6 ways to apply machine learning algorithms to business problems
Every business faces different challenges, and applying the right solution gives you a competitive advantage. See how machine learning models or algorithms can help solve business problems and optimize processes!
Machine learning (ML) is considered a branch of artificial intelligence (AI), where models are used to automatically learn about data and process the data into useful information. This can be employed by many companies into solving problems.
Professionals have the opinion that ML enables performance of tasks on a large scale, especially for those tasks that were initially seen as impossible. Hence, ML algorithms are used to optimize the performances of businesses to accomplish goals and objectives more efficiently and effectively.
Machine learning algorithms have the ability to capture, manipulate, index, combine and perform predictions with available data as applicable in data science and big data. To this end, innovation-oriented organizations are harnessing the power of machine learning use towards the improvement in operations as well as propel new opportunities.
Application of machine learning
1. Manual Data Entry
Most businesses face the risk of inaccurate data points capture and duplication which serve as a problem especially when it comes to process automation.
With machine learning solutions, data can be entered into a database with ease and done automatically thereby saving time for workers or customers which could be spent in solving higher value problem tasks.
Applications of machine learning can be used to scan text, image (image recognition and classification), video and create relationships which can be used to write reports such as financial and management.
Also, when it comes to processing of information, ML functions in identification of unstructured data and then processing them into categorized contents.
By considering social media communications, text and video recordings in different languages can be automatically translated into a single language so that companies can understand customer’s sentiments as well as compute the ratio of positive to negative comments which can be used to aid company decision.
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BOTWISE also helps minimize repetitive, manual work. Custom ML algorithms automatically process documents from various sources and deliver exactly the information you need at that moment.
2. Product Recommendation
Through the use of unsupervised learning, supervised learning and deep learning, machine learning can learn about the needs of customers and make recommendations where necessary.
ML can analyze the behavior of customers, identify their likes and preferred choices which can be used to display bespoke products to customers.
Hence, it brings directional digital marketing towards recommending products which customers may actually be interested in and want to purchase. The machine learning algorithm notes patterns among products previously ordered and uses a model of decision making to recommend clustered products to customers as a form of value added service.
With the aid of demographic information such as location, gender and social group, machine learning can be employed in recommending different users into connecting with each other as seen with Facebook.
3. Customer Curation
One of the problems faced by some companies in recent years is identification of specific needs of customers in product or service. What may be desired by customer A may not be desired by B. What may be desired by customers in location A, may not be desired among customers in location B.
Hence, the need to understand the behavior of customers through deep learning which can be tracked over time through a system that learns in real time. Information obtained about customer’s behavior can be used to target advertisements to the right set of customers thereby saving cost and marketing resources.
Knowing what customers want will bring about better customer engagement and excellent curated experience.
For example, Netflix uses machine learning to identify the needs of customers after analyzing some variables such as viewer’s history, age, gender, location, etc., to recommend specific shows and movies in order to create that curated experience.
4. Customer Relations
As a company grows into gaining more market shares, this can be attributed to the customer growth of the organization. Hence, business growth cannot be possible without the growth of client base, products and services.
One problem faced by organizations is the aspect of keeping customers and growing them. Customer’s will not stay with a brand or get attracted if they do not feel some form of customized experience and so it proves to be a challenge when new ones are converted but old ones are lost.
What businesses need to remedy this problem boils down to effective customer relationship management (CRM). This is where machine learning comes into play in solving problems of this nature.
Machine learning uses algorithms to analyze several variables to determine how satisfied clients have been when they interact with the business brand.
Data obtained from customer’s feedback serve as inputs which are run using machine learning software for which presented results are used to ensure maximum customer engagement as well as boost customer’s loyalty.
5. Dynamic Pricing Tactics
Every business wants to succeed and this can be tied to the amount of revenue generated while effectively managing their cost.
Businesses have to sell products or services at the right price so as to generate revenue. This can be considered a business problem if set market price is too high or considered as too low. To this end, organizations can mine historical price data just to understand certain dynamism with their prices as well as that of the market.
Machine learning algorithms can learn from such information in combination with market trends and client data in order to help businesses set the flexible prices that fits the client's budget and still maximizes revenue based on the moment.
For example, the price of bus tickets can be increased if there is a surge in demand and reduced when the demand falls.
6. Predictive Maintenance
Preventive and corrective maintenance can be inefficient and costly to businesses especially with regards to manufacturing companies. However, predictive maintenance helps to minimize the risk of unexpected failure to machine parts and reduces the amount of irrelevant preventive maintenance and excess cost associated with corrective maintenance.
Machine learning can be used to identify significant patterns in factory data for which predictive maintenance activities can be designed.
Machine learning algorithms help to process historical data and operational environment into identifying early failure in devices which can be addressed in a timely manner and prevent unnecessary shut down.
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For example, Microsoft Machine Learning Azure platform is a software used to simulate an aircraft engine and predict failure so as to demonstrate predictive maintenance modelling. Through this means, businesses can save on maintenance cost as well as maximize operational efficiency through the application of machine learning algorithm software.