CMO interview: Bringing the power of cognitive computing to marketing and customer engagement

OpenText global CMO shares how he's tapping into machine learning and artificial intelligence to improve the way the enterprise software vendor engages with customers

Cognitive computing and machine learning are among the top buzzwords of 2016, and judging from the product roadmaps of most martech and adtech vendors, they’re going to dominate headlines again in 2017.

OpenText is one of a raft of software players heavily investing into next-generation cognitive computing capabilities to complement its information and communications management offerings. And much like its rival, IBM, the vendor is looking to external partners, line-of-business executives and importantly, marketers, to come up with the best use cases for its application.

During a recent visit to Sydney, OpenText’s global CMO, Adam Howatson, sat down with CMO to discuss how cognitive computing can be applied to marketing and customer experience and engagement, how his team is using the power of machine learning, and what the future of digital marketing looks like in a world where automation of intelligence-based tasks is commonplace.

How is cognitive computing playing out in OpenText’s product roadmap?

Artificially intelligent cognitive systems and automation of those human intelligence tasks is at the forefront of what we are doing.

Adam Howatson
Adam Howatson


Cognitive computing for OpenText is based on our foundation of EIM [Enterprise Information Management]. Core capabilities include using pattern analysis and machine intelligence in an analytics platform that can derive insight, create prediction from large data sets. But to do that, you have to have a few fundamental building blocks, such as natural language processing, image and video recognition, text mining and semantic analysis. We have all of those, as well as predictive and prescriptive capabilities.

We’re applying this cognitive analytics to our EIM solution, which really is the memory of an organisation. It houses every contract, commercial interaction, electronic data transaction, customer experience via the website, ecommerce – the list goes on. As an organisation, you’re not going to want a shared cognitive platform trained on information similar to your competitors. Nor are you going to want to submit your proprietary data to train a system that would be used by competitors, because you lose the competitive advantage in doing that. We’re able to create and train that system based on the information specific to you.

Then as you build cognitive systems, you need to understand what insights you’re looking to garner.

A project I’m undertaking right now is using cognitive to take and analyse every RFP, RFX, RFI and proposal we have ever written as an organisation. That’s tens of thousands of pieces of content that would be impossible for a group of humans to analyse. We’re looking to come up with an objective opinion as to what, as an aggregate, customers are asking for.

As a customer, you might be specifically doing high-volume claims processing, and you need a handling process tied to machine intelligence that can do exception revenue tasks. If we can help to improve that process, it’s going to set you apart from the competition in a big way.

For marketing, cognitive will allow us to gain insight across a massive volume of data to understand exactly how our customers engage and what they ask us for, and will help with what language should I be using to communicate with them. For example, when I do segmentation, account-based marketing [ABM] or I look at a specific industry or use case, am I using their language? If I’m not, that is a problem.

How are you identifying the business opportunities cognitive can cover?

We are developing what we call strategic development partnerships externally. One of these is with a large US shipment and logistics company. They are looking at customer experience and interactions and how to make things simpler from a shipping perspective, using a cognitive agent to complete things for the customer intuitively.

Let’s say you have a number of items you’re looking to ship, such as shoes, handbags or phones. These logistic firms ship massive volumes of them, but as an individual user, you don’t necessarily know the dimensions, how much a unit weighs, or what customs needs to know about that artefact. But if I were to start describing that to a cognitive system that can go through the reams of data previous users have submitted, it could auto-complete that order for you within you typing in the first few letters.

Pharmaceutical is another great example around drug development and trials, and the use cases go on. The vendors in this space have the technologies, but we need to understand that if you could automate any human intelligence task, what would it be and what problems are you trying to solve? We can build something for the market and hope it hits, but we’d prefer you to articulate exactly what problem you’re trying to solve and we’ll build accordingly.

How do you respond to fears that cognitive could take jobs away from humans?

We are on the precipice of the biggest labour disruption since the depression. But it’s different this time, because it’s not economic uncertainty that’s driving it, it’s automation. The majority of tasks we’re talking about when it comes to cognitive really are repetitive. You talk to any working professional and ask them: If you freed up 75 per cent of your labour force, do you have a project backlog big enough to keep those staff busy for the next few years? Everyone is going to answer yes.

This particular revolution around cognitive/machine intelligence is also going to create jobs we can’t even conceive of. It’s going to be a big challenge for education. You’re teaching them for jobs that won’t exist by the time the student has graduated.

Yes, there will be a labour displacement, but I don’t think it’s necessarily the terrifying scenario most people imagine. We are an industrious civilization and species, we will find ways in which to apply that human effort and creativity.

Marketing functions have had to completely reskill in recent years thanks to digital disruption and the data now available, and technology continues to exponentially transform industries and jobs. So how can you prepare your marketing function now for even three years’ time?

One thing I tell my teams, particularly in field and product marketing, is that you’re not going to have access to email in five years. You’re not going to be allowed to email anyone who’s not triple opted in. I’m telling my teams and management I need to see a proposal this year for how you’re going to replace all of the demand you’re generating from email over the next 3-5 years. You need to start today to train your teams and develop systems to replace this.

We have deployed a digital hub model, linked into social, and we can track, correlate and see direct return on investment in social. We’re focusing on digital engagement instead of click rate or operate or pass through rate. We went through a full training program in all of our regions throughout the organisation, to up the skillset around digital marketing and ABM. Those are the types of changes CMOs need to be urging their teams to make.

Are there a couple of specific things marketers must do if they’re to survive the next 3-5 years?

Companies need to put investments in place, and have a plan. For organisations further behind, it has to start from the top, and the ultimatum is very simple: You will be vaporised if you don’t start changing now. Because the competitive insurance company is going to come up with a digital strategy and disintermediate their broker network,they will be able to provide services cheaper, have better algorithms to make better predictions.

As a CMO, are there other trends exciting you as we go into 2017?

It’s analytics, analytics and analytics, and taking action on that. We need to be building algorithms, have a vibrant analytics platform, but right now it’s about meaningful insight.

Once we complete the RFX project I mentioned earlier, I’m going to have 25 years of history of every customer interaction, and every request they’ve made to us. I’m then going to plug in our customer support ticketing system to understand where customers run into trouble, what are they challenged by, what they ask for help for, and what we are able to resolve for them. If I find things we’re not able to resolve, I am going to get together with our head of customer support and ask why and if there are ways we can make it better.

The smartest people in the industry right now are working on marketing and these transformations, being able to provide meaningful customer interactions in a way that doesn’t turn people off the brand.

In the next five years, we’re going to see diminishing access to email, and over the next decade, we’re going to see true cognitive presence that gleans insights we could never have imagined. To date, we have sat, guessed, predicted, and tested; in less than 10 years’ time, we will be told to take five actions and with 98 per cent certainty, we’ll get results.

What’s your parting advice then to marketers?

Do not rest on your laurels. Do not sit here and think you will be able do next year what you did this year and it will still be successful. You have to keep abreast of these emerging trends, both from a technology landscape perspective, and from a market perspective and the way humans are interacting with advertising and marketers.

It’s also about the way legislators mandate how we should interact with people and leverage data. As a CMO, you need to be a trustworthy individual. How you use your customer’s data, or don’t use it, is extraordinarily important. As we go through this transition where all things are possible, maintaining an impeccable moral standard in how you leverage data is paramount. Those who ignore those factors will simply cease to be in business.

Read more CMO coverage on cognitive computing and machine learning in marketing:

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