But before we get going, remember you can skill up with the following Econsultancy resources:
- The Fundamentals of Marketing Measurement and Analytics
- 2017 Measurement and Analytics Report
- Advanced Data & Analytics Training.
Analytics for the masses
Kieran Kilbride-Singh, head of marketing, Prodlytic:
Analytics is still a pain for the average internet user and analytics companies know this. The focus in 2017 has been on making it easier for people to access data and take action in less time. This will continue in 2018 with things like ‘track everything’ and visual recording of events/metrics (only a handful of companies offer this right now).
David Wharram, commercial director, Coast Digital:
The introduction from Google of Data Studio set out with the ambition of making analytics data easier to interpret for users. We have seen many cases where it’s been possible to remove the need for custom dashboards – a time save and positive for any agency. This has allowed us to present strategic findings without getting into the weeds of showcasing how Google Analytics works and explaining its quirks.
As machine learning marches on I predict predictive analytics making more of a splash for both product and marketing teams, too. Things like predicting customer churn so you can catch people before they churn or highlighting common behaviours between different users that makes them more likely to complete onboarding or become a power user.
Analytics companies are also trying to provide more context to your data, helping you understand the why behind the what. For example, answering questions like “why do only a handful of people make it to the sign up page?” once the data shows only a handful of people did actually make it there. Long-term, this goes hand-in-hand with predictive analytics.
As AI and machine learning become more sophisticated, the need for relevancy and personalised marketing communication will only increase. Channels like email will be left behind If organisations push messages in a world where customer behaviour is predicted and recipients inboxes are tailored to their needs and desires.
To take a positive stance on GDPR, from a data strategy perspective organisations should be getting data cleaned up, consented and well organised. Not only will this be necessary to be compliant, but it will help layer relevant data at each touchpoint in the user journey. The new era of tidy, consented data forces us into best practice, and encourages engaging personally with users who want that approach.
…But beware ‘AI’ hype
Andrew Hood, founder and MD, Lynchpin Analytics:
This year there has been an explosion in the noise around artificial intelligence (AI) and machine learning (ML). Reading the marketing buzz, many might conclude that simply supplying more big data into a black box will lead to better outcomes and novel solutions. In truth, there is a lot of hype here, but as always smart applications of computer science and statistical techniques will help companies leverage data more effectively in 2018.
Integration means more than just systems
Ben Barrass, head of data and analytics, Centaur Media:
The proliferation of tools and the requirements and pressures specific to digital marketing have meant that marketers have long been able to ‘hack together’ processes and flows to match various tasks. This tends to also include the approaches of ‘agile’ and ‘mvp’ (minimum viable product). All of which are generally taken out of context and munged to fit a task that suits the marketer.
As we all now have part used, badly implemented, compromised systems, and with the increasing demands for quantifiable results from substantial digital investments, the role of the marketing technologist has never been so important.
With the speed that an organization can transform to meet a digital challenge, it’s not about culling the rogue entities from the organization (either from marketing or from the technology) – as that would circle everyone right back to where we were before when we had to go to IT cap in hand for every tech implementation. It’s in fact, the opposite – fostering a strategy or approach that allows for entrepreneurialism within the system.
Establishing a strategy that goes outside of just the technology to focus on business requirements, talent, competencies, integration with processes not systems etc to concrete in the main business strategy, and make room to play and experiment – bringing the successes into the fold and not losing everything on the fails.
By taking this approach and applying clear responsibilities and tasks, ‘hacking’, ‘agile’ and ‘mvp’ can actually become valuable to an organization.
GDPR will start to become a bigger reality in 2018, and as organisations navigate compliance there are both challenges and opportunities for analytics roadmaps as the urgent review of data models and strategies takes place. As well as the obvious impact on consent and data governance, questions around service design and how to effectively and ethically target customers are likely to come to the foreground.
GDPR has a significant impact on tracking and targeting opportunities. Cookies are likely to be affected by GDPR (in particular those that have a User based ID). This will affect reporting, particularly with cross device attribution. Furthermore there will be an issue with user based targeting options for Paid media channels. In May next year website owners will need to be open about the data they are storing on a user and the tools the site uses to capture information. This could end up including options for users to opt in and out of how they are targeted, retargeted or reported on in paid media – a significant step change for all advertisers.
Lots of companies – big and small – are worried and confused by the GDPR. Those that can offer GDPR compliancy with their analytics will probably do quite well in 2018.
Customer experience over campaign optimisation
James Collins, SVP Global Product Strategy, Attribution – Rakuten Marketing:
In 2018, I hope more businesses will move on from just using attribution data for immediate performance reporting and campaign optimisation to also using it to improve the customer experience.
Brands that are able to implement the infrastructure (across the business, not just in the marketing department) to effectively track, manage and effectively act upon their customer data are those that will succeed.
Those brands who are able to take advantage of AI and machine learning to provide these personalised – and most importantly, relevant – messages at scale (across the many different devices consumers access) without losing the ‘human touch’ will be even more successful.
Prescriptive analytics and data viz
2018 will see more flourish across prescriptive analytics and visualisation of data as the need for data-driven decision making and sharing of insights increases. But in this vital area a lot of things don’t change: the need for a comprehensive analytics strategy that breaks down silos, makes insights transferrable across the business and impacts decision-making at all levels.
More than just data collection
James Parker, Global Head of Data and Planning, Jellyfish:
The biggest change we’ve seen in 2017 is that projects are no longer simply large data collection exercises. Analytics is now a critical medium for a business to achieve its targets, with more teams in an organisation now leaning on analytics data to improve their day-to-day roles.
This has resulted in a large growth within analytics strategy, training and insight, essentially using the data to generate an outcome, for example, increase conversion rates through personalisation or increase return on ad spend through building more effective audiences.
What do you think, fellow marketers? Let us know in the comments.