Four Applications of Artificial Neural Networks (ANN) for Marketers

Four Applications of Artificial Neural Networks (ANN) for Marketers

While deep learning and artificial neural networks seem to live in the tech world’s buzz bubble, they are already proving useful to marketers in a number of ways.

As computing power becomes more powerful and affordable, machine learning applications once proposed by science fiction become our burgeoning reality. Among the most promising types of machine learning are artificial neural networks or ANN. Loosely based on mammalian brain functioning, artificial neural networks utilize interconnected layers to decipher patterns and develop an understanding of the data being fed. While artificial neural networks are largely still in the infancy of reaching their potential, they are already proving instrumental to marketers in several ways.

Before we go more deeply into how artificial neural networks can aid marketers, let’s get some understanding of how they work.

How do Artificial Neural Networks Learn?

First of all, what are they? You’ve likely come across examples of artificial neural networks already such as image recognition and speech processing. Other applications of ANN include nonlinear systems modeling, estimation and prediction of parameters, pattern matching, identification and control. In terms of marketing, artificial neural networks can aid in customer segmentation, market segmentation, customer behavior analysis, and sales forecasting, but we’ll get there in a bit.

Neural nets are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. The most rudimentary example of this is an example of how toddlers learn. Imagine teaching your child about different kinds of animals. First, you show them flash cards with pictures of sheep, cows, dogs, etc. and you label the flash cards for them (i.e. “This is a sheep. This is a cow.”). During this phase of learning about animals, you’ll likely point out any animal on television, on the internet and in real life. These are the child’s “inputs”. With enough inputs, the child will begin to identify animals on their own (“What’s this?” “SHEEP!”). The child’s ability to identify animals is equivalent to the ANN’s outputs.

So, how do artificial neural networks do this? With layers of algorithms that interpret different characteristics or patterns before passing it on to the next level. For instance, in the case of image recognition, a neural net might first identify the brightness in the image before passing it on to the next algorithm which would identify the lines/shapes in the image. After passing through the various layers of the neural network, the ANN would presumably be able to label the animals (outputs) with impressive accuracy.

Artificial Neural Network Applications in Marketing

While ANNs are still undergoing countless studies to test the abilities and limits of their potential, there are several possibilities for marketers already proving useful. Among those possibilities are four examples I’ll be exploring today: audience segmentation, SEO, content recommendations and ad targeting.

Audience Segmentation

A study by a collection of computer scientists, Marknadsanalys Finland Ltd and Tampere University of Technology studies the efficacy of artificial neural networks for audience segmentation and identifying relevant prospects against humans’ capacities. Using three segmenting classification criteria (potential, profitability and loyalty) the aims of the study included:

·      Improving decision making efficiency in sales and marketing

·      Presenting a better general view of present and prospective customer base

·      Enable organizations (specifically, a bank) to better allocate sales and marketing efforts

The conclusions of the study are promising:

·      The ANN yielded better classification results than sales offices on average

·      The ANN was able to classify more than 200,000 enterprises into the three segments (potential, profitability and loyalty) in just one day

·      It made three times as many correct decisions as humans

·      The ration of incorrect classifications of the ANN versus humans was 1.5:7

With these conclusions, it’s clear that artificial neural networks have great potential in audience segmentation (and likely other forms of segmentation, including market segmentation).

SEO

The search engines are already using deep learning (artificial neural networks) to analyze content and ensure the searcher gets the most relevant answer to their query. ANNs can also prove useful to marketers in analyzing their content’s relevancy for them so they can tweak their content to better fit customer needs. Jonathan Ronzio, from Cramer, predicts that “machines are inevitably going to overtake engineers in the field of predicting ranking optimization” in the coming years.

Content Recommendations

Using historical data, similar to how many streaming services give content recommendations to users, neural networks can also provide content recommendations to content creators/organizations producing marketing content. Companies like Spotify have been studying and implementing the use of artificial neural networks to recommend the most relevant songs (movies, videos, etc.) to their users, but what if you could have an idea of what content best serves your various audience segmentations? That is exactly how neural nets will be used when recommending content to marketers. Through analysis of the content your audience segments consume, a neural network could identify the content most relevant to those segments and recommend relevant content for your content creation team to act on.

Ad Targeting

Combining the three topics I’ve discussed above, artificial neural networks can be extremely beneficial in ad targeting. Let’s say you’ve already utilized neural networks for audience segmentation, SEO and content recommendations to create your ad and target audience, neural networks can analyze the real-time performance of your ad. After analysis, a neural network could also maximize the impact of the ad content, then provide outputs to your audience segmentation and content recommendation neural nets.

The Marketing of the Future

With artificial neural networks and machine learning at large on the rise, we are at the dawn of a new era in marketing. Consumers will have more timely and relevant content than ever, while marketers will have increasingly accurate and specific details about the performance of their content, who their audience is and what their audience wants. This gives the marketers with initiative to learn new skills and remain agile a competitive edge and uniqueness, while leaving others in the cold. Which will you be?  

look forward to your comments and feedback.You can find more blogs on Machine Learning (ML) trends, Sales Enablement, Marketing Technology and related topics on my LinkedIn profile and Twitter.