It's no surprise to anyone it’s the age of data, 1’s and 0’s are getting stored at record speed and the amount and type of data that we collect on every customer is mind boggling. Even though we all know that marketing teams need to be data-driven to be successful, only 26.5% of organisations describe themselves as data-driven and 19.3% feel they have established a “data culture” - and these numbers have continually dropped year-on-year.
Why is that? Today, 67% of CMOs state that they are simply overwhelmed with the amount of data. Where in the past, marketing teams collected just a few data points on a customer such as an email address, first name, company name and job title. Now, the types of data we collect and means of collecting are extensive - capturing everything from website activity, social media interactions and product preferences, to transactional data and customer sentiment.
Context and Interpretation
To make use of data, we must first understand the data.
Simply having access to data isn’t enough - it requires context and interpretation. How you are interpreting it, how you are using it to optimise and enhance how you are engaging with your customers. Most importantly, it is the actionable insights that we draw from that analysis that provides the value.
So - what does an actionable insight look like and how can we embed a data-driven approach to decision making in our own teams and organisations? Let’s look at an example:
Without any context, it looks like Email 2 performed poorly without any click engagement, even though the open rate was above their average. However, when taking into consideration the context of the campaign, the purpose of this email was to send an operational message about the company’s support hours over the upcoming holiday period. So with no intended call to action to achieve a resulting click engagement, this outcome isn’t entirely unexpected and should not be viewed as a poor performance.
But adding context does not make it an actionable insight, that comes down to how you bring multiple sources of data together, and interpret these in the context of your business KPIs or key metrics...
Data Relationships and Business Metrics
The most effective approach for creating actionable insights is simply by first developing a set of assumptions, and then conducting analysis on your data to prove or disprove these. Looking beyond a single data set to bring together multiple data points and how they relate to each other to be able to prove out your assumptions and hypotheses will provide you with a more complete picture. Scouring through all of the different data points that have been captured and trying not only to find the data, but piece together how it can be related to each other.
As an example, if you worked in Customer Success and regularly measured your NPS score with customers, and in one particular month you noticed that the NPS score was down across your customer base by 15 points. This alone will tell you that your customers are less satisfied than they were previously, but doesn’t tell you why or indicate what actions you can take to improve the situation.
However if you look at other key data sources, such as the volume and type of support tickets that have been logged recently, you find that there’s been a few significant bugs with some of your platform’s key features, and this has been causing frustration across your customer base. With the goal of improving the NPS score, the key business KPI, you can make decisions around what you will do. Such as reaching out proactively to customers or providing services to compensate for the drop in service. And there’s your actionable insight.
In another example, if you had an ecommerce retail business and you started to see patterns with the types of customers who purchase a particular set of products, you could use this insight to look at how you could drive more revenue and sales. For example, you could implement product recommendations based on customers or prospects with similar profiles, or if this was a high margin product you could consider an acquisition strategy that targeted lookalike audiences.
In essence, actionable insights are drawn from:
Analysing and interpreting the data in light of its context
Drawing relationships between external and internal data points to see the complete picture
Linking this to business metrics and KPIs that matter to drive informed business decisions
This enables you to anticipate the needs of your prospects and customers, and help build better end-to-end customer experiences - and in doing so, help drive better revenue and business outcomes.
If you aren’t sure where to start or need help sifting through your data to create actionable insights, Idea Science is ready to help you start creating more meaningful interactions with your customers. Talk to us today to organise a discussion.