helping you master customer insight leadership

Festive Others

Christmas_lights_in_Sloane_SquareAs we approach December and experience ‘Black Friday’ becoming more established in the UK Calendar (perhaps without Thanksgiving we can make it a holiday to remember that retailers systems do crash too?), it’s time for a more festive post. This time I’m sharing a few Christmas related insight posts from others. Hope you enjoy them. Ho ho ho…

First, how about some gift ideas for your insight team?

 

Otherwise, perhaps it’s time for an infographic on the big bird for the big day?

 

If you’ve avoided the queues, enjoy reflecting on the number of Behavioural Economics biases being used by retailers on ‘Black Friday’ shoppers…

 

Hope that was a little fun as you look forward to those Christmas parties. Do you have any favourite festive data, analysis, research or database marketing stories to share? If so, please let us know below.

Poll: Which is your favourite ‘top tip’?

Billiard_Chalk_and_CueThanks for all the positive feedback I’ve had on our recent series of ‘top tips’ for the different technical teams which make up the full breadth of customer insight. It’s encouraging to know that those tips did help other leaders.

In fact it set me to wondering which top tip was the most helpful. So, to once again help shape future content here on CIL, see below for our latest poll.

Please vote for your favourite ‘top tip’, the one which was most useful to you…

 

I’ll share the results of that poll once participation is high enough to give us robust results. But I’m not arrogant enough to think that wisdom resides here, so please do also share your ‘top tips’. I’d love to hear about the lessons you’ve learnt while leading a data, analytics, research, database marketing team or whole customer insight department. Go on, use the comments section below & see if others have found the same as you.

Those sharing the most useful or popular tips may be invited to be a guest blogger here (and I’ll find a prize for them as well).

I look forward to hearing your thoughts & seeing your votes – let’s help each other succeed as Customer Insight becomes a profession.

Global Reviews’ UK Digital Leaders Summit (#UKDLS)

IMG_2587This is a new event in the UK and one I attended yesterday, as a speaker on Behavioural Economics. It’s well worth digital leaders attending in future.

Right from the start things boded well as they had chosen a different and engaging venue. Bounce (121 Holborn, London) is the official ‘home of ping-pong’ and is literally the place where the game was invented in 1901. This gave guests the promise of a ping-pong championship after the event, as well as a more visually interesting location.

Another bonus was the approach taken by Global Reviews – for this to be a relaxed meeting of like-minded leaders across a number of verticals. There were no sponsors pushing their wares, no exhibition stands & no sales pitches. It really was a relaxed time to be challenged by other viewpoints and discuss what it might mean for your own business.

The day was divided up into three sections of panel debates (including attendees not speakers, which was a good approach) followed by keynote speakers. There was also a team exercise before and after lunch, to give plenty of outlet to those competitive urges. Compere and inquisitor of panels was the excellent Clare McDonnell of Radio 5 Live – a very likeable and effective facilitator.

A further bonus was the hiring of a graphic artist to visually capture (with flip charts & coloured markers) the main themes of each session – which made a much more amusing set of notes to review. Clive was later available for caricatures in between ping-pong bouts & was very good at those too. I’ll upload some of his most useful visualizations of key points when I get them. It could be a whole new genre of ‘conference cartoons’.

To give you a more bite-sized breakdown of my learning points from that day, I’m also going to innovate and just share the key tweets sent during the day (as inspiration struck):

How was that as means of writing up an event? Did it work for you?

3 tips for maximising your Data team

Data team

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.

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.

Please let me know of any series you would like to see here on Customer Insight Leader. We want this site to help customer insight leaders thrive & grow in their roles.

Get your geek on and enjoy the visuals

SEO-ChartFor those you who have been asking for more digital specific insights and technical advice, I have been listening. To complement our recent content more focussed on effective insight leadership (see our ongoing ‘3 tips’ series), here are two guest items with a digital and technology emphasis.

First up is a post from BraveMedia’s blog, shedding some light on the murky and ever-changing world of Google’s search algorithms, for those challenged with Search Engine Optimisation (SEO) for their digital marketing content. What drew me to this post in the first place is the rather cool and eye-catching visualisation, so it is also another example of effective information visualisation.

The next guest content covers the potentially dry, but vitally important, topic of data modelling, when building or enhancing databases. I’ve recently been reviewing training material on this topic for a client who now needs an ‘in house’ CRM system. What struck me about LearnDataModelling.com is the simplicity and effective summarisation of material which is too often left to appear overly complex, perhaps so non-IT types don’t challenge too much? So, I share this as an example of both an important skill for customer data management teams (within customer insight not just IT) and as an example of keeping it simple.

I hope those were helpful and you enjoyed some unashamed ‘geekiness’ this time. But for those of you are thinking “all I heard was blah, blah, blah, blah”, don’t worry I’ll be returning to the English language next week.

Have a good weekend and do share your best tips for SEO or Data Modelling, I’m sure there are plenty of equally good resources out there.

3 tips for maximising your Analytics team

analytics teamFollowing on from tips for your research and database marketing teams, this week we turn our attention to the 3rd quadrant of customer insight, your analytics team.

Given the emphasis of advertising & events for this community, it’s tempting to focus on technical skills.

So much social media airtime is given over to new software, coding skills or data solutions, you would think that was all that was needed for effective analytics teams. But there is so much more to developing analysts who will make an impact in your business.

So, without further ado, here are 3 top tips for maximizing the impact of your analytics team (mostly lessons learnt from getting this wrong first time)… (more…)

Correlation is not Causality

Causality bookI don’t know about you, but one of the perennial issues I experience when communicating analytical findings to clients, or fellow business leaders, is to help them avoid the pitfall of assuming that correlation equates to causation.

Once a relationship can be shown between some customer characteristics and the objective of interest, say likelihood to purchase, people love to rush to hypotheses as to why this makes sense – even when it is extremely unlikely and causation has not been proven.

Now there are plenty of studies showing examples of spurious correlations, like the proportion of blue-eyed customers coming into a store in Moscow and the murder rate in Los Angeles. So, an extreme example can normally be thought up to illustrate this danger. However, too few people actually understand causality and how it can be proven statistically. This is also important because of the unconscious bias that we all have to seek to simplify problems and attribute causation as soon as possible; thus it can feel like ‘swimming up stream’ to suspend judgement and seek robust evidence.

So, I’m pleased to share this guest content, by Vincent Granville, recommending a classic text to help with this very challenge:

 

Have you read this? How do you help others understand whether to not they have proven causality?