Predictive
Analytics

Data-driven predictions about future events are a necessary capability for companies to maintain a competitive edge. Today companies can use data to predict possible business risks, unknown future events, and potential business opportunities. While forecasts and predictions have always been a part of business usage, until recently these were either straight extrapolations or gut based ones, not always accurate but also statistically significant.

Driven by the explosion of data, we believe Predictive Analytics finds its relevance in end number of industries and functions by providing a more fact-based vision. By leveraging broader pools of data through different technologies & platforms, our team of certified professionals creates more data mining opportunities for you to gain predictive insights.

We have extended traditional Analytics into the predictive side for several use cases like:

  • Fraud Detection-Merging analytics methods to make pattern detection better and prohibit criminal behavior
  • Customization of marketing campaigns- Tailor messages or outreach as per customer response
  • Limiting risk and expanding markets- Generate credit score to determine buyer’s creditworthiness

Reach new insights with
Predictive Analytics

Predictive Analytics is the efficient use of data, machine learning, and statistical algorithms to boil down on possible future outcomes based on historical data.

At Quosphere, we apply Predictive Analytics theories in business by using cutting-edge algorithms like Arima, Random Forest and many more to find hidden insights in your data.

  • Identify most valuable audience segments via machine learning and data clustering
  • Generate insights for effective customization of customer experiences across channels
  • Deliver propensity models to predict customer conversion
  • Real-time data feeds for the right business decisions and better engagements

Predictive Analytics Use Cases:
Solutions

Issue

Retention and churn are two most important aspects of a business. One of our customers from the retail industry wanted to have a cost-effective strategy built around retention and churn.

Solution

While this is one of the oldest statistical problem to solve, the data cleansing and preparation as well as identifying the attributes to figure out which customer is likely to churn and why in order to implement a targeted retention campaign effectively were some of the challenges that were resolved.

Issue

Customer needed an efficient demand-forecasting model to cut short the inventory and enhance the service levels.

Solution

After analyzing the need we came up with a custom demand-forecasting model that collects historical data to outline future trends, while considering constraints like supplier lead time and organizational processing time.

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