Why do people do what they do?
Developing new offerings that customers will predictably purchase and use requires a systematic approach based on a deep understanding of the progress and experiences that customers seek. Enter jobs-based innovation. Developed by Harvard Business School Professor Clayton Christensen with Lippincott’s Taddy Hall and coauthors Karen Dillon and David Duncan in their book Competing Against Luck, “jobs” is an approach to innovation that helps companies uncover opportunities that are systematically overlooked.
Jobs-based innovation replaces traditional models of backward-looking data correlation with deeper insights on human behavior and why people do what they do. By understanding the jobs that people are struggling to perform, companies can systematically design better products, services and relationships to get them done.
There is a simple but powerful insight at the core of Jobs Theory: Customers don’t buy products or services; they pull them into their lives to make progress. This progress defines the “job” they are trying to get done, and customers will “hire” products or services to solve these jobs. If a product or service doesn’t live up to expectations, customers will “fire” it and look for alternatives.
All too often, leaders view the marketplace through the lens of their products — “We are in the mortgage lending business” — rather than through the customer job lens — “I could use some help figuring out how to pay for our first home.” What predictably follows is a costly string of innovation failures as leaders introduce an array of perplexing variations on mortgage rates, terms and features that leave the would-be homebuyer struggling to make any decision with confidence.
A jobs view (see exhibit below) reframes markets by revealing the opportunities, and answers, that matter most to customers. In doing so, it uncovers a true map of competitors — helping leaders to identify and anticipate previously overlooked threats.
Figure 1: Product Mindset vs. Jobs Mindset
Life is not an algorithm — and neither is innovation. There’s a misconception embedded in many organizations that if we can just collect enough data and model it the right way, the truth will be revealed. High-powered analytical tools help this effort, but innovation insights are fragile constructs, easily washed over by the smoothing effects of averages and regressions.
Through our process of jobs-based innovation, we help teams build and deploy the mechanisms that ensure their data reveals what they need to see: people in the world struggling to make progress.