In today’s guest post, Greenlight Digital’s Wojciech Bednarz, examines the key challenges facing marketers, as they strive to harness multi-channel data in an effort to develop a single customer view.

One of marketing’s greatest hurdles is fragmentation; customers are becoming increasingly difficult to define, and as brands are looking to improve segmentation and targeting more and more, it’s key for marketers to marry their data to create a single customer view.

But where to start when it comes to breaking down all this data to truly understand who their current and new customer is? Data management services are fast becoming the solution, combining interaction, descriptive and behavioural data to help brands understand what makes their customer tick. And with such valuable data comes great responsibility for marketers – they need to ensure that it’s used to improve customer experience and truly tap into their needs. Through this customer-centric approach, marketers can then impact brand perception and brand value as a means to support business growth in the long-term.

Making sense of all the data

Tapping into rich data resources which hold first-party data sounds like a no brainer, but surprisingly there are few marketers who capitalise on this resource. The reality is that many brands don’t know how to make sense of the data available on their audiences nor can they merge cross-channel data, making it difficult to create meaningful insights on how to optimise processes. This ultimately leads to a disjointed experience as well as inconsistent reports due to data coming from multiple channels.

There are many reasons for this, such as the technologies in play which are often siloed across separate departments. These include search, display, social media and more, which effectively results in fragmented insights on audiences and their behaviour. When it comes to reporting, Facebook Audiences differs from AdWords, as does Twitter from Display, so aligning data is inevitably overwhelming and incredibly difficult. And that’s not even the end of it – there’s also an analytics layer of technology which reports behaviour on the site, user engagement and source to name a few.

This is all well and good, but none of the above – apart from social media channels due to their audience-driven nature – offer more insights into who the customers are, their interests or how they spend their free time.

The most reasonable thing a marketer can do is to try to get a channel and technology agnostic data management platform (DMP) which can take audience science to the next level. This opens up new possibilities, bringing customer data from all channels to unprecedented granularity and building a fuller picture of who they are, what channels they engage with and what their demographic characteristics are. The most important aspect of this technology is that marketers can immediately act on the findings and help optimise all aspects of digital marketing campaigns on the fly.

Capturing the cross-channel experience

With increasing usage of mobile devices in the last couple of years, cross-channel marketing strategy has become the holy grail for many digital marketers who are trying to figure out the best way to approach this conundrum. While more than three devices per user is already complicated, it seems 2017 will bring even more complexity to the world of multiple interfaces per user reality, as it’s not only screens marketers need to consider, but also the growing universe of IoT devices, such as smartwatches and voice-controlled devices such as Amazon Echo.

In fact, Google is predicting that in the next two years, a third of searches will be initiated by voice. On top of IoT, VR is beginning to make waves, alongside the growing capabilities of AI, all of which will drastically change how people consume media and, thus, behave on devices.

With consumer technology moving so quickly, so does the technology digital marketers can use to understand them. The data management solutions are becoming more agnostic and can pick up any signal with the ability to stitch together information from other devices based on machine learning to determine if they belong to one person or many. For example, Greenlight’s Data Management Platform allows data collection across devices and interfaces to process it all as one user ID. This has tremendous implications for ensuring that marketing efforts deliver the best possible experience for customers. Being able to identify the ownership of devices with more confidence will allow for more precise messaging, and thus more effective campaigns, leading to better CTRs, lower costs and better ROI for clients.

Data-driven marketing is here to stay, and will become a standard modus operandi in the foreseeable future, particularly with the increasing usage of AI and machine learning. This will result in a higher degree of automation of marketing activity and a greater focus on data and its interpretation, elements which will be key factors of success for brands going forward.

*If you’d like to learn more about this topic, you can catch-up with Wojciech at the Data & Insight Leaders Masterclass in Manchester, where he’ll be delivering a session on –  Joined-Up Data to Deliver a ‘Single Customer View’

Author Biog:

Wojciech has over six years of experience in digital marketing, four of which he has served at Greenlight. During his career at Greenlight, he’s worked on various international clients such as RS Components and Nespresso alongside others. He joined as part of the Client Services team, where he was responsible for delivery of SEO, paid search and display activation campaigns.

He was particularly focused on data-driven strategies which involved advanced analytics, data modelling techniques and manipulating large data sets. In his current role as Data & Insights Senior Strategy Manager, Wojciech is responsible for advancing Greenlight’s Data Science & Audience Insights department, which involves managing the implementation of the DMP technology across the agency to deliver cutting edge audience insights, data-driven attribution models and advanced activation strategies through data analysis.