If you’re not using data to influence your marketing (and general business) decisions, you should be. Analysing data can uncover all sorts of insights into your business and activities which can then be used to fuel new actions and strategies designed to improve performance. It’s the equivalent of choosing where to go on holiday by reading reviews and talking to people, rather than putting on a blindfold and throwing a dart at a map.

There’s no limit to the data that can be extracted from your current activities. Any business with its own website should have a Google Analytics account at the very least, from which you can discern all sorts of relevant information that can highlight things you’re doing well, things you’re not doing well and things that might surprise you.  However, you then have to analyse it to uncover the insights that will drive your business forward.

Where can data analysis help drive business decisions?

Data can be used to analyse and improve the performance of practically any area of an online business. The examples below are just the tip of the iceberg.

  • Personalised shopping experiences – the interests and past purchases of repeat customers can be utilised to recommend products they might be interested in on their next visit
  • Detect underperforming sections of your website – simply by comparing user engagement between mobile and desktop users you can decide whether your website actually works well on mobile devices
  • Get to know your customers better – looking at the terms that users tend to search on your website can help you understand your customer’s needs
  • Improved customer service – use features like feedback forms to pinpoint what customers like and dislike about your website, services and products
  • Improved content marketing – by having a better handle on customers’ likes and dislikes, you can produce content that mirrors their interests and results in a more engaged and loyal audience

If you have the data to hand, you can use it to help with almost anything – it depends what you’re trying to improve or change.

Work to a process

Data must be examined objectively – while you will be hoping that it reveals something you can work to improve or build upon, it’s important that you enter into the process with an open mind, otherwise you may end up suffering from confirmation bias where you try to fit the data around a preconceived theory.

  1. Question – what is the business’s need? What is it looking to achieve (increase in sales, more brand awareness, etc.)?
  2. Find potentially relevant data – once the question is specified, only a subset of the data in hand will be of interest. Carefully pick metrics that reflect the pre-specified question. Don’t get bogged down in or waste time on parameters that won’t have an impact on the question you’re looking to answer.
  3. Select methods and algorithms which are suitable to answer the pre-defined question.
  4. Interpret the results and suggest actions

Using a working method like the one above will ensure you retain your focus and don’t fall down a rabbit hole of data that may or may not be relevant. Efficiency is important – once you know what you’re trying to find out, you will be able to pinpoint the data you need to be looking at.

Can you get results solely by using data?

Having access to data is a great start, but it is just a start. You have to use your knowledge and understanding of your audience and business to identify areas in which the data you’re collecting can make a difference. Let’s say that, at its most basic level, data reveals a slump in sales of a certain product. While you might think that offering a discount to boost sales might be a good idea and could bring in more customers, you’re probably right – but is this going to be sustainable? How many of those new customers will stay with you when the discount period comes to an end? You have to interpret data in the right way to see what it’s really showing you.

Looking at the data you have collected about your repeat customers may help pinpoint what it is about your business that keeps them coming back and allow you to target new customers – for example, if a lot of people return a lot via your email newsletter then that means your emails are a strong source of traffic and a good extension of your brand – but it’s your wider knowledge about the business and insight into your audience that will allow you to use that data in the right way.

Use data to look ahead

Data doesn’t need to (and shouldn’t) be used in a reactive way – it can be used to plan ahead proactively in order to optimise marketing campaigns and business operations. By predicting the likelihood of user conversion in the future, you can reduce campaign expenditure, thereby spending less for greater results.

One way that some car insurance companies plan their future activities is by placing sensors inside vehicles and using the data it pulls off them to adjust the cost of the premiums they offer to customers. While your business might differ in the way it gathers relevant information and data, the principle remains the same – you can still take the data you have and use it to alter your offering if the numbers indicate that it’s the right thing to do.

Is it always a manual task?

Mining and analysing data is extremely time-consuming and in many cases it would be a herculean task to comb through it all to find the nuggets of gold that are actually valuable to you. With that in mind, if you’re planning to take data mining to that sort of level, you may want to begin experimenting with a supervised or unsupervised machine learning model to discover interesting patterns in your data. Understanding the mechanism that lies behind those patterns is again the key that will lead to solve the specified problem. As we said previously:

“… an ecommerce website could use a supervised machine learning algorithm to predict which of their customers are at risk of churn in the near future based on their previous browsing and/or purchasing behaviour. Those customers differentiate significantly from visitors that according to their behaviour will most likely convert soon. Therefore, a different data-driven strategy could be implemented for each type of customer, reducing campaign costs and improving acquisition/retention rates.”

What insights into your business and operations have you gleaned from analysing data? Would you like to make greater use of the insights you have access to? Get in touch with us via @RocketMill to see how you can take your data and insight analysis to the next level.