PharmExec Blog

Focusing On the Metrics That Matter: A Quick Guide to Marketing Analytics

The medicines business is all about data. Drug discovery relies on the meticulous interpretation of experimental results; clinical trials involve carefully analysing streams of information relating to patient response; even physician decision-making around the most appropriate therapies is becoming more and more data driven.

Peter Houston

Peter Houston

There is no question, data is big on the science side of the pharmaceuticals business. But, despite all the tub-thumping about making use of data throughout the enterprise, things are a little less clear in the pharma marketing department.

All modern marketers are squeezed for time and it is getting increasingly difficult to prioritise in a multichannel world. Campaigns seem to run forever; when you think you’ve got one channel squared away, another kicks off. The tweaks that you think should take two minutes simply don’t, and you’re reluctant to take anything new on because you’re just not sure if it’s going to be worth the effort.

Sitting down to analyse the numbers would probably help, but a mammoth data dashboard session is the last thing the average marketer feels they have the time to do.

Let me refer you to a 15-minute guide to marketing analytics I read last week on the eConsultancy digital marketing blog. While it won’t solve all of marketing’s multichannel problems, it offers an interesting DIY approach to focusing in on the metrics that matter.

The post was written by Jeff Rajeck, director of digital marketing and analytics at Singapore-based online recruitment firm Maachu. His advice to anyone suffering ‘analytics anxiety’ is to develop your own, which he promises ‘doesn’t take much time and can be done using any old spreadsheet’. He makes the point, ‘your analytics don’t need to be complicated. In fact, simple analytics can provide better results than complicated systems.’

I don’t know that DIY data reports are going to fly at every stage of the pharma marketing process, but Rajeck makes some real sense in his self-help suggestions for how to approach analytics.

The core of his approach is to focus on what you really want to know. Yes, that fairly obvious, but anyone that has ever sat in front of a digital analytics dashboard knows that identifying the data line you really want is easier said than done. Rajeck says it’s not about measuring things that are easy to measure, it’s about measuring things that will make a difference to the business.

Taking the DIY approach makes this easier to do this because you’re working forward from your objectives — what you really want to know about your marketing – not backwards from an off-the shelf menu of data reports.

He illustrates his point by running readers through a ‘data model’ (a spreadsheet to you and me) that he uses daily to compare conversions rates. It matters to every business how much they have to spend on advertising to get a new customer; Rajeck keeps on top of this by looking at the daily cost of two ad campaigns — one on Facebook, the other LinkedIn. He then uses the daily conversion figures to calculate which is delivering better.

The specifics of LinkedIn and Facebook conversions are interesting, but the real learning is in moving your data out of a sophisticated dashboard environment into a home-made spreadsheet that gives you the ability to play with the numbers and see what happens when you move the dial on individual metrics. To put that another way, what happens when you prioritise specific activities.

This is pretty basic stuff on one level. What’s interesting is the way basic stuff like creating metrics around your own objectives and manipulating the data yourself can be effectively used to set priorities, from use of creative resources, audience targeting and even campaign spend. All from a home-made 10-column spreadsheet.

For Rajeck, analytics is simple. He says it’s just setting an objective, finding what you need to achieve it, and then using data to measure your improvement. “Without analytics, you can get bogged down with work which is going nowhere, gives you no pleasure, and may turn you into an anxiety-ridden and unhelpful marketer.”

The real takeaway from this post is to avoid ‘analysis paralysis’ by identifying the one thing that matters most, working to improve it then measuring the impact your work is having. That sounds so much better than trying to manage some of the real-time, live-response, big-data control panels that we’re being sold as the cure to all marketing ills.

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One Comment

  1. Dr Graham Leask
    Posted July 30, 2014 at 10:10 am | Permalink

    The problem with this approach is that Pharma data is complex and may contain a myriad of relationships. Whereas I greatly endorse the idea of looking at your data using graphs to go beyond this using Excel is not to be recommended. Inappropriate use of analytics is a major cause of unsound decisions in my opinion. Whereas Galton’s method is great for simple data , pharmaceutical data rarely falls into this category. Linear regression is predicated by 4 critical assumptions most if not all of which are violated by most pharmaceutical data. Modern computational analytics has moved on since the time of Darwin and can certainly improve marketing productivity. Like many worthwhile activities however many of these methods are complex and require a sound understanding of modern computational methods to carry out effectively.

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