Today, customers expect more than a good product or service – they want businesses to understand them, know them, and deliver a truly personalised experience. To stay relevant, businesses are collecting and storing more data about customer habits and preferences, which will help them learn what their customers want and deliver a satisfactory experience.
This unprocessed data is collectively known as Big Data, and it is now a business’s most precious asset as it provides actionable insights that can make or break a business. But with larger volumes and more complex data being generated daily, more sophisticated analytics is needed to modernise applications and the data interpretation process for the best accuracy – which is where Big Data analytics comes in.
Collecting Big Data
Businesses have many ways to collect personal, behavioural and engagement data from customers, ranging from tracking their browsing habits on their websites to more traditional surveys and feedback forms.
On websites, businesses can use cookies to track a customer’s purchase journey and learn everything from how long they spend browsing to how likely they are to drop off at point of purchase. It can also tell brands what offerings are most popular, when customer traffic is at its highest or lowest and how customers are discovering the site.
Social media is also one of the best ways for brands to engage with and learn about their customers. Brands can learn the demographics of their target audience based on social media profiles, evaluate the performance of a campaign, product or service based on audience feedback and reactions, and even find out where their customers are based.
Processing and analysing Big Data
The collected data is stored in a data warehouse or data lake, where it must then be organised, configured and cleaned for easier analysis. Next, analytics software is used to make sense of the data – it will sift through the data to search for patterns, trends and relationships, which can then be used to build a customer profile or predictive models that can forecast customer behaviour.
Analysing such volumes of data in a short amount of time requires immense computing power and can take a heavy toll on networks, storage and servers. As such, many businesses opt to offload this task to the cloud, which is capable of handling these demands efficiently and quickly. This enables businesses to be more agile and responsive in making customer-centric decisions.
The benefits of Big Data analytics and customer engagement
With the valuable insights derived from Big Data analytics, businesses gain significant customer insight that they can then use in everything from product research and development to marketing strategies and campaigns. The goal is to resonate with the customer and build an emotional relationship that will increase customer stickiness and brand loyalty.
Some of the most famous success stories include Spotify, which uses machine learning and artificial intelligence to offer personalised ‘Discover Weekly’ playlists that recommend songs to users based on their song history. Another is Amazon, where Big Data helps them make better product recommendations to customers and improve the delivery experience with an intelligent logistics system that chooses the nearest warehouse.
The Big-Data bottom line
It is clear that business success and the brand-customer relationship is more tightly linked than ever, which is why businesses need to invest in their Big Data collection and analytics to reap the most benefits – especially with an increasingly saturated marketplace in the digital era.
At Cloud Kinetics, we understand the value of intelligent data analytics. Our Data Engineering team has helped many companies collect, manage, and extract valuable insights from their data, enabling them to provide an improved customer experience and enjoy better business outcomes. Speak to us today to start your journey into Big Data analytics.
