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Why you should make your AI product harder to use: The Egg Effect
- Making a product slightly more demanding can make it more appealing to users, according to behavioural scientists Michael Norton, Daniel Mochon and Dan Ariely.
- Their research shows that making a product harder to use can create a psychological investment that makes end products more valuable to clients. Also, when people help create things, they tend to value them more, whether the items are functional or recreational.
- The egg effect is an analogy taken from the 1950s, where cake manufacturers struggled to push their instant cakes onto the market because consumers felt there was no sense of achievement in merely mixing powder and water.
- When the egg was added, increasing the effort needed to bake the mix, sales began to rise for cake mixes made by Duncan Hines and General Mills.
- Yet, despite the research on human involvement and investment, many product designers appear to be moving in the opposite direction, particularly in behaviour-design tech products.
- AI products require user involvement, but designers need to find a balance between too much user input getting too burdensome and not enough input to create an investment in its use. When users add effort in training, customising and directing an AI system, they are more likely to trust the results and value the outcome.
- Some companies that have already taken user effort into consideration include Salesforce, which has an upfront investment to increase user adoption and renewal rates. Function of Beauty also has users conduct an extensive quiz to customise products and justify premium pricing.
- Betterment and Wealthfront require users to complete an extensive personal financial assessment to get customers to set specific life goals, connect existing accounts, and choose investment themes and justifying premium pricing.
- Glean, an enterprise AI search tool, succeeded by understanding that companies wanted to teach AI about their data and how they operate. By letting organisations put in the effort to train a system on their systems, it becomes more trustworthy and companies trust it more.
- Product teams must try to find areas where users can meaningfully contribute their expertise and figure out how to make user input feel valuable instead of burdensome.
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