Personal Data Is Now On Consumer’s Terms
Personal data is now on consumer’s terms. Today’s brands face a tough task, to drive continuous improvements in marketing performance in a world where personal data is less available and consumers have ever-increasing expectations around privacy.
Privacy and data protection regulations have recently given people greater choice around sharing personal data with brands.Technology companies have also begun to require opt-ins before consumers’ personal data is shared, while third-party cookies are being phased out, preventing the tracking of online behaviour.
While some marketers may see this as a loss of consumer insight, the shift has driven companies to reconsider their approach, opening up more robust ways to collect data while adhering to evolving privacy norms.
It was in this context that Meta’s VP of business product marketing, Goksu Niebol-Perlman hosted a panel of digital marketers from lloyds Bank, pandora and EssenseMediacom at the Meta Marketing summit EMEA 2023. the discussion ranged from how brands are upholding their customer’s privacy rights online to how they are adapting their digital strategies, ensuring they can not only trust their data but also use it most effectively, throughout the marketing funnel.
As consumers become more aware of their data rights and require brands to engage on their terms, advertisers need to reconsider their approach to gaining marketing consent.
For Pandora’s global paid social manager, Kasper Moll, the key to ethical data collection is to do it when it is necessary and adds value by improving the customer journey.
“We only collect data if it creates a big uplift for Pandora and creates a better user experience for our consumers and potential customers,” he says. “That’s the trade-off we always evaluate.”
According to Nic Travis, head of digital marketing at Lloyds Banking Group, this means understanding when it is the right time to ask, as well as explaining why it is necessary and the value exchange behind it. The bank changed its approach recently, he said, because it wanted to avoid the appearance that it was a “box-ticking” exercise for the brand.
“When a customer is out of the product application journey, and downloading our app, we deemed that’s the right context for permission,” he explained. “It’s then a relationship journey rather than a purchase journey. It becomes: ‘How can I get the most out of this product that I bought? How can I get the most out of this service?’ And therefore, asking for consent to deliver personalised advertising in that context is the appropriate place. We’ve seen very high opt-in rates from that approach.”
Trust, optimisation and efficiency
Given the principle of data transmission and deprecation of third-party cookies, marketing teams need new ways to show incremental gains from their activity, given they rarely have a full picture of a customer’s path to purchase. A key tactic her is to integrate the brand’s first-party data anonymously with an ad platform’s API, which enables marketers to understand how customers convert to a sale without compromising their privacy.
Nic Travis agreed that using API technology is now “table stakes” for getting a reliable picture of customer behaviour, given many customer journeys can no longer be tracked. “There’s only so much modelling can get right,” he cautioned, arguing it brings confidence when platform data can be matched to actual sales data. “We’ve seen a huge success in the conversion lift studies that we’ve run after putting in CAPI.”
Kasper Moll added that the benefits of API technology are tangible enough to justify the investment in first-party data infrastructure: “If I were at a brand where we didn’t have it in place, I would certainly make a business case for it,” he said. “And that’s fairly easy when you get the average uplift that we see from getting it in place. The business value is there, and quite apparent.”
He emphasised that Pandora sells around 280,000 items of jewellery per day, so it uses CAPI “triangulated” insights from multiple data points, as well as to optimise advertising campaigns
Full funnel, not just conversions
Moll believes brands should be upbeat about data signals from consumers’ digital activity becoming less available, and get back to the fundamentals of marketing throughout the funnel rather than becoming fixated on treating digital channels as conversion opportunities.
This shift to giving digital marketing a role throughout the full funnel is also happening at Lloyds, said Travis. “We want to make sure our consumers understand the depth and the breadth of our product offering and to do that we need to get beyond broadcast media,” he said.
“Our focus is on generating great creative at scale, using different formats in more channels and making sure we personalise those messages so they are relevant to the customers’ various life stages, regions or different kinds of demographics. We then need to make sure we can measure marketing effectiveness through the full funnel. That’s a really key point for us.”
Predictive personalisation is the future
Learning algorithms displace uncertainty in customer knowledge. In accordance with the historical ambition of market research, they quite considerably reduce the uncertainty surrounding the customer’s tastes and behaviour.
They do, however, induce a new type of uncertainty, linked to the explanation of scores (black box effect), but above all to their interpretation. Contrary to symmetric critics and praises regarding machine learning algorithms, they do not produce a cold, seamless, and automated process of knowing and governing data points. Until the last moment, sadly humans often adjust the parameters and nature of algorithmic action on people.
What happens to the consumer in this process? Far from being dissolved within algorithmic computation, their taking into account constitutes both the horizon and the material support of a set of varied practices.
Organisational settings actively aim to embed the usual categories and perceptions of the consumer in the choice of analysed data, and in the types of questions for the algorithm to solve. The algorithmic predictions demands a specific interpretation work, or “casual theorising”, that reattaches them to “palatable” figures of the consumer and plausible courses of action. Predictive personalisation software (PPS) is just one example, in which highly likely imminent purchases are identified and emailed to the consumer before competitors know about it, or the moment is lost.
These moments, before and after the calculation, are key conditions of its possibility, success and performativity upon the social world. In other words, it is because data and algorithms are continuously readjusted to exogenous preexisting forms of knowledge that personalisation of entire databases can happen.
Classical categorisations of the consumer are essential to the organisational as well as to the epistemic success (i.e. producing “useful” predictions) of predictive marketing.
Algorithmic modelling seems to be a mechanistic knowledge instrument, in which the automated abstraction of calculation should guarantee the elimination of human preconceptions and biases, and would thus produce more “objective” results than those generated by traditional market research.
Nevertheless, predictive personalisation in ecommerce marketing now delivers enormous return on marketing investment, through application of precise and enormous volumes of personal data on each consumer.
Further practical and academic articles on ecommerce marketing are available here.
SwiftERM is a Microsoft Partner company.