We work with a lot of organisations at various levels of advancement along the data journey.
Many are just setting out with copious amounts of data at their fingertips but no clear strategy on how to use it.
Along the way we’ve learnt that there are some common challenges that can hinder the effectiveness of a data analytics project.
1. Be clear on your problem.
“Find something interesting” is a common brief for an analytics team but will rarely uncover interesting results, plus it’s time-consuming and costly. A clear brief will get the best results but can be hard to pull together if you’re not well versed in what is easy to achieve from an analytics standpoint. The solution is to focus on clearly articulating the problem that you want to solve or the outcome that you want to achieve. Let the analytics team define the how.
2. Big but small.
Tackle the most significant problems facing your business right off the mark but do it in small chunks. Analytics can have the biggest impact on the challenges your business is facing, but it’s common for data and analytics projects to fail because they bite off more than they can chew. It’s best to start with smaller easy-win projects to test the robustness of your data and build confidence and buy-in, and progress from there.
3. Data isn’t just marketing automation and BI.
There seems to be a conflation in the New Zealand market between ‘data analytics’, ‘marketing automation’ and ‘Business Intelligence’. While marketing automation and BI are important and should be invested in, they are often not the most valuable applications of data and analytics. Areas that can be solved with data analytics like pricing, promotions, ranging, store layouts, network planning, customer acquisition and retention and many others are often much more impactful.
4. Source the right data.
Most organisations already have a lot of data at their fingertips. The quality of this data is often seen as an issue but using it is the best way to identify where the quality issues exist and to create incentives to get them fixed. Curating this with external data inputs such as Paymark transactional data or Zavy social media data can exponentially increase data’s power.
5. Be prepared to be challenged.
We’ve found that intuition and fact often don’t align, with many marketing teams thinking that their customers behave one way and the data showing something entirely different. Be open to having your pre-held notions of your customers challenged.
6. Bring it to life.
Analytics and data projects don’t often directly deliver value themselves. These projects will almost always need some action from other parts of the business to actually deliver any value. This means that communicating the findings from these projects and driving action with stakeholders is critical. There are two ways to do this: firstly, using information design to ensure outputs are engaging and communicate the most critical information clearly; and secondly, the result must be put in the context of the business strategy, what does this mean for where the business should play and how it should win.
