3 tips to get the most out of your Database Marketing team
The latest in our series of ‘top tips’, are some thoughts on getting the most out of your Database Marketing team. By this name (or DBM), I mean the team who provide the selections for targeted direct marketing or pre-scored leads for inbound channels.
This may involve your team also developing that targeting, normally a mixture of explainable ‘trigger event’ and filtering by propensity to respond (say from a logistic regression model), or that targeting may be provided by your analytics team. The database marketing team will almost certainly be responsible for ensuring robust experimental design, with feedback loops and control groups, to ensure that targeted marketing effectiveness can be measured – they may also encompass measurement of wider marketing spend effectiveness, including use of econometrics.
I share all that clarification on the role of the team, because one of our earlier surveys identified that only 6% of our readers viewed this capability as part of Customer Insight. One of the advantages of having database marketing within the Customer Insight department is delivery of measurable commercial benefit in the short term. Many years ago when I first advised and then integrated a separate database marketing team into my CI function, it was both because I believed joined-up working with analysts and researchers could improve the quality of database marketing execution and because the measurable incremental profit delivered from this team would help fund/justify the more strategic longer-term profit impact work of other teams.
Anyway, as I did for research teams, here are 3 top tips for maximising the impact of your database marketing team (lessons learnt from getting this wrong before I saw it really work in practice):
Tip 1: Visit touch-points (Customer Closeness):
Getting out to experience first-hand who your customers are, and the experiences they have when interacting with your business, is something that I’ve always recommended to all my teams. However, I’ve found it to yield particular benefits for database marketing analysts. Often the kind of people who excel in this team are both analytically strong and commercially driven – they are motivated by seeing the difference they can make to tangible business results. This can be a real bonus to your wider customer insight team, but it also runs the risk of a team who are much more internally focussed and over time see their role as optimising a process and achieving key numbers. In other words they can become both rationally & emotionally distant from real customers.
Requiring database marketers to get out, to see & hear customer experiences (especially when those customers are confronted with the targeting leads or marketing which those analysts have executed), can be both a revelation and a strong motivation for these good folk. At its best it can provide a ‘double whammy’ of benefit. First your DBM team are powerfully reminded that it is real people for whom they are designing interactions (which often leaves them feeling more empathy for customers & passion to make a difference). Secondly, being there ‘at the coal face’ (whether listening to calls, seeing customers interact in a branch/store, or watching customers using digital devices) can highlight practical problems or give ‘eureka’ moment ideas to improve the interaction. So, it’s also important to empower these team members to come back and change things as a result of what they have learnt and felt.
Tip 2: Share commercial targets with sales/retention teams:
In recent years customer insight teams have risen to greater prominence and influence within large companies. However, one silo of skepticism that many have still encountered is the perspective from sales teams or those ‘on the spike’ for critical commercial targets, including customer retention metrics. Historically, these teams can sometimes view, not just customer insight, but all marketing-related teams as at worst ‘fluff’ and at best rather ‘Teflon’ teams who manage to avoid being accountable to such hard numbers (that directly impact their performance ratings and bonuses). This is unfortunate for both sides, as customer insight as a whole should be a benefit to an entire business and certainly has as much to share and learn from sales teams as it does from marketing. Database marketing teams can be a key to overcoming this barrier.
As measurement of the effectiveness of targeted direct marketing and lead generation improves, DBM teams should be able to more and more accurately predict both the volume of leads that can be provided and the likely sales (or retained customers) they will generate. The first stage of warming relations with your sales and retention teams is, of course, to share this with those teams. Communicated well, this can help to overcome a misconception that customer insight is all about theoretical maths or fluffy concepts like ‘understanding the customer’. But the signature action, which I found made a step change in relations, was for the customer insight leader and database marketing team to also take commercial targets. To calculate the proportion of overall sales which can be generated from leads provided by the team and be ‘on the spike’ themselves for hitting those numbers. Of course that means the result if not fully within the control of the CI team, and people will feel uncomfortable that their good analytical work could be undermined by a poor sales experience, however this was more than made up for by the respect earned from sales teams and the cooperation to improve results together which this fostered.
Tip 3: Invest in commercial understanding:
One of the many mistakes it’s possible to make as a customer insight leader is to assume that your team know more than they do or are fully equipped for the challenges you set them. Over the years it has been eye opening for me to see that many very capable analysts, who may wow audiences with their statistical work of data visualisations, actually understand very little about how the business really makes money. Such commercial naiveté can really trip them up in future, either through inappropriate recommendations or when they are talking with business leaders about their requirements or how insight can be acted upon. Beyond this potential for embarrassment, it is also a huge missed opportunity. So much of the quality of database marketing work comes from thorough domain knowledge; really understanding where the opportunities lie, where things are not working and what can be done to improve results.
It can prove very valuable to invest in ‘commercial understanding’ training. This topic covers both understanding the general principles of understanding how a business makes money. One of the best presenters I have heard on this is Dave Meckin, also author of ‘Naked Finance‘. Then building on that improved financial understanding, your DBM analysts will also benefit from a better understanding of the market your business operates in, how it competes and the major profit levers being used by its particular business model. Here, getting experts in from your market & competitor intelligence or strategy teams can really help. A bit like the customer ‘eureka’ moments that I explained in Tip 1, above, this greater understanding can produce significant improvements. As DBM analysts look at their data, processes and customer behaviour with fresh eyes – they often spot areas for improvement which they now know will impact the bottom line.
I hope those tips help. The above list is by no means exhaustive and I’d love to hear your thoughts on what has helped. But I hope it gives you food for thought & perhaps help you lead an uplift in commercial performance from customer insight, of which you and your team can be proud. I’ve led a CI department with a well performing DBM team to add over £10m to the bottom line each year – so there is rich potential in these great people.
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...