Embedded Analytics in a SaaS application

We helped a multinational software company to attract new customers and increase market share by implementing Machine Learning and Business Intelligence modules, which help end-users to increase retention rates and improve operational efficiency with embedded BI.

THE CHALLENGE

Personalized offers for end users

Nowadays personalization has a huge impact on sales growth. A company wants to create a system for products recommendation, where preferences of individual users will be taken into account.

Powerful analytical platform

A company wants to use the full potential of data, find new correlations and patterns in an automatic way. Also, the company wants to give SaaS users a possibility to analyze customers’ data in different dimensional layers, because usage of standard static widgets couldn’t uncover all problems and gain useful insights.

100+ excel reports

SaaS generated hundreds of reports that were difficult to analyze by users. A company wants to create a flexible reporting tool for end-users which could avoid a large number of requests.

Data integration

Nowadays it’s not only transaction systems that generate data. Consumers generate data from wearable devices and CRM – distributed data sources that were not adding any value to the business. A company wants to integrate all available data sources for a deeper analysis of all aspects of customers.

OUR SOLUTION

We developed an analytics system with self-service interactive dashboards and reports to analyze customers’ data and improve customer experience (customer 360). Additionally, we implemented a customized machine learning system for customer churn prediction, sales predictions, and recommendation systems which results were visualized using BI. We created a tailor-made data integration solution for both structured data and Big Data sources, combined together in a data warehouse.

Customer 360 panel with machine learning models to predict user behavior.

Based on customers’ behavior and preferences we developed custom machine learning algorithms that predict customer churn, detect anomalous behavior and automatically create segments of users. The results are integrated with CRM and are presented in the analytical panel, which shows all the needed information in one place.

Embedding of self-service BI into SaaS application

Self-service BI software was embedded into the SaaS application to meet all end-users needs. Drill down and drill through capabilities were enabled to enrich data analysis and decision making. Embedded analytics solutions are more flexible to integrate with web applications and cost less money, as well as take less time compared to building your own BI application.

Data integration

Data from 3 various sources were integrated into one analytical data storage. A multidimensional data model was developed to improve queries and ad-hoc analysis performance.

Custom recommendation engines

We created a data-driven POS and business-oriented recommendation engine that analyzes customer behavior with a huge amount of transactional and business-specific data to increase sales, overall business performance, client engagement, and satisfaction.

BENEFITS

The implemented solution supports automated daily reporting, provides a possibility to analyze insights instantly, increases upsells on customers, grows retention by detecting high risky customers and automated decision-making processes. Solutions are fully integrated with SaaS and complemented with its unique functionalities for end-users.

Money graph

19%

Increase in sales

Money

11%

Increase in customer retention

Graph

10

Automated reports

Cloud

3

Integrated data sources

 TECHNOLOGIES USED

R programmers
MS SQL Server developers
code integration
power bi consultants
R programmers
MS SQL Server developers
code integration
power bi consultants

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