Foto oleh Google DeepMind: https://www.pexels.com/id-id/foto/abstrak-teknologi-penelitian-digital-18069160/
Now, the wonderful word “Machine Learning” has become the buzzword spread over the flimybox dusky – clouding the mind of those it shines upon with visions of mysterious algorithms and wonderful sci-fi worlds. With all the hype, and all the way to go, it would be easy to see it as just another over-hyped technology of an over-hyped future world, or worse still, a failed technology from the past. It is a how-to-guide that provides call-specific common-sense examples of how insight is used –examples of successful, insight-driven applications built or revised repeatedly, with the call center or contact center in mind. Tabloid-fueled Hollywood hype aside, the real promise of ML rests in its practical application to solve actual problems in an impactful and measurable way. This post emphasises on how machine learning applications help industries and businesses and gives an overview of some of the main and practical applications of machine learning for them.
Enhancing Customer Experience and Personalization
Customer experience is one of the biggest, most successful applications of machine learning. Internet juggernauts like Amazon and Netflix have set the standard for personalized service, and ML is the engine that drives their recommendation systems.” ML algorithms can forecast what a customer might like next by analyzing huge amounts of user data such as transaction history, browsing history and ratings. It’s not just product recommendations but truly personalized and social online experience,for a more efficient conversion and a superior lifetime value.
But these are more than just tips — they represent a new era in machine learning driven chatbots and virtual assistants that’s revolutionizing customer help. These are systems that utilize NLP so they can comprehend and respond to customer queries on the fly. They can handle a high volume of routine questions, allowing human agents to focus on more complicated or strategic questions. This ensures a high level of customer satisfaction, as they receive rapid responses, and saves hundreds of thousands – if not millions – of dollars on an enterprise level.
Optimizing Sales and Marketing Strategies
Machine learning is the next-generation lens pour sales and marketers can crystal clear view of who their audience and how they can connect with them. Predictive analytics, a key type of ML, forecast sales and demand with high accuracy. By combing through data, including sales figures, market trends, seasonality or even other items like the weather, businesses can better manage inventory, production and even personnel. The end result is less waste, a more optimal supply chain, and greater profit.
In addition, ML let’s you kick-start high sophisticated, hyper-targeted marketing campaigns. It achieves this by grouping cutomer in micro market segments based on their behavior and preferences and in doing that enabling businesses to tailor near personal ad content and email campaigns. and is key to ensuring the message is heard by the right audience, resulting in more engagement and better marketing returns.
Enhancing OP and Predictive Maintenance
And in manufacturing, logistics, transportation and other industries, machine learning is a game changer when it comes to operational efficiency. Predictive maintenance, for example, is one of the greatest uses, where ML algorithms analyze data from sensors placed on machines and devices. Patterns and anomalies suggestive of failure can be sensed and the power system informed a warning prior to a failure. This preventive measure eases costly unplanned down time, extends the life of your processing equipment and provides safety. For a company that manages a fleet of trucks, for example, this could mean forecasting when a truck’s engine is likely to fail or break down from use, so that maintenance can be scheduled in time and users avoid having to wait for roadside repair.
Above maintaining the logistics, ML is applied to optimize logistics networks. Traffic volume, weather conditions and delivery schedules can all be crunched to determine the most efficient routes for delivery trucks, reducing fuel consumption and time on the road. For a warehouse, ML can help to minimize storage and picking, so that personnel spend less time picking and fulfilling a personal order – this speeds up service and increases the total throughput.
Strengthening Fraud and Risk Detection
The commercial banking field is one that rapidly adopted and applied machine learning as a subset of AI for such things as fraud detection and risk mitigation. Older rule-based systems for detecting fraud would have the effect of being slow and constantly playing from behind in the face of the continually shifting strategies of criminals. ML models, on the other hand, can scan millions of transactions in a click and discover what might be abnormal patterns — including outliers that might be anomalies that might be human evidence of fraudulent behavior. It can also make out fraudulent transactions on the fly and could act in time to stop the money disappearing.
And, in evaluating credit risk, ML models can process reams of other information beyond a credit score to produce more accurate predictions about loan default risk than can human beings. This leads to a more equitable and objective approval, whether for the financial services firm or for the consumer.
A Pragmatic Approach to Implementation
Machine learning is a very promising technology, there’s lots of potential, but you have to be practical and narrow in order to actually pull it off. Companies need to stop the “let’s do AI” attitude and solve specific well-defined problems. The most successful ML efforts start with a clear business goal: Are you trying to reduce costs, boost revenue, increase customer satisfaction, enhance a product?
Furthermore, success depends on data. Models are only as good as the data on which they were trained. What companies must build on top of is data strategy: making certain that data is clean, accessible and properly formatted. Finally, there needs to be a hybrid team of domain experts who grok the business problem and where to take action because of the model, combined with the data scientists who are building and deploying the machines.
Lastly, machine learning is no longer the tech giants’ holy grail. And then there’s the game-changing factor available to everyone: data, which has empowered the democratization of data-driven decision making and a new competitive advantage for companies both large and small. By focusing on the pragmatic side of things — problem solving — organizations can utilize the magic of ML to cut through to the chase and create a smarter, more productive, more profitable tomorrow.