Statista reported that revenue from big data and business analytics will likely generate $186 billion worldwide this year. This isn’t surprising considering how much the data analytics industry benefits businesses across all industries. In fact, more than 80% of respondents in the Big Data Executive Survey 2017 said that their big data investments have been successful.
These managers experienced positive outcomes in the form of cost reduction, product and service innovation, and more. But perhaps the biggest advantage of big data is that it can help you know your customers better.
Data-based insights provide a glimpse into who your customers are, what they like, and how they behave. Based on this information, you can know how to position yourself in a way that will appeal to them. It’ll also help predict how your customers will behave in the future. All of this can improve customer experience and boost revenue.
In this post, we’ll take a closer look at all the ways big data can help you know your audience better and provide actionable advice on how this can benefit your business.
Use Pattern Data to Accurately Predict Customer Goals
Big data helps uncover customers’ behavioral pattern data. This includes information on what they purchase, how long they stayed on a website, how many times they needed customer support, and more. Instead of using this information in isolated packets, try combining it to uncover pattern data that you can use across the organization.
For example, let’s say you work for a bank and notice that some customers have a similar pattern of regularly overdrawing their checking accounts. The credit risk team can use this data to identify customer likelihood of defaulting on their mortgage, enabling the marketing team to make use of the same data by pitching services like overdraft protection and financial planning.
Amazon collects data from customers as they browse through the website, then uses it to make accurate recommendations. The company uses collaborative filtering to develop an accurate customer profile for each shopper based on pattern data such as browsing and search history, purchase history, etc. They also use external datasets such as customer demographics. Amazon’s recommendation technology then offers products popular among certain groups to others with similar profiles.
Similarly, retailers like SHOEPASSION realized that people who buy shoes are highly likely to buy matching belts. They used this data to make accurate cross-selling recommendations, resulting in a 10% increase in their cross-sell revenue.
Understand When and How Your Customers Want to Be Reached
Big data can also be used to personalize outreach and earn better response rates. Track response rates and combine the data with the rich customer profiles you’ve developed as a way of determining which content format and distribution channel is most ideal for each customer segment.
For example, an insurance company may use big data to discover the starting point of a customer’s journey to purchasing travel insurance, prior to receiving their first quote. Let’s say they also collect information on the number of signals taken before they make a purchase.
They may see that most customers tend to make inquiries via email rather than a phone call. Using this data, they can personalize the tone and channel of outreach for each customer. They’ll also be able to tailor the right timing for sending outreach emails or making outreach calls.
According to McKinsey, this type of personalization can increase sales by more than 10%. It can also improve the ROI on marketing expenditures five to eight times.
Celcom, which deals in mobile communications, used a data-driven approach for their digital marketing campaigns. A main challenge for mobile communications companies is to make sure customers remain loyal, and in a fiercely competitive environment, that’s not easy.
Celcom realized that the only way to retain customers and grow their subscriber base would be to come up with more relevant offers. They used existing customer data to create marketing messages likely to appeal to customers based on their usage.
They urged customers with low weekend usage to buy credits on weekends for free airtime. By using real-time data, they were able to make their offers more meaningful, and the messaging became more relevant for each customer they targeted. As a result, campaign performance improved by 70%.
Understand How to Better Help Your Customers
Big data identifies what customers need, giving you a better understanding of how you can serve them. In the first section, we discussed how businesses can use big data to make accurate predictions about what their customers might like. Let’s now explore the idea of using customer data to understand what they need.
Consider something simple like a customer’s browsing history. Perhaps you noticed that they checked out different service pages but left without making a purchase.
Maybe they simply didn’t find what they needed or had a hard time figuring out which service would be most suitable for them. You could introduce a live chat agent or create a service selector tool, recommending products to visitors who follow similar browsing patterns. This can reduce website abandonment and drive purchases.
Financial institutions like RBS use big data to analyze transactional figures as a way of identifying when a customer has paid twice for the same financial product, such as an insurance policy already included in the account package. They also provide real-time recommendations to customer call centers and branch staff.
56% of organizations have been able to improve their customer service by using big data. Not only can data implementation result in increased sales and revenue, it can also help deliver a better customer experience. This, in turn, can improve customer loyalty and fuel business growth.
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