Rarely was performance data from multiple channels being placed side-by-side to give a full picture of success. So saying that joined-up data wasn’t a big priority would be quite an understatement.
We’ve come a long way since then
Today, integrated data is not only essential to proving marketing efforts, but we are now flooded with “big data“, which has huge potential but can often be confusing to digest.
When implemented well, it is being harnessed to drive more revenue through digital strategy than ever before.
All of this has created its own unique set of challenges: not just the volume of data available to be harnessed, but the sheer number of data sources required to collate the data, often in different formats, with plenty of room for error.
In this brave new world of truly integrated digital activity and huge volumes of available data, how do we collect, utilise and interpret the exact data we need to aid strategic decision making without overwhelming ourselves with more data than we can make sense of?
Getting it ‘Just Right’
In the fairy tale, Goldilocks finds an empty house owned by three bears and decides to help herself to their food before having a nap, only to find that their porridge and beds were either too hot or hard, or too cold or soft.
It took several attempts to find the ideal. This is the essence of The Goldilocks Principle, which states that something must fall within certain parameters, as opposed to reaching extremes.
Getting data requirements ‘just right’ is essential for companies who want to be market leaders.
With insufficient data, decision-makers suffer from blind spots, and at worse make incorrect assumptions based on only partial data available to them.
Too much data can have a similar effect, making it difficult and time-consuming to find the relevant information, or even missing key insights due to data overload.
I spend a great deal of time speaking with digital marketers who have grand plans of what they’d like do with lots of data, but get understandably overwhelmed as to how to actually implement their ideas.
Their first impulse is often to invest in sophisticated technology to make sense of the data (which is why I’m usually speaking with them in the first place), and while I agree it’s essential, technology is unlikely to magically solve the problem on its own.
Here are a few key requirements I have learnt are necessary to ensure you have the right data, to provide the level of insight you might require.
1. Start with the outputs
What do you want to see? Which business decisions will the data influence? Who needs to use the data? How do you want it visualised and how will it be communicated and shared?
When speaking to customers about their data requirements, I always start at the end.
It may seem counter-intuitive, but people often forget why they’re embarking on a ‘data discovery journey’ – it shouldn’t be to use data just because it’s available. In order to use data effectively it’s essential to first understand what you’re trying to achieve.
Start with your key performance indicators (KPIs). I can’t stress that strongly enough.
It may seem obvious, but it’s amazing how often these get forgotten when there’s a lot of exciting juicy nuggets of information available to use.
KPIs can, and should, change throughout the life of a business, and particularly within the digital industry as it rapidly evolves. Some, however, should remain constant in order to be able to truly compare performance over time.
For instance, many SEOs have reduced the priority of search engine ranking data as a KPI – in part due to keyword traffic data being more difficult to come by, but in many cases because search engine result pages are now complicated by localisation and universal search, and are merely a stepping stone to the more important priorities (for instance, rankings + traffic + conversions = revenue!). The list, however, goes on.
For most commercial businesses, the essential KPI that won’t change is revenue and profitability.
In most cases, all business activity, including digital marketing, should be aimed at increasing revenue, and other KPIs should be helping support this objective in some shape or form.
There are some exceptions as it’s not an exact science – there are still many cases where marketers can’t directly link marketing activity to revenue – but data should still be able to show correlation even if not causality.
It’s not perfect, but it’s better than flying blind.
Even with all the data in the world, if you can’t show return on investment, and use the right data to drive improvements on this, you would seem to be missing a fairly big trick.
2. Clearly communicate your objectives
Does everyone in the team understand the end goal? Does the tech team understand what the digital marketers need to see and vice versa?
Once everyone is on the same page, you may find that you don’t need as much data as you originally thought.
For instance, I heard of a large organisation that gave its digital agency an enormous budget to ensure its products were ranking well in natural and paid search and drive traffic to key product pages.
Monthly reports took days to put together due to the sheer number of keywords and campaigns being monitored. Traffic to the product pages from these efforts was high, and therefore the agency’s KPIs were met.
However, customers weren’t converting. They were dropping off the product pages like flies because the commodity service just wasn’t competitive.
In the end, all the promotion in the world wasn’t going to drive revenue unless the products themselves became what people were looking for.
While all the reports showed campaign success, the organisation wasn’t happy.
An enormous amount of data at a granular level was available to see the problem and provide a solution, however everyone was so focused on the detail they lost sight of the bigger picture.
Their report was too complex and missing the most important KPI – customers converting.
All the data in the world will miss the mark if the most important objectives aren’t aligned and communicated effectively.
3. Evaluate the inputs
Which data sources are necessary to achieve the agreed output? What are the technical, legal or cost limitations? How will you obtain and manage the data?
With so much information being created every day, when aggregating and reporting on data it’s very easy to find yourself in a situation that is probably best described as “information overload”.
Instead of using some of it, you end up using none of it because it becomes unmanageable.
Once your KPIs are defined, technology can handle more data aggregation and output than most companies need, often at affordable prices.
Finding the right technology to meet your exact requirements will avoid you ending up in a cloud-based superstorm of data.
However technology is only just one part of it. Can you legally use the data? Do you have to pay for it and is it worth it? Is the data reliable and going to remain consistent in the future? Perhaps you don’t need granular data, but just a high level summary.
If you can’t extract useful actionable insights, then the data you are collecting has no value.
4. Start Small. Think BIG.
Don’t get caught up in the big data hype, believing that all companies should be investing big budgets into collecting and analysing all available data immediately.
The reality is that most businesses can achieve exactly what they need by starting with just a few key data sets and utilising technology already on the market.
It’s essential to avoid flooding yourself with too much data if you don’t initially have the resource to use it effectively.
Learn what works and keep adding to it, but avoid adding data for data’s sake – ensure that any additional metrics enhance the measurement of your objectives and KPIs.
Don’t make the mistake Goldilocks did by going straight for what appears the “biggest or best”, but instead spend a bit of time figuring out what is right for you.
It will save you a lot of pain in the long run and provide a much more valuable journey.