How to Turn Data into Insights and Actions

Data analytics is becoming increasingly important as businesses face growing demands for competitive differentiation, enhanced customer service and sustaining financial growth. With company structures transitioning away from ‘top-down’ to ‘bottom-up’, more junior employees are being allowed to make decisions so it is vital that employees at all levels understand how to interpret data efficiently. So exactly how do we make the most out of big datasets? How can we turn data into stories that reveal the secrets of your business and industry? Here are four effective ways to turn your data into insights:

1. Simplifying Your Problem and Ensuring Your Dataset is Accurate

With tonnes of data available at your disposal, it is important to be able to understand the problem that you face and define what metrics you need to look at to best tackle the problem. It is very easy to overcomplicate your problem with multiple metrics and different analytical tools. To avoid getting sent on a wild goose chase during your analysis, it is much better to simplify your solution as much as possible.

It is a well-known fact that accurate data is a prerequisite to any analysis, so it is very worthwhile to tidy up any database to prevent mistakes being made. Sometimes, it is your analytical tool itself which reports inaccurate data. For example, it can be very easy to place tracking code in the wrong place, copy it twice or simply not put it in at all. It is good practice to always check every analytical tool with your back-office system to see if sales or any other data collected are accurate.

2. Use a Combination of Analytical Tools

To truly understand your business, you must look at it from multiple angles, this involves using a variety of analytical tools or techniques. Let’s suppose that the home page for an e-commerce is doing particularly well in making customers go on to another page. Google Analytics might tell you what page customers went to next and although this good for answering the ‘what’ component to your problem – what it will not be able to tell you is how customers engage with your page.

Why is my homepage effective in moving customers down the sales funnel? Overlaying this with a heat-map analysis (for example) can allow you to see where your customers are clicking, record user sessions to see how they interact with your page and highlight where customers place their cursors to see what is keeping customers interested. We might find out that the bright-red ‘see more’ call to action button does very well in catching the customer’s attention straight away.

3. A/B Testing

A/B testing (or split testing) is comparing two versions of a webpage where everything except one variable is exactly the same. Let’s say you want to see the impact of a new website layout. You can randomly send half your traffic to your ‘control’ webpage, which is what is was previously and the other half to your new webpage. It would probably be better to make a small change such as, changing the colour, a headline or button as opposed to full transformation because you would struggle to pinpoint what exactly led to the result if there were many changes. This might give you an idea on which headlines resonates better with your customers, but how do you know if it is the best? There are endless variations you can test out and the only way to know which is better is through more tests.

4. Data Segmentation and Visualisation

Segmentation is an effective way to turn your data into insights and learn how different customers (segmented by age, gender, location for example) respond to your business. Another example is looking at mobile vs desktop traffic. Perhaps you might find that people use mobile for browsing and desktop for purchases through looking at session times and conversion rates. Maybe traffic from Facebook is converting a lot better than your PPC which might give you an idea on where to spend. There are many ways you can divide your data to get really informative insights. Visualising your data as opposed to comparing numbers will allow you to spot trends, perhaps by charting your data on traffic you find that you have more traffic during the Christmas period from people browsing for gifting ideas, so you might want to time a promotion around this time. To find out more about data visualisation click here.

Ultimately, with the volume of data made available to us growing exponentially and technology becoming more prevalent, the demand to analyse data effectively has never been higher, especially with ‘The Internet of Things’ gaining more momentum. A successful data analyst will be able to mix both quantitative analysis (i.e. looking at metrics from an analytical tool) with qualitative analysis (the interpretation of metrics and understanding why this is the case). To help with the interpretation side, I find that it is beneficial to hold meetings where members of different departments are present, to gain a mutual understanding, hear new insights but also to empower your employees to question any data they get. If you follow these four tips you will be able to understand your business by turning your data into insights.


Brightblue Consulting are a London based consultancy which help businesses drive incremental profit from their data. We provide predictive analytics that enable clients to make informed decisions based on data and industry knowledge. Through Market Mix Modelling, a strand of Econometrics, Brightblue has a proven track record showing a 30% improvement in marketing Return on Investment for clients’ spend. If you are interested to find out more please contact us through email by clicking here and one of our consultants will get back to you shortly.

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