Hackernoon logoThe Adoption of Machine Learning in Data Driven SaaS Products by@v2stech

The Adoption of Machine Learning in Data Driven SaaS Products

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@v2stechV2STech Solutions

Dev at technology consulting for SaaS | Python | Chatbots | Product Development

SaaS solutions have been gaining popularity over recent years to the point where most software products are using SaaS based model.

SaaS has been widely accepted by industry as it requires little to no installation, software can be instantly dispatched via cloud and cloud computing offers flexibility in computing power and resources.

The cloud based scalable SaaS model offers both businesses and vendors significant efficiencies and cost savings. With the recent advent of big data, machine learning is proving to act as a product differentiator and also adding significant value in terms of product features.

Evolution of Machine Learning:

Machine learning has evolved from small pattern recognition to allowing computers to perform tasks without being programmed. Refined learning models and iterative aspect of machine learning allows models to independently learn and adapt. Learning from previous computations, models are now able to produce reliable, repeatable decisions and results.

Due to its inherent nature of scalability and flexibility SaaS keeps evolving continuously with feature extensions and upgrades.

The improved performance of machine learning models coupled with the almost ubiquitous concept of big data in today’s day and age has further fueled the adoption of machine learning in data driven SaaS products.

Uses of Machine Learning:

Typically machine learning is used for:

  1. Natural language processing
  2. Speech recognition
  3. Predictive analytics
  4. Computer vision

Applications of Machine Learning:

Recently machine learning is being widely used for predictive analytics, identifying patterns and anomaly in big data, creating risk profiles, fraud detection, smart metering in utilities network, churning data from IoT devices and much more.

The application spans across various industries such as:

  • healthcare,
  • fintech services,
  • logistics and transportation,
  • energy and utilities,
  • e-commerce,
  • e-governance among others.

Let's take a look at some applications of machine learning in SaaS:

Fintech and Financial Services:

Fintech and financial SaaS apps leverage machine learning primarily to gain insights into customer’s spending patterns and for fraud detection. The insights can help businesses offer better financial products, personalize their product offerings or help users with other basket of products such as investment, trading, insurance etc.

In a similar vein the data points captured from customer’s spending patterns can be used to generate risk profiles and beef up security across application. Any outlier behaviour can pinpoint to a fraudulent activity and timely intervention can be provided.

Considering the vast number of transactions and volume of data that is typically generated in a financial dashboard, machine learning can automate the processing and save time while simultaneously learning from the new data points.

Healthcare:

Application of machine learning is gaining fast-adoption in the healthcare SaaS offerings. This has been further accelerated thanks to the advent of affordable wearable devices and plethora of sensors that can use data to assess a patient’s health in real time. The insights can help medical experts analyze data to identify patient’s health trends or red flags that may lead to improved diagnosis and treatment.

Energy and Utilities:

Energy and utilities business can benefit most from integrating machine learning into their SaaS and SCADA applications. Machine learning can be integrated across multiple layers within the SaaS, with IoT enabled devices and also with embedded systems.

Such smart solution can give business accurate predictive analytics, improve the system monitoring, help in early detection of sensor failures. Such insights can help business plan predictive maintenance. It even allows monitoring of earlier remote or inaccessible sites.

Such machine learning and IoT enabled smart solutions are cost effective as it allows centralized monitoring of the entire system efficiently. Furthermore the site engineers and field personnel can also get vital information and alerts on the move.

Logistics and transport:

Logistics and transport generate a multitude of data points. Supply chain and logistics rely heavily on SaaS and mobile apps for maintaining an efficient operation, communication and status updates.

  • Analyzing this wealth of data with machine learning can enable one to identify patterns and trends affecting the business.
  • Identifying such gaps, bottlenecks, uptime of assets or machinery can make an impact in increasing profitability.
  • Predictive analytic can be used for predictive maintenance of vehicles, machinery.
  • Geo spatial data can be tracked for optimizing route planning, improving just-in-time delivery and identifying road blocks or bottlenecks in routes to improve delivery times.

The data analysis and modeling aspects of machine learning can also be applied to public transportation and other transportation organizations to track arrival times, improve fleet efficiency and route planning.

With machine learning businesses can get a competitive edge in the market as businesses are always looking for accurate insights across the business functions that enables better and quick decision making.

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