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Generative AI is everywhere now. You see it in content, campaigns, customer experiences, and even internal workflows. It promises speed, scale, and efficiency, but there’s a harder question sitting underneath all that progress; Do your audiences actually trust it?

Trust in the Age of AI: What Consumers Really Think About AI Generated Content

Today, PR teams aren’t just tracking media coverage and share of voice. They’re also measuring how their brand shows up in AI-generated answers, search summaries, and large language models (LLMs). This new LLM visibility layer is quickly becoming a critical part of PR performance.

15 Must-Have PR KPIs and How to Track Them

See how one platform can power every decision across PR, communications, and marketing.

The Meltwater Blog

Blog Topics: Generative Engine Optimization, Media Intelligence, Media Relations, Social Listening, Consumer Intelligence, Influencer Marketing

Find out how you can leverage Facebook to boost your brand's online presence and gain a competitive edge. Explore the Meltwater category page today!

Facebook Blog | Meltwater

Find out how you can leverage Instagram to boost your brand's visual appeal and gain a competitive edge. Explore our category page today!

Instagram Blog | Meltwater

Find out how you can leverage LinkedIN to grow your professional network and gain a competitive edge. Explore the Meltwater category page today!

LinkedIn Blog | Meltwater

Find out how you can leverage TikTok to elevate your brand with creative content and gain a competitive edge. Explore the Meltwater category page today!

TikTok Blog | Meltwater

Find out how you can leverage X / Twitter to engage your audience effectively and gain a competitive edge. Explore the Meltwater category page today!

X / Twitter Blog | Meltwater

Find out how you can leverage YouTube to create engaging video content and gain a competitive edge. Explore the Meltwater category page today!

YouTube Blog | Meltwater

Generative Engine Optimization (GEO) is transforming how brands show up in AI-powered search. Explore insights, strategies, and best practices to help your content rank and resonate across conversational engines like ChatGPT, Gemini, and Copilot.

Explore insights and strategies for Generative Engine Optimization (GEO) to help your brand stay visible and discoverable in AI-powered search.

Generative Engine Optimization (GEO) Blog Collection | Meltwater

Every day billions of conversations take place on social media: tweets, posts, comments, blogs, reviews, podcasts, and much more.
Learn how to use social listening to make sense of all of that data.

On our social listening category page, you find all our blogs covering topics around social listening and social media monitoring. Read more.

Social Listening Blogs | Category

Business Reputation Management: What does it take to add reputation management to your marketing strategy? More importantly, how do you keep your reputation sparkly and shiny? Here’s your all-access guide to online reputation management for the long term.

On our reputation management category page, you find all our blogs covering topics around business reputation management. Read more.

Reputation Management Blogs | Category

From your brand's logo to your website, your social media presence to your customer support—these elements combined are what constitutes a brand in the minds of customers. But, there are thousands of other businesses that have a logo, website, social media presence, and website. So, how can you position your business on the market and make it stand out from the competition?
The key to success is branding & brand management!

On our brand management category page, you find all our blogs covering topics around brand management. Build your strategy and reach your goals!

Brand Management Blogs | Category

Competitive intelligence and the act of collecting competitive data can be seen as growth drivers for a company. Our guide will help you understand the concept of competitive intelligence (CI) by answering questions such as: What is the definition of competitive intelligence? How is competitive intelligence useful to organizations? What is the goal of competitive intelligence? And more.

On our competitive intelligence category page, you find all our blogs covering topics around competitive intelligence. Learn how to stay ahead.

Competitive Intelligence Blogs | Category

Let’s dive into the customer journey framework and how to optimize the process by creating a customer journey map and using metrics that matter when analyzing the customer journey.

On our customer journey category page, you find all our blogs covering topics around mapping and understanding the customer journey.

Customer Journey Blogs | Category

What Is Data Analytics? [Beginner’s Guide 2023]
How can I grow my business? 
How do customers interact with my newly launched product?
How do I increase the productivity of my employees?
How do I know if my marketing efforts are working?
We have just two words for you...Data analytics!

On our data analytics category page, you find all our blogs covering topics around AI and data analytics. Technical and still easy to understand.

AI & Data Analytics Blogs | Category

Public relations and media relations can add impact to any marketing strategy. It’s an opportunity for brands to grow their reach beyond traditional sales messages. PR helps to shape the image you want people to have of your brand.
So, what is PR and how do you use it? This guide shows you everything you need to know to build a strong public relations foundation.

On our public relations (PR) category page, you find all our blogs covering public relations and communications topics. Step up your PR game.

Public Relations (PR) Blogs | Category

We all know exactly what the roles of sales or accounting are in a company, right? We might even have a slight idea of what the IT guys are doing.
But there is another business discipline, that plays a crucial role in organizations, even more so during the pandemic – internal communications!
But what exactly do we mean by that, what purpose does it serve and what steps can you take to ensure you and your employees will communicate effectively?

On our internal communications category page, you find all our blogs about internal comms topics. Learn about the intranet, internal newsletters, and more.

Internal Communications Blogs | Category

As the internet has taken root in more areas of our lives — everything from entertainment to shopping to paying bills and more — traditional marketing is becoming less impactful. As any marketer knows, you need to focus on where your audience is if you want to reach them. Since the average person spends about seven hours a day online, it’s no surprise that content has maintained its hold on marketers.

Here's a closer look at what content marketing is and why it matters more than ever in our digital-first economy.

On our content marketing category page, you find blogs about content marketing topics. Learn about strategies, KPIs, tips, and more. Make your content king!

Content Marketing Blogs | Category

The rise of the influencer marketing space – today a $10 billion industry, growing 50% per year – is one of the clearest examples of how social media is transforming traditional marketing. These days having a business website, presence on social, PR coverage, and your own content isn’t enough to compete.
We'll dive into the intricacies of influencer marketing to help ensure that you're equipped with the insights needed to be successful in this space.

On our influencer marketing category page, you find blogs about influencer marketing topics. Build your strategy, find influencers, and measure results.

Influencer Marketing Blogs | Category

Have you just launched a new product or service that you want to get the word out about? Maybe you want to share with consumers why it is awesome and how it is so much better than the one your competitors offer.

Well, then, there is one thing you can't do without...Marketing!

On our marketing category page, you find blogs about marketing topics. Learn about all the different angles and types of marketing and improve your results.

Marketing Blogs | Category

In this guide, we want to give you a peek into the definition of social media management, the daily schedule of a social media manager, social media platforms to promote your business, and the do’s & don’ts of social media management.
Are you ready to begin the journey of becoming a social media superstar?

On our social media management category page, you find blogs about social media management topics. Build and manage your online community.

Social Media Management Blogs | Category

Building a following and using social media platforms to reach a wider audience requires a well-planned social media marketing strategy. In this guide, we’ll be looking at why social media marketing is so essential and how you can make it work for your business.

On our social media marketing category page, you find blogs about social media marketing topics. Learn how to get the highest paid social ROI!

Social Media Marketing Blogs | Category

In the fast-evolving realm of digital marketing and public relations, utilizing the right tools is crucial for success. Our interdisciplinary category showcases all the pertinent tools for digital marketing & PR. These tools help in enhancing your online presence and effectively reaching target groups, whether through reputation management, influencer marketing, or social media management.

On our digital marketing and PR tools category page, you find all our blogs covering topics around mapping and understanding the customer journey.

Digital Marketing & PR Tools Blogs | Category

We create 2.5 quintillion bytes of data every day. That’s a lot, even when you spread it out across companies and consumers around the world. But it also underscores the fact that in order for all that data to matter, we need to be able to harness it in meaningful ways. One option to do this is via deep learning.

Deep learning is a smaller topic under the artificial intelligence (AI) umbrella. It’s a methodology that aims to build connections between data (lots of data!) and make predictions about it.

Here’s more on the concept of deep learning and how it can prove useful for businesses.

What’s the Difference Between Machine Learning vs. Deep Learning?

Types of Deep Learning vs. Machine Learning

How Can You Apply Deep Learning to Your Business?

How Meltwater Helps You Harness Deep Learning Capabilities

Let’s start with a deep learning definition — what is it, exactly?

Deep learning (also called deep learning AI) is a form of machine learning that builds neural-like networks, similar to those found in a human brain. The neural networks make connections between data, a process that simulates how humans learn.

Neural nets include three or more layers of data to improve their learning and predictions. While AI can learn and make predictions from a single layer of data, additional layers provide more context to the data. This optimizes the process of making more complex and detailed connections, which can lead to greater accuracy.

We cover neural networks in a separate blog, which you can check out here.

Deep learning algorithms are the driving force behind many applications of artificial intelligence, including voice assistants, fraud detection, and even self-driving cars.

The lack of pre-trained data is what makes this type of machine learning so valuable. In order to automate tasks, analyze data, and make predictions without human intervention, deep learning algorithms need to be able to make connections without always knowing what they’re looking for.

Machine learning and deep learning share some characteristics. That’s not surprising — deep learning is one type of machine learning, so there’s bound to be some overlap.

But the two aren’t quite the same. So what's the difference between machine learning and deep learning?

When comparing machine learning vs. deep learning, machine learning focuses on structured data, while deep learning can better process unstructured data. Machine learning data is neatly structured and labeled. And if unstructured data is part of the mix, there’s usually some pre-processing that occurs so that machine learning algorithms can make sense of it.

With deep learning, data structure matters less. Deep learning skips a lot of the pre-processing required by machine learning. The algorithms can ingest and process unstructured data (such as images) and even remove some of the dependency on human data scientists.

For example, let’s say you have a collection of images of fruits. You want to categorize each image into specific fruit groups, such as apples, bananas, pineapples, etc. Deep learning algorithms can look for specific features (e.g., shape, the presence of a stem, color, etc.) that distinguish one type of fruit from another. What’s more, the algorithms can do so without first having a hierarchy of features determined by a human data expert.

As the algorithm learns, it can become better at identifying and predicting new photos of fruits — or whatever use case applies to you.

Another differentiation between deep learning vs. machine learning is the types of learning each is capable of. In general terms, machine learning as a whole can take the form of supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning requires labeled or structured datasets. This type of learning requires human intervention to ensure data is labeled appropriately.

Unsupervised machine learning doesn’t require datasets to be labeled. Instead, it focuses on pattern recognition in the data and will connect the dots based on shared characteristics. As you may have guessed, there’s no human involvement in feeding unlabeled data into an unsupervised machine learning algorithm.

Then we have deep reinforcement learning, where a model “learns” to become more accurate based on a feedback and reward system. This technique is considered semi-supervised.
Reinforcement learning trains machine learning models to make a series of decisions while earning penalties or rewards based on the effectiveness of its decisions.

Deep learning applies mostly to unsupervised machine learning and deep reinforcement learning. By making sense of data and making complex decisions based on large amounts of data, companies can improve the outcomes of their models, even when some information is unknown.

In deep learning, a computer model learns to perform tasks by considering examples rather than being explicitly programmed. The term "deep" refers to the number of layers in the network — the more layers, the deeper the network.

Deep learning is based on artificial neural networks (ANNs). These are networks of simple nodes, or neurons, that are interconnected and can learn to recognize patterns of input. ANNs are similar to the brain in that they are composed of many interconnected processing nodes, or neurons. Each node is connected to several other nodes and has a weight that determines the strength of the connection.

Layer-wise, the first layer of a neural network extracts low-level features from the data, such as edges and shapes. The second layer combines these features into more complex patterns, and so on until the final layer (the output layer) produces the desired result. Each successive layer extracts more complex features from the previous one until the final output is produced.

This process is also known as forward propagation. Forward propagation can be used to calculate the outputs of deep neural networks for given inputs. It can also be used to train a neural network by back-propagating errors from known outputs.

Backpropagation is a supervised learning algorithm, which means it requires a dataset with known correct outputs. Backpropagation works by comparing the network's output with the correct output and then adjusting the weights in the network accordingly. This process repeats until the network converges on the correct output. Backpropagation is an important part of deep learning because it allows for complex models to be trained quickly and accurately.

This process of forward and backward propagation is repeated until the error is minimized and the network has learned the desired pattern.

Let's look at some types of deep learning models and neural networks:

Convolutional neural networks (or just convolutional networks) are commonly used to analyze visual content.

They are similar to regular neural networks, but they have an extra layer of processing that helps them to better identify patterns in images. This makes them particularly well suited to tasks such as image recognition and classification.

A recurrent neural network (RNN) is a type of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior.

Unlike feedforward neural networks, RNNs can use their internal memory to process sequences of inputs. This makes them valuable for tasks such as unsegmented, connected handwriting recognition or speech recognition.

Long short-term memory networks are a type of recurrent neural network that can learn and remember long-term dependencies. They are often used in applications such as natural language processing and time series prediction.

LSTM networks are well suited to these tasks because they can store information for long periods of time. They can also learn to recognize patterns in sequences of data.

Wondering what challenges deep learning and AI can help you solve? Here are some practical examples where deep learning can prove invaluable.

Using Deep Learning for Sentiment Analysis

Sentiment analysis is the process of extracting and understanding opinions expressed in text. It uses natural language processing (another AI technology) to detect nuances in words. For example, it can distinguish whether a user’s comment was sarcastic, humorous, or happy. It can also determine the comment’s polarity (positive, negative, or neutral) as well as its intent (e.g., complaint, opinion, or feedback).

Companies use sentiment analysis to understand what customers think about a product or service and to identify areas for improvement. It compares sentiments individually and collectively to detect trends and patterns in the data. Items that occur frequently, such as lots of negative feedback about a particular item or service, can signal to a company that they need to make improvements.

Deep learning can improve the accuracy of sentiment analysis. With deep learning, businesses can better understand the emotions of their customers and make more informed decisions.

Deep learning can enable businesses to automate and improve a variety of processes.

In general, businesses can use deep learning to automate repetitive tasks, speed up decision making, and optimize operations. For example, deep learning can automatically categorize customer support tickets, flag potentially fraudulent transactions, or recommend products to customers.

Deep learning can also be used to improve predictive modeling. By using historical data, deep learning can predict demand for a product or service and help businesses optimize inventory levels.

Additionally, deep learning can identify patterns in customer behavior in order to better target marketing efforts. For example, you might be able to find better marketing channels for your content based on user activity.

Overall, deep learning has the potential to greatly improve various business processes. It helps you answer questions you may not have thought to ask. By surfacing these hidden connections in your data, you can better approach your customers, improve your market positioning, and optimize your internal operations.

If there’s one thing marketers don’t need more of, it’s guesswork. Connecting with your target audience and catering to their specific needs can help you stand out in a sea of sameness. But to make these deeper connections, you need to know your target audience well and be able to time your outreach.

One way to use deep learning in sales and marketing is to segment your audience. Use customer data (such as demographic information, purchase history, and so on) to cluster customers into groups. From there, you can use this information to provide customized service to each group.

Another way to use deep learning for marketing and customer service is through predictive analysis. This involves using past data (such as purchase history, usage patterns, etc.) to predict when customers might need your services again. You can send targeted messages and offers to them at critical times to encourage them to do business with you.

Advances in machine learning, like deep learning models, give businesses more ways to harness the power of data analytics. Taking advantage of purpose-built platforms like Meltwater gives you a shortcut to applying deep learning in your organization.

At Meltwater, we use state-of-the-art technology to give you more insight into your online presence. We’re a complete end-to-end solution that combines powerful technology and data science technique with human intelligence. We help you turn data into insights and actions so you can keep your business moving forward.

The Fundamentals of Deep Learning

The digital explosion has enhanced media consumption as audiences splurge over 58 minutes on several platforms to catch up on the latest and trending stuff circulating globally. From traditional print media to cutting-edge digital platforms, the UK's media sector offers a wide array of sources for news, entertainment, and information.

You will notice that the UK media has made the shift to catering to clever audience choices by upping its ante in creativity and advertising to a wider population.

The competition for mass audience content has had a steep decline. Bingeing on episodes or movies made available in libraries showcases the fragmentation of the viewing experiences of the people. The core audiences have diversified their viewing and are hooked to streaming services—The Video Streaming (SVoD) market in the UK is expected to grow by almost 10% by 2027 resulting in a market volume of US$5.48 billion.

The changing need to engage audiences and public perception of content consumption has garnered a positive sentiment among creators and marketers. Recognizing the power of British influencers and social media to cater to the demography of viewers has been the prime goal for media in the UK.

Navigating the media landscape in the UK requires an understanding of the diverse sources and platforms available. Meltwater has been tracking the latest in terms of data research on understanding the media in the UK.

Whether you're seeking breaking news, in-depth analysis, or entertainment, the UK's media companies offer something for everyone, reflecting the country's dynamic and ever-evolving society.

Media Consumption Of The UK Population In 2023

Trending Media Moments In The UK, Presently

Media Challenges Currently Faced In The UK

Revenues of several media houses had a rundown in the past couple of years. There was a rejig in the order and finding their feet back post-pandemic challenging economic conditions that impacted how content was consumed not only in the UK but across the globe.

Meltwater has put together the list of the three most impactful British media companies that ruled the roost in 2023. Statistics showed these companies not only survived the three lockdowns but bounced right back and established their ranking in terms of revenue, news, media, and information:

RELX is one of the most formidable media companies in the UK with revenue earnings of £8.6bn through news and information brands. It is a media house that worked on the lines to tread on the stability of legal and scientific brands.

They have cracked the market by understanding the market need by banking in popular data products that last the course. With an events company and publishing division all faring well, their revenues will likely soar in 2023.

BBC remains one of the largest British news organisations with approximately 91% of UK adults using BBC radio, television, or online media weekly.

The organisation is also responsible for 42% of the total investment that goes into original UK TV content. Their annual income for FY2023 was more than £5.5 billion; of which approximately 65% comes from licence fees paid by the citizens.

ITV, the oldest commercial network in the UK, is a British free-to-air public broadcast television network.

Following a relatively low revenue in 2020 due to the coronavirus epidemic, ITV Studios' revenue grew again, reaching more than £2 billion in 2022. This is driven by their constant investments in new streaming services—ITVX—to upgrade their customer’s experience.

ITVX contains bespoke video packages along with a special news section; which the viewers have always found ITV entertaining. Additionally, having some of the trusted brands on board has added to the brand value.

UK media ownership is concentrated, with few major conglomerates controlling various news outlets in the UK. This is majorly because only creating content isn’t enough, you need to know how to distribute it too.

For example, Lachlan Murdoch, who owns the British Media company ‘News Corp’, is a business magnate, media proprietor, and investor. Through ‘News Corp,’ he owns some of the most renowned media magazines such as Fox News, and The Wall Street Journal.

If you want to better understand how to distribute newswire effectively, watch Meltwater’s webinar on ‘How to make the most of Newswire distribution with Meltwater’!

Coming back, here is a list of some of the most common channels through which the UK population consumes media:

Tip: Take a look at UK social media statistics as well!

Although there has been a decline in the consumption of print newspapers, with digital platforms substituting them, recent data shows consistent print newspaper reach between 2022 and 2023.

The UK is also home to some of the world's most renowned newspapers, including:

The Times: Known for its comprehensive news coverage and analysis.

The Guardian: A progressive newspaper with a global readership.

The Daily Telegraph: Providing in-depth reporting on politics and business.

The Sun: A popular tabloid focusing on celebrity news and entertainment.

Tip: Take a look at our list of the largest US newspapers and read about the state of print monitoring.

The UK boasts a thriving magazine industry covering diverse topics, from fashion (Vogue) to politics (The Economist) and culture (The New Yorker). While there wasn’t a significant increase in reading in the recent year, ‘Time’ remains the most popular magazine among adults, having seen a 13% increase in reach since 2022.

Tip: Take a look at our list of the most popular magazines in the US and read our customer story with The Economist to learn how Meltwater helps The Economist strategically grow its readership through new subscriptions.

The major broadcasting services in the UK are:

Tip: Learn more about TV and broadcast monitoring with Meltwater.

1. British Broadcasting Corporation (BBC)

While publicly funded, the BBC is a cornerstone of the UK's media landscape. It operates multiple television channels, radio stations, and an extensive online platform.

The BBC is known for its impartial news reporting and high-quality programming, including dramas, documentaries, and entertainment shows, with an increased viewership of 3+ hours a day.

Independent Television (ITV) is a major commercial television network in the UK, offering a wide range of programs, including popular soaps and reality shows.
Also, BBC One remains the most-watched channel among adults in the UK, followed by ITV1, BBC News Channel, and Sky News Channel.

The UK has a thriving radio industry, with BBC Radio stations covering diverse genres, including news, music, and talk shows.

Despite a decrease in radio consumption since 2018, BBC One remains the most-used news source across platforms—among the adults that use radio for news, 7 in 10 continue to listen to BBC radio station.

Additionally, BBC iPlayer remains the fourth most-used source for news, behind ITV1 and Facebook.

If you're interested in always staying on top of the UK media, get a free demo of our Meltwater Suite today. Our huge source base has got you covered.

If we acknowledge that the media plays an important role in how we identify, agree, rally, communicate, and progress as a society, media monitoring becomes an extremely critical activity for brands seeking a presence in the digital world.

Media monitoring refers to how companies acquire insight into their consumers' hearts and minds, perceptions of their brand, themes, and conversations relating to the job they do or the services they give, and how their brand exists and performs in comparison to the competition.

Tip: Download the ultimate guide to media monitoring for free.

Some of the most common digital modes through which media companies reach their audience are:

Online news platforms like BBC News, The Guardian, and The Independent have a significant online presence, delivering breaking news and in-depth analysis to a global audience.

Social media platforms like Twitter, Facebook, and Instagram are widely used for news consumption and social interaction—they have become essential tools for journalists and news organisations to reach audiences.

According to reports, almost 50% of UK adults use social media for news nowadays. Plus, among the various social media channels; Facebook is the most used for local news, Twitter for breaking news, and TikTok, Instagram, and Snapchat are all used for celebrity news.

Tip: For more specific information on social media in the UK, take a look at UK social media statistics.

The popularity of podcasts has surged in the UK, covering a wide range of topics, from true crime to politics. They offer an alternative way to consume news and entertainment and are particularly popular among young adults and Gen-Z.

YouTube and Spotify continue to be the most popular platforms among adults who claim to consume news via podcasts, followed by BBC Sounds.

Tip: Check out the best tech podcasts, the best social media podcasts, the best retail podcasts, the best PR podcasts, the best finance podcasts, the best e-commerce podcasts, the best marketing podcasts, the best SEO podcasts, and the best Shopify podcasts.

Global trends percolate into influencing UK media consumption, and the patterns reflect the trends you see in 2023. Engaging and sensational content often catches the attention of viewers.

Audiences have made their choices and subscribed to major streaming platforms to enjoy more diverse content. Traditional platforms struggle to keep up and use above-the-line marketing campaigns to derive new revenue streams to stay relevant.

The following changes came about to keep up with the evolving content consumer’s needs.

Most traditional broadcasters have revamped their content according to media consumption patterns, such as ITV and Channel 4 rebranding.

BBC had an uphill task this year as they had to deal with several controversies and check out a window for digital acceleration to meet the future of the market of UK media.

The overall ad spend growth prediction for 2023 is 3.6%, which is lower than the 5.9% predicted in the July 2022 report.

Embracing commercial media models and not having safety cover from macroeconomic challenges harms the performance of several marketing organisations.

Storytelling and press releases have shifted to TikTok and Instagram, and news publishers are switching over to cater to younger audiences.

One of the long-awaited news was the passing of the online safety bill.

Media has significantly transformed, and consumption in all age groups has posed challenges. The advent of the latest devices, extensive streaming platform services, and varied forms of media supposedly sidelined traditional forms.

Here are some of the most common challenges that the UK media currently faces:

The UK Media Landscape: Media Companies, News Outlets, Ownerships

As machine learning advances, so do its many capabilities. One area of interest for businesses is the use case for neural networks. This subset of machine learning takes on a brain-like form to connect dots between data and learn more than what’s on the surface.

Neural networks support a slice of the artificial intelligence pie called deep learning. It’s what powers some of the technologies we use every day, such as voice assistants on smartphones and Google’s automatic translator. The idea of neural networks in computer science dates back more than 70 years. But the use of neural nets has really taken off in the last 10 years or so as machine learning is applied to various areas of our everyday lives.

Here's what you should know about neural networks and how you can apply them to critical business use cases.

How Meltwater Helps You Leverage Neural Networks and Deep Learning

Neural networks are a type of artificial intelligence (AI) that is modeled on the human brain. These brain-like networks are composed of multi-layered interconnected neurons, or nodes. The nodes are connected by synapses, which transmit information from one neuron to another. Neural networks can learn to recognize patterns of input data and can make predictions based on these patterns.

Companies use neural networks for a variety of tasks, including image recognition, identification of spoken words, and prediction of financial markets. Neural networks have been shown to be particularly effective at these tasks because they can learn to recognize patterns that are too complex for humans to discern.

Neural networks have been found to be particularly useful for tasks such as image recognition and classification, data mining, and pattern recognition. In recent years, neural networks have also been used for more complex tasks such as natural language processing and machine translation.

For marketing purposes, neural networks can add context and clarity to the customer journey. Because neural networks look for hidden connections and patterns that would otherwise be harder to detect, marketers can get richer insights into their customers’ experiences.

For example, neural nets may be able to pinpoint niche behaviors about a particular area of the customer journey, such as how users engage with content or make a purchase.

Knowing how customers move through their digital journeys can help brands cut the clutter and create a more direct route to success. You can eliminate stumbling blocks that might prevent customers from engaging or purchasing, such as slow page-load times or complex navigation.

Another opportunity for using neural networks is to predict customer churn. Churn is a costly problem that makes brands rely more heavily on new business. However, if you can predict churn, you may be able to take the right steps to keep those customers longer.

Using neural networks, you can find answers to questions like:

What steps do customers take before they churn?

When does the churn process really start?

Neural networks look for patterns between clusters of data to draw richer insights. By looking at lots of customer journeys from a bird’s eye view, you may be able to identify at-risk customers and see what behaviors trigger certain outcomes, like churn.

Using neural networks in marketing is not a substitution for your conventional ways of mapping the customer journey. Rather, machine learning complements it by making your current models clearer and more accurate.

Computers can more quickly and accurately look at empirical customer data to find common denominators between actions and behaviors. They can be sorted into segments or groups based on these findings.

You’ll have information about your customers that you didn’t have before and had no real way of knowing. Use that intel to strengthen your positioning, provide better experiences, and shorten the path to conversions.

The Meltwater Blog

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