Getting back to the ‘human’ heart of your data with next gen martech

You are in database hell, right! You have an accounting database, an order history database, a contact database, a loyalty management database, a complaints database, a transactional NPS database, an ATL media database, a digital database, a sales database, ex-factory/warehouse databases and supplier databases – all separate from one another. Sound familiar?

You’re not alone. Far from it in fact. This era of data proliferation began a long time ago, and as different parts of organisations jumped onboard with the idea of ‘save it, it’ll be useful some day!’, each solution expanded organically but often haphazardly, creating multiple disconnected data lakes within your organisation.

Each part of your data environment exists because someone recognised an opportunity to understand some facet of your organisation. However, some parts work better at different aspects than others, whether that be collecting or storing data, making the data accessible, or providing general exploratory or detailed investigative analysis. And they don’t always play well together. In fact, these disjointed systems have accumulated data to now unmanageable volumes, and the big end of town’s consolidated data integration platforms and solutions are still too often over-engineered, expensive and generally inappropriate to specific environments and needs of individual businesses.

Data ecosystems are now reverting to smaller, focused tools for specific areas and with better connectivity features. Yes, marketing technology (martech) is finally growing up… thankfully.

Identifying the right information for the right stakeholders is at its heart a human endeavour.

While the work to revolutionise martech has just begun; it is already providing new opportunities to create value from data sources that are now better designed to be usefully connected. Creating value from the martech of the future will come in the same two ways required from martech of the past:

  • getting the right information into the hands of the right people
  • providing more and better insights made possible through the harmonised data ecosystem.

This first requires an audit of both the information available and the stakeholders into whose hands it should go. The design of data capture, access and visualisation systems that are both timely and relevant should be driven by:

  • understanding your purpose
  • identifying the goals that you are trying to achieve
  • and most importantly, how the system will help your business achieve KPIs and objectives (who needs what information to make what decisions and how this data will be used)

Whether this is the disconnected systems of the past, the consolidated but underwhelming systems of the present, or the integrated but heterogeneous martech of the emerging future, the set-up for the system requires a human understanding of organisational structures, goals and data-driven decision-making protocols. And while some systems can shortcut the implementation of these requirements, intelligent implementation by the analytics community is the critical ingredient to success.

Similarly, unlocking the value of connected data requires connected models of human behaviour. What is possible with single source individual customer records? What is possible with stratified longitudinal databases of input and output metrics for the business? How can you connect the two?

For example, how do CX and brand influence each other? How do their feedback loops deliver returns in terms of sales, retention and increased spend? Where should you invest to get greatest return and uplift, and where are you already over investing? How do strategic segmentations for product and service development relate to tactical segmentations for targeted marketing communications? And how do these segmentations work within the martech systems to monitor and support achieving these objectives?

Understanding your organisation’s unique questions and data ecosystem is a creative process.

Some systems and frameworks can be generalised for common corporate questions, but every business’s ecosystem will have its own KPIs and unique quirks of data availability requiring proxies and substitutions and intelligent model design before the computers start building forecasts.

Remember, it’s not a sprint or a marathon, it’s a relay. With each iteration of technology, the tools and structures of data ecosystems are getting better. And with this evolution, layers that slowed access and interfered with our desire to be more data-driven are being stripped away.

This next version of technology is making the data even more accessible and, importantly, empowering marketing professionals – and your partners in accounting and the C-suite – to start getting better answers to the questions you’ve always had. Ideally, data and systems will be engineered to inspire you to ask new questions – some of which will, in turn, inspire the next generation of technology and the analysts and marketers who will use it. In the end though, the accelerating pace of development of the exciting tools of our trade will have us madly off in all directions unless we apply the thought and expertise necessary to build toolboxes that work for us.

John Cucka
Head of Analytics, Kantar Analytics Australia

Rob Kramer
Associate Director, Kantar Analytics Australia

Maree Taylor
Head of CX, Kantar Australia