A bit overly-simplistic but probably ok way to think about things is with a traditional conjoint analysis (CBC or even ACBC) we're going to show predetermined packages to people and ask them to choose the one they like the best, or accept or reject those bundles. With a menu-based approach, we're going to invite the respondent to build their own bundles and vary pricing, availability, bundling discount, etc.
All approaches could be used, but there's an attractiveness to MBC if it more closely matches what a respondent would end up doing in the real world to build their own bundle. A traditional or adaptive CBC could definitely be used as well, though. Adaptive CBC is potentially a nice option because it allows you to treat price more like a continuous range and apply price adjustments based on levels. This means you can, on average, show bundles with lots of things at higher prices.
The second skill set needed would be some data manipulation capabilities so you can take the CBC design and choices from the software and put them together in the right format to begin working with the data in the MBC software. Think scripting capabilities to read in two CSV files and spit out a joint CSV file of both.
The third skill is being familiar with experimental designs and regression analysis in general. The MBC will do the hardest work on running the models for you, but when you import the raw design and answers you have to tell the software which variables are independent and dependent variables, and specify the structure of the model (variable 1 was hamburger price and should be an input into variable 10, hamburger choice, but also variable 2, cheeseburger price, is an input into hamburger choice as well because we expect them to be substitutes). The MBC software does contain some tools to help you decide on specifying the structure/effects of the choices, but at the end of the day you start out with a blank slate and have to structure things from scratch. In a traditional CBC, at most we tell the software we want an interaction effect or a constraint, but most of the time we just hit the "Estimate" button and run a model.
The MBC software does contain a simulator that works very similarly to a traditional conjoint simulator, and your primary output would be the share predictions given a specified menu of options. Although utility scores are generated, because of the complex nature of the model (inherently desirability of an item plus a price utility plus a substitution effect, for example) we generally don't report average utilities, try to figure out attribute importance, etc.
Hopefully that gives a nice overview. We definitely don't want to scare anyone away from an MBC project, but it is definitely a lot more difficult to pull off than a traditional conjoint. We'd be happy to set up trial licenses for you for any option if you wanted to take a stab at things first before deciding what direction to go.