Transform your marketing analytics to outperform your competition

James Forbes

James is the head of marketing and digital for InfoReady. The pure-play information management and business analytics consultancy specialises in helping organisations transform data into actionable intelligence.

As digital and offline brand experiences diversify, more customer data is becoming available to marketers. At the same time, the number of tools available to analyse this data is increasing rapidly. Leading marketers are taking advantages of these shifts and transforming their marketing analytics practices to outperform their competitors.

At its core, marketing analytics is about understanding customers better so marketers can improve the way they are served. Intelligence from marketing analytics informs decisions to help optimise marketing activities, improve efficiency and lift ROI. More data and improved analytical tools means we can better understand our customers across the entire journey, generating better insights and make more effective decisions.

However, to take advantage of these advances requires a transformation of traditional marketing analytical practices. High performing marketing teams are putting marketing analytics at the centre of their operations, creating an end-to-end system of activity that is data-driven and that thrives on continuous learning and optimisation. They are adopting a wide array of tools and employing new team members to exploit the intelligence that that lies within their data vast repositories.

Let’s take a look at some of the characteristics of a high-performance marketing analytics practice.

Real time

Looking in the rearview mirror on a weekly or monthly basis is a thing of the past. Even teams operating on a daily cadence are still up to 24 hours behind their customers and leave themselves exposed to competitors.

Leading marketing analytics practices support the continuous analysis of customer interaction data in real time, using a combination of new tools and business processes. Huge volumes of interaction data from multiple channels are able to be processed and analysed with real time responses generated across both inbound and outbound channels.

End-to-end

Truly understanding the customer experience requires the combination of data across all touchpoints, online and offline, from point-of-sale to transactional systems, websites and apps.

While online data abounds, many marketers still struggle to gain an understanding of the online footprint of their customers. There are the challenges aplenty when trying to join the dots between owned, earned and paid media. Even within owned media, the story is not straightforward: For example, trying to identify whether a user of a mobile app is the same person as a user visiting a website.

While there is no perfect solution, these challenges represent an opportunity to analytics savvy marketers. Those who can piece together the journeys of their customers across as many touchpoints as possible are best positioned to exploit their understanding.

The best tools and the right people

Unfortunately, there’s not a single tool that will provide you the end-to-end visibility you desire. Understanding your customer’s journey across all touchpoints requires piecing together capability from a variety of vendors and joining the data between them.

When buying technology, be wary of vendors who promise an end-to-end solution, and look for evidence of open APIs that will allow you to move data around without restriction in real time.

And without skilled practitioners, the best tools are of little use. You can’t expect your technology investment to deliver dividends without investing equally in your team. When hiring, knowledge of a specific tool is useful, however, look for practitioners who have strong business analysis and problem-solving skills as well as a natural curiosity.

Increasingly, data scientists are being hired into marketing teams. Data scientists bring with them capabilities to perform advanced data modelling using techniques including artificial intelligence and machine learning to perform predictive and prescriptive analytics.

Automation and agility

The huge volume of data being gathered across multiple channels demands that processes must be automated across the full data lifecycle, from collect, to combine, to analyse and act.

At the same time, leading marketing organisations are adopting an agile approach to analytics that thrives on a continuous cycle of learning and optimisation. Take a flexible approach that embraces and supports the execution of many small data-driven experiments and learns as it goes.

Accessibility and visibility

While you need a core team of dedicated analysts and data scientists, analytics must be part of everyone’s role. With data central to marketing decisioning all team members need access to data and analytical tools and should have the capability to perform analysis relevant to their role.

Whatever stage of maturity your marketing organisation is at, ensure analytics is performed with purpose. Ask yourself why you are collecting and analysing data and what business decisions this will inform. Avoid ‘analysis paralysis’ and take action with many small experiments to test your hypothesis, learn from the outcome and optimising your approach continuously.

Soon enough, the benefits you realise from your initial investment will support the business case to invest further in analytics.

Tags: data analytics, data-driven marketing

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