Predictive Analytics

Gain the ability to see into the future with predictive analytics

The number of data sources has grown exponentially in the past decade using IoT-enabled sensors, smartphones as well as web systems. Coupling this with the increasing power of data storage capabilities and the ability to process large volumes of data in the cloud, edge devices or on-premises systems has given rise to predictive analytics techniques unlike ever before.

Predictive Analytics applications cover numerous business applications for various industries. Stridely Solutions helps businesses to predict future outcomes like customer churn, sales forecasts and others with predictive models.

Predictive Analytics Models And Use Cases

01. Customer Retention

Keep your customers happy by analyzing historical data that includes past purchases, app interactions as well as social media engagement with customer retention analytics.

02. Recommendation Engine

Cross-sell and up-sell unlike ever before by identifying patterns that recommend the right products or services to your customers before they even know that they need them.

03. Predictive Maintenance

Avoid costly downtime that can affect production and keep your machines and equipment up and running by predicting when they need maintenance and repair.

04. Risk Management

Calculate and assess every bit of risk associated with your business by analyzing past data and manage risk with a ranking model that better equips you to deal with unaccounted behaviour.

05. Customer Segmentation

Get to know your customers better by dividing them into groups that share similar characteristics for superior customer service as well as satisfaction.

06. Sentiment Analysis

Calculate and assess every bit of risk associated with your business by analyzing past data and manage risk with a ranking model that better equips you to deal with unaccounted behaviour.

07. HR Analytics

Utilize data to gain higher employee retention and attract top talents and optimize recruitment channels. Predict and evaluate compensation to stay prepared in case of employee churn.

08. Sales Forecasting

Calculate and assess every bit of risk associated with your business by analyzing past data and manage risk with a ranking model that better equips you to deal with unaccounted behaviour.

09. Inventory and Supply Chain Prediction

Predict the inventory you will require for better planning with stock replenishing, tracking inventory costs, forecasting prices, multi-channel inventory synchronization and real-time updates.

10. Compliance Adherence

Determine functions and processes that are likely to miss out on compliance adherence by analyzing granular data and keeping a check on internal processes and external policy changes.

12. Fraud Detection

Analyze past and present data to detect abnormal behaviour that deviates from normal behaviour and may result in fraudulent activities.

13. Next Best Action

Analyze everything from customers’ buying patterns to consumer behavior to social media interactions which gives insight into the best times and channels to connect to those customers.

Our Predictive Analytics Implementation Approach

01

Dataset understanding

We help organizations to improve their data literacy which plays a key role in understanding the data sets and share information on data collation and aggregation for existing as well as new sources.

02

Patterns and trends identification

We determine trends, patterns, unique characteristics along with inconsistencies and outliers from datasets allowing companies to get highly contextual insights from data.

03

Create predictive models

We create predictive models using historic data for forecasting future events, which help organizations with improved processes, optimized operations and cost reductions.

04

Data distillation

We use raw data for distillation to break it into structured formats. This process of data refinement ensures higher data quality which is imperative for data preparation.

05

Evaluation

We constantly work on evaluating the performance of the predictive model with unseen data to improve future performance for increased accuracy, fine tuning as and when necessary.

06

Deployment

Our deployment approach uses industry-defined best practices to ensure that the model transitions smoothly into production without causing any stoppages to your business practices.

Why Stridely Solutions For Predictive Analytics?

Depending on the quality of training data, predictive models gain accuracy over time. When ingested with newer data, they can adapt to the dynamic data which adds to their learning in order to provide better forecasts.

At Stridely Solutions, we employ a pragmatic approach for our clients to determine future outcomes based on their data. This allows you to concentrate on driving better business decisions and stay ahead of the competition. Predictive Analytics help in determining future events using current as well as historical data through identification of trends, patterns and results. Computing as well as mathematical techniques like machine learning, artificial intelligence, statistical modeling and others.

Our data science experts help companies to collect, analyze, and visualize historical data for accurate forecasts with increased reliability.

We would love to hear about your project.