Unleashing True Customer-Led Innovation: Going Beyond the Data

Most organisations are not short of data. In fact, many have data coming out their ears. Sales data, Net Promoter Score data, employee engagement data, purchasing behaviour data, customer segmentation data, brand health data …the list goes on. Yet don’t be fooled; the volume of data does not necessarily correlate with the power of the insights that it generates. In these data rich times, it’s important to remember that data is only helpful if it leads to value-creating insights.

Best case scenario, if you’re using the right analytics tools, your data can provide insight into patterns and correlations and tell you ‘what’ is happening at any given point in time. However, where some organisations trip-up, is that they make critical decisions based on the ‘what’ without truly understanding the ‘why’. This is a big watch-out when it comes to innovation. We know that the juicy insights that sit behind successful innovation require looking beyond the data to get to the true customer motivation.

One of the most famous theories that speaks to this idea, was developed by former Harvard Business School Professor Clayton Christensen, called Jobs-to-be-done theory’. (You’ll hear us bang on A LOT about this theory at Inventium).

The idea of Jobs-to-be-done theory is that your customers are not simply looking for products, services, or features (which we tend to focus most of our attention on), but instead are seeking solutions to problems. These problems can be functional, emotional, or social in nature. 

By understanding the specific job or need that your customers are trying to fulfill, you can design products and services that are more likely to create value. (Notice how this theory takes the focus off what you offer your customers, and instead encourages a focus on what problem your customers want solved. This shift helps open your mind to a whole range of potential solutions beyond what you offer today.)

Importantly, it’s very hard to unearth your customers most important ‘jobs’ through data alone.

As an example, P&G were looking to innovate in the home-mopping category. They’d identified through their data (and P&G have A LOT of data), that sales of traditional mop-and-bucket cleaning systems were declining. They’d also identified through their data that people were switching to disposable cleaning wipes as an alternative, despite them being less effective. 

Rather than jumping straight into launching ‘new and improved’ disposable wipes (which is what their data might have led them to do), they went and spoke to their customers. Through conversations, the P&G team identified that their customers hated the mess and effort of having to clean the mop and bucket afterwards. They wanted quick and easy cleaning with minimal mess. 

Armed with new understanding of their customers ‘job’, they went on to develop the Swiffer cleaning system, which uses disposable pads that attach to a lightweight, handheld mop. Low and behold, the Swiffer was a huge success and quickly became a popular household cleaning product. 

By understanding the job that customers were trying to accomplish, and the trade-offs they faced, P&G was able to develop a product that better aligned to their customer’s needs. 

The data pointed the team in the right direction; however, it was only through customer conversations that they were able to identify the true problem and get to the ‘why’ behind their data. And thankfully they narrowly avoided launching another me-too disposable wipe.

Data can help guide us to the opportunity space, however, when it comes to successful innovation, data’s true power is realised when it’s combined with the art of customer conversation.