Dig in deeper with these half-day tutorial sessions led by experienced and plain-spoken practitioners. You’re sure to come away with deeper understanding on how to execute your upcoming quant research studies. The longer format also leads to greater opportunities for Q&A and networking.
Determining the optimal product or portfolio pricing has always been a critical strategic decision and in the light of the pricing dynamics we are observing these days it is becoming even more important to have data driven evidence to make these decisions. And while theory of conjoint analysis is very important and much discussed at the A&I Summit, we want to take the practical approach with this workshop and run you through an entire pricing (shelf) CBC study. We will first describe different pricing research approaches touching upon pros and cons of methods such as Van Westendorp, Gabor Granger, Monadic experiments and conjoint. Then we will take you through both set up and analysis steps of a shelf conjoint including building a customized statistical design, data re-coding, counts, Logit and HB. During each of these steps we will show tips and tricks to get the most out of your data. How do you model the data, interpret the result and what are the key things to look out for? All of this will be discussed based on a real data set using Lighthouse Software.
Driver analysis and segmentation are two things marketing research analysts do about as frequently as they run choice experiments. Worse ways of doing driver analysis are easy to identify and also easy to avoid by using demonstrably better methods. The first part of this tutorial covers how and why the poor methods should be avoided and why and how to use better methods instead. Hint: if you’re running correlation or regression, you really need better methods for driver analysis. Less happily, a constellation of mutually reinforcing problems can thwart the segmentation analyst. The longer second part of the tutorial focuses on understanding these several problems and on how to try to avoid them through (a) well-planned research objectives, (b) understanding the nature of your data with diagnostics, and (c) ameliorating as many of the problems as possible using the imperfect tools available to us, namely questionnaire construction, variable selection and clustering algorithms.
