How Neural Networks Are Redefining Marktech

A crucial subset of Machine Learning (ML) comprises Artificial Neural Networks (ANN). Computer scientists utilise them to tackle challenging tasks like forecasting, planning, and spotting patterns. Neural Networks (NN) learn from experience, exactly like humans, as opposed to other machine learning algorithms, which may organise data or crunch statistics. 

NNs are already working in a variety of fields, including engineering, finance, and healthcare. While neural networks have been around for a while, the recent evolution of Big Data has now made this technology particularly effective for marketing. It is now possible to create sophisticated, precise predictions that can aid CMOs in choosing the best course of action and the channels to devote more resources to.

Predictive analytics is the other area in which ANNs are most frequently applied at present. By identifying tendencies from earlier marketing initiatives, neural networks are able to help marketers forecast the results of a campaign. 

Additionally, this technology is enabling a more dynamic level of automation, which is modernising the marketing workflow and vastly improving the consumer experience.

How can CMOs go about implementing NN?

• Determine your marketing requirements: Identify what marketing problems need to be solved before choosing a neural network. Knowing your needs can help you select the best neural network, whether you're enhancing customization, producing interesting content, or boosting ad targeting.
• Evaluate your options: Research and compare the various neural networks that are out there. When making a choice, take into account elements like price, functionality, usability, and customer service.
• Properly train the network: Accurate and efficient outputs require proper neural network training. The training data should also be updated frequently to keep the network accurate and current.
• Incorporate it into your game plan: The neural network must be carefully planned and built into your marketing strategy. Also, think about how the network will affect your marketing objectives and how it will integrate into your overall marketing plan.
• Track and analyse: Assessing the neural network's performance on a regular basis is vital to making sure that it delivers the intended outcomes. 
• Get expert help: If you need guidance on how to use a neural network efficiently for marketing purposes, consider hiring a professional who may offer helpful direction and support.  

Challenges to the adoption of NN
 While neural networks have the potential to completely transform the marketing industry, a number of roadblocks need to be addressed in order to achieve this. 

• One problem is the necessity for high-quality data. The networks might not be able to produce valuable insights if the data is inaccurate or of low quality.
• The requirement for specialised knowledge poses another difficulty. Many businesses might need to rely on outside suppliers or consultants to design and implement these networks because they lack the necessary internal skills.
• Finally, there's the matter of data security. Companies must take precautions to safeguard client data and guarantee that it is used in an ethical and accountable way.

Nonetheless, the prospects for neural networks in marketing are considerable, and organisations that adopt this technology sooner rather than later stand to gain an edge in the competitive landscape.

Amit Tiwari

Guest Author The author is the Global Head of Marketing Demand Centre of Tata Consulting Services, Amit leads MarTech strategy and operations to support the business and marketing objectives. For over 21 years, Amit Tiwari has proved a key thought leader in the MarTech space. He understands and adapts technology faster and is also keen on experimenting and exploring new technology and new ventures in marketing.