Forget personalised brand experiences, it’s all about “individualised” moments, argues Citi’s top data expert Satya Upadhyaya.
As vice-president campaign optimisation, marketing capability and change at the global financial services company, Sydney-based Mr Upadhyaya is tasked with lifting the quality of customer communications across different channels.
This sees him focus on the three core areas of data, process and technology as he attempts to make complex things simple by transforming fragmented ad hoc operations into a coherent processes.
And the new theme “buzzing up”, that should have marketers talking and brands tuning in, was the hot topic of individualisation, Mr Upadhyaya believes.
While marketers may have become comfortable and confident with efforts around personalising comms to targeted audience and customer “groups”, this is only the beginning as users want and need much more.
“Individualisation is different to personalisation. It’s purpose-driven one to one communications which helps customers accomplish more meaningful goals,” Mr Upadhyaya said.
“It looks at that one to one connection for specific individuals. It’s the ‘show me you know me’ with all information about me, take the charge and recommend to me products and services, surprise and delight me offerings which are truly engaging and personal. To provide this 1:1 conversation at scale needs the power of artificial intelligence (AI) and machine learning (ML).”
Most companies are not there with this level of one to one and individualisation. Typically, for brands, a one to one conversation is managed manually, is very cumbersome and time consuming and errors can bubble up. Two-way conversations between brands and customers tend to be a reaction to interactions as opposed to a natural, real time style of one to one communicating. As a result, there was a clear “promise gap” in brand experiences meeting consumer expectations and this was being driven by broader competitors and their agenda.
Mr Upadhyaya said Netflix, which crunches viewing data using AI and ML to provide him with bespoke content, is setting the bar for seamless one to one experiences.
“So now when I’m actually going to my bank, to my telco or my insurance provider, I forget that I’m actually at a different location, I’m actually interacting with a different brand. I’m thinking hey, Netflix can do that for me, why can’t you do that for me?” he said.
“That’s where the perpetual competitors are coming in. They have nothing to do with my competition line of business, but there becomes this fear of a missing out type scenario that everyone’s doing it, so people start to question what they can do to get that individualisation piece.”
To achieve that, Mr Upadhyaya said you needed a complete and accurate picture of your data to allow for intelligent decisions. Thereafter, you need a mechanism for precise delivery that converts the decisions into actions.
“It’s about building an entire page of experience on canvas from scratch, from the millions of interactions and the fragmented content that’s floating around within the organisation to deliver that unique customer experience,” he said.
Many marketers may think they are already winning with current personalisation efforts, but Mr Upadhyaya said individualisation was a big step beyond this.
“It is in nascent stages; some organisations have started doing it, some are still in the process of doing it but almost everyone’s on that journey with no one mastering it yet,” he said.
“The question is, does anyone know how to do it and that’s the challenge; looking at what it requires from the people, process, data, technology, system and maturity point of view.”
In addition to getting a handle on individualisation, Mr Upadhyaya said beyond the big data boom, marketers needed to more specifically turn their attention to “big ops”, which is the integration of data into design and delivery by placing the experience layer over the data layer with seamless orchestration of apps, automation, processes and workflows.
With as much as 43 per cent of data captured not being used, he said the mission of big ops was to bring that “dark data” to light and harness its value.
“If you listen to your data you are able to analyse customer needs, predict needs and manage risk,” he said.
“Data is actually speaking to us and it’s our job to listen but are we listening? Big ops ensures that we are.”