Implementing Machine Learning Strategies for Business Success

We've been helping companies grow for over a decade! Check our success stories

x
buzz
 
back

Implementing Machine Learning Strategies for Business Success

Share with your friends










Submit

With each passing day, machine learning’s business implications are becoming clearer. Machine learning is a branch of artificial learning in which systems identify patterns from data, learn from insights, and make autonomous decisions with very little human intervention.

As the number of smart devices connected to internet increase, so will the data generated by them. This deluge of data is also known as Big Data, and machine learning applies complex algorithms to understand patterns in Big Data to make decisions. Machine learning can provide real-time insights based on data, giving businesses a competitive edge over their peers. In this article, let us take a look at how machine learning is going to influence businesses across the spectrum.

Where is machine learning used?

Currently, machine learning is being used across industry verticals for business success. Here are some examples:

  • In the media, machine learning is used to personalize content and to make recommendations, predict paywall price, and to optimize layouts.
  • In marketing, data insights can be used to make upsell forecasts and churn predictions, while it can also help in lead scoring. Machine learning also assists in making KPI predictions such as CLV (customer lifetime value).
  • The eCommerce industry has begun to use machine learning to promote products in a targeted manner. The retail industry, on the other hand, uses machine learning to make predictions related to inventory and store layout.
  • Financial services use machine learning to predict churn rate and to reduce it. It is also used to predict loan outcomes and identify risky customer behavior patterns.

Three scenarios in which you can implement machine learning immediately

  1. Make better sales forecasts, improve marketing campaigns, and enhance customer satisfaction

You can start using machine learning to consume and analyze data from unlimited sources. You can also rapidly process analyses and make predictions related to sales and marketing campaigns. In addition, you can use machine learning tools to evaluate past behaviors of customers. According to Forbes, 84% of marketing organizations currently use some form of machine learning or AI to enhance their services.

Use cases

Example 1: Azure Machine Learning can be used to analyze customer churn and minimize it as well. This is more cost-effective than other traditional and time-consuming methods to minimize customer churn. Interactive Pricing Analytics Pre-Configured Solution (PCS) is a Microsoft Azure machine learning solution that helps to determine the pricing elasticity of every product that you may sell. In other words, this tool can be used to offer contextually relevant pricing.

Example 2: Salesforce Einstein is a great example of what machine learning and AI can do to enhance existing CRM solutions. Salesforce Einstein can be used to implement predictive lead scoring, and the tool looks at various demographic and behavioral data sets. It can also help recommend products to your customers based on their interests, and to cross-sell and up-sell products more effectively.

  1. Offer predictive maintenance and avoid downtime

Most businesses rely on corrective maintenance to fix machines and applications. Corrective maintenance requires one to wait until an issue arises, but the costs in downtime, unscheduled maintenance requirements, and labor can increase the overall expenditure exponentially. Some businesses have begun to use preventive maintenance, which urges customers (and their own staff) to replace spare parts regularly or to ensure certain security and upgrading protocols for software tools. Even scheduled downtime and under-utilization of spares before their full lifetime can result in unnecessary losses.

Machine learning helps businesses to undertake predictive maintenance at the right time, whether onsite or for customers. It is the smartest way to ensure that equipment and systems are used to their full lifetime and that problems are identified before they cause issues. You can implement predictive maintenance to reduce over-corrective maintenance, scheduled downtime, and labor costs by analyzing user data and identifying when interventions need to be taken. Specific benefits include:

  • Detecting anomalies in system performance or in equipment
  • Predict when an asset may fail
  • Estimate how long an asset may remain useful
  • Recognize the reasons for an asset’s failure
  • Recognize what steps need to be taken to offer maintenance support to

Azure Machine Learning and Microsoft Azure AI platform can help in the predictive maintenance of both onsite infrastructures and provide support for customers.

  1. Detect fraud and enhance security

An important function of machine learning in businesses is to detect fraud and enhance security. Machine learning technology can be used to manage portfolios, engage in algorithmic trading, underwrite loans, and detect financial fraud.

Here are a few ways you can implement machine learning to enhance security:

  • eCommerce websites can make use of machine learning to prevent credit card fraud.
  • Create real-time behavioral profiles that interpret the actions of customers, merchants, individuals, and other entities.
  • Supervised machine learning that uses algorithms to detect fraud after having “learned” from innumerable examples of fraudulent and legitimate transactions.
  • Supervised machine learning can only detect fraudulent activity that has taken place previously, and thus, unsupervised machine learning is the next step. This self-learning algorithm predicts fraud and by detecting outlier behavior and transactions.
  • Adaptive analytics helps machine learning models to continuously learn from feedback. These models can be used to detect spam and thwart IT security threats as well. For example, PayPal uses an open-source based homegrown AI and ML engine to detect fraud. After implementing this model, PayPal reduced fraud by 50%.

Implementing machine learning in your business

Before you implement a machine learning model, follow these steps for a customized solution:

  • Recognize the problems which machine learning will solve
  • Identify the data sets that will help the machine learning model to solve a problem
  • Determine which machine learning platform you will use to build your custom model
  • Consult a data engineer or determine yourself how you will stream data into the machine learning platform
  • Build or choose the right machine learning model to address your issues
  • Continuously test and adjust the model

Machine learning does something for every business

With proper planning, you can implement machine learning to enhance sales and marketing campaigns, make better product recommendations and build new products. You can also protect yourself and your customers from financial fraud, and ensure IT security. In addition, you can quickly increase CSAT metrics by offering predictive maintenance to enhance customer satisfaction. There are many other scenarios in which you can use machine learning, based on your unique business situation. To learn how you can custom-implement machine learning to suit your specific needs, contact us today.

Share with your friends










Submit