Why is data analysis so critical?
Business intelligence aims to help managers, executives and other operational employees make more informed decisions supported by accurate data. Ultimately, this will help them see new business opportunities, reduce costs, and identify inefficient processes that need to be redesigned.
Users of business intelligence systems such as Power BI analyse and present data in dashboards and interactive reports, visualising complex data in an easier, more accessible and understandable way. With this analysis, it is possible to show both historical and current data. Of course, BI-based data analysis does not tell us precisely what to do, but by indicating the company’s current situation, as well as archival indicators and easy comparison, it gives the management more opportunities to make informed business decisions.
What are the benefits of business analytics?
The benefits of business intelligence and analytics are numerous and diverse, but they all have one thing in common: they give you insight into all areas of your business. We present three benefits of data analysis based on fascinating case studies:
A better understanding of customer needs
Andre Chaperon, the leader of the email marketing industry, once said that “the business that is most successful is the one that best understands its customers”. In practice, this means that researching the needs of our customers gives real opportunities to tailor our offerings very accurately to their needs. This is perfectly illustrated by a case study by Versatel, a German telecom operator.
Versatel did quite well but was facing increasing competition and price pressure. So senior management decided to look for new ways to reduce the churn ratio, which means the percentage of customer losses. After all, acquiring a new customer is more expensive than keeping an existing one. To this end, they decided to analyse their data carefully. It turned out that their customers did not like having to deal with an external call centre to get support. After the changes, Versatel was able to maintain the lowest churn rate in its industry in Germany.
Stimulating the growth of the company and its revenues
McKinsey completed a case study of a fast-food chain. The company wanted to focus on its staff and analyse any data about their employees more closely to understand what they are doing and what they can do to improve their business performance. The board felt that solving the staff turnover problem would be critical to improving the customer experience and leading to increased revenue.
To this end, the company began work by defining objectives and finding ways to translate employee behaviour and experience into data to model it in relation to actual results. The goals were multiple: revenue growth, customer satisfaction and speed of service. Then the analysis of three areas was started: a selection of employees and their employment, daily staff management and analysis of employee behaviour and interaction in restaurants.
The data collected allowed them to introduce changes that led to an increase in customer satisfaction of over 100% within four months, service speed improved by 30 seconds, loss of new employees decreased significantly, and sales increased by 5%.
The identification of sales trends
The business analysis was also used by the world-famous basketball club Boston Celtics. Thanks to the data they collect about their customers, they were able to analyse who they are, where they sit and how much they pay for their tickets. This helped them quickly create promotions to sell more tickets and analyse revenue based on sales trends. Moreover, the visualisation of the data allowed them to see how much revenue a particular venue generates in a given season and compare different stadium areas.
As you can see from the examples above, business intelligence and data analysis systems are much more than the technology used to collect and analyse data. Instead, it’s all about using data to understand reality better. This allows companies to make better strategic decisions instead of relying solely on instinct and predictions.
You can read more about data analysis and Power BI on our blog. We particularly recommend the article “Do you want to analyse data more efficiently? Discover Power BI!”.
