Neural Networks implementations in Market

Anshika Sharma
5 min readMar 14, 2021


Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.

It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.

  • Neural networks are a form of Deep Learning.
  • They are also used in Machine Learning.
  • The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.

Artificial Neural Networks Used for?

Artificial Neural Networks can have ample number of use cases, that are:

  • They can be used to classify information, cluster data, or predict outcomes.
  • These include analyzing data
  • They are used for transcribing speech into text
  • To powering facial Recognition software.
  • To predict the weather.

Now lets have a look on how Artificial neural networks helping to improve market strategies.

Artificial Neural Networks are Improving Marketing Strategies

By adopting Artificial Neural Networks businesses are able to optimise their market strategies.

They provide some important capabilities to the systems, that are:

  • Systems powered by Artificial Neural Networks all capable of processing masses of information.
  • Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation.

This application of Artificial Neural Networks can save businesses with both time and money.

Implementation of Neural Networks at multiple places

1. Developing Targeted Marketing Campaigns

Starbucks has used artificial neural networks in it’s highly marketing campaigns
  • Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic.
  • This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.
  • Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy. Like unsupervised systems, they can also segment customers into similar groupings.
  • However supervised learning systems are also able to match customer groupings to the products they are most likely to buy.
  • This application of technology can increase profits by driving sales.
  • Starbucks has used Artificial Neural Networks and targeted marketing to keep customers engaged with their app.
  • The company has integrated its rewards system location and purchase history on their app.
  • This allows them to offer an incredibly personalised experience, helped to increase the revenue by $2.56 billion.

2. Applications of neural networks in the pharmaceutical industry

clinical trial phases

Artificial Neural Networks are being used by the pharmaceutical industry in a number of ways.

  • The most obvious application is in the field of disease identification and diagnosis.
  • It was reported in 2015 that in America 800 possible cancer treatments were in trial.
  • With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyse and interpret it.
  • The IBM Watson Genomics is one example of smart solutions being used to process large amounts of data.
  • IBM Watson Genomics is improving precision medicine by integrating genomic tumour sequencing with cognitive computing.
  • With a similar aim in mind, Google has developed DeepMind Health.

3. Developing Personalised Treatment Plans

A personalised treatment plan can be more effective than adopting a standardised approach.

  • Artificial Neural Networks and supervised learning tools are allowing healthcare professionals to predict how patients may react to treatments based on genetic information.
  • The IBM Watson Oncology is leading this approach.
  • It is able to analyse the medical history of a patient as well as their current state of health.
  • This information is processed and compared to treatment options, allowing physicians to select the most effective.
  • The aim is to allow medical professionals to get a better understanding of how disease forms and operates.
  • This information can help to design an effective treatment.

4. Neural Networks in the Retail Sector

Ocado Smart warehouses utilise a host of AI technology including neural networks

This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector.

  • For instance, Artificial Neural Networks are, when given the right information, able to make accurate forecasts.
  • These forecasts are often more accurate than those made in the traditional manner, by analysing statistics.
  • This can allow accurate sales forecasts to be generated.
  • In turn, this information allows your businesses to purchase the right amount of stock. This reduces the chances of selling out of certain items.
  • It also reduces the risk of valuable warehouse space being taken up by products you are unable to sell.

Online grocers Ocado are making the most of this technology.

5. Artificial Neural Networks in Financial Services

Ray Dalio, Founder & CEO of Bridgewater Associates is at the forefront of technology with neural networks

When it comes to AI Banking and Finance, Artificial Neural Networks are well suited to forecasting.

  • This suitability largely comes from their ability to quickly and accurately analyse large amounts of data.
  • Artificial Neural Networks are capable of processing and interpreting both structured and unstructured data.
  • After processing this information Artificial Neural Networks are also able to make accurate predictions.
  • The more information we can give the system, the more accurate the prediction will be.

6. Fraud Detection Applications

As technology advances, and more importance is placed on online transactions, fraudsters are also becoming more sophisticated.

  • Luckily Artificial Neural Networks can help to keep us, and our finances, safe.
  • Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud.
  • For example, this application can identify unusual activity, such as transactions occurring outside the established time frame.
  • Visa has used smart solutions to cut credit card fraud by two thirds.
  • Their sophisticated anti-fraud detection systems are working towards biometric solutions.
  • However the company also analyses information such as payment method, time, location, item purchased, and the amount spent.
  • Even a small deviation from the norm in any of these categories can highlight a potential fraud case.

I hope these application of Neural Networks will help you to get some information about how much Neural Networks are important to us, in multiple sectors.

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Anshika Sharma

I am a tech enthusiast, researcher and work for integrations. I love to explore and learn about the new technologies and their right concepts from its core.