Customer experience revolution underway thanks to AI and ML
From columns and rows to collaboration and compliance, data-led insights and new technology is enabling a brave new world of data-led marketing opportunities.
Data is a four-letter word that can overwhelm or excite a business in equal measure.
The deluge of details collected by most modern businesses on transactions, customers, sales and even electricity usage has changed the way many businesses work, upending how technology and customer marketing work together.
Data and cloud-based technology offer the capability to automate, personalise, create efficiencies and engineer better customer outcomes than any analytics or data expert who remembers floppy disks could ever have imagined.
Yet data and technology-enabled businesses also risk higher costs, complex compliance demands and must constantly adapt to new business practices like greater collaboration and better staff attraction policies to retain skills that are in high demand.
“You don’t understand if you have a good data scientist until you’ve spent millions of dollars and got it wrong - it can be an expensive mistake,” said Peter Bonney, general manager of Technology, Engineering and Data at grocery giant Coles.
“People have to remember that technology, data and things like artificial intelligence are only tools to solve business problems, not solutions in and of themselves.”
On the flip side, head of marketing at telecommunications giant Optus - Melissa Hopkins - said there was an abundance of new companies, vendors and technologies enabling new opportunities to connect with customers and deliver business value in brave new ways.
“We are at an intense point in time that is very exciting. We need to rewrite the rules to take advantage of the opportunities,” she says.
Data informed decision-making and marketing
Liquid CX founder Simone Blakers - who has worked at the bleeding edge of data marketing for two decades - says the rise of ‘plug and play technology’, or SaaS products and APIs, has opened the door for all levels of business to take advantage of automation and personalisation.
“It’s now easy to combine sales data, online data and customer data to a highly sophisticated level that can be accessed by all sizes of business,” she said.
Larger companies still have greater advantage, as they can quickly leverage resources to explore new technologies and gain commercial advantage.
Blakers has seen big companies leverage artificial intelligence technology to test and predict the impact of communications, packaging and simulated retail experiences. The technology enables this testing to be done in real time and at scale before going to market, offering large companies the ability to optimise their customer experience faster than ever before.
“Using predictive and real time analytics on the fly changes the very nature of creative and retail marketing, as ideas are tested before they are even made or put into market,” Blakers said.
“There is now business intelligence technology with voice assistant interfaces that let you pivot, understand triggers quickly and analyse a campaign – all through one verbal query.”
“The secret world of data intelligence is now democratised,” Blakers said. “You can, however, get lost in a sea of data if you don’t manage it well.”
Hopkins said corporations have gone from relying on large partners like software vendors or agencies to working with more nimble partners who can allow big business to ‘dip their toe’ into new technology offerings.
“The small indies that are popping up have leaner teams. Technology partners like Facebook and Google and Salesforce remain important but so do the smaller AI companies that specialise in particular areas - smaller organisations have less fat where you deal with the thinkers and doers,” she said.
As an example, Optus worked with a supplier to place a media value on the telecommunications giants owned assets - everything from the screens inside Optus retail stores to the company’s email list, website traffic, app usage and more was valued as a media asset for the business.
There are an abundance of clever startups leveraging new ML and AI technologies to partner with businesses to create better insights and better customer outcomes.
Blakers cites dragonflyai.co and albert.ai as two interesting examples but predicts that technology offering the greatest business impact will be most in demand. “The value always lies in the use case, not the technology itself,” she said.
Leveraging artificial intelligence and machine learning for prediction
Collecting data may be easier than it used to be. But working with data has rarely been.
Merging data sets, cleaning data and finding the right insights is as hard as it was before technology enabled more opportunities.
“There is a small grey patch of hair on my head for data quality issues,” Peter Bonney said.
What’s more, the business use cases for machine learning and artificial intelligence may be exciting, but it can be complex to navigate.
As Melissa Hopkins said: ““Yes, AI is important for marketers and businesses but you can’t take your hands off it. Unless you know how to drive it, stay away from it or leave it to the experts.”
Algorithms and models can degrade over time, or become entirely inaccurate if a brand new team member in another department isn’t keying in data correctly.
Blakers said business leaders needed to understand the role artificial intelligence and machine learning could play in their company strategy.
“Investing in innovation is not just about the ROI, it is also the COI – cost of inaction. You don’t want to wake up and realise your competitor has spent the last 5 years training AI to better service their customers and now you can’t possibly catch up,” she said.
“Executive leaders who aren’t data experts and rely on the data geeks to tell them what’s happening need to know the right questions to ask and break through the complexity.”
Collaborating across the business
How organisations solve business problems with data and technology is more nuanced than “just do it”. Leveraging plug and play packaged applications can turn an organisation into a fast bowler, but that doesn’t always win the long game.
“If you’re a company that relies on (technology) partners and SaaS products, you will have to build data and technology capabilities in house and it will be a longer journey for you,” Peter Bonney said.
“If you do have the technology and data discipline in an organisation, then you can build analytical products for fast-changing areas. The real competitive advantage is how quickly you can get things in market.”
Bonney said large and complex businesses needed business managers to partner with data and technology teams to manage projects more like a product than a one-off implementation.
“Businesses need senior data scientists with a product development mindset - it’s about a hypothesis, test it, test the outcome, measure it and rinse and repeat,” he said.
“In the old days, technology teams would hold a governance forum that the rest of the business didn’t understand or care about. But now it’s about what’s broken, how can we fix it and how can we get more insights to deliver a better experience for the customer.”
Hopkins said human insight will drive the best data and technology strategies. “If you want to use AI, make sure there is input from humans. It’s human insight that will drive the 360 degree customer experience,” she said.
She said the Optus marketing team have in-housed functions like SEO and SEM but their sophisticated digital team blends with other business functions rather than solely focusing on ‘digital’ or ‘data’.
The job spec for marketing has to change and it is changing. We all have to innovate and do more than merely use different channels and models.
Machine learning means businesses can now personalise communications and marketing and data drives more decisions across the business value chain.
“But data is like crude oil - it’s valuable but worthless unless it can be refined to come with insights,” Hopkins said.
The skill shortages in data and technology
Fast and furious change across the data marketing landscape inevitably leads to skill shortages, which can make it hard for all businesses to adapt to the new opportunities.
Blakers says the strategic lens and skillset is more necessary than ever, as specialists have evolved at the channel or platform level but not necessarily understanding the complexity of the business landscape.
For Bonney at Coles - which faces rising disruptive threats from global data giants like Amazon - the shortage is in cloud engineer specialists and data scientists with deep market experience in areas like forecasting or personalisation.
Compliance issues - especially around security, privacy and personal information collection - were forcing senior executive leaders to understand data and technology issues more broadly than they ever needed to in the past.
“Skills have always been challenging, but ultimately you don’t want anyone with their head in a spreadsheet without having their eye on the customer,” Bonney said.