Since the widespread implementation of big data analytics tools at companies across the world, few business functions have been left unaffected. One of the key areas where big data analytics has made the most impact is in improving the customer experience, in part due to its ability to harness customer data extremely effectively.
What is big data analytics?
Big data analytics is the process of collecting, storing, processing and analysing large and complex data sets.
The purpose of big data analytics is to uncover hidden patterns, correlations, and other insights that can be used to make informed decisions and drive business value. This process requires the use of advanced technologies, algorithms, and statistical methods. The volume, variety, and velocity of big data can pose challenges for traditional data processing methods, but the insights gained through big data analytics can lead to significant competitive advantages and improved outcomes.
Four ways you can utilise big data analytics to enhance the customer experience
1. Hyper-personalisation
Thanks to the growth of platforms like Amazon and Netflix, that offer a deeply personalised experience for users, customers expect all interactions with businesses to take into account all of their preferences, needs and tastes. Before big data analytics, this was a very difficult task,’ with humans simply being unable to join together all customer data points.
Today, big data tools can automatically collect and analyse large amounts of often disparate data and provide actionable insights to staff or automatically offer what a customer wants directly to them. Everything from call transcripts, social media comments and demographic information can be taken into consideration by these innovative tools to quickly answer customer queries and give them what they need before they even know themselves.
2. Trend prediction
No matter what industry a business operates within, a major challenge is working out what trends are on the way. In the past, guesswork was a typical way to forecast future changes in demand, but the advent of big data analytics enables massive data sets to be analysed to uncover hidden customer trends as early as possible and providing them with a positive experience.
In practice, businesses will be able to prepare for big changes to demand and ensure there is enough of a specific product in-stock so that shortages are reduced or eliminated. Trend forecasting can be turned into more of a science thanks to the huge volume of data that can be processed.
3. Next-generation customer targeting
The process of acquiring new customers can be difficult. Scattershot marketing campaigns can have a high cost and result in low conversion rates. Yet, in the both the business-to-business and business-to-consumer spaces, big data analysis can unlock comprehensive customer profiles that increase the chances of turning potential customers into new clients.
By gaining a clear understanding of potential customer behaviour, account managers and marketers alike can tailor approaches to achieve the highest possible conversion rates with little human time or effort.
4. Removing friction
Even a minor obstacle during the onboarding or purchasing journey can lose a new customer. For example, an online check-out error can quickly turn a happy customer into an ex-customer. By deploying big data tools, the most impactful roadblocks can be identified and work can get started to eliminate these.
By understanding what parts of the customer journey sees the most users drop-out gives staff the ability to improve these sections to hopefully increase the conversion rate. Customer support agents, too, can be supported by comprehensive data about the customers they are assisting, making their job easier.
