Cebit conference panel including Canva, Google, LinkedIn and RedBalloon looks at the most effective ways of taking a data-informed approach to marketing.
Control group data, economic graphs, path to purchase insights and ad measurement are just some of the ways brands including Canva, Google, LinkedIn and RedBalloon are tailoring products, services and content to customers and audiences.
The panel of marketing and sales analytics experts at last week's Cebit event in Sydney shared strategies on their different approaches to segmenting customers and find new opportunities as well as how their very different companies are finding insights in their data. Panelists included Michelle Huang, CRM/email and messaging lead, Canva; Andrea Rule, head of enterprise sales, LinkedIn A/NZ; Matthew Cavalier, general manager, RedBalloon; and Ben Jarvis, head of sales analytics and insights, Google Australia.
Huang began by explaining how, with a small marketing team, data enables Canva as an organisation to set up and automate email campaigns.
“It gives us a lens to evaluate the effectiveness of the campaigns. We can look at what impact it has on revenue and the impact on active users. And based on that, make decisions about stopping, starting or continuing with campaigns,” she said.
Huang also explained how data aids the speed at which campaigns can be tested and reviewed. “We can put out something to a 5 per cent control group and within days we have something that is statistically successful in telling us if it’s successful.”
Over at LinkedIn, an economic graph including data from member profiles, company, open jobs, skills and universities is vital to decision making. “When you put all that together, you can get a very good understanding of what is happening at a particular vertical or a company at any one time,” Rule said.
It’s possible to accurately target audiences using unique data points you can’t get elsewhere. And the data is actually the competitive advantage for LinkedIn, Rule claimed.
At RedBalloon, the path-to-purchase journey is a little different because people come to the site to purchase experiences, which can vary enormously from customer to customer. Cavalier explained how data plays a part in all site activities from path to purchase experience data from all the way along the funnel to what’s trending and re-targeting using AI.
“We include social, paid search, abandoned cart data and it’s all fed through to the customer service team so if you call in they know last transaction and history and can provide a level of service that’s contextual,” he said.
When it comes to Google, data and advertising start with measurement, and Jarvis noted improvements in measurement can give you a competitive advantage to know more about your customer base.
“The ultimate goal is to you know the customer lifetime value of your customer base if you’re an advertiser,” he said.
Yet with so many organisations continuing to struggle to maximise the effectiveness of their data, it's clear more needs to be done to make it actionable. Cavalier nominated data silos as standing in the way of getting all the data across systems.
“You can go a little data crazy and go down rabbit holes. It’s about looking at what you’re measuring and why and what metrics will actually move the needle,” he advised.
Rule said putting sufficient time into communications plans for projects is just as important as the data-driven marketing project itself, while Jarvis suggested amassing more data isn’t the answer, it's instead about looking for stronger insights from data. To do this, Google uses the NARC framework.
"For something to qualify as an insight, it has to be ‘novel’ where something is new or stands out, and it needs to be actionable, relative so it’s in context and credible from a reliable source," he said. "In our experience, outliers are rare.”
Huang said one of the challenges Canva faces is how to best collect the most relevant data for its users and then turn relevant and useful data points into a story it can take action on.
“It's how you share this data that may be relevant to another team with them across the organisation,” she said.
Starting with the end in mind to understand what problem you’re trying to solve for will help focus the efforts around useful insights, Rule continued. In RedBalloon's case, this sees teams keeping to its central strategy of acquisition, retention and customer service.
For Cavalier, it’s a question of assessing whether you can take an outlier or cluster and learn from that. “I’d give about 15 per cent of time and resources to examining the outlier data points. It might mean iterating and testing and learning, and maybe pivot, but it doesn’t interfere with BAU,” he said.
Jarvis identified data maturity as a key part of a data strategy, but one that is often overlooked. “Where do you sit relative to your own industry in terms of data maturing? Get information on how you benchmark relative to your industry," he said.
"Where are you at in journey? Improved measurement. Get the foundations in place first."
The panel finished by sharing their views on where data-driven marketing is heading. Huang said data is increasingly a vehicle for powerful personalisation, provided it’s done in an ethical way to give the most relevant and personalised content, and nominated YouTube and Spotify for their great user experiences. Rule saw artificial intelligence (AI) doing its job to aid personalisation, and doesn’t anticipate a deceleration in the use of the technology, it’s more about creativity.
“We’ll see a lot more creativity in how the data is used and looking at things like cross-functional leadership and strategy,” Rule predicted.
Yet Cavalier sounded a note of caution about hyper-personalisation and hyper-individualisation and asking customers what they want and giving what they want can make it hard to introduce new products or services to customers.
"We’re reaching a place of maximum complexity, from the number of daily emails to the world of online marketing, and at the same time we have intelligent machines getting smarter, but it’s not at the point where it can reduce the complexity," Jarvis concluded.
“We will get to a point when the machine can do most of the work. And so what will be the skills we then need to drive data-driven marketing forward?”
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