A Simple Introduction to Conjoint Analysis
Each day we experience many “trade-off” scenarios such as “Where should I go to eat?”, “What flight should I choose for my trip?”, and the list goes on and on. Making these trade-offs are very natural and are an everyday aspect of the consumer lifestyle. This is one of the principals that make Conjoint Analysis so valuable. What is Conjoint Analysis? In short, it’s one of the most powerful research techniques. Today we’ll take a deep look at what Conjoint Analysis is, how it works, and share a case study of how Conjoint Analysis was used to optimize pricing strategies.
What is Conjoint Analysis?
Conjoint analysis, aka Trade-off analysis, is a popular research method for predicting how people make complex choices. Conjoint asks people to make tradeoffs just like they do in their daily lives. You can then figure out what elements are driving peoples’ decisions by observing their choices.
One of the leading providers of Conjoint software, which we use extensively, is Sawtooth Software. They recently released a video explaining how Conjoint Analysis works. The video is worth your time. Trust me.
Conjoint Analysis Case Study – Optimizing Pricing Strategy
There are numerous applications of Conjoint Analysis but today we will focus on optimizing pricing strategy. We recently worked with a municipal owned utility, SMUD (Sacramento Municipal Utility District), on getting more people to enroll in their Green programs. Before we get to how SMUD used Conjoint Analysis to optimize pricing, it’s important to share some of the background steps to help frame the case study.
First we tried to better understand how people felt about Green programs and their deeper motivations/needs using an online brainstorming tool. We took what we learned from the online brainstorming session and overlaid it with the details about the SMUD Green program.
This is where Conjoint Analysis came into play….
Using Sawtooth’s Conjoint software we tested 7 attributes and 29 messages/levels to identify the optimal Green program. Think of attributes as “buckets” or individual parts of an offer. For example, “price” is an attribute. Underneath the attribute of “price” are multiple options or levels. If you’re trying to identify the optimal price point, you would include various prices in your “levels” ($1, $5, $20, $50, etc…)
The results showed the attributes that had the greatest impact on participation in SMUD’s Green program. By analyzing the data we were able to identify the different price points that would maximize both participation and revenue.
Using Conjoint Analysis, SMUD accomplished 3 big things:
- Identified the areas their current Green program was lacking
- Identified the attributes of their Green program that have the greatest impact on participation
- Identified the optimal mix of attributes that drive the highest rate of participation and revenue.