Data-driven decisions - The methodology

Blog | 14 Mar, 2018

 

In my opinion, ‘data-driven decisions’ is simply another way of saying ‘wiser decisions’. But what is the methodology to being data-driven when it comes to making digital marketing decisions?

 

Humans are a species that make decisions based on data
We always are, and always will be - this is instinctive. We are ‘coded’ to ensure we learn from our mistakes, which leads us to make better decisions in the future. Experience is the first and most natural source of data.

Historical data is now not sufficient
I recently bought a flat which involved plenty of data-analysis (well, being a significant purchase and commitment, I needed to ensure I was making the right decision right?) I then had to buy furniture, and since I’m aiming for longevity I was looking for the good quality pieces! Hence, I found myself browsing online for hours, reading reviews, doing price comparables, researching quality etc etc... all of this, your customers do in the very same way.

Whether we’re launching a new business or product, evolving a website or buying something online - we make data-driven choices. Consumers, marketers, business owners, creatives, architects, lawyers - all have to work from data in order to maximise their chances of achieving a satisfying result. For example, consumers do their own research prior to buying, much in the same way I did (by checking reviews, investigating the product, and looking for promotions and savings), whilst people launching a new business will investigate the market, run product analysis, testing, investigations, experiments…and so forth.

Data-driven decisions in the digital marketing field
As a Conversion Optimisation Specialist, my process with clients is no different. I need data to ensure the changes I drive are wise and focused on what matters most. So although I may have gut feelings based on previous testing with other clients, I always attach numbers to my recommendations (traffic, opportunity, why we believe it will help...) and support them with some extra user data.

At Jellyfish, we always recommend starting with an optimisation audit (basically a compilation of data investigations) with a view to providing prioritised (traffic / impact / effort) and actionable recommendations, whether this be direct implementation or testing ideas.

We investigate the historical data from analytics platforms, logs of live chats, email campaigns and any other content that seemed to be the most efficient. Then we run some new data collection and analysis including funnel analysis, product performance, user behaviour, user profiles, user journey lengths and purposes, competitor analysis, eye tracking, user testing etc. the list goes on!

All of these investigations will point towards issues, frictions and opportunities that impact most on the customer journey and from these, we create solutions whether it’s direct implementation or experimentation.

 

My three golden rules when it comes to formulating my hypothesis:

 

1. Be KPI focused
Understand Key Performance Indicators for your business and break them down so that you can turn them into goals that you can actually influence. For example, you can’t magically make money appear on an account (if you can, please reach out asap! fabien.caublot@jellyfish.net) However, you do know that you can maximise revenue by improving your sales, your average order value and your retention rate.
An example of how you could improve your average order value would be by creating a combined offer - and there you have it, one way to impact revenue.

If your actions don’t directly inherit from the KPI that you’re working against, most likely the impact won’t be the one you expected.

2. Data-driven
If you attach a number to your recommendations then you can ensure you’re always focusing on the best opportunity. Hence it’s important to understand the volumes that you are targeting, the impact you’re expecting, then the “where to act” will become very obvious.

You can have a thousand good ideas on how to improve a KPI, but only data can help you understand which one is worth focusing your efforts on.

3. Problem-solving
Last but not least, make sure that your work is problem-fixing or opportunity-oriented. Do this via a strong data analysis. Investigate your customers UX, then identify issues and frictions that actually damage their experience (and hence your KPIs). You will then see where the opportunities are, therefore giving you something solid to work against.

To guarantee a correct approach, use this popular syntax:
“I want to implement <solution>..., so that it works against <friction/problem>... since we know…<data>”

That’s all folks! Hopefully, this short guide has explained why and how to have a data-driven approach when it comes to making digital marketing decisions. I will follow up with another article around the different data tools and analysis currently available as well as the type of indicators you can get on each of them. 

 

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