Introduction to the World of Data Analytics !!
Warren Buffet in his second year in grade school would wander about the town collecting thrown away bottle caps of Pepsi, Coca-Cola, ginger ale, root beer. As a way of relaxing, he would sort these caps and count them every day. This not only told him which soft drink was doing well but also amazed him with the fact that there was so much of free information floating around.
What is Data Analytics ?
Data analytics is the process of analysing raw data to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions. It helps you to make sense of the past and to predict future trends and behaviours; rather than basing decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you.
Data analytics is so important in business simply because it enables an organisation to make more informed, data-backed decisions, as well as providing invaluable insight in key business areas, including:
- Customer behaviours and trends.
- Security and risk mitigation
- Business productivity and efficiency
- Customer personalisation
- Measure performance of marketing campaigns
What Is Behavioural Analytics ?
Behavioural analytics is the process of collecting and analysing data recorded from user activities on an app, website, IoT device or any other digital touch points. With this data, one can see exactly how users interacted with the product and make decisions about how to enhance customer experience and increase customer engagement in the future. Few of such examples are :-
1. Sign up
2. Searching for a Product
3. Viewing product details
4. Adding Product to cart
5. Purchasing a Product
These behaviours can be instrumented as events (or event-based analytics) and can be tracked to reveal user activity, preferences, and habits. It helps us to understand our customer better and gives us the opportunity to offer more value in product.
How Analytics can fuel Explosive Business Growth !!
It is important for organisations to drive product growth by understanding Customer’s actions as it conveys much more meaning than words. The good news is that for the first time in history we can measure practically every interaction a customer has with our business. If one understands what’s influencing their customers at every stage of customer journey, they can increase customer’s engagement, customer’s conversion into a purchase/subscription and customer retention.
The best way to ensure that our efforts will pay off is by building product knowing exactly what the customer wants. As per Mckinsey’s research, Organisations that leverage customer behavioural insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin. A study published in the Technology blog of Fortune Business Insights says that market size of Big Data Analytics was valued at $271.83 billion in 2022 and is projected to grow from $307.52 billion in 2023 to $745.15 billion by 2030.
A look at diagram below helps us understand how analytics tools can be used at different stages of customer journey.
What are the different types of Analysis:-
Once you have collected customers data, you can run different types of analyses. Below are few such Analyses: -
1. Measuring Successful conversions using Funnel: Funnel analysis is the process of mapping the flow of website visitors, mobile apps, emails, or other digital touch points inside products to a set of specific funnel steps leading up to a point of conversion. It can be used to trace the user journey throughout the product and see how many customers end up in each stage of the funnel.
2. Understanding trends using Segmentation: Segmentation analysis is based on common characteristics which allows companies to split their customers or products in different groups. It becomes a marketing technique giving companies the ability to create tailor-made and relevant advertisement campaigns and products. Few types of Segmentation are behavioural, transactional, needs-based, demographic, geographic, etc.
3. User Journey: User Journey analysis helps companies understand the common paths their customers take through the product, mobile app, or website. It also analyses the top paths customers take before or after a specific event.
4. Experimentation analytics using A/B Testing: A/B Testing is a randomised experimentation process allowing companies to compare multiple versions of the same web page or component. Different versions are shown to different segments of customers at the same time to determine which version leaves the maximum impact and drives business metrics.
5. Grouping similar users using Cohorts: Cohort analysis breaks the data in a data set into related groups before analysis. All entities in each group, or cohort usually share common experiences within a defined period. This allows a company to “see patterns clearly across the life cycle of a customer”.
Let’s have a look at few of the inspiring examples of companies using data analytics to their benefit: -
1. Netflix: Netflix ran predictive analysis on their customer’s behavioural data to learn what exactly their customers would be receptive and interested to watch. By analysing over 30 million plays a day as well as over 4 million subscriber ratings and 3 million searches, they were able to make winning bets on developing widely acclaimed hits such as “House of Cards” and “Arrested Development”.
2. Google: Employees are the backbone of any organisation, and maintaining their morale is critical for business to thrive, expand, and innovate. Google’s people analytics team looked deep into their data and evaluated employee performance reports and feedback surveys to better understand how to “build a better boss”. This helped in the creation of a list of data-driven insights into what employees valued and helped to improve the manager quality 75% of their lowest-performing managers.
3. Uber: In pursuit of solving demand-supply gap , Uber analyses historical data and key metrics that include number of ride requests and trips getting fulfilled in different parts of a city, the time and day where this is happening. This helps the company to gain insight into what areas have a supply crunch, allowing them to preemptively inform drivers to move to those areas to capitalise on rise in demand.
By Pragya Deep and Ashwin Rajani
References
- https://www.mckinsey.com/capabilities/quantumblack/our-insights/capturing-value-from-your-customer-data
- https://www.wikipedia.org/
- https://www.financialexpress.com/archive/a-simple-american-life/375104/
- https://www.fortunebusinessinsights.com/big-data-analytics-market-106179
- https://unscrambl.com/blog/data-driven-companies-examples/
