The four most common mistakes made by customer insight leadersby
Customer insight leaders perform a crucial role in today's customer-centric businesses. But they must be wary of several common pitfalls that can undermine their effectiveness.
Insight leaders have multi-disciplinary technical teams to manage and are also increasingly in demand other areas of the business, so when you consider the breadth of responsibility within their roles, it is little surprise that there are certain common mistakes that insight leaders are prone to.
Most of the lessons I’ve learnt over the years have come from getting it wrong myself first. So, there’s no need for any of my clients or past colleagues to feel embarrassed when they make an error.
Here are four common mistakes I see customer insight leaders making, and hopefully by flagging them up I might help others recognise if they are about to fall into the same trap:
Leaving data access control with IT
Data ownership or data management are not the sexiest responsibilities up for grabs in today’s organisation. To many they will appear to come with much greater risk of failure or at least blame, than any potential reward. However, such work being done well is often one of the highest predictors of insight team productivity.
Ask any data scientist or customer analyst what they spend most of their time doing and the consistent answer (over years of asking such questions) is ‘data prep’. Even with al the access they need, most of the time significant work is needed to bring together the data needed, explore, clean and categorise it ready for any meaningful analysis.
But, given the negative PR and historical role of IT in this domain, it can be tempting for insight leaders to leave control of data management with IT. In my experience this is almost always a mistake. Over decades (of often being unfairly blamed for anything that went wrong and involved technology) IT teams and processes have evolved to minimise risk. Such a controlled (at times bureaucratic) approach is normally too slow and too restrictive for the demands of an insight team.
I’ve lost count of how many capable but frustrated analysts I have met over the years, prevented from making the difference they could because of lack of access to the data needed. Sometimes the rationale is data protection, security or even operational performance. At root, customer insight or data science work is by nature exploratory, innovative and requires a flexibility and level of risk that runs counter to IT processes.
To avoid this foolish mistake, I recommend insight leaders take on responsibility for customer data management. Owning flexible provision of the data needed for analysis, modelling, research and database marketing is worth the other headaches that come with that territory. Plus, the other issues that come to light are well worth insight leaders knowing well. Whether they be data quality, data protection, regulation or technology related. Data leadership is often an opportunity to see potential issues for insight generation and deployment much earlier in the lifecycle.
Underestimating the cultural work needed to bring the team together
Data scientists and research managers are very different people. Data analysts, working on your data quality challenges, also see the world very differently from database marketing analysts focussed on lead performance amd the next urgent campaign. It can be all too easy for a new insight leader to underestimate these cultural differences.
Over more than 15 years, I had the challenge and pleasure of both building insight teams from scratch and integrating previously disparate technical functions into an insight department. Although team structures, processes and workflows can take considerable management time to get working well, I’ve found they are easy compared with the cultural transformation needed.
Stepping back from the day-to-day concerns, this should not be a surprise. Most research teams have come from humanities backgrounds and are staffed by ‘people people’, interested in understanding others better. Most data science or analysis teams have come from maths and science backgrounds and are staffed by ‘numbers people’, interested in solving hard problems.
Most database marketing teams have come from marketing or sales MI backgrounds and are more likely to be motivated by business success, interested in proving what works and makes money. Most data management teams have come from IT or Finance MI backgrounds and are staffed by those with strong attention to detail, motivated by technical and coding skills, but wanting to be left alone to get on with their work.
As you can see, these four groups of people are not natural bedfellows. Although their technical expertise is powerfully complementary, they also tend to approach each other with natural skepticism. Prejudices common in society and education often fuel both misunderstanding and reluctance to give up any local control to collaborate more. Many maths and science grads have grown up poking fun at ‘fluffy’ humanities students. Conversely, those with a humanities background and strong interest in society can dismiss data and analytics folk as ‘geeky’ and removed from the real world.
So, how can an insight leader avoid this foolish oversight and lead cultural change?
There really is no shortcut to listening to your teams, understanding their aspirations/frustrations/potential and sharing what you learn to foster greater understanding. As well as needing to be a translator (between technical and business languages), the insight leader also needs to be a bridge builder. It’s also worth remembering classic leadership lessons like ‘you get what you measure/reward’ and ‘catch people doing something right’. So, ensure you set objectives that require cooperation and recognise those who pioneer collaboration across the divides. It’s also important to watch your language as a leader, to ensure it is inclusive and valuing of all four technical disciplines.
Avoiding commercial targets due to lack of control
Most of us want to feel in control. It’s a natural human response to avoid creating a situation where we cannot control the outcome and are dependent on others. However, that is often the route to greater productivity and success in business.
The myth still peddled by testosterone fuelled ‘motivational’ speakers, is that you are the master of your own destiny and can achieve whatever you want yourself. Collaboration, coordination and communication are key to making progress in the increasingly complex networks of today’s corporations. For that reason, many executives are looking for those future leaders revealed by their willingness to partner with others and take risks to do so.
Data scientists and research managers are very different people. It can be all too easy for a new insight leader to underestimate these cultural differences.
Perhaps it is particularly the analytical mindset of many insight leaders that makes them painfully aware how often a target or objective is beyond their control to achieve. When a boss or opportunity suggests taking on a commercial target, what strikes many of us at first is the implied dependency on other areas to deliver, if we are to achieve it.
For that reasons, many stress wanting objectives that ‘measure what they can control’. Citing the greater accountability and transparency for their own performance, can be an exercise in missing the point. In business life, what customer insight can produce on their own is a far smaller prize than what can be achieved commercially by working with other teams. Many years ago I learnt the benefit of ‘stepping forward’ to own sales or marketing targets as an insight leader. Although many of the levers might be beyond my control, the credibility and influencing needed were not.
Many insight leaders find they have greater influence with their leaders in other functions after taking such a risk. Being seen to be ‘in this together’ or ‘on the spike’ can help break down cultural barriers which have previously prevented insights being acted upon and so generate more profit or improve more customers’ experiences.
Not letting something fail, even though it's broken
A common gripe I hear from insight leaders (during coaching or mentoring sessions) is suffering for ‘not dropping the ball’. Many are working with disconnected data, antiquated systems, under-resourced teams and insufficient budgets. Frankly, that is the norm. However, aware as they are of how much their work matters (because of commercial, customer and colleague impact), they strive to cope. Sometimes for years they and their teams work to manually achieve superhuman delivery from sub-human resources.
But there is a sting in the tale of this heroic success. Because they continue to ‘keep the show on the road’, their pleas for more funds, new systems, more staff or data projects often fall on deaf ears. From a senior executive perspective (used to all their reports needing more) the evidence presents another “if it ain’t broke don’t fix it” scenario. They may empathise with their insight leader, but also know that they are managing to still deliver what’s needed. So, their requests get deprioritised.
In some organisations, this frustration can turn to resentment when insight leaders see other more politically savvy leaders get investment instead. Why were they more deserving? They just play the game! Well, perhaps its time for insight leaders to wake up and smell the coffee. Many years ago, I learn that you have to choose your failures as well as your successes. With the same caution with which you choose any battles in business, it’s worth insight leaders carefully planning when and where to ‘drop the ball’.
How to avoid this foolish mistake? Once again it comes back to risk taking. Let something fail. Drop that ball when planned. You may get the infamous Ferguson “hairdryer treatment” initially, but hold your nerve. If you’ve built a good reputation, chances are it will also increase the priority of getting the investment or change you need. You might just be your own worst enemy by masking the problem.
I work with exceptional leaders & their teams, so they can master the people side of data & analytics.
That means helping them maximise the value they can drive from using data, analysis & research to intelligently interact with customers. It also means developing teams & enabling them to sustain their improvements through...