What’s the first image you have when it comes to data analytics? Complicated line graphs and bar charts that describes the past? You’re not alone.
Unbeknownst to smaller firms, corporate giants use data analytics to do more than look at past performances. Many have invested heavily into analytics to predict trends, identify new opportunities and prescribe solutions to a wide-variety of complex challenges. But how does that exactly work?
While you need to be a data scientist to understand the technicalities of analytics, it is useful for professionals to appreciate the benefits of data analytics applications.
Here are five effective business applications that SMEs can adopt.
Categorization is an important first step in data analytics, as well as being an analytics tool in and of itself. Common real-life examples of categorization include the classification of junk email, and identifying potential customers. In the case of Spotify, it can even be used to predict if you’ll like a song.
Categorization classifies your data into sub groups. Like washing our clothes by separating our white clothes from the colours, categorization helps to clean data for analysis. Categorization can be done in any number of ways including the use of decision trees, linear regression models, and neural networks to name but a few.
A common usage of profiling occurs in the sales and marketing industry, where datapoints are clubbed together to create user profiles; this information helps decision makers to reduce their many customers into a few personas with identifiable and distinct attributes.
Advancement in profiling techniques also means better detection of fraud patterns. It would be able to separate rare customer behaviour from abnormal fraudulent acts. The advancement in this area can help banks detect evolving credit card fraud techniques and telecom services identify fraudsters leeching on to legitimate accounts.
Profiling analytics falls under unsupervised learning (a branch of machine learning). It identifies commonalities in test data points and reacts when new pieces of data are added to the set.
3. Demand Forecasting
Moleskin, maker of the legendary notebooks, now operates in 105 countries managing over 1,000 SKUs across 25,000 points of sales. With demand forecasting, it was able to optimize inventory to minimize lost sales in stores. This reduced working capital by 15%, increasing profitability and also freeing working capital to fund growth.
Demand forecasting is a field of predictive analytics which estimates future customer demands with historical data and other information. By knowing market potential, managers can make better decisions in pricing, stocks & operations. Increased accuracy in forecasting means businesses can optimize their costs while maintaining (or increasing) profits.
Without data-backed decisions, businesses risk far-reaching negative impacts on inventory holding costs, customer satisfaction and, ultimately, profitability.
Since 1998, Amazon has been using recommendation algorithms to personalize shopping experiences and reaped tremendous benefits for its ecommerce business. In 2013, Amazon reported that 35% of its revenue came from product recommendations.
This technique recommends courses of action based on inferences from historical data. This is intuitive to many people, because we are constantly exposed to recommendations in the digital world; from Facebook posts, to ecommerce shopping suggestions, our usage behaviour has been compiled and applied to us. Recommendations make decisions easier for end users.
5. Sentiment Analysis
Sentiment analysis is an important tool that provides behavioural insights to build better understanding of customers. Large corporations will often engage in sentiment analysis to gauge public opinion of a brand during times of crisis, or to understand consumer sentiment during the launch of a new product to determine if that product is well received or not.
A relatively recent development, sentiment analysis has become popular in the age of social media. It is fundamentally based on Natural Language Processing (NLP) analysis, and uses categorization methods to come to an inference on direct, comparative, explicit and implicit opinions.
Bonus: Data Cleaning and Preparation
Data cleaning and preparation can include simple things such as correcting punctuation or removing excess spaces from input fields.
It is the process of removing corrupted or inaccurate data from a data set. Bad data can distort your analysis and invariably lead to errors in assumptions. It is estimated that up to 80% of a data analyst’s time can be taken up by data cleansing, but new technology is making it easier than ever to obtain clean data so that proper analysis and insights can be discovered.
Technology has come a long way in recent years, making effective data analytics attainable and achievable for SMEs. DataVLT makes it easier than ever to supercharge your business with the power of data analytics.
Interested to learn if your company is ready tap into advance analytics? Get a Custom Feasibility Reports from DataVLT. The report will assess your collection methods allowing you to obtain preliminary insights to your data as well as give you ideas on how you can adopt analytics for your organization. Get in contact with us for your custom report today.
DataVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DataVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
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