In this age of e-commerce and web analytics, there is an abundance of data available at your disposal to evaluate how consumers are responding to your website, marketing campaigns, or user interface. Naturally, analysing data is an extremely powerful tool in any business’ arsenal when designing a business or marketing strategy.
However, the sheer abundance of data can be daunting. Where do you even begin to look? What types of analytics should your business be purchasing in order to fortify business performance? Is qualitative data gathered from interviews with focus groups a more reliable reflection of consumer preferences for a promotion you’re running, or should you be looking at click-through rates for the promotion on your website? In an absolute sea of every answer imaginable, it is imperative to ask the right questions.
When we leveraging consumer insights, the general framework is as follows:
First, we identify the business objective we want to meet. These objectives could be specific to the goals of your team or department, or could be company-wide objectives, such as improving the e-commerce website’s performance, or improving the return on advertising spending. Having clear objectives sets a boundary for what you’ll consult the data for.
Once you have identified objectives, then you can determine your metrics for evaluating your success. These metrics will likely be based on the consumer data you can collect – an example from my work in e-commerce analytics would be the percentage of users who abandon their cart. Of course, picking the right metrics that can shed light on the objectives is key. We will discuss this in greater detail later.
Let’s say your metrics will show that your performance can be improved. How do you convert the data into an actionable step? This is where you analyse the data and generate a hypothesis on why the KPIs are not being met. Doing so requires looking past the superficial KPI figure and looking at the actual data. What is the problem that is hampering consumers?
The last step in this framework is devising a solution that addresses the problem proffered in the hypothesis. Typically, if you have rigorously analysed the data and have a clear and specific hypothesis, the solution to the problem will be obvious.
However, many a time, you will inevitably have to try different solutions before you stumble at the perfect one. Don’t be afraid of not getting it right the first time. This analytics process is ultimately iterative – there is rarely a point where your solution is the actual best solution to the problem. Test, learn, iterate. If you analyse the problem using the steps in this analytical framework, you will put yourself in a position to be closer to identifying the “correct” answer, even if it takes a few rounds of iteration.
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Head of Digital Analytics (Customer Sentiment)