Case Study on Recommendation Engine: Predict Future Customer Purchases
Before describing the technology:
Here are some use cases of Recommendation Engines:
- According to a recent study, 75% of consumers of Netflix watch contents that are recommended by the system. The executives of Netflix say their recommendation system saves about $1 billion each year. Netflix had a 32.3% year-over-year growth of about 5.2 million subscribers in the second quarter of 2017.
- Amazon is also making 35% more revenue using recommendation systems. They credited a 29% increase in sales which is approximately over $135 billion.
- Best Buy, another big retailer, recorded a 23.7% increase in sales using recommenders.
- Linkedin also uses a recommendation system to recommend useful pages or newsletters to the user for better user experiences.
- Youtube’s recommendation engine is powered by Google Brain. Google shows advertisements during videos using recommender and earns a revenue of more than $22 billion as of June 2017.
- Alibaba and its affiliate partners process an over 70% increase in parcel deliveries and credited approximately $42 million a day every year using an AI-based recommender. And these numbers are increasing every hour.
- Spotify confirmed more than 60 million subscribers and 140 million active users spread across 61 countries in July 2017. Spotify serves 30 million songs to them and credited $376.8 million in the second quarter. Pandora did also earn the same profit margin during this period of the year.
Filtering virtual information or data by using recommender algorithms will provide the most relatable and relevant items to the user based on their choices. This set of algorithms are known as recommendation systems or recommender engines. Most tech giants use a few different recommender algorithms that are required to build engines. These algorithms can be simple to complicated and range from simple threshold to black-box neural networks.
The Importance and Benefits of Recommendation Engines:
Recommendation engines have become important needs for a growing industry which use the information to grow their market share. Also, they can earn a large amount of money by increasing their content or products to the consumer using recommenders. Organizations can also save their investments with the help of recommendation systems.
Applications of Recommendation Engines in Various Industries:
Commercial recommendation engines utilize either “Collaborative Filtering,” “Content-based Filtering,” or a combination of both algorithms. These approaches effectively generate playlists for video or music services, recommend products on online shopping platforms, and suggest content on social media or blogging platforms. The engine generates targeted suggestions to promote additional content or advertisements to users.
Content-based systems, compare the content of any items with a user’s profile to recommend useful items to the user. User preferences are vital for this sort of system. Important points to be noted for content-based recommender must be performed are –
- A complete description of items.
- Define user profiles based on their previous engagements.
- Suggest to each user the items matching most with the user profile.
Tailoring Recommendations Based on User Preferences”
In our comprehensive Case Study on Recommendation Engine, we delve into the algorithmic steps employed by this system in marketing scenarios. When a recommendation is required, the system leverages the user’s preferences, such as their fondness for smart devices, to suggest relevant content or products. This approach ensures that users receive tailored suggestions aligned with their interests. While the content-based engine excels in its performance, it does have limitations in detecting intricate behavior and interdependencies. Thus, the Case Study on Recommendation Engine highlights the significance of considering both user and item aspects in recommendations.
The Role of Collaborative Filtering in Recommendation Engines:
The collaborative system has the most uses in the market. These sorts of recommendations are also based on the preferences of the user. The user’s most liked items are the data for this recommendation. A collaborative engine uses those data to evaluate the market to provide the most relevant products and items to the user. The product user liked in the past will also like in the future, on that basis recommender suggest similar things but with more features that might be liked by the user. The exploitation depends on the behavior of users and items in terms of transaction history, product ratings, selection, and purchasing data. To get more users, connection recommenders use the data of existing users. Collaborative filtering is used to suggest complex items such as movies and so on.
Adapting to User Behavior Changes:
Suppose, a user looked for a specific smartphone brand’s product with good displays and better battery life. Then the collaborative recommender will save the data of searching of the user and use it to suggest a product. The recommended product will be smartphones with good displays and better battery life but the brand will be different from the searched products. This recommendation will change continuously based on the behavior and the searching or liking information. If the behavior change, then the recommendation will also change according to that.
Interactions between the user and the shopping or media content platforms are noted every time it runs. There are no privacy issues with the filtering because the recommendation system gathers data carefully without breaking the barrier of the privacy policies. Companies implement recommender systems to-
- Improve retention to make users more likely to remain loyal.
- Increase sales by doing various research up to 10-50% by recommending accurate products.
- Save time up to 80% for analysts when tailoring suggestions for further research.
- Build strong habits and influence the usage patterns of users.
There are benefits in utilizing the recommendation engine in most businesses and many business cases. An online platform can use recommenders to attract consumers and make a profit. E-commerce, Retail, Media, Banking, Telecom, Utilities and many more platforms should use a recommender engine to increase consumer engagements and make people stay loyal, increase user satisfaction, reduce churn, etc.
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Advantages of Machine Learning in Financial Forecasting
Using AI to robotize the monetary estimating measure presents a few remarkable advantages for senior money heads and their groups. The key advantages are summed up beneath.
1. Capacity to Produce More Accurate Forecasts, Faster
As referenced before, AI empowered estimating can free monetary gauging of the concentrated work of gathering and accommodating information. The apparatuses can be designed to gather and accommodate extremely enormous informational indexes in a mechanized style. In addition, AI apparatuses can assist with deciding business drivers and enormously diminish estimate blunder. AI calculations are intended to gain from the information after some time and foresee which drivers have the best effect on monetary execution. After some time, the model turns out to be more precise and produces figures all the more rapidly.
2. Hedge between best and worst case scenario
Accounting page driven gauging measures impose limits on the number of information sources and the amount of information that anticipating models can process and consume. AI devices greatly enhance the volume and types of information that can be utilized because these tools can hold more information and process it faster than humans. This enables us to better navigate between the best and worst case scenarios, increasing our chances of mitigating risks.
3. Empowering Value-Adding Activities
Customary estimating measures ordinarily expect examiners to invest the vast majority of their energy accommodating and gathering information as opposed to chipping away at esteem added investigation and collaborating with the business. Utilizing an AI answer for production at any rate a standard estimate can help investigators move away from these commonplace assignments and spotlight on understanding operational drivers, key business occasions, and microeconomic and macroeconomic elements that may affect the business, carrying those experiences into the determining cycle. Utilizing AI can eventually help monetary investigators accomplice all the more intimately with the business and backing dynamic.
The Emergence of AI in Accounting and Finance
There are sure businesses that are generally helpless to the effects of AI. The most referred to report from NPR predicts that:
- Bookkeepers have a 97.6 percent possibility of seeing their positions computerized.
- Accountants and examiners have a 93.5 percent possibility of seeing their positions mechanized.
- Financial examiners have a 23.3 percent possibility of seeing their positions mechanized.
To summarize, occupations that are comprised of repeatable, precise errands have higher danger of being mechanized than those that require judgment, examination, and relationship building abilities. Thus, in the event that your present place of employment regularly expects you to show those attributes, at that point you need not concern. For Finance and Accounting, AI and robotization are viewed as suitable answers for successfully managing consistency and danger challenges across different areas. To stay serious, organizations are moving from work exchange and seaward unrest to mechanization insurgency. Man-made intelligence speaks to an occasion to decrease the weight on money experts, especially around conventional monetary exercises, for example, exchange handling, review and consistency. These exercises in their present structure keep money from being more essential colleagues.
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