In our last blog post, we talked about how to get the basics right when using data to understand your customers. Once you’ve mastered this first step, it’s time to look at how you can use data to gain real customer insights to guide your marketing decisions. This is where data science comes into play.
In layman’s terms, data science is the process of extracting information and insights from data using scientific methods and algorithms. Essentially, data science tells you what you don’t already know about your customers. And it’s pretty new on the scene, the term ‘data scientist’ was only coined in 2008, so the vast majority of people won’t have been taught it in college or have even been aware of it as they climbed the ranks in business. For this reason, many companies shy away from it as an unknown area. It also sounds expensive, which doesn’t help!
Data science is a key piece of the puzzle when it comes to customer insight. You don’t know what you don’t know. Data science will tell you. It provides the non-obvious insights that can be gleaned from your data. Whilst a data scientist can answer a direct question, the real power is when they’re not asked to and instead interrogate the data to find connections, patterns and correlations.
"Businesses are using customer altitude data to save millions of pounds on their retention marketing."
A great example of this is businesses using customer altitude data to save money on their retention marketing. For one company, data science unearthed that customers living above a certain altitude were far more likely to remain loyal to the brand due to the difficulties they experience with receiving deliveries. It tested this theory and stopped all retention marketing for customers living above a certain point. It saw no decrease in revenue and saved millions on its retention marketing programme. This is not a question you would necessarily think to ask, but the data provided the insight.
"New tyre searches over-index for men in homes with a pregnant woman."
Data science will also find relationships and correlations that affect your KPIs in ways you would never dream of. For instance, you might find it surprising that new tyre searches over-index (positively deviate from what’s expected) for men in homes with a pregnant woman. Would you think to check your data to see if there’s a correlation between tyre sales and pregnancy? Probably not. But data science finds the relationship within your customer data and allows you to test assumptions and make decisions based on its findings.
Basic data science is inexpensive
Data science sounds expensive, but there are ways you can experiment with some basic analysis.
Google Analytics is good enough to plug into data science tools to gather some initial insights. This is probably half a day’s work for a data scientist. You could look to use a freelancer or go via an agency so you can dip in and out rather than having to hire a costly full-time head. Do some basic work, get the insights, test them out and, most importantly, measure your ROI. This will become a great business case to get more budget for future work.
Create a data lake
Pull all your data into one centralised place, beyond a customer relationship manager (CRM). As we touched on when talking about data basics, this type of activity is practically impossible with a standard CRM but customer data platforms (CDPs) can help. Cloud computing platforms such as Microsoft Azure and Amazon Web Services (AWS) make this easier than ever. Having a data lake in place will allow for far more complex analysis and surfacing of powerful insight.
The final step in your data science journey, and probably the most important one, is testing. Our third and final blog in this series, How to truly understand your customers through data and insights: Testing, will talk you through why it’s imperative to your strategy and how you can ensure it’s done right.
Or, you can check out our full data and customer insight webinar for more guidance and interesting stats.