Using Big Data Analytics To Know Your Customers Better

Today, customers expect more than a good product or service – they want businesses to understand them, know them, and deliver a truly personalized experience. A whopping 78% of consumers are more likely to choose a brand that offers personalized experiences, according to PwC’s Consumer Insights Survey 2023. 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 modernize applications and the data interpretation process for the best accuracy – which is where Big Data analytics comes in.

Big Data use cases for businesses

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.

Beyond the personalized recommendations and targeted ads, here are 5 innovative ways that brands can leverage big data for deeper customer understanding:

  • Predict churn: Traditional churn models often lag behind customer behaviour. By leveraging real-time data like browsing patterns, cart abandonment and engagement metrics, brands can quite accurately predict which customers are likely to churn. Brands can then proactively intervene with personalized incentives or targeted outreach in an effort to control churn.
  • Map customer journeys across the business ecosystem: Businesses need to analyse data from all customer touchpoints – websites, apps, physical stores, social media – to understand the complete customer journey. They can then effectively identify friction points, optimize pathways and create seamless omnichannel experiences that cater to individual customer preferences and buying stages.
  • Understand the emotional footprints of customers: Businesses need to stretch their learning beyond demographics and purchase history by using text analysis tools such as sentiment analysis, to understand the emotions behind customer reviews, social media mentions, and even customer support interactions. Such an analysis can reveal unspoken frustrations, desires and brand affinities, making it a little easier for brands to tailor their messaging and experiences.
  • Decode hidden needs with unstructured data: It is not enough to only focus on structured data like purchase history and demographics. To gather a deeper understanding of customer preferences, businesses need to analyse unstructured data like images, videos and voice recordings from customer interactions. Such details can reveal subconscious preferences, cultural nuances and emerging trends that surveys or focus groups might completely miss.
  • Create hyper-personalized feedback loops: The importance of real-time feedback cannot be undermined. Brands can use dynamic surveys and AI-powered chatbots to collect real-time feedback from customers as they interact with the brand. Such data allows brands to instantly customize product offers and recommendations, as well as content based on individual preferences and changing needs.

Processing and analysing Big Data

The collected data is stored in a data warehouse or data lake, where it must then be organized, 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. Here are some examples of how cloud-based data and analytics solutions can be used to gather, process and translate business data:

  • Multi-source data acquisition: Data can be gathered from diverse sources such as business websites, apps, customer interactions, social media and IoT devices.
    • Point of sales and transactional data is a starting point for many businesses.
    • Demographic data enables businesses to understand who is buying what depending on age, gender, economic condition and much more.
    • Altitudinal data  gathered through market research and social media sentiment analysis are other rich data sources too.
    • Social media profiles, reactions to promotional campaigns, products or services are all valuable sources of data.
    • Consumer trends, local preferences and acceptable prices can all be understood from such data. Businesses also get to know about the most popular brands, when consumer traffic is the highest or lowest, and customer browsing styles, among many other attributes.  Cloud-based data integration platforms can then be utilized to unify these various data streams.
  • Scalable data warehousing: The massive datasets are stored in secure, flexible cloud data lakes, data warehouses, lake houses like Google BigQuery or Amazon Redshift for efficient retrieval and analysis. Such warehouse tools usually support all types of data, can work across clouds and have built-in business intelligence and machine learning. 
  • Data quality management: The data is then cleaned and transformed using cloud-based data cleansing tools to ensure the data is accurate and consistent before analysis. Data management teams must ensure that the data is in alignment with global and domain rules. Ensuring that certain data quality metrics are adhered to increases the quality of the data gathered. A few common metrics include accuracy, completeness, uniqueness, validity, consistency and linkage to relevant items.
  • Advanced analytics engines: Cloud-based data analytics platforms can then be employed to run large-scale statistical analyses and build predictive models for customer behaviour. Such advanced engines can work on complex datasets and derive customer behaviour details. 
  • Data storytelling dashboards: This is the process of translating data analyses results into understandable terms that can be used to influence a business decision or action. Cloud-based business intelligence (BI) tools like Tableau or Power BI can be employed to visualize insights and translate them into actionable strategies for improved customer experiences.

Big Data Analytics: Building customer-brand relationships & 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 big data analytics success stories include Spotify which uses machine learning and artificial intelligence to offer personalized “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.

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. Connect with us today to start your journey into Big Data analytics.

Tags: Business on Cloud Data & Analytics Data Engineering Data Modernization Data Transformation