Digital marketing has never been easier in some ways, or more difficult in others. Even as data gives us more power to identify each customer and determine what they want, consumers expect personalised, relevant experiences, and they expect those experiences to work seamlessly across every device they use.
In this series of articles, we’re analysing the five key components of optimising your FSI customers’ journey: bringing data together, creating individualised content, leveraging decisioning and algorithms, delivering fluid experiences across channels, and continuing to test, learn, and optimise over time.
We’ve already spoken about data—how to identify who the customer is and what content we should show—and explored key concerns around unifying data and having a KPI framework. But once we’ve got that data, the next piece of the puzzle is to create the personalised content our customers expect.
Google’s 2014 Zero Moment of Truth survey found that customers engaged with 10.4 pieces of content before making a purchase, and that number has only increased since then. What’s more, a recent Nielsen survey found that customers are five times more dependent on content than they were five years ago.
Forrester’s study Proving the Value of Digital Asset Management reports that most brands are creating more than 10 times the assets they used to, in order to support today’s proliferation of new channels. A full 76 percent of those brands agree that consumers’ expectation for personalisation increases the need for assets.
In short, consumer expectations, new channels, and the personalisation expectation all create demand for great content. But how do we actually go about delivering that content?
Freedom within consistency
We want to give our designers and creative teams the freedom to create, within the limits of brand consistency and guidelines. That means we need templates. It also means we need to give our creative teams access to the right tools, and put the right workflows in place to enable them to collaborate—both with each other and with outside creative agencies—as well as to leverage user-generated content from customers. The goal is to speed up the entire creative workflow, at scale. This is where I have seen organisations adopt traditional agency models, or publisher models, to successfully deliver content quickly and efficiently.
Once you’ve optimised the process of creating new content, you need to manage it from a central repository—a digital asset management (DAM) system, which gives you the ability to search for content. To achieve that searchability at scale, you’ll need machine learning to analyse and extract the semantic content of an image, and to automatically tag images with helpful keywords. You’ll also want your DAM to integrate with other systems, whether that’s enterprise resource planning (ERP) or a content management system.
Another key benefit of a DAM system is the ability to automatically generate hundreds of different variations of the same asset, each tailored for a particular screen resolution, bandwidth, or customer segment. Each of these assets may start with the same background image—or with a range of images you’ve specified—then dynamically overlay many versions of messages and calls to action (CTAs), each to be delivered to a particular customer at a specific time, on a certain channel.
Assembling all those assets by hand would take years of labour, but a cutting-edge content management system can assemble millions of them on the fly, and deliver them as needed.
Optimisation and benefits
Now that you’re delivered millions of personalised assets at scale, it’s time to start optimising your campaigns. A digital content management system makes this much easier, because it records analytics on every asset it delivers. This means you’ll be able to run A/B and multivariate tests on each version of every asset, analysing its performance in ways that will help you optimise its contribution to the user experience as a whole.
One obvious question is, “How does each asset contribute to revenue?” As your analytics come rolling in, you’ll be able to start personalising assets even further, by bringing that real-time performance data back to your designers, explaining which audience segments especially like which types of content, and assigning them to focus on creating more content that’s likely to resonate.
This data-driven process not only mitigates risk, it also drives engagement by making content more interactive, and breaking down communication barriers as you learn more about what your audience segments want and expect. Meanwhile, it also helps you streamline production by cutting down on repetitive tasks, and eliminates the costs of creating redundant or useless assets. Most of all, the right creative processes help you go to market faster, and reach each of your customers with precisely the right message before your competitors do.
On-demand data management
Financial services firm UBS had a worldwide portfolio of marketing assets, including a flagship global website offering more than 50,000 pages in multiple languages, highlighting financial planning services, credit cards, and a wide range of other products. However, without a centralised repository for the vast amount of assets required to support marketing, media, and e-commerce, UBS was slow to launch new campaigns, updates, and events.
By switching to an on-demand DAM platform, UBS improved collaboration with its video production teams, and with agencies handling other types of asset creation. They strengthened brand management and consistency and improved their ability to share large files, as well as to collaborate with international offices and external vendors, achieving a quick time-to-value on their investment.
The results have been striking. UBS has achieved three times faster and more efficient publishing, with increased personalisation; up to 40 percent faster updates through reusable assets; increased security in managing authoring rights; and new insights into user activity.
As data and content continue to proliferate, it becomes impossible to manually deliver personalised experiences at scale. To meet your customers’ expectations, you need to automate by leveraging data science. That’s what we’ll be talking about in the next blog of this series: how you can use machine learning in digital marketing to deliver standout customer experiences. See you there!