Tag Archives: insight team

4 Ways to Avoid Being a Foolish Leader

April Fools’ Day is just one day a year, but there are common mistakes an insight leader is prone to (and that could end up making him look like a fool) all year ’round.

This isn’t surprising when you consider the breadth of responsibility within the customer insight leadership role. Such leaders have multi-disciplinary technical teams to manage and an increasing demand across from areas of modern business to improve decisions and performance.

Like most of the lessons I’ve learned over the years, the following has come from getting it wrong myself first. So, there’s no need for any of my clients or colleagues to feel embarrassed.

Beyond the day of pitfalls for the gullible, then, here are four common — but foolish — mistakes I see customer insight leaders still making.

1. Leaving data access control with IT

Data ownership and data management are not the sexiest responsibilities up for grabs in today’s organizations. To many, they appear to come with a much greater risk of failure or at least blame than any potential reward. However, this 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 my years of asking such questions) is “data prep.” Most of the time, significant work is needed to bring together the data needed and explore, clean and categorize it for any meaningful analysis.

But, given the negative PR and the 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 that involved technology), IT teams and processes have evolved to minimize risk. Such a controlled (and, 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 who were prevented from making a difference because of lack of access to the data needed. Sometimes the rationale is data protection, security or even operational performance. At the root, customer insight or data science work is, by nature, exploratory and innovative, and it requires a flexibility and level of risk that run counter to IT processes.

See also: 3 Skills Needed for Customer Insight

To avoid this foolish mistake, I recommend insight leaders take on the responsibility for customer data management. Owning flexible provision of the data needed for analysis, modeling, research and database marketing is worth the headaches that come with the territory. Plus, the other issues that come to light are well worth insight leaders knowing well — whether they be data quality, data protection, or something regulation- or technology-related. Data leadership is often an opportunity to see potential issues for insight generation and deployment much earlier in the lifecycle.

2. Underestimating the cultural work needed to bring a team together

Data scientists and research managers are very different people. Data analysts, working on data quality challenges, see the world very differently from database marketing analysts, who are focused on lead performance and the next urgent campaign. It can be all too easy for a new insight leader to underestimate these cultural differences.

Over more than 13 years, I had the challenge and pleasure of 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.

This should not be a surprise. Most research teams have come from humanities backgrounds and are staffed by “people people” who are interested in understanding others better. Most data science or analysis teams have come from math and science backgrounds and are staffed by “numbers people” who are interested in solving hard problems. Most database marketing teams have come from marketing or sales backgrounds and are more likely to be motivated by business success and interested in proving what works and makes money. Most data management teams have come from IT or finance backgrounds and are staffed by those with strong attention to detail, who are motivated by technical and coding skills and who want to be left alone to get on with their work.

As you can see, these types of people are not natural bedfellows. Although their technical expertise is powerfully complementary, they tend to approach each other with natural skepticism. Prejudices that are common in society and education often fuel both misunderstanding and a reluctance to give up any local control to collaborate more. Many math 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 as 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 worth remembering classic leadership lessons such as “you get what you measure/reward,” and “catch people doing something right.” So, ensure you set objectives that require cooperation and recognize those who pioneer collaboration across the divides. It’s also important to watch your language as a leader — it should be inclusive and value all four technical disciplines.

3. Avoiding commercial targets because of 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-fueled motivational speakers is that you are the master of your own destiny and can achieve whatever you want. Collaboration, coordination and communication are key to making progress in the increasingly complex networks in today’s corporations. For that reason, many executives are looking for those future leaders who have a willingness to partner with others and to take risks to do so.

Perhaps it is particularly the analytical mindset of many insight leaders that makes them painfully aware of how often a target or objective is beyond their control. 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.

See also: The Science (and Art) of Data, Part 1

For that reasons, many people stress wanting objectives that “measure what they can control’.” Citing greater accountability and transparency for their own performance can be an exercise in missing the point. In business life, what customer insights 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 learned 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 that have previously prevented insights being acted upon and that generate more profit or improve more customers’ experiences.

4. Not letting something fail, even though it’s broken

A common gripe I hear from insight leaders (during coaching or mentoring sessions) is a feeling of 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, as 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 the reports needing more), the evidence presents another “if it ain’t broke, don’t fix it” scenario. They may empathize with their insight leader but also know they are managing to still deliver what’s needed. So, requests get de-prioritized.

In some organizations, 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 learned 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 do you avoid this foolish mistake? Once again, it comes back to risk taking. Let something fail. Drop that ball when planned. 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!

Phew, a longer post than I normally publish here or on Customer Insight Leader. But I hope those leadership thoughts helped.

Please feel free to share your own insights. Meanwhile, be kind to yourself today. We can all be foolish at times….

How to Pick Your Insight Team

Amid the merry-go-round of new objectives, targets and budget allocation that keep many a leader of an insight team busy, there is a question of “Who?” Who will do the work? That is probably accompanied by “how many people will there be in my team?” and “do they have the skills and motivation they need?” At first, this can all feel rather daunting. But it will be helped by, first, being clear on your goals. If you know what matters most and why, you’re in a better place to make those ever tricky people decisions.

Staffing your team has more potential options than in years past, with more data, analytics and research agencies, consultancies and contractors. Determining which route to take requires some thought. In my conversations with leaders, it sounded like different businesses favored different resourcing models, but it was unclear which was most popular.

For that reason, we ran a survey among our readers about customer insight team resourcing models. Thanks to all of you who took part. The time has come to share those results.

First, we asked about how customer insight leaders currently resourced their four technical teams that make up holistic customer insight:

1

  • Customer Data: For this team, as you can see, most leaders (67%) replied that all members of the team were employed by their company. The only alternative resourcing approach captured was a mixture of employed and contractors — but still all part of an in-house team. Perhaps it’s the greater ease of recruiting these skills, or the sensitivity with regard to customer data, but this team doesn’t appear to be a focus for outsourcing at the moment.

2

  • Customer Analytics: For this team, there was a similar picture, with an even bigger majority of leaders (80%) stating that all the members of the team were employed by their company. Once again, the only other alternative captured was a mixture of employed and contractors as part of an in-house team. These results were perhaps more surprising, given the much-touted difficulty recruiting analysts or data scientists. Perhaps many businesses are still recruiting rounded analysts rather than the more limited pool of data science graduates. The result certainly flies in the face of advertising by many outsourcing analytics providers.

3

  • Customer Research: Here, we began to see a slightly different picture. Only 50% of leaders replied with the most popular resourcing model thus far, that all the members of the team were employed by their company. The other half were split between outsourcing their research provision entirely and a mixture of both approaches. Sadly, this doesn’t surprise me. I’ve found many a CMO or CEO who assumes that research is an ideal candidate for outsourcing and just asks the agency to “do more.” Sometimes, this is down to the internal team not demonstrating clearly enough the value they provide, so they are simply being perceived as “research buyers.”

4

  • Database Marketing: Last, but definitely not least, is this commercially focused insight team. This category showed the most variation in resourcing models. More leaders (40%) still chose the most popular option; all the members of the team were employed by their company. But all the options we have seen so far were also used — contractors, outsourcing and a mixture of all of them. Given the more visible dependency that most businesses have on this team to hit income targets, I was slightly surprised by this.

As always in business, past approaches are no guarantee of business strategy, funding priorities or resourcing model preferences going forward. So we added a couple of questions to capture personal preferences. Experience has taught me that the preferences of two key parties tend to influence the way customer insight teams are resourced. First is the CEO and any recruitment policies he mandates; second is the customer insight leader who is leading the recruitment.

See Also: Leveraging the Power of Data Insights

So, how did you vote for those two personal preferences in resourcing models, and does that give us any clues as to how customer insight teams may be resourced now?

5

  • CEO preference: This reflects that CEOs value customer insight and view it as a potential competitive advantage, so the majority prefer all the members of the team to be employed by their company. There were also votes for use of contractors within in-house teams, a mixture of both and “no preference” (it depends on the team). This appears to be a continued opportunity for customer insight leaders to build on in 2016, to demonstrate to their CEOs that they offer that competitive advantage and are a key internal skills within their business.

6

  • Your preference: So, we finally come to the resourcing model that customer insight leaders themselves favor. What has given them the best results, that they would prefer to have at their disposal to achieve 2016 targets? Well, based on votes, it would seem the answer is definitely in-house teams. 60% favor all the members of the team being employed by their company, with the other 40% voting for a mixture of employed and contractors making up this internal team. For what it’s worth, that was my own experience, too. Growing your own talent internally worked best.

I hope you found the results useful. Do they accord with your experience?

One final thought, if you are seeking to build a strategic insight capability within your business, one that will empower your company for years to come, are you thinking long-term? Rather than be at the mercy of whether the jobs market has the candidates you require or graduates have the skills and aptitudes you’re seeking, why not shape the latter? I know a couple of businesses that have seen real value through building strategic partnerships with local universities.

See Also: A Wedding’s Lessons on Customer Insight

If you are fortunate enough to have a local university with a good reputation for numerate graduates (from business school or maths/stats faculties), why not work with them? Are there opportunities for internships to try out potential future employees? Would it benefit the university for you to go in and speak to students, even teaching them some of the skills they will need within business? How much better would it be for you to know  that students are being trained in the skills you require?

A great example of building this kind of partnership was the Data Talent Scotland event. I’m proud to have delivered a workshop at this gathering of data science students, academics, industry experts and businesses, helping to forge the kind of partnerships customer insight leaders will need.

Please let me know if you have built a sustainable pipeline of talent to resource your insight team for years to come. What’s working for you?