Tealium CRO: Customer data boon yet to be unlocked
- 03 March, 2020 08:17
There is a massive opportunity for brands to engender even more consumer loyalty, as the true potential of data and the harnessing of technologies like customer data platforms (CDPs) and artificial intelligence (AI) become apparent in coming years.
That at least is the view of Tealium chief revenue officer, Ted Purcell, who told CMO his mind boggles to think just how much growth potential and experience potential there is yet to be unlocked as businesses continue on a data journey arguably in its infancy.
He said the biggest impediment to this end so far is the level of maturity of both the market and the people in it.
“The biggest gap we have is experience. There is no question that this concept of customer data and leveraging customer data platforms is its here to stay. And the only gap I would see is the maturity and experience of people in teams so that they can embrace the power of CDPs,” he told CMO.
“The latest Gartner reports are saying only 4 per cent of the market is even utilising a CDP. And then you could ask, of the 4 per cent, what percentage of those are properly leveraging a CDP?
“As we move forward, mature companies are going to have resources and the people and skills, which may not yet even exist, to not just leverage these data technologies, but know what to do with them, how to create business process to optimise the utilisation of the data, and to review the data to make proper business decisions.”
Purcell previously served as the SVP/GM of Marketo’s commercial business and was a member of the team when the marketing automation vendor was first acquired by Vista Private Equity Partners in 2016 and then by Adobe Systems in 2018. Before Marketo, he also held a number of senior roles at B2B organisations including Clarizon and SAP.
He said the appetite for data has not lessened in the face of privacy regulations and the death of the cookie, and in fact, this will ultimately lead to happier businesses and happier customers.
“We're seeing this burgeoning data fever people have around the convergence of not only trying to drive better personalisation along the customer journey, but then the regulatory aspect of data, the data privacy,” Purcell explained.
And CDPs are fast becoming positioned as the key to tackling the customer data challenge. Tealium was one of the early players in the CDP industry and now faces competition from a tonne of businesses using the term CDPs. According to Purcell, many are not even CDPs as the vendor defines it, but may have some element of customer data they're using.
"We are still seeing that strategy being confirmed not only in the market with our customer growth, but with our growth as well," he commented, noting company revenues doubled over 100 per cent in the Asia-Pacific and Japan region in the past year.
“People are still trying to get that data foundation in place around tag management. It hasn’t lost its importance, but there is definitely a lot more excitement around arranging audiences in what's now commonly referred to as a CDP strategy. So we are not moving away from the data layer strategy at all. In fact, we're looking at the the latest layer and the data layer strategy and the AI component, and the machine learning component, which we've now launched this quarter."
The privacy conundrum
Purcell said data privacy regulation does not have to be a challenge, provided businesses are prepared for the changes and devise a data strategy. In fact, he sees this change as being a positive for the industry.
“I view it as really positive because it's incredible the level of personalisation that the consumer can experience in the way these brands can now interact with their own customers throughout their customer journey using the right data," he said.
“Everything can be so personalised based on all the context and learnings you can gather, with consent. But if you're not properly prepared, there can be negative regulatory and financial implications to come. It can hurt your brand, due to brand dilution that comes from a negative customer experience based on irrelevant or duplicative content, or data or information on a consumer that is bad. And that hurts brands all over the place.
"On the positive side, if people are prepared, and are looking at it through that lens of personalisation, it's amazing the brand credibility and the authenticity and the likeability brands can achieve, and that connection to brands because of proper preparedness around data."
The concept around real time facilitated by better data is even more important because if information is not real time, it could be irrelevant by the time the experience occurs, Purcell continued.
"We are all in the business of trying to put the right content in the right context in front of the right people at the right time," he said.
“There are so many amazing technologies out there now that offer great experiences based on whatever experience consumers are interacting with, whether it's text or email, or adtech, or Web experiences or even offline experiences in the store the point-of-sale, to enable them to offer the customer in front of them the greatest experience possible.
“That level of real-time personalisation is not only great for an experience perspective, but it's good for business because it converts."
For Purcell, the next era of marketing is predictive and creating these machine learning algorithms and use cases around the data, to not just react in real time but even more prepared in a predictive context. And this isn't just about marketing either.
Purcell saw personalisation coming into every aspect of the actual experience, affecting other areas of the business such as manufacturing, supply chain, demand and inventory - all of which are starting to impact the business performance of the company.
"Thanks to the machine learning algorithms, we're now starting to get deeper into the science of analytical scenarios, to help drive better predictions so that the business can be even more prepared for whatever may come," he said.
The hygeine factor
But to deliver these great use cases, organisations must strive for high-quality data, and to ensure information is well orchestrated, integrated, cleansed and properly set up.
“If you don't have the data, properly cleansed, properly prepared, or properly set up, then the value of the predictable model is going to be lower," Purcell said. "The concept of really understanding data sources, data collection, data cleansing, data orchestration, is still the fundamental aspect of where I would start before I'm thinking about getting into predictive models.
"This may be a really interesting result or piece of information that you present, but if it's not accurate information, then the teams, the executives of businesses, the people that will be presented that data in meetings, or in discussions of strategy, will lose credibility because you will spend too much time fighting over the data quality.
“And that's just something you don't want to accelerate. But if you've got that data foundation in place, and baseline data credibility in place, your ability to then set up those machine learning models to help drive more predictable use cases is unbelievable.”
Purcell does not just mean data cleansing from a regulatory perspective, but from an ethical one, also.
“Companies in general are still plagued with the classic challenges of alignment and communication and how organisations collaborate. And now as companies move to a data-first mindset, if the data isn't isn't properly prepared, if it isn’t agreed upon by all parties that are involved in the conversation, it just creates so many challenges," he said. "We need to be prepared with our data and our information so we know how to be the best we can be for the company and our customers.
“The early era of big data was about operational data. And that was about looking in the rearview mirror to really understand what has happened in the past to just understand the results in a backwards looking perspective. But now this modern era of real time, that's the real key - the concept of now leveraging the experience, to impact real time and then layer predictive aspects over the top. The machines can start calibrating around experiences based on buying info and customer information down to the personal level. It's just insane what we will see in the future.
“A lot of people are wary and almost nervous about this, but it does at the end of the day offer a much better, more personalised experience for people, and then brand loyalty grows from there.”
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