For brands, a huge benefit of a customer’s digital footprint is it provides them with a better understanding of the customer as an individual.
The plethora of online user data that brands collate – combined with offline data – is supposed to facilitate a more consistent and tailored consumer engagement. Yet, how do consumers really feel about their ever-growing data trail?
Our recent Experience 2030 research suggests that the gathering and use of personal data is being met with a strong sense of concern by consumers. While 46 percent of respondents are willing to trade personal data for free products and services, 73 percent felt concerned about how brands are using their data. So, how do brands turn the tide and win back their trust?
A concerned customer
The volume of data available to organisations opens up a world of possibilities within the customer experience, for both the consumer and the brand. However, most brands are not delivering on this potential yet. Instead, their approach has left consumers cold, with 71 percent believing companies shouldn’t even be allowed to share their data with other brands. This is likely to stem from the fact that 73 percent of customers are questioning exactly how brands use their data. Unless you’re able to take this data, apply analytics to drive better decisions and execute those decisions in the moment, you aren’t going to keep up with the competition when it comes to customer experience.
This uncertainty has manifested into a feeling of distrust amongst consumers. It’s now reached a point where 61 percent feel that they have no control over the level of privacy they need for themselves, their family or their children.
A combination of recent data scandals and widely publicised hacks may have contributed to this feeling of concern. However, it’s also worth considering that many consumers may also feel they aren’t getting a fair exchange when they share their data.
So, what are brands doing wrong? Why is it that despite access to so much valuable data and significant investments in analytics and data science, customers are still not receiving the type of tailored and personalised experiences they expect in exchange for sharing their data.
Unfortunately for most organisations, the process of filtering through vast amounts of data, spotting patterns in customer behaviour and then using this information to drive decision-making is a painstakingly slow process. By the time the insights are deployed into customer interactions, they lack the relevance and context required to engage customers, leading to frustrating customer experiences and limited business value.
Showing customers products and services they’ve just purchased from you, or that they’re no longer interested in, is a sure-fire way to annoy customers and lose their trust.
Brands must find a way to bring their operational structure in line with their vision of a customer-centric future and create a customer insight process that accelerates the time from data to insight to decision to impact in a rapid, repeatable and scalable way.
You’ve got to make that change
To achieve this, organisations need a way to rapidly bring together a complete view of their customer.
This covers what they have done across all channels, past and present, and predicting what they may do in the future. It involves combining online data, offline data and even third-party purchased or collected data with data quality processes that ensure that your customer data is trustworthy, valuable and ready for analysis.
Analytics needs to be democratised with marketers given access to advanced analytics delivered to them in an uncomplicated, easy to consume way. Analytical decision helpers should be deployed to embed real intelligence directly into the marketing process to support marketers in crafting messages, offers and content across channels.
Predictive analytics and machine learning should ensure that accurate insights are delivered at the right time, dramatically increasing the reach and value of data and enabling brands to make evidence-based decisions at every stage of the customer journey.
Finally, this all needs to be operationalised, so it becomes an ongoing, dynamic process, embedded directly into the customer interaction.
Insights derived from self-learning customer and behaviour analysis must be fuelled by real-time contextual data from every customer interaction, to dynamically refresh every customer recommendation, offer or message and deliver instant experiences that are highly relevant.
By providing such a personalised service, consumers will have greater visibility into how their data is being used, giving them a greater incentive to share.
Making the impossible possible
This may well seem like a daunting task for a lot of brands, but it’s proven both feasible and worthwhile by ICA Banken. Offering financial services to Swedish customers, ICA Banken has committed to providing a personalised marketing approach to each of their 750,000 customers. As is the case with many retailers in the digital age, the brand’s interactions with their customers are largely digital.
Using SAS Customer Intelligence 360, the bank has been able to collect the customer’s data in real-time, tracking the user’s behaviour on the app and how they got there in the first place. By collating this digital behaviour, the bank has been able to gauge the core interests of each consumer and provide them with customised offers in response to this.
The impact has been immense. ICA Banken’s customer engagement increased by 70 percent and their efficiency levels have improved several times since adopting the new approach. Where it used to take them weeks to design a marketing campaign, it can now be produced in a matter of days. This is a level of efficiency which all brands can achieve by collecting, integrating and, crucially, analysing the correct customer data.
Providing data has to be a two-way street where both the customer and the brand benefit. For the customer, offering up their personal data should be the gateway to a more personal experience. This can only be achieved if brands accelerate the process of collecting data, analysing it for valuable insights and making data-driven decisions, even in real time if needed. As demonstrated by ICA Banken, the results are worth the effort.