Advanced choice methods are where a lot of agencies hit a wall. Conjoint and MaxDiff are among the most powerful tools in research for understanding what people actually value and how they trade off, but they've historically demanded specialty software, a particular kind of design expertise, and a statistician available to run the Hierarchical Bayes estimation and sanity-check the output. That overhead meant these methods got reserved for the biggest projects, or quietly outsourced to a specialist shop.
The methodology was never the obstacle. The production apparatus around it was. When the advanced methods are built into the platform, the rigor stays and the overhead goes.
What used to make conjoint expensive
The cost of a conjoint study was rarely the thinking. It was the machinery: a separate stats package, a designer who knew how to build a balanced experimental design, someone to run the Hierarchical Bayes estimation, and someone to translate raw utilities into something a client could read. Each of those was a specialist step, often outside the firm, and each added time, cost, and a hand-off.
So conjoint became a premium, occasional methodology, not because clients didn't want the answers it gives, but because the operational lift made it impractical for routine use.
Built in, not bolted on
When Hierarchical Bayes conjoint and MaxDiff are native to the platform, the heavy lifting is handled without assembling a specialist pipeline for every study. The experimental design, the estimation, and the output are part of the system, so you can field a rigorous choice study without a stats package on the side or a statistician on call for the mechanics.
That doesn't remove the researcher from the method, it removes the production drudgery. You still decide what attributes and levels matter, how to frame the choices, and what the results mean for the client's decision. The platform runs the estimation correctly and consistently; you bring the design judgment and the interpretation. Which is the right division of labor: the machine does the math, the researcher does the thinking.
Rigor is the point, not the casualty
A research director's first question is whether "easy conjoint" means "watered-down conjoint." It doesn't. The estimation is proper Hierarchical Bayes, the same method a specialist would run, and it sits alongside the platform's other methodological machinery, two-stage IPF weighting and effective-sample-size-adjusted significance, so the numbers are defensible end to end. The goal is to make the rigorous method routine, not to substitute a simpler one for it.
If anything, building it in makes it more consistent. A method run the same correct way every time, rather than reassembled by whichever specialist is available, is easier to stand behind.
What it changes for the firm
The practical effect is that conjoint and MaxDiff move from premium specialty work to part of your standard toolkit. You can offer trade-off analysis and preference ranking on routine studies, not just flagship ones, without outsourcing the estimation or assembling a stats pipeline for every project. That's more capability you can sell, delivered faster, at a cost that no longer restricts these methods to the biggest budgets.
The expertise that makes a conjoint study worth commissioning, the design and the interpretation, stays firmly with your researchers. What goes away is the specialist production overhead that kept the method on the shelf. Conjoint and HB, without the stats bottleneck.