Marketing managers are consistently being challenged to collect and analyse increasing amounts of data to optimise their marketing expenditure. Thus, as communication platforms increase in number, collection and measurement methods improve and storage options abound; it is becoming more and more necessary for marketing managers to understand and work with data.
In order to keep up to speed with these technological advances, marketers are increasingly finding themselves in dire need of upgrading their analytics knowledge and skills. There is a pressing need for marketing managers to educate themselves as to what analytical options are out there and obtain a fundamental grasp of industry terms.
There are excellent resources online that focus on upskilling marketing managers in this way. For example “Five essential principles for understanding analytics” is a great blog for learning the difference between big and small data or what predictive vs prescriptive analytics is.
In this blog however, we are going to explore some basic concepts that you might want to consider when delving further into the world of analytics.
The way you frame your question determines the analytics that you will need to solve it.
Framing the question is a vital first step in any data analysis. Take for example, an investigation into the success of a historical campaign. In order to do such an analysis it is important to understand the KPI’s that reflect success. Is it sufficient to know how much different campaigns costs and the interest that they generated, or the sales that were generated or the profit? The amount of data, the data source as well as the product determines how accurate one can be in determining these different metrics. A good understanding of this allows you to frame the question that you want the analytics to answer appropriately.
Time is precious and automation is king.
Automation is fairly straight forward when it comes to collecting the data but how much of the analytics can be automated? Once an analysis becomes a report it should be automated as most repetitive tasks can be automated. This allows the analyst and the manager spend more time understanding what the insights are rather than spending excessive amounts of time building reports.
Don’t immediately discount the value of correlations.
What is the difference between causation and correlation and when is it appropriate to use either as a basis for action? Correlation is defined by the Merriam-Webster Dictionary as the relationship between things that happen or change together. Causation is defined as the relationship between an event or situation and a possible reason or cause. While it is obvious that strategy decisions based on causation makes sense, it is also reasonable to make strategy decisions based on correlation at certain times. The skill lies in knowing when decisions based on correlation is appropriate. While correlation does not always imply causation – there are many times when it actually does.
Find the true value of your data.
When all is said and done the true value lies not in the data inherently but in testing, learning and adapting strategy according to what the data reveals.
The importance of data stream integration
There are many aspects of analytics that need to be understood. In addition to gaining a thorough understanding of the fundamentals of analytics, marketing managers need to be able to ensure that their data (that is sourced across various points of sale, interaction and communication channels) is integrated and stored safely, as it is this data that will form the basis of sound strategy decisions.
Data integration (followed by cleansing of the data) is a vital step that is easier to recommend than to achieve. Having said that, the effort to set it up correctly is well spent, as it enables an accurate overall view of the company’s performance and a detailed view of each campaign.
Overall view is important for general strategies such as where to best place your marketing budget in order to maximise your ROI. In contrast to an overall view, a detailed view of each campaign is able to tell you important efficiency markers such as unforeseen interruptions or actions that can be taken to minimise interruptions to the sales engine. Only a combination of both views enables a thorough understanding of your target markets behaviours and allows for effective optimisation.
Analytics is here to stay
The application of analytics in marketing has become a theme that is repeated across the internet by companies such as PSFK Labs who have found that by “capitalizing on this ability, businesses can create more personalized and compelling experiences for their customers”.
While analytics in marketing became popular with the onset of digital marketing, measuring and analysing data online is relatively simple, with a host of free and paid for tools that are readily available to help out.
What will really set competitors apart is their ability to collect, measure and analyse data from other platforms (aka those other than digital). The application of analytics across every marketing campaign and platform points the way forward for every marketing manager.