“Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

Digital Marketing and its elusive promise of complete and instantaneous visibility across all activities has tantalized marketers for the best part of three decades. The notion that any interaction between a customer is trackable, and therefore measurable has an obvious appeal to those marketers tired of being seen as the ‘Colouring-in Department’.

However, as many have found out, effectively capturing and utilising the troves of digital marketing data available is easier said than done. The reality is, most companies have no idea how to capitalise on the considerable data intelligence that is available to them. Yet, for the brands that are able to do so – the positive business impact is immense. With a recent study from Mckinsey which surveyed 1000’s of MNC showed those companies who achieved the greatest overall growth, were able to attribute a significant proportion of that boost from their ability to utilise data and analytics (Read: Catch them if you can, how leaders in data and analytics have pulled ahead).

So what are the steps that brands take to fully realise the potential of data that is available to them?

1. Build a 360-View of the CustomerFirstly, brands need to build a 360-degree view of a customer – or at least as close as possible - by capturing, storing, and organising data in such a way that it can be meaningfully dissected. This means capturing as many of the millions of data signals that customers provide each and each and every day. These signals can range from simple behaviours such as opening an email, clicking on a banner ad or applying for a loan to more passive signals such as turning 18 or letting your gym membership expire.

Most companies have elements of a 360-degree customer view, but often they reside in silos without the ability to ‘stitch’ all of the signals together. To overcome this, data leaders are increasingly turning to Customer Data Platforms. And while this relatively ‘new kid on the block’ technology has transformed a brands ability to build a holistic view of the customer, the technology alone will not solve the challenge.

Critically, brands need to strategically decide on what specific data they want to collect and how they will collect it (Cookies, Interactions, forms etc.). Too often brands skip this step – expecting data to answer questions without first defining those questions.

Brands also need to ensure that all this collected data is clean and consistent, which can be both a strategic and technical challenge.

Creating a logical data taxonomy is one of the most painstaking but important tasks that a brand can undertake on their data journey. Having a standardized naming convention means your data is consistently categorized across multiple sources and channels, thus allowing you to undertake the second step of a data journey - meaningful analysis.

2. Data Analysis: Mining data for actionable signals Data is only beneficial to an organisation if it can be actioned upon. However, marketeers (and their agencies) often fall into the trap of undertaking quasi analysis on a host of vanity metrics that provide no relevant insight on how a digital experience or media program can be improved.

Often this takes the form of isolated channel analysis – i.e. the Open Rate on the eMail campaign was 10% - without any degree of audience level analysis or action orientation.

While even the most rudimental segmentation can lead to actionable insights (the Open Rate for Segment A was 5% and Segment B was 15%.) such simplistic analysis can distract brands from the true power of data.

True strategic analysis should allow marketers to gain a deeper understanding of their customers and the unique relationship they have with a brand – who they are, what they have in common with other customers, what content they are most likely to engage with etc. etc.

Consider the diverse audience of a university’s website. At the highest level the organisation may cluster into prospective students, current students, researchers and staff. However, this is only a surface level understanding – direct or indirect signals may lead us to learn the faculty they belong to, level of engagement they have with the organisation, tenure of engagement and so on.

With the right technology in place, advanced data models allow for clustering of customers into narrow segments based on a continuous loop of signals, with each interaction leading to a progressively fuller picture of the customer. With this, marketers are then able to dissect their audience into increased granularity and uncover a host of actionable triggers and insights.

Decisioning and Distribution: Personalising the experience Armed with a deep understanding of their audiences and a host of actionable insights and triggers against them - the next step for a marketer is to decide exactly what to act upon. It is important to note here that not all need to be actioned upon.

It is here that an integrated technology stack, combined with rule-based automation becomes the most powerful tool in a modern marketers arsenal.

Whether it be a fully-fledged Experience Platform, stand-alone Content Management System, Marketing Automation Platform or simple Email Service provider – marketers now have a plethora of technology solutions that allow for the distribution of personalised experience.

Continuing on with the University example; this stage may include using a CMS to personalise website content based on whether a visitor is an existing and prospective student. Or a triggering email based with tailored content based on the course guide that was downloaded.

The potential scenarios that can be delivered by these technologies are practically endless – only restricted by volume of actionable data and content available.

3. Ongoing Testing and Learning The last step that markets need to be aware of in making the most of their data is the capacity to test, learn and evolve these decisioning strategies and content.

Data-leaders are those that have an embedded test culture, looking at results to understand “what did I learn” rather than “can this data prove I made the right decisions”.

Marketers who understand that a successful data journey comes from a collection of incremental improvements in strategy and execution rather than a single “home run” are well placed.

In conclusion Data enables brands to understand who a customer is, how and where they are interacting with a brand – and, more importantly, data makes it possible to craft and influence those interactions. In essence, data is the single most important ingredient that allows a brand to improve the entire customer experience through personalised content and tailored journeys.

For marketers that fail to act on the data they have at their disposal they are at risk of losing relevance and remaining as the ‘colouring in department’.