What Oracle’s marketers are learning from machine learning
- 21 May, 2018 07:17
Machine learning has become one of the hottest topics in marketing circles in 2018, as organisations strive to understand what it can do and how it works. But as with most emerging technologies, there is limited experience in market against which to make decisions.
So when your company is one of the world’s largest providers of machine learning technology for business purposes, it makes sense to be selling that technology from a position of practical application.
For Oracle chief marketing officer for EMEA and JAPAC, Amanda Jobbins, her machine learning journey started with applying it to the vendor’s lead scoring methodology. Although still at the testing phase, the results have been remarkable.
“The Holy Grail here is to identify a customer right at the moment when they are ready to fully engage or ready to make a transaction, or have a question where they really want to talk to you,” she tells CMO. “We ran millions of previously successful leads through a machine learning algorithm, so that it could deduce what typically turned out to be a right prospect. And then we applied that algorithm to our current lead engine.
“And it looks like it has a 10 times better prediction of the quality of a lead than our previous methodology.”
The experience so far has also produced some interesting learnings about just how far machine learning can be trusted.
“If I turned the machine learning algorithm on at 100 per cent and rejected everything the machine learning algorithm didn’t score at a certain point, lead volume would go down very substantially,” Jobbins says.
For this reason, Jobbins keeps the recommendations of the machine learning engine suitably broad.
“What you are trying to do with machine learning is sift through a huge amount of data and come up with patterns and conclusion that the humans can’t do based on the volume,” she says. “One of the challenges with machine learning is, does the machine learning algorithm have inherent bias? The machine learning algorithm is a black box. You don’t know how it calculates the outcome, it is just learned on a historical quantity of data.”
Jobbins joined Oracle two years ago after an extensive career in marketing positions elsewhere in the technology industry. She says she was particularly attracted to the demand generation challenge of her new role.
One problem to be tackled was product knowledge. The company’s huge breadth of offering meant even existing customers weren’t always aware of how many solutions Oracle had to offer. With the cost of customer acquisition directly related to brand awareness, Jobbins says Oracle needed to present itself differently.
“We still have a job of work to do around awareness of our brand, and awareness of the capabilities and offerings and services that we actually have to bring to a customer,” she says.
Like many technology companies, Oracle had also come to realise customers were using online tools to get at least halfway through their buyers’ journey before even speaking to a sales rep. There was a need for the marketing organisation to create a more integrated journey for buyers, and help them find their way through a richer collection of online assets and materials.
But doing this effectively also meant overcoming an age-old problem found in many organisations -the divide between the sales team and the marketing team – to help sales teams engage in new conversations with clients.
“We are obviously going to do our piece around digital demand gen and search engine optimisation,” Jobbins says. “But at the same time, we have a large sales force, and they have good customer relationships. So how can we enable them to have a more informed and engaging conversation with our customers?”
The key to winning the trust and confidence of sales was to understand their commercial targets almost better than they do.
“You must actually go out and talk to customers yourself, so you can talk to them about the reality of their customer needs from a point of authority,” Jobbins says. “And you need to spend time with them. We are all in it together.”
Oracle has also implemented account-based marketing, including appending deep customer insights developed in marketing to sales records.
“They are able to see that activities their customers have actually done online with us, what content they have already downloaded, how they have already engaged with Oracle on the marketing journey, and that has really helped them have a more informed conversation with their customer,” Jobbins says.
Future work includes continuing to enhance what happens on mobile sites, and the addition of more self-service and chatbot capabilities.
“The more we can decrease that cost of sale by enabling the customer in that way, the better the company will be and the better the solution will before the customer,” she says.