the resulting decision is likely to be less rational. Marketers take advantage of Intertemporal Choice in involvement, affective and informational advertising.


  • Maciejovsky, B., 2012. Ebbinghaus Illusion, Consumer Behaviour. Imperial College London, unpublished.
  • BBC NEWS | Business | Advert banned for inflated sofas. 2008. BBC NEWS | Business | Advert banned for inflated sofas. [ONLINE] Available at: [Accessed 12 November 2012].

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Pricing – Insufficient Adjustment Anchoring

This article concerns the underlying psychological factors business managers should consider when thinking about effective pricing. The definition of price this article will be working with is the following: Price of something is the total cost of adopting the product which includes purchase price, cost of switching, inconvenience and disposal. Each market segment can be characterised by dissimilar price sensitivity factors including: ratio between total and disposable income, level of involvement in purchase, ease of comparison, switching cost and perception of the market price of product (anchoring and heuristics).

The decision to buy, as far as the price of the product or service is concerned, depends on valuation. It is positive if the valuation a consumer makes of the good is greater than the actual price, and vice versa. Valuation is a cognitive process and consists of two underlying factors: Anchor and Insufficient Adjustment. Anchor is a piece of information (a reference price of the same or similar product) stored in person’s memory which is relevant to the item subject to valuation (a product). The process of valuation continues until adjustments fall within an implicit range of plausible values and this is an Insufficient Adjustment. (Epley and Gilovich, 2012).

Reference Pricing is a process of displaying the price of a product together with price of the same product sold elsewhere. A reference price is a kind of ‘artificial anchor’ where the seller facilitates the process of insufficient adjustment by subjecting the buyer to the anchor (in this case at the POS – point of sale). The routines in the process of buying are structured according to Heuristic. The human mind develops rules (software of the mind) which govern the processes of information processing when making decisions to buy. Marketers can blur proper functioning of Heuristics by using arbitrary and random numbers to distort valuations.

Most basic pricing methods are:

• Demand pricing – prices are altered or lowered according to the supply and demand for the product or service. The higher the demand the higher the price.

• Skimming – is the practice of starting with high price for the product than reducing it progressively as sales level off.

• Timing – is a procedure in pricing which helps managers predict repeat purchases. Marketers, mainly using CRM systems, can remind their customers about subscriptions. Here, the most obvious catch is if payments are disassociated from benefits the consumers gradually forget about them.


  • Maciejovsky, B., 2012. Anchor and Insufficient Adjustment, Consumer Behaviour. Imperial College London, unpublished.
  • Epley, N. and Gilovich, T., 2012. The Anchoring-and-Adjustment Heuristic. Why the Adjustments Are Insufficient. [ONLINE] Available at: [Accessed 10 November 2012].

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How concerned are you about your reputation online?

We asked Londoners how concerned are they about what is being said about them online and how does this affect their good name?  The interviews took place in London in May 2012 as a part of the consumer research project for the Online Reputation Management.

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