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We consider a problem of marketing decisions for the choice of a product with maximum customer appeal. A widely used technique for this purpose is TURF, or Total Unduplicated Reach and Frequency, which evaluates a union of the events defined by the sample proportion of many products, or flavors of one product. However, when using TURF, it is often impossible to distinguish between subsets of different flavor combinations with practically the same level of coverage. An appropriate tool can be borrowed from cooperative game theory, namely, the Shapley Value, that permits the ordering of flavors by their strength in achieving maximum consumers' reach and provides more stable results than TURF. We describe marketing strategy reasons for using these techniques in the identification of the preferred combinations in media or product mix.

User driven innovation (UDI) is a popular term in policy and corporate circles. However, it is not clear exactly what UDI means and how such practices are used across the spectrum of companies and over the innovation life cycle. The present study compares 58 UDI showcases in Finnish companies in order to analyse the diversity of UDI practices and their evolution over time. We identify five main modes of UDI and show how the ways of using UDI develop over time in individual companies. In almost half of the examined cases, the dominant mode of UDI changes at least once, and in some cases, up to three changes in dominant mode are observed. We then proceed to identify six qualitatively different ways in which companies’ orientation to UDI evolves over time. The study has implications for innovation management and policy: It calls for greater attention to UDI diversity and particularly to the management and support of the continuity of UDI efforts.

Discrete choice modeling (DCM) is widely used in economics, social studies, and marketing research for estimating utilities and preference probabilities of multiple alternatives. Data for the model is elicited from the respondents who are presented with several sets of items characterized by various attributes, and each respondent chooses the best alternative in each set. Estimation of utilities is usually performed in a multinomial-logit (MNL) modeling and software for Hierarchical Bayesian (HB) technique is usually applied to find individual utilities by iterative estimations. This paper describes an easy and convenient empirical Bayesian way to construct priors and combine them with the likelihood on individual level data. This allows the modeler to obtain posterior estimation of MNL utilities in noniterative evaluations. Logistic modeling for the posterior frequencies is performed using the linear link of their logarithm of odds that clarifies the results of DCM modeling. The problem of overfitting is considered and the optimum balance between signal and noise in the precision of individual prediction and the smoothing of overall data is suggested. Actual market research data are used and the results are discussed.

Maximum difference (MaxDiff) is a discrete choice modeling approach widely used in marketing research for finding utilities and preference probabilities among multiple alternatives. It can be seen as an extension of the paired comparison in Thurstone and Bradley–Terry techniques for the simultaneous presenting of three, four or more items to respondents. A respondent identifies the best and the worst ones, so the remaining are deemed intermediate by preference alternatives. Estimation of individual utilities is usually performed in a hierarchical Bayesian (HB)-multinomial-logit (MNL) modeling. MNL model can be reduced to a logit model by the data composed of two specially constructed design matrices of the prevalence from the best and the worst sides. The composed data can be of a large size which makes logistic modeling less precise and very consuming in computer time and memory. This paper describes how the results for utilities and choice probabilities can be obtained from the raw data, and instead of HB methods the empirical Bayes techniques can be applied. This approach enriches MaxDiff and is useful for estimations on large data sets. The results of analytical approach are compared with HB-MNL and several other techniques.

User driven innovation (UDI) is a popular term in policy and corporate circles. However, it is not clear exactly what UDI means and how such practices are used across the spectrum of companies and over the innovation life cycle. The present study compares 58 UDI showcases in Finnish companies in order to analyse the diversity of UDI practices and their evolution over time. We identify five main modes of UDI and show how the ways of using UDI develop over time in individual companies. In almost half of the examined cases, the dominant mode of UDI changes at least once, and in some cases, up to three changes in dominant mode are observed. We then proceed to identify six qualitatively different ways in which companies’ orientation to UDI evolves over time. The study has implications for innovation management and policy: It calls for greater attention to UDI diversity and particularly to the management and support of the continuity of UDI efforts.

The paper seeks the policies and strategies that Greek libraries apply in order to discover, communicate and attract potential users. Theoretically, every non-user is a potential user of a library and it is common sense that most of the people avoid visiting libraries. Being so, libraries undertake the responsibility to appeal to their clients. The paper:

Seeks the communicative methods that libraries apply in order to increase the users,

Examines the strategy for appealing new users or new group of people,

Seeks the state of the art of Greek libraries, their communication policy,

Describes the marketing research on the above theme, and

Proposes best practices.