Previously, we gave you a bit of a teaser in our post, Using Anchoring to Make MaxDiff Stronger. We wanted to tell you a little more about what led to the “how we got three buckets for 80 attributes” problem, and we didn’t want to wait until the SKIM conference. Here’s the background:
Our client, a multi-national hotel chain, had a well-structured loyalty program running in more than 50 countries. One of the key ongoing challenges they faced was attracting and retaining customers. This raised a question: What did their customers really want?
- Top-notch service at lower prices
- Enhanced stay experience
- Exclusive privileges as a loyalty program member
- Freebies (of course)
It costs a lot more to attract a new customer than to retain an existing one. With that in mind, we partnered with our client to revamp their loyalty program.
When You Have Lots of Options to Rank…
The study’s first goal was to identify which services meet customer wants. Next, we would create priority groups of services and tie these priority groups into a loyalty program. What this solution really needed was a technique to organize those 80 services into a relative order – like a good-better-best scheme.
Our initial idea was to do a basic rating scale. But the sheer number of attributes involved deterred us. For a customer, ranking or rating 80 services could easily lead to bias or fatigue. And we wanted to avoid that.
MaxDiff was clearly the best choice in this situation. It would give us an order of relative importance for different services. Furthermore, adding an anchor would differentiate the important from the less-important.
…Use Anchored MaxDiff
However, we wanted three groups, not just the standard important/not important. Therefore, our solution needed to be capable of:
- Outlining three groups (by level of importance) from the MaxDiff results.
- Creating multiple anchor points.
- Dealing with a large number of attributes.
So that’s what we did, using anchored MaxDiff.
Why Multiple-Anchor MaxDiff Is the Way Forward
More researchers are adopting MaxDiff as a technique. There’s also a trend towards an increased use of anchoring. It is human tendency to want more; every researcher wants more from study output. Multiple anchor points can give them this ‘more output’; it is always better to have more information about consumer wants. In our next blog, we’ll talk about creating multiple anchors using MaxDiff.