Restaurants, Insurance, Bundling, and Conjoint – How it All Fits Together

Conjoint analysis is the best way to get the most out of bundles. How does a team collaborate with this powerful tool to get the most out of their bundling strategy?

In a previous post, here, we introduced conjoint – its background, uses, and values. In this mini-post, we’ll focus on two specific industries we work with extensively – insurance and restaurant providers. What do these seemingly unrelated industries have in common? Bundles.

Insurance providers are certainly known for the bundled products and services – think of all those “I saved with a home and auto bundle” commercials you see. This makes immense sense – the more products a customer has within your ecosystem, the more Lifetime Value (LTV) that customer generates and the “stickier” they become as a customer. But generating appropriate product pricing and bundles that both make sense for customers and from a revenue/margin perspective is not easy.

 

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Restaurants do the same thing – think of your #2 with a large coke or a two-for-one deal. The idea of the bundle is to move higher margin products with the entry-level food that someone initially wanted. But how do restaurants decide on what to put in a bundle? It’s actually far more complicated than you’d think, and often involves hundreds and thousands of simulated (and sometimes real) experiences to judge how customers respond.

Conjoint is the easiest way to trial various bundles with prospective customers to come to a perfect blend of product, pricing, and messaging that is going to resonate. And if you’re working with a team internally – you’ll need the ability to collaborate effectively. Corus has the tools to enable you to comment on various questions, track survey changes, and also determine levels of control throughout your organization – without worrying about seat licenses.

Bottom line – if bundling products or services is a portion of your research, you need self-serve conjoint and Corus is simply the best option available.

 Read more in this series here: Data Analysis

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