Looking to make a breakthrough in marketing sciences?
The Sawtooth Software Conference brings together research and analytics professionals to deepen their knowledge and get hands-on training on the latest conjoint analysis methodologies. As a longtime Sawtooth Software partner, a team of SKIMmers will once again share our choice modeling expertise with industry peers in Orlando.
Our methods and analytics experts from across the U.S. and Europe will present various technical papers, workshops and tutorials on a range of choice modeling topics. Be sure to attend of our sessions or chat with us to learn about exciting career opportunities.
Deepen your analytics knowledge: Join SKIM expert presentations
Practical Conjoint Solutions
Sometimes standards are not enough to answer the research questions at hand, and one must deviate from the roads most travelled. We would like to get you on board for this 4-hour tutorial on advanced applications. Here we will focus on several challenging but very interesting extensions of choice modeling, including:
- Custom statistical design techniques (designs using Lighthouse Studio and Excel; e.g. alternative-specific, frequency imbalance, pricing rules) and implications of these customizations
- Showing different coding methods
- Duct-tape solutions for the red-bus-blue-bus problem
- And some other practical solutions
Co-Clustering with Covariates: Maximum insight, minimal effort and with covariates even more actionable
Co-clustering is the simultaneous clustering of rows and columns of data. For example, when used for rating questions, or MaxDiff scores, it provides excellent insight into the underlying heterogeneity of this data: which respondents are similar and which items are similar. Adding covariates in the process – both for respondents and for the variables! – adds another layer of insights. This paper will show different ways of visualising co-clustered data and explain the heuristics on how to do co-clustering with covariates.
Innovation via pragmatic & accessible insights for PayPal
PayPal and SKIM recently developed a pragmatic approach to isolate behavioral drivers of consumer shopping journey at the digital checkout stage among online merchants. We innovated on survey design experience by simulating checkout experience and combining conjoint and non-conjoint analytics. Further, we translated answers into actionable business recommendations that were accessible to non-technical audience as well. This innovation elevated Checkout workstream as a strategic priority at PayPal and adoption of these recommendations to the product, marketing and sales roadmap.
Are we overfitting our models with how we are estimating our price parameters?
Complicated pricing studies can end up with a lot of part-worth levels of price. From recent conferences, a piecewise function that uses from 2 to 6 breakpoints (aside from the endpoints) is recommended and 12-20 breakpoints have been seen as potentially useful. We would want to investigate whether a dozen or more breakpoints is an overfit and we are better off with a more parsimonious approach. This investigation will also test if it would be best to have multiple simpler price effects. RLH, Holdout Hit Rate, and % of effects that don’t need to be constrained will be used as testing criteria.
Thompson sampling in multi-attribute CBC
Often, we run into the challenge that we can only test a certain number of levels per attribute to make sure the estimation remains robust. However, there are scenarios in CBC studies where we want to test many more levels and we just want to find out what the best levels and combinations of levels are. By employing Thompson Sampling, we select preferred products to oversample for each new respondent.
Volumetric Conjoint and the role of assortment size
Predicting the volume of a new product to be launched is a task that many researchers have done at least once. There are a variety of methodologies to accomplish the task, which are grouped in three families: real-life tests, benchmarking, and replication of market environments. In this paper, we show the pros and cons of each of them and explain an approach that oversteps some of the limitations of these methods by using them in combination.
Vector Autoregressive Modeling of Longitudinal sales data using simulated Populations Informed by Conjoint experiments
Vector Autoregression (VAR) is often used for modeling sales of P items over time. VAR forecasts sales at time tnew using previous sales at tlag, coupled with attributes explaining those changes like price, distribution, and trend. We also model correlated sourcing among P items using a simulated population ~ Multivariate normal(α_lag, ∑). We show how to use conjoint experiments to inform ∑ and how that significantly improves predictions versus modeling ∑ from sales data alone.
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About Sawtooth Software
Sawtooth Software provides advanced analytics and insights to help organizations understand what’s important to their customers and to predict what they will buy or choose. They do this through their solutions platform, consulting, and educational services.
The Sawtooth platform handles traditional survey questions, but they are best known for an integrated predictive analytics solution called Conjoint Analysis, or Choice Analysis.