How Can Apache Spark Help with E-Commerce Data Analysis?
In any e-commerce business, providing the best consumer experience is what all the research are all about. A majority of e-commerce companies have started leveraging data to provide better services to their customers.
Among the numerous big data tools available in the market right now, Apache Spark is one of the most popular and effective software of all. Apache Spark is an open source framework which is used to store and process big data in various clusters. Though Hadoop was initially the go-to software for big data analytics, Hadoop had severe limitations that restricted the handling of data like low processing speed, inability to handle data pipelining, challenging to use and lengthy coding processes.
Spark was able to overcome all the drawbacks of Hadoop and present an easy to use system that blends in well with the real-time operations. Many top e-commerce businesses are depending on Apache Spark consulting services
for their big data analytics in increasing their business visibility and customer satisfaction.
For example, Alibaba, a giant in the e-commerce sector, has been using Apache Spark and GraphX
to work with a set of mining algorithms which focusses on reusability, providing better recommendations to the users and helping customer to make better product decisions. They have been charting out the data obtained from Spark on the graph which has enabled them to improve their performance significantly.
Personalized Predictive Analysis for Product Recommendations
Product recommendations are an essential section in an e-commerce website which actively helps the business to suggest products that the users would want to buy. It is also a section where the user can easily find the products they want without searching for it.
Several factors are considered in the product recommendations other than just choosing the right products to display – the format of display, the right image choices and the sizes and the categories to select also plays a significant role in the conversions.
Through the automated big data analytics of Apache Spark, e-commerce businesses can display products in the right frame that will increase the likeliness of purchases. By taking into factor the user’s product history and the searches, it can choose the products from the possible categories which will look attractive to the user.
Apache Spark can increase the click rates, the conversion rate and thereby the revenue from the e-commerce website. Spark is used by data scientists to predict a user’s purchases instead of just relying on the intuition. Every recommendation section can be personalized according to the specific tastes of the user and displays them in an attractive way that captures the eye of the user.
Additionally, the predictive algorithm of the recommendation section needs a lot more concentration than the sessions on a computer. Apache Spark can still increase the probabilities of the conversions even in a limited display frame and with vast options.
Designing The E-Store for High Customer Engagement
The use of the right web design goes a long way in capturing the customer’s attention. From the placement of the images to the use of specific colours wholesomely decide the engagement of the e-commerce store. With the big data analysis from Apache Spark, one can take into account the previous histories of users, analyse the customer behaviour, find out the reasons for the customer leaving quickly and gain a complete insight into what runs in the minds of the customers when they land on the website.
By mapping the hot and cold maps and analysing the areas where the improvements could do better, Apache Spark will be able to provide a complete set of options which when implemented could make a whole new level of positive difference to the customer engagement.
Spark takes into account the user journey as they work their way inside the website and pulls out the insights about the actions of the users in specific areas. This will be an eye-opener for companies that have been struggling to understand why a particular feature or a strategy doesn’t work.
Forecasting Demands and Data-Driven Supply Chain
Again, the predictive analysis of Spark comes into play but in a much broader sense. Apache Spark analyses the data collected from your website and from external sources in your industry to understand the movement of the market. It can predict the boom and fall of certain products in advance which will give one enough time to be ready for the market fluctuations.
Apache Spark along with MLlib, the machine learning library, is a great boon to forecast the demands and the falls earlier. You can easily plug in the library with any data source and use it in integration with Spark to gather quick real-time insights that let the business to take advantage of the market conditions.
Just imagine being able to give your user exactly what they want on a silver platter. This is precisely what you can do with Apache Spark – provide a personalized shopping experience to every single user who visits your e-commerce store.