This is the last in our series of 3 top tips for each of the technical teams that make up a fully effective Customer Insight department. I’ve already shared tips for database marketing, research and analytics teams. Now, last but definitely not least, your data management team.
As I have written for Customer Experience Magazine, there is a danger that the new generation of more senior customer insight leaders neglect their data teams. They could be tempted to focus on the ‘sexier’ analytics teams, obviously commercial database marketing work, or high profile research like NPS tracking.
But that would be a mistake as the provision of quality customer data is an essential foundation to all the other work of the team (including targeted research samples & matching analytics to NPS/CES results).
So, without further ado, here are those 3 top tips for maximising the impact of your customer data team (lessons learnt from getting this wrong before I saw it really work in practice):
Tip 1: Avoid becoming an alternative IT department:
In my experience, in many large corporates, once other teams across the business find out that your data team is capable of providing customer data extracts quicker & more cheaply than your IT department – you will be overrun with demand. Hence there is a need for the customer insight leader to protect this team from being just another MI/BI/data team at the mercy of ad-hoc questions. One way can be to highlight the difference between marketing data systems where matching and data quality is designed to be suitable for marketing purposes and operational systems were for security reasons data matching may have to be stricter & more complete.
This can mean that data extracts for some purposes, e.g. communications mandated by your regulator, should come from operational systems and you can legitimately claim that your marketing data sources are not appropriate for such extracts. Either way, one key to getting the best out of your customer data team, is to protect them from having their time taken up providing data that does not drive insight.
Tip 2: Scheduling and Automation:
Many organisations, despite significant IT infrastructure investment, still rely on such data teams to be able to merge new data sources or pull together a semi-manual Single Customer View solution that meets the needs of the rest of your insight team. This is valuable work & strong SQL skills as well as an understanding of database design are core skills for members of this team. However, after the initial enthusiasm of appreciating such flexibility, the leader may become frustrated as more and more of your data team’s time is taking up running regular jobs or refreshing the Single Customer View each day/week/month.
This is the point at which it is important to invest in scheduling/automation software for your data team. There are many different software packages for different hardware platforms and database management systems – the important thing it to have a process for manually created jobs to be automated & ultimately transferred to IT support. That will again free up precious resource who should be able to be data experts advising others across Insight team and wider projects.
Tip 3: Customer dashboards:
Another bane of corporate life can be performance management systems. With the best of intentions, these processes can end up only rewarding those individuals who have more visibility with senior stakeholders and penalising those who are more ‘back room’. This is a particular threat to technical teams like data management ones, where the quality of their work and reliability of output may only be appreciated by the other technical teams they supply. One way I found useful to help overcome this is to challenge your data team to produce a dashboard which should be of interest at the highest levels in the company. This resulted in an EIS dashboard of basic customer data (number of customers, products held, channel of acquisition, retention rates, by segment, by brand etc etc).
From the data team’s perspective the advantage is this does not require complex statistical analysis and gives a way to visualise the latest data updates of you Single Customer View (which can also play a role as a data quality check). From the directors’ perspective, it enables them to ‘have their finger on the pulse’ of how their business is performing from the perspective of customer relationships, not just product, brand, channel silos. So, it can be a ‘win win’ and an artefact that can be easily cited to explain the important job the data team members do, to those who rarely if ever interact with them.
I hope those tips prove as popular as the previous 3 team updates.