There’s an eagerness to use metaphors when highlighting the value of data to organisations. Particularly to those that are yet to see the true potential from their data assets. Data was first compared to oil; a phrase we couldn’t seem to escape, despite its flaws. But data is now being positioned as the “new sun”.
On the surface, this may sound like a poor analogy too. But those who cite the comparison make a persuasive argument.
Bill Schmarzo is the author of ‘Big Data: Understanding How Data Powers Big Business’ and ‘Big Data MBA: Driving Business Strategies with Data Science’. He was one of the first people to call data the new sun. Schmarzo noted a similarity in the way data can “power an infinite number of uses at zero marginal cost, never wears out (or depreciates) and never depletes”.
Google’s chief financial officer, Ruth Porat, is another exponent of the sun analogy, pointing out that the oil comparison implies that data is a finite resource.
The “data is the new sun” analogy is certainly one we can get behind – not only does it work in illustrating data’s evergreen qualities, but it also alludes to its dangers. After all, it’s possible to be burnt by the sun.
Why it’s worth the risk
Few would argue that it’s best to stay indoors rather than risk any exposure to the sun. And more and more businesses are coming round to the need to increase their business intelligence with the use of data and technology, despite the associated risks (compliance, bias and loss, for example).
In all sectors, from retail and telecoms to healthcare, data has the power to be a competitive edge in multiple areas of business – none more so than customer experience (CX). After all, it’s not the data itself, but the outcomes of the insights that matter; generating benefits and value to people every day.
With customer touchpoints proliferating, CX is becoming a crucial competitive differentiator. It’s up to organisations to connect the dots – the touchpoints – to provide a 360-degree view of the customer.
Once this unified view is created – by aggregating the ‘hard’ and ‘soft’ data the company captures about its customers and their interactions – it enables an organisation to offer the best customer experience across all channels. It’s the foundation for a personalised, seamless and valuable experience for the customer, every time.
The experience they receive is what customers base their loyalty on, as opposed to price and product. Three-quarters of customers have stopped using an organisation’s services because of poor customer experience, according to Loyalty360.
Get CX right and your customers will spread the word. According to Esteban Kolsky, 72% of customers will share a positive experience with six or more people. On the other hand, if a customer is not happy, 13% of them will share their experience with fifteen (or even more) people, becoming a detractor.
That’s why CX is the new battlefield for businesses. And data is your best weapon in the fight for supremacy.
Customer expectations are at an all-time high. In fact, customers today expect brands to be one step ahead of them. To deliver on these expectations, however, the prerequisite is that organisations need to be proactive. More than having the ability to understand what customers need today, brands have to predict what customers want tomorrow.
That’s where predictive analytics comes in – using your data to make predictions and then taking action to influence customer behaviour. For example, if your data indicates that a customer is on course to churn – perhaps data shows the customer has ‘checked out’ and is no longer engaging with the brand – you can try to come up with a remedy.
To practice predictive analytics, however, you need large quantities of quality data relating to the issue at hand. Not only do you need the data, but you also need to be able to bring it together – aggregated and formatted – so that you can detect patterns and signals in your customers’ behaviour.
This presents a serious challenge to organisations who are operating with disparate systems, producing data in different formats, which makes it impossible to generate predictive insights.
In PwC’s 22nd Annual Global CEO survey, CEOs highlighted the ‘lack of analytical talent’ (54%), followed by ‘data siloing’ (51%), and ‘poor data reliability’ (50%) as the primary reasons the data they receive is inadequate.
With organisations struggling to glean usable insights from their data, Gartner classified more than 87% of companies as having low business intelligence and analytics maturity.
Business intelligence is a technology-driven process for analysing data and presenting actionable information which helps executives, managers and other corporate end-users make informed business decisions.
Machine learning & AI
While the path to predictive analytics can seem like a long one at the outset, you’re ultimately simply leveraging data analytics that your customers already provide on a daily basis.
For a comprehensive view, that data needs to be combined to give a 360-degree picture of the customer. But you’ve got to sieve out the ineffectual data, leaving only the high-quality content, which will inform your predictions.
Machine learning and artificial intelligence have a significant part to play here. Working together, they can pull out notable interactions at every touchpoint along the customer journey, to reveal where issues might lie with individual customers. Then you can set about taking timely action – whether that’s to turn passives into promoters or following up with detractors.
Your priorities will be informed by how CX initiatives will impact financial outcomes. The latest predictive analytic models allow you to generate profit and loss for each client, so you know exactly which groups to prioritise.
Anticipate Customer Behaviour
Predictive analytics can be used to anticipate your customers’ behaviours and estimate a client’s potential value. By understanding the probable next actions of different clients, you can help your organisation increase loyalty, improve your customer experience, and build value for your business.
It’s key to driving growth — by delivering a much more meaningful experience to all your customers, whether they respond to a survey or not.
However, predictive analytics only delivers real value if the insights are made accessible to all who would benefit from them in the business. In bringing data scientists and business stakeholders together, you can create the optimal solution and make it easy for users to access the insights which will inform CX initiatives.