Share in our experience of customer insight and data management

Back

Made for each other: Celebrus and Data Science

Published: Thursday, 9th August 2018 by Pragati Jain, Data Analytics

The rich breadth of data that Celebrus captures makes it one of the best sources for use in more advanced analytics and data science initiatives such as real-time propensity scoring, predicting purchase behavior or serving personalized offers to customers. Capturing data in real time and using Machine Learning models - along with the ability to execute without involving too many additional systems - means that while other data science initiatives are still on the drawing board, our clients can test and implement real world projects such as:

  • Real-Time Scoring: Already used in multiple areas such as detecting fraudulent credit-card transactions and product recommendations to customers while browsing products online. These use machine learning algorithms like Random Forests, Support Vector Machines, Naive Bayes among others.
  • Predicting Customer's Purchase Behavior: Celebrus captures a lot of relevant information from retail websites like pages visited, products browsed by customers online and products added to baskets. Considering customer's historical data related to product purchase, we can predict whether the customer will make a purchase in the future or not, or what kind of sales will happen at a later date, and whether sales will increase or not. Learning algorithms like Logistic Regression, Decision Trees, Neural Networks, etc. are typically used to predict purchase behavior
  • Product Recommendation to Customers: offers related to products already purchased or browsed by customers are typically provided by using clustering algorithms like K-Means. Implementation of this can be seen in areas like telecommunication, where companies try to target customers with similar monthly usage of data plans, for sending out targeted offers based on their monthly usage

A typical process looks like -

Diagram showing a typical process

A suggested approach would be to start simply and iterate towards a more complex model to improve accuracy and confidence while at the same time proving its business value. Once you have done this, you can calculate the return-on-investment and if appropriate migrate it to a production ready environment.

To find out more about how Celebrus data can be used to underpin data science initiatives, why not speak to our Celebrus Customer Success team – contact us now.

Our use of cookiesv20190815

At D4t4 Solutions we are all about the data. We are serious about data protection and your privacy and we will only collect your personal data and use it with your permission. We use necessary cookies to make our site work. We'd also like to set optional analytics cookies to help us improve it. We won't set optional cookies unless you enable them. Using this tool will set a cookie on your device to remember your preferences.

For more detailed information about the cookies we use, see our cookies policy & settings page.

Necessary cookies

Necessary cookies enable core functionality such as security, network management and accessibility. You may disable these by changing your browser settings, but this may affect how the website functions.

Anonymous browsing

We like to set Celebrus cookies to help us to improve our website by collecting and reporting information on how you use it. For more information on how these cookies work please see our 'Cookies page'.

By selecting anonymous browsing, these cookies will not allow us to identify you.

SET ANONYMOUS

Opted-in browsing

In addition to collecting and reporting data on how you use our website, we will also collect information that allows to identify you which in turn allows us to communicate with you more precisely. You will be able to opt-out of our communications at any time. In the course of dealing with you, we may need to pass your personal data on to third-party service providers contracted by D4t4 Solutions.

SET OPTED-IN