In 2011, Coca-Cola in Australia thought that Coke had become “too familiar, too predictable”, and growth in a saturated market was proving difficult. In response, the company’s creative team came up with a truly disruptive idea that would make headlines and capture the country’s attention. It launched a novel marketing campaign — replacing the company’s name on its bottles with 250 of Australia’s most popular first names. The results were astounding: a 7 per cent increase in Coke consumption by young adults, multiple award wins and further rollout in 80 other countries.

The immediate appeal was that consumers saw their own names on the bottles they were drinking; an instant, very personal connection had been established between brand and customer.

Technology has come a long way, and the power of personalisation can be extrapolated from the above physical example to an infinite variety of virtual instances. Customers often voluntarily share a tremendous amount of data with brands, and this data can be anonymised and deployed in personalising the experience of each consumer according to his or her tastes, preferences, demographics and even location. Artificial intelligence (AI) and machine learning (ML) algorithms plugged into big data analytics platforms are able to understand customer purchasing or behavioural patterns at a very granular level, millions of users at a time.

Today’s C-suite is tightly integrated — especially the chief information officer, chief technology officer and chief marketing officer — and mapping out customer journeys involves orchestration across several arms of the enterprise to make it a truly seamless experience. As the marketing function becomes increasingly quantitative and technology-centred, close collaboration between the technology and marketing functions is vital in ensuring that the hard and soft expertise gel to create a truly “human” experience for the users.

No matter how closely the C-suite works together, however, deploying cutting-edge technologies can still be risky. While there is lots of hype in the market about artificial intelligence (AI), machine learning (ML) and predictive analytics, integrating them with existing platforms can prove to be a challenge for small IT departments. Managed services can be a big help in such cases, where experienced system integrators can combine best-in-class solutions and customise them to a specific vertical or use case. Open APIs are also a critical component, as they allow the IT team to weave a web of data through disparate, proprietary legacy IT systems, building an ecosystem of services that can interconnect as required.

Although everybody knows that their bank or telco has thousands of employees across different geographies, nobody wants to spend hours on the phone with them, being transferred from department to department. AI, ML analytics and data management — the core of the customer experience engine — can put an end to all that. In the new reality, the customer can be seen as an individual, with likes and dislikes and habits, rather than as just an entry in (often-siloed) databases.

With so much customer data available, the enterprise owes it to its customers to organise and harness this data to provide relevant, even bespoke, experiences.