It’s not down to lack of effort. Nor is it down to a lack of expertise. So could it be organisations’ inability to tell the right stories about customers?
Humans love stories.
From an early age, life lessons, moral values and even entire religions are all passed on through simple yet mesmerising parables and stories.
Anecdotally, the greatest storytellers of each generation are celebrated and immortalised. Academically, Stanford research suggests stories are 22 times more memorable than facts alone. The scholar Jonathan Gottschall even argues stories ensure human survival. The consensus is clear: humans love stories.
So here’s a question. Which loves stories more?
Claiming data loves stories might seem odd. Stories are all about sentences, protagonists, challenges and journeys.
Data, on the other hand, usually takes the form of numbers and codes – or nuts and bolts.
In the right hands, though, those nuts and bolts can be ordered to create a thing of beauty. Today, with the help of skilled data scientists, data can reveal insights-fuelled stories that offer organisations a competitive edge.
Consider Nintendo in 2006.
In 2006, Nintendo had fallen a long way behind its rivals.
After a long period of dominance, sales of its video games consoles had been leapfrogged in just a few short years. With every passing day, Sony and Microsoft were chomping away at Nintendo’s market share.
A few years before, Nintendo had attempted to fight back. With the launch of its GameCube, the company sought to shed its kid-friendly status and appeal to an older generation of more-serious gamers.
The plan failed.
Both Sony and Microsoft continued to outsell Nintendo, whose GameCube sales failed to match even those of Nintendo’s previous model.
Then, in 2006, Nintendo released the Wii.
Critics were crestfallen.
The Wii’s hardware was inferior to its Sony and Microsoft-backed counterparts. Games developers bemoaned the Wii’s technical specs, claiming they lacked the power necessary to run the advanced software being developed for rival consoles.
Sales, meanwhile, shot through the roof.
Throughout the first half of 2007, US Wii sales topped those of the latest PlayStation and XBox consoles combined. In Japan, the gap in sales was even greater.
For almost every month following launch, sales of the Wii outranked those of its rivals.
And all with what critics saw as an inferior console.
How did Nintendo pull off the feat?
Former Nintendo president Satoru Iwata hinted at the answer years later, in a discussion published in 2011.
‘Shortly after the Wii console was released, people in the gaming media and game enthusiasts started recognising the Wii console as a casual machine aimed toward families’, Satoru explained. He referenced the differences between ‘causal’ and ‘core’ gamers, before matter-of-factly stating, ‘I certainly do not think that Wii was able to cater to every gamer's needs.’
Years earlier, Nintendo had attempted to beat its rivals at their own game.
But when developing the Wii, Nintendo instead asked the right questions.
The company gathered data on what people liked about Nintendo and what people liked about competing consoles. Equally importantly, it gathered data on what people disliked about Nintendo and what people disliked about competing consoles.
Crucially, they used the data to help draft a new narrative for their business and the industry.
This narrative led them to create a new type of games console that catered to a far larger market – leaving their rivals fighting for leftover scraps.
Today, advanced companies employ skilled people to tease out similar stories and take advantage of new trends. And yet, successful examples are usually few and far between. Why?
Perhaps it's because so many stories get lost in translation. In many organisations, there remains a gulf between the data scientists tasked with interrogating big data and the commercial managers tasked with shaping data into stories – and it’s a gulf few businesses have so far managed to bridge. Even those that have, repeatedly come up against challenges of scale: data scientists today are few and far between. Commercial managers often end up fighting for their time.
Speed – or, rather, a lack of it – compounds the problem. For the most part, today’s data queries take hours – and sometimes days – to run. And it’s hard for commercial teams to piece together a coherent narrative without the facts at their fingertips.'
Equipping business intelligence teams with software that lags is akin to equipping Shakespeare with a hammer and chisel. Yes, you might get a story. But, through either a lack of refinement, waning interest or both, the story is likely to jump.
Fortunately, intuitive data platforms offering real-time analytics are finally taking centre stage.
The new platforms – Polymatica included – overcome the challenges of miscommunication and scale by putting non-expert users in the driving seat. And their real-time analytics overcome issues of speed.
The platforms appreciate unlocking the collective genius of entire organisations will be key to future success – and they therefore empower everyday people to create and then share data-driven stories.
As time goes on, we’d suggest businesses will increasingly rely on new data platforms offering scalable real-time analytics to develop insights, narratives and, ultimately, a competitive edge.'
Does data love stories as much as humans love stories?
With the advent of a new generation analytics platforms, time will surely tell.