6 Top Use Cases for Neural Networks
By: Mohammad Ahmad
Tags: Machine Learning, Technology, Tech, Artificial Intelligence, Data Science

Customer Experience Enhancement

(Example: Sephora)

Customer data is collected by different organisations for commercial or analytical usage. Customer data can contain information like demographical data, economic status, purchase patterns etc. Using these data, ANNs can be applied to segregate customers in multiple segments. Sephora , a big cosmetic company in the world, has already started to implement ANN in their marketing campaign and going towards becoming speciality beauty retailer. They are using Augmented Reality having ANN in the core to let customers try makeup virtually. They are also implementing ANN to produce virtual fragrance sample for their customers so that the customers can feel their products. These marketing strategies have been powered by ANN and is boosting Sephora in their sales.

Digital Marketing Enhancement

(Example: Starbucks)

Now-a-days, digital marketing is a huge thing as many customers prefer to shop online and browsing is one of the most sought-after method for a very large number of customers. ANN can be implemented in digital marketing strategies to increase the customer base.

Starbucks, one of the leading coffee shop brands in the world, has used ANNs in their marketing campaigns. They have used Supervised and Unsupervised Machine Learning methods along with ANN to divide their customer groups.

Based on these, they have been able to send customers targeted content over email and other social media and has been able to improve their sales.

Their Starbucks Rewards program has been successful, and they have been able to provide the customers with great personalised experience and it has increased their revenue by $2.56 billion.

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Improve Search Engine Functionality

(Example: Google)

Search Engine Optimisation is a huge factor for Search Engine companies. To improve their search engine capabilities, search engine providers are now leaning towards ANNs . Google has already implemented a 30-layer deep ANN in their system to allow the search engine to process complicated searches such as shapes and colours.

There have been significant improvement in search experience already and as a result, the error rate has dropped to 8% from 23%, according to the company report.

Customer Loyalty

(Example: FedEx)

Deep Learning can be used to determine whether an existing customer can switch to a competitor or not.

Neural networks can be used to predict which customers are most likely to switch to another organisation and how to retain them using a tailored service. FedEx has implemented a neural network to enhance and personalise their consumer’s experiences.

With the help of neural nets , FedEx has been able to predict which customers are likely to switch to their competitors with an accuracy of 60–90%.

Market Movement Forecasting

Example: LBS Capital Management

Predicting the prices of assets has been one of the oldest professions as traders through time have tried to take goods from one market and sell them in another. Nowadays, this is sped up a lot by hedge funds who can trade electronically from their offices in London, Tokyo, or even in the middle of nowhere.

Given this technological jump, it’s also possible to introduce more complicated models into the mix. Statistics is the foundation to Financial Mathematics so it paved way for the developments in Machine Learning . This has been greatly beneficial as, companies like LBS Capital Management, who’ve implemented neural nets to get positive results. Based on neural network models that utilise 6 financial indicators, they’ve been able to predict the average directional movement over the last 18 days.

Note: This isn’t financial advice and nor do I recommend anyone to take these approaches.

Reference

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Insurance Provisions

(Example: Allianz Travel Insurance)

Insurance providers often have to provide customised travel insurance and that’s no easy task. People generally have differing holiday plans in terms of cost, age, reason and length of trip.

Given these variables , it’s important to give the right recommendation because otherwise, the responsibility ultimately falls with the insurance provider.

Neural networks are being used by Allianz to help segregate different types of policyholders to customise and offer appropriate pricing plans and provisions.

Allianz Travel Insurance has implemented a neural network in their travel insurance system which analyses many factors like length of the trip, cost of the trip, traveller’s age, reason for travelling, air miles used etc. and then comes up with the best policy for the customer.

This has helped customers to get most relevant coverage for the trip and it also reduces the time to research for the trip, making the holiday planning a less worried item.