Big data promises to revolutionise marketing analytics and drive results. But what is big data and how do you use it? Luke Clum explains how your existing data could help you to understand your customers better.
What is big data? It’s any large, complex database that is difficult to process using traditional data processing applications (such as Excel). But this data has potential – if you know how to use it.
Do you know the value of your customers? Sales and marketing data – information that you may already hold – can tell you a lot about your customers. Big data provides valuable analytics – but the information must be curated and organised in order to give you a complete view of your customer.
Since marketing is all about reaching the right customers at the right time, big data can be used to predict purchases, analyse customer behaviour and better understand the people buying your product. But many companies are paralysed by the sheer amount of information and find it hard to identify actionable sources of customer insight.
While big data is incredibly useful for learning about your customers, it isn't the whole story. First you need to understand the human element – customer personas.
Customer personas and why you need them
Personas are essentially characters that represent various segments of your customer base. They contain in-depth information such as demographics, sources of influence, motivators, average income and so on. This information is then used to inform marketing and ad campaigns.
Commonly collected demographic characteristics include:
Commonly collected non-identifiable personal information includes:
Which customer is your best customer?
Knowing which customers are your most valuable buyers is important because it helps you further focus your efforts. Traditionally, companies often define their Most Valuable People (MVPs) as the buyers who spend the most money. However, you may find these customers are the most expensive to keep and are the least loyal over the long term.
Big data comes into play here by calculating several factors that help you get more information about your buyers. Now that you have your personas, let's find out more about them by calculating the metrics below:
How do these metrics correspond with your previous assumptions about your customers? If they are still intact, great. We can still use these calculated metrics to group buyers together and further adjust your targeting.
This is where big data analytics comes in to play. Try to tease out demographic and behaviour trends that correlate with your best customers (buyers whose lifetime value is greater than the combination of acquisition and retention costs) and match them to your personas. Also keep an eye out for customers who are moderately valuable and those who don't seem to fit the mould.
The desired end result is several groups of customers defined by behaviour, demographics and merit. These groups should all be prioritised by the value they provide to your company.
A great result of teasing out behaviour trends is identifying purchasing drivers and then tailoring marketing touch-points. Say you have a price-conscious customer who abandons the shopping cart. Sending that customer a 20% off discount with his/her cart items just might do the trick. For emotionally driven or socially aware customers, a product with proceeds benefiting a specific cause may sell more than a price promotion.
Giving data a human face
Volumes of data won't help your business unless you can give it a human face. By connecting data to human experience, you have the power to craft buyer personas that can help drive digital strategy and shape highly-targeted campaigns.
Luke Clum is a digital marketer with Distilled.
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