Somos colores, insights para crecer

Se acabó la era de ser colores primarios puros de lunes a domingo las 24 horas del día. ¿Conducimos igual para ir a trabajar que para relajarnos el fin de semana? ¿Cocinamos igual para cenar un miércoles que un sábado? ¿Nos vestimos igual un lunes que un viernes? ¿Elegimos el mismo restaurante para ir con nuestros hijos que con los amigos?

El azul es conocido por ser uno de los tres colores primarios en la rueda de colores. Como color primario, el azul no puede hacerse mezclando otros colores, pero puede combinarse con otros colores primarios y secundarios para hacer una variedad de diferentes tonos y matices. Si combinamos el rojo y azul se produce el color morado, si mezclamos azul y amarillo se producen el color verde, etc.

Ya no somos azules, ya no somos un color primario,  ahora somos azul turquesa, azul pálido, azul acero, azul nocturno, azul egipcio y cientos de variedades más durante la semana e incluso a veces somos rojos, amarillos, naranjas y verdes.

Sabemos que siempre hay un color que prima por encima del resto y que nos define de forma primaria, pero que en nuestro estilo de vida, en nuestra actitud vital, en nuestra forma de comprar, en nuestra manera de relacionarnos con las marcas, en nuestro hogar, en nuestra forma de desplazarnos, en cómo entendemos la moda, en nuestra interrelación con redes sociales y la tecnología, etc… hay una paleta de colores interesante que matiza nuestro comportamiento primario.

Vivamos el día a día del consumidor para descubrir estos colores, no es una tarea sencilla ya que muchos de ellos surgen de combinaciones casi imposibles.  Es fundamental interactuar, dialogar, vivir y sentir  el comportamiento del consumidor.

Obtener un insight no es una tarea sencilla, no hablamos de mapas motivacionales que son relativamente sencillos y nos dan la visión estática de una categoría o mercado.

La solución pasa por descubrir las palancas finales que mueven un mercado, aquello tan profundo que es capaz de cambiar el hábito de un consumidor, construyendo territorios de insights para crecer, soluciones latentes no trabajadas por la marca y que componen el efecto dinámico a futuro del mercado.

Estos insights para crecer son la base futura de segmentos de población cuya paleta de colores es difusa y líquida, pero cuyas soluciones son tan potentes, que nos debe abrir un nuevo paradigma de inteligencia de mercado, una nueva visión del negocio de las marcas y nuevo marco de interacción con el consumidor.

Jordi Crespo

Knowing more about the Maximum Difference Scaling (MAX DIFF)

Maximum Difference Scaling, also known as Best-Worst Scaling, is an approach for understanding the preference and importance scores allowing researchers to analyze a higher number of items generating discriminating results as respondents are asked to choose the ‘Best’ and ‘Worst’ option which simulates real-world behavior. Max diff is a powerful tool used by Hamilton to further understand and identify which attributes in a product /service / offer are most important.

Traditionally, to determine the importance of the items for the interviewee, the attributes have been asked through Rating Scales and/ or Ranking Scales. Listed below are the main characteristics of each one:

Rating Scales: asking the respondents to choose one response category from several arranged in hierarchical order. In example: how much do you agree or how satisfied are you, etc. The main benefits of the rating scales are that are easy to ask, provide data that can be analyzed statistically and are stable on repeated measures. By contrast, the main problems with rating are that the results may not be discriminating because some respondents rate everything as important, the scale is arbitrary and doesn’t tell the strength of importance and, also, rating scales cannot handle a long list of items and depending on the country the rating scales used are different.

Example of Rating Scale:

Ranking Scales: asking the respondents to rank their views on a list of related items, comparing different objects to one another. Through the use of these scales, interviewees can establish what matters and what doesn’t matter. The main benefits of the ranking scales are that each element receives a unique ranking because respondents cannot assign the same value to each item, also, the question technique forces discrimination between choices, which provides more statistical power. Otherwise, the main contras with ranking scales are that respondents are good at picking the extremes but their preferences for any item in between might be fuzzy and inaccurate, this technique only explain the order of importance but not the strength of importance and, as rating scales, cannot handle a long list of items / characteristics.

Example of Ranking Scale:

Which are the main characteristics and benefits of Max Diff Scales?

• Max Diff always generates discriminating results as respondents are asked to choose the BEST and WORST option which simulates real situations (in the real life people make choices and trade-offs no ordering or ranking, for example, on a purchase in a supermarket).
• Max Diff is a simple method for all the targets involved in the project: researchers, end user and respondents. The question is simple to understand, so respondents from children to adults with a variety of educational and cultural backgrounds can provide reliable data less monotonically. For researchers and end users is easy to use and applicable to a large variety of projects and market research situations.
• Since respondents make choices rather than expressing strength of preference using some numeric scale, there is no problems of scale use bias, so cultural differences are absent in the Max Diff scales. Comparisons between items are referenced against other attributes tested, rather than pre-defined points of a scale.
• In Max Diff scales more items can be included due to the question is simple to perform and understand providing to the analysts a preference value for each attribute reflecting its relative importance in comparison to others.

At a methodological level, the respondents see a list of items and they are asked to determine from that list what is the most important to them and what is the least important. The items are not shown all at one time. The technical teams determine how many items must be shown and how many sets of these items each person has to go through in order to move to next question.

MaxDiff it’s easy for researchers and respondents. The studies with MaxDiff scales may be conducted via CATI, CAPI and also PAPI, so the technique allows apply it through different research methodologies.

Example of Max Diff Scale:

How to analyze the Max Diff Scales?

There are three main techniques that can be used:

1. Count Analysis: the simplest alternative, tallying of the number of times each item is chosen as ‘Best or ‘Worst’ important by respondents. A simple form of summarizing MaxDiff scores combines the two measures: percent of times each attribute has been selected as BEST less the percent of times each item has been selected as WORST.
2. Logit Model: a more complex but fast alternative, using a Logit model to obtain the importance value of each attribute in percent-shared utility scale.
3. Hierarchical Bayes or Latent Class: a more advanced statistical technique that provides respondent-level utilities and can be used in simulators or segments of respondents with similar needs / preferences.

When can be used the Max Diff Scales?

The Max Diff method is similar to the Conjoint Analysis but much easier to use and is applicable to a wider type of studies and objectives like:

• Brand preferences: to identify a brand market position, relative to its competitors.
• Advertising: to identify which messages are most preferred by key targets.
• Concept and / or product testing: to determine which variety of products has the greatest potential for success.
• Customer satisfaction: to identify the key strengths and enhancement opportunities to improve quality index.
• Needs-based studies: to determine which attributes are critical vs. those consumers are willing to sacrifice.

Therefore, Max Diff is an appropriate research tool that provides richer information about respondents’ preferences and attributes importance through trade-off analysis instead traditional Ranking or Rating scales in a robust and easy application.

Jennifer Varón