Data science in B2B marketing under the microscope
REA Group’s Isaac Smith says analysing and acting on data in real-time is a valuable marketing asset.
While most B2B marketers acknowledge the importance of understanding data and analytics, “data science” sounds like something from CSI or a futuristic sci-fi flick. But at least knowing how data science impacts on marketing performance will soon be an important part of a marketer’s armoury.
Data science in marketing is more than just gathering statistics about customers. Done well, it involves developing ways to record, store and analyse data and using this information to make intelligent decisions about what to do next. Increasingly, it’s about using rapidly evolving tools such as artificial intelligence (AI) and machine learning to analyse things quickly, using this knowledge to take action in real-time.
So should B2B marketers who are unaware – or perhaps even a little scared – of the concept of data science be worried? Should they feel a severe case of FOMO (fear of missing out)? Perhaps even more worryingly, will data scientists soon take marketers’ jobs?
Isaac Smith, who spent much of his successful career as a sales and marketing leader, was recently named Executive Manager, Audience Modelling and Analytics, for Melbourne-based REA Group. Four areas make up his professional remit at the global online real-estate advertising company – strategy, personalisation, insights/analytics … and data science.
Smith acknowledges B2B marketers might feel a sense of “discomfort” dealing with issues such as data science and machine learning. He thinks this is most likely felt by marketers who work at organisations that still think of customers as “companies”, not as a diverse group of “internal customers”.
“I think if you’re still running your CRM and thinking of customers at a company level, then you’re absolutely behind the pack,” Smith says. “With multimillion-dollar contracts, you’ve got to be working with multiple stakeholders – maybe the CTO, sales director, CFO and CEO.
“You need to be thinking of the individuals within those organisations making those decisions. They’re the end-users of your products. You need to be trying to develop a database where you can communicate one-to-one with them.”
Smith says B2B marketers who still think of customers at a company level don’t understand individuals within those companies expect to receive relevant information aligning with their roles and interests. This is where data science and its ability to deliver targeted information can be invaluable.
“The ones who are more sophisticated have more of an account-based kind of approach in their marketing teams – particularly those engaging in high-value, long-duration sales,” he says.
Making intelligent use of data
REA Group has a diverse range of B2B and B2C customers – from homebuyers, sellers and renters to residential and commercial real-estate agencies. Smith admits most of REA Group’s data-science work to date has been on the consumer-facing side.
“Our data-science team over the years has already built a number of predictive models based on the consumer audience,” he says. “We’re looking to anticipate the journey they’re on and serve them content, information and tools on-site to help bring them down that funnel.”
Real-estate agents use REA Group’s intelligence to reveal market trends and show their expertise. “The digital world is about transparency and empowering consumers,” he says. “The agents who understand that are more likely to stand out; they will be more successful in attracting leads or inquiries.”
“You can publish a lot of potential data points and some insights but if they can’t be applied or you haven’t helped your customer, they’re not going to shift the needle.”
Smith says large-scale B2C organisations are ahead of their B2B colleagues when it comes to using the power of data science.
“I know we [REA Group] have invested more on data modelling on the consumer side and certainly a lot less on our customer side,” he says. “We should have predictive models looking at next best product-to-purchase, for example. That’s common on B2C platforms, certainly those that deal at scale. My sense is there are stronger use-case stories on the B2C side than there are on B2B.”
Smith doesn’t think B2B marketers should be worried about the advance of data science, especially developments in machine learning and AI that offer fast and accurate customer insights.
“Like anything, you should be cognisant and understanding of the key principles,” he says. “You don’t need a deep understanding of linear regression models; you need to appreciate there are tools that look at consumer behaviour that inform why they should take certain approaches and develop certain models.”
What concerns Smith and others is a marketing world dominated by “too much data, not enough insight”. He believes now is a good time for marketers to add their expertise to the conversation.
“Marketers who have a good grounding in market research and insights know the journeys consumers are going through – their needs, fears and hopes,” he says. “The model’s only as good as the data points you put into it.”
Smith believes organisations have to acknowledge marketers are in the best position to make sense of customer data and turn this knowledge into action. “I see marketers as multidisciplinary – masters of many trades. They need to be the advocate of the customer: ‘Of all of these millions of data points, what are the ones that will make a difference to my customers?’
“You can publish a lot of potential data points and some insights, for example, but if they can’t be applied or you haven’t helped your customer, they’re not going to shift the needle.”
Room for improvement
Glenn Flower is one of Australia’s most experienced B2B martech specialists. The former marketing head at Telstra Wholesale and Nextgen Group, who is currently Chief Marketing and Product Officer at 5G Networks, believes we’ve only scratched the surface around data science and what it means to be truly “data-driven”.
“I think there are some pockets doing it well and I’ve seen some evidence of journey mapping and customer-experience management through AI,” he says. “To me, it’s about automation – the ability to scale a function based on data – and getting some type of workflow or system that acts on that data almost in real-time.
“Now who’s doing that? Well, I’m not sure.”
Flower says he sees some organisations doing parts of it well, and listens to agencies talking up the idea of using it to support customers. “To me, there’s still a lack of maturity and really understanding how to get these different systems to sing together with the same harmonic approach to a business outcome,” he says.
“You hear someone doing something and you go ‘wow, that’s really cool’. And then you explore it and find it doesn’t fit your business model and it isn’t worth the time and effort to get that outcome. You go: ‘That’s nice, but really does it put bums on-site or does it grow the top line by certain numbers?’ I’m almost sure.”
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Further reading: Getting martech right in high-growth environments