Deep learning is a machine learning technique that imitates the human brain functioning to help us in complex decision-making processes. Deep learning imitates the human brain to analyze unstructured data to help machines solve complex problems.
The greatest advantage of deep learning is that it is capable of learning and improving the analysis of data sets. Deep learning has many useful real-world applications such as speech recognition, image processing, detecting fraud, predictive analysis, language translation, complex decision making, and many more. Deep learning uses artificial neural networks just like the human brain which enables data processing using a non-linear approach.
Deep learning has been nothing but a boon to numerous businesses today that use it to simplify and optimize complex business operations. Letâ€™s look at some practical applications and use cases of deep learning in business.
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Applications of Deep Learning in Business
1. Digital Adoptions and Business Process Automation
Today, every minute that an employee can spend on productive business tasks is precious. Therefore, no company wants to waste this precious time on repetitive tasks and workflows that can be easily automated. Deep learning algorithms can help businesses identify such repetitive processes and automate them so that employees can spend their time on other important tasks leading to an increase in ROI.
Deep learning can help with element detection to automatically identify different elements on a page during the creation of business flows. Digital adoption alternatives for WalkMe that use deep learning can help to optimize content for better performance and provide personalized 24/7 intelligent digital assistance.
2. Detect Fraudulent Activities
Deep learning can be highly beneficial for businesses to detect even the slightest possibilities of fraudulent financial transactions. It analyses customer behavioral patterns based on their transactions. Even the slightest change in the pattern can be observed and detected.
Deep learning applications have the capability to detect changes in usual patterns such as transaction amounts, the location from which the transaction was made, time of the transaction, frequency of transaction, etc. After detecting such anomalies, deep learning applications can even form connections between different unusual activities. This can be super-helpful for businesses for the early detection of financial frauds.
3. Better Content Discovery Recommendations
With the advancement in technology and social media platforms, the amount of content being produced and consumed has increased like it was never seen before. Stats show that we are consuming 2 to 3 hours of content daily.Â
With the amount of content being bombarded from all the places, we tend to lean towards the content that is more customized and fits our interests. This is why most content production platforms have resorted to employing deep learning applications for better content discovery and providing better content recommendations to consumers.
Some great examples are Netflix, Twitter, and Pinterest. These companies use deep learning systems to analyze consumer content consumption patterns and provide ultra-customized content recommendations to them. This fact has definitely helped these companies to increase their popularity amongst consumers.
4. Improved Customer Experience
Businesses have been able to provide top-notch customer service using deep learning and artificial neural networks based systems. Right from employing smart assistants, self-learning chatbots, geo-mapping, cloud computing, to identifying cybersecurity threats, deep learning has empowered businesses to provide a better customer experience in numerous ways.Â
Using self-learning chatbots, businesses have been able to attend to different customer queries and issues on-time. Such chatbots can learn from previous customer conversations, detect the tone and sentiment behind the interactions, and lead the conversation in a way that is beneficial to the business while providing the best possible customer support. One of the best examples is Facebook Messenger chatbots, Microsoftâ€™s smart virtual assistant Cortana, and Amazon Alexa.
5. Optimized Marketing Campaigns
Digital marketing is another area where deep learning has added valuable contributions for obtaining better results from campaigns. One of the most important success metrics for marketers is the conversion rate which indicates the ratio of the number of people targeted vs the number of people who actually converted. Deep learning has empowered businesses to maximize their conversion rates.
Deep learning enables marketers to have a laser-targeted marketing approach. By detecting usersâ€™ buying patterns, interests, and behavioral traits, deep learning systems can pick users who are most likely to be interested in the businessesâ€™ products. Such highly optimized marketing campaigns have led to massive improvements in profits for companies.
6. Customer Relationship Management
Customer relationship management systems are gaining increasing popularity in more and more industries today. Be it B2B or B2C, efficient customer relationship management to improve customer experience, increase customer satisfaction index, and maximize customer retention rates has proven to be beneficial for both the businesses and the consumers.
Operations such as lead prediction, lead scoring, constructing detailed customer profiles, identifying customer journey touchpoints, and similar ones can be handled effectively by deep learning systems. Popular CRMs such as HubSpot and Salesforce are using deep learning to improve their business processes.
Cybersecurity threats are a huge risk for many businesses today, ignoring which can lead to massive monetary losses to the company. Hence, more and more businesses have started investing in cybersecurity solutions that can help them with the early detection and resolution of any potential threats. Deep learning plays a major role in the cybersecurity space as it has been known to have threat detection rates as high as 99.9%.
Deep learning can be helpful in functions such as malware detection, automatic intrusion detection, malicious code detection, and fishing detection. Employing deep learning systems for cybersecurity has helped businesses to avoid potential threats that could have been quite expensive for the company. Hence deep learning is a small cost to invest in comparison to the number of gains that companies can get out of it.
8. Image Detection
The human brain can understand different visual entities of the world, find similarities, and cohesive patterns. Almost the same level of accuracy can be gained by using deep learning for image detection applications. Deep learning systems can identify patterns in visual content such as images, videos, graphics, etc to sort and detect relevant information.
This capacity of deep learning systems can be used to attain an advanced understanding of digital images and videos. Facebook uses deep learning for image detection in pictures for their â€œtagâ€ feature. Another great example of this is Pinterest, a visually-driven social media platform, that allows users to discover similar images based on colors and visual patterns of the original image.
After centuries of hard work, we, humans, have come up with algorithms like deep learning that can form artificial neural networks just like those in our brains to enable machines to imitate human behaviors and decision-making capabilities.
Deep learning has empowered many businesses through practical everyday applications such as image detection, predictive analytics, cybersecurity, detecting frauds, etc. Investment in deep learning systems is a small cost to pay for businesses when compared to results that they have been able to achieve in return.
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