data analytics

How to get started with analytics: Let the data tell the story

3rd Mar 2016

With CEOs increasingly recognising the value of digital data, marketers are under growing pressure to explore ever more sophisticated analytics techniques. But what is the best way to move from basic demographic segmentation to one-to-one personalisation? How can a company achieve effective multichannel attribution or accurately understand the complete online customer journey?

In this series of four articles, we're going to explain the importance of the 'crawl, walk, run' approach to gaining true business advantage from Big Data analytics.

The biggest question facing those organisations yet to embark upon any serious analytics activity is often where to start? With so much data and so many analytics techniques to explore, it is easy to feel like freezing in the headlights. But doing nothing is not an option when two-thirds of respondents to a recent Forbes¹ survey report that big data and analytics initiatives have had a significant, measurable impact on revenues.

Asking difficult questions

Companies need to be honest with themselves. What is our current level of analytics maturity? How do we compare to the competition and to other market sectors? Have some teams demonstrated more expertise than others? It is only with this level of tough evaluation and understanding that a company can identify and fill gaps in skill sets and confirm its situation regarding the underlying technology – both data and analytics tools.

Improving analytics maturity is key to achieving data-driven decisions. The emphasis is not solely on better data collection and sophisticated analytics; it should be about exploring these resources to drive organisational change. Great analytics alone is not enough; organisations need to put in place a change process that actively explores the newly created insight.

Having said that, none of this can happen without granular data. Attempting to manage and prepare tagged data is extremely resource intensive and leaves companies with little or no time to actually explore analytics driven opportunities, which is where the value lies.

Prioritisation is key

Armed with the right, detailed data, a company can begin to define and prioritise use cases to help them decide where best to start. There are a number of questions to ask:

  • What current challenges is the business facing?
  • What is the value of addressing each challenge?
  • What data is needed?
  • What analytics is required?
  • How complex is the work to develop the required analytical insight?
  • Is the subject matter expertise available?

The results can be mapped to a matrix - one axis being ‘expected value’, the other ‘level of difficulty’ – to provide a clear prioritisation of where best to focus first.

To get a measured, trusted view of what activity would deliver the most business value and understand the analytics/ data complexity associated with achieving that goal, companies clearly require the insight of cross-functional decision makers. This process should include data owners, IT infrastructure experts, functional business leaders who can identify the ways in which data can, for example, improve the customer experience, and analysts who understand how to use data to create valuable insight. The combined expertise of these individuals is key in determining the potential ways in which analytics can be used, identifying realistic time scales and prioritising activity.

But remember, while this process can flag business challenges or improvement opportunity, it is the data that must guide an organisation as to the specifics of the root cause and how to tackle that challenge or deliver improvement. Let the data tell the story.

Injecting realism

Given the sheer scale of opportunity offered by analytics, it is incredibly important to balance objectives with realistic goals - don’t try to run before you can walk, let alone crawl. The prioritisation process is not only about identifying key business objectives, but also ensuring those goals are achievable given the data and analytics skill sets currently in place.

This exercise will provide a company with a clear roadmap for the future – from those projects that can be addressed immediately because they are both valuable and achievable, to those that can be scheduled in for the future when the organisation has addressed any data, analytics or skills gaps.  

Once up and running, a company can quickly evolve through the crawl, walk and run methodology to embrace ever more complex analytics toolsets and drive even more value; but they need to start taking baby steps to make any progress.

The next three articles in this series will outline the way organisations can take a 'crawl, walk run' approach with three key digital analytics techniques; attribution, segmentation and customer journeys, to progress from simple to sophisticated analytics and deliver ever-increasing business value.


Replies (0)

Please login or register to join the discussion.

There are currently no replies, be the first to post a reply.