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Influencer marketing is evolving, and so is the way brands measure success. That’s why Meltwater is proud to announce a new partnership with YouTube, integrating YouTube’s Creator Partnerships API directly into our Influencer Marketing platform.

Introducing Verified YouTube Data in Meltwater Influencer Marketing

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

Decades ago, reading the minds of your customers might have seemed like an unrealistic superpower. Today, we have consumer intelligence — which isn’t the same as mind reading, but it’s pretty close.

With increasing competition, staying connected to your customers is becoming more and more necessary. Knowing what they want and need from you allows you to meet them where they are. This helps you to adjust your products and services, reveal and remedy issues, and give your customers a reason to keep choosing you.

Consumer intelligence helps you to learn more about your customers by creating a more holistic image of your customers and their experiences. You can make decisions based on data, not guesswork, so you can maintain a competitive edge.

Let’s explore the role of consumer intelligence and how to go about collecting and applying it in your business.

Definition: What Is Consumer Intelligence?

What Are the Benefits of Consumer Intelligence?

Consumer Insights vs. Consumer Intelligence: What's the Difference?

Making Meltwater Your Consumer Intelligence Solution

We define consumer intelligence as the byproduct of collecting and analyzing customer data. You might also hear it referred to as customer intelligence and customer intelligence analytics.

This intelligence comes from various internal and external sources, including but not limited to:

Consumer intelligence can help brands deliver more personalized experiences across the entire customer journey. It plays a role in marketing campaign planning, audience segmentation, and improved communications.

The goal of consumer intelligence is to gain a 360-degree understanding of your target audience’s needs, preferences, and motivations so you can make informed decisions for your business.

Consumer intelligence provides brands with a path to improve their customer experience — including customer service, product and service offerings, go-to-market strategy, communications, and accessibility.

In order to improve each of these, companies need insights into their customers’ behaviors and motivations.

Surface-level “insights” like age, gender, and income level aren’t enough to craft tailored experiences. Understanding what customers need at each stage of the buying journey allows companies to deliver on those expectations.

Consumer intelligence uses data and analytics to create comprehensive customer profiles that go beyond the surface. These insights provide actionable recommendations for improving the customer experience and building stronger relationships.

When implemented correctly, consumer intelligence can deliver the following benefits:

The ability to choose the right marketing and communications channels to get in front of more customers and increase conversions

Improving sales efficiency and marketing effectiveness by creating messaging and offers that resonate with your audience

Building stronger customer loyalty by providing programs customers use and improving social media customer service

The ability to take pre-emptive action with customers at risk of churning to reduce revenue volatility

Helping you introduce new products or services that customers will purchase

These benefits are just a few examples of what consumer intelligence can do for your business.

Consumer insights and consumer intelligence represent two sides of the same coin. They’re not quite the same, so let’s explore the nuances between consumer insights vs. consumer intelligence and the value they bring to your business.

We define consumer insights as the deep understanding of consumers that comes from gathering consumer intelligence and analyzing it carefully. The result is actionable  data your business uses that takes into account customer behaviors, feedback, and conversations and turns them into conclusions about your customers’ preferences.

To benefit from consumer insights, you need good data quality and reliable data sources, as well as a way to collect, organize, and translate the data. In other words, to gain real consumer insights, you need to first gather real consumer intelligence.

Tip: Take a look at some Consumer Insights Example Reports!

Where Do Consumer Insights and Consumer Intelligence Overlap?

Both consumer insights and intelligence play an important role in developing your products, services, and marketing strategy.

Consumer insights and intelligence can help you learn more about what makes your target audience tick. Insights are built by identifying trends in behaviors and actions throughout the customer journey that help you learn more about sentiments and the drivers behind customer decisions.

The results delivered by insights and intelligence differ from traditional market research in that you don’t know what you’re going to get. Instead of creating specific questions ahead of time, you receive answers to questions you may have never thought to ask. 

The data you receive from an insights and intelligence strategy can answer questions like:

How and where should you position your products or services so customers take notice?

How do customers feel about your competitors?

Is there a demand for your products or services, and how does demand change over time?

Are there opportunities to create new products or services?

What does your audience like, dislike, need, want, etc. and how can you connect with them wherever they are?

To get a better understanding of the value of consumer intelligence, it helps to see it in action. Let’s look at a few examples of consumer intelligence and how you can apply it.

Consumer intelligence tools like Meltwater can track customers across the entire customer journey, from how they discover your brand to the content they engage with prior to purchasing.

Pinpoint where customers lose interest or the steps they take that lead to purchasing. These details can help you identify where you might need to improve your sales funnel (for example, you’re losing lots of leads around the same point) or increase your cross-selling and upselling.

Tip: Download our free Buyers Guide: AI Enabled Consumer Insights Platforms

Consumer intelligence can help you segment your audience on factors that might not be visible to the eye. For instance, you might segment campaigns based on products purchased, buying patterns, or new customers that responded to a specific offer in the past.

Segmentation may also prove effective in marketing to audiences who share similar interests that aren’t directly related to your brand. For example, some of your customers might be soccer fans, moms of toddlers, or avid watchers of Love Island. You can tailor your marketing messages to cater to these interests and be more likely to get your customers’ attention.

These resources might be helpful as well: Ultimate Guide to Market Segmentation & Personas, What is Audience Segmentation?, Lifestyle Marketing Guide, Customer Segmentation Guide, The Top Customer Segmentation Tools, Customer Segmentation Examples, Customer Segmentation Models, The Top Audience Segmentation Tools.

Learning more about your customers’ interests and preferences can give you a starting point for personalized marketing. You’ll have a more informed idea of what drives consumer behaviors and decision-making.

Use this data to offer incentives to customers they care about and will want to act on. This might be a percent-off coupon, a free gift with purchase, free shipping, an invitation to a customer loyalty program, or a similar offer. When your offer speaks directly to their needs and wants, potential customers are more likely to convert.

The longer you track your customers’ behaviors, the more valuable this data becomes.

This proves especially helpful with demand forecasting. The more a customer buys from you, the more you can learn about their purchasing frequency and volume. This helps you to maintain a healthy inventory of products to avoid backorders when customers are ready to buy.

Banks and financial service providers leverage consumer intelligence to detect possible fraud or other suspicious activities. They look for patterns in the data to spot risks of bad debt, unauthorized purchases, or identity theft.

This not only prevents financial loss for the organization, but also gives the company a chance to strengthen customer relationships by looking out for customers’ best interests.

Consumer intelligence comes from a variety of internal and external sources. Combining data from multiple sources gives brands the most comprehensive view of their customers.

Consumer insights tools and customer intelligence platforms can ingest data from multiple internal and external sources to create these holistic profiles. Some of the best places to mine for customer data include:

Corporate databases (such as an ERP system), CRM, sales software, and call centers can all provide rich details about your customers.

Accessing this data might require cross-department collaboration with IT, customer service, procurement, and sales departments.

Be clear about the type of data you’re looking for, how you plan to use it, and why it could be beneficial to your strategy.

Customers are open to sharing personal details on social media. They’re also not shy about airing their grievances with brands and businesses when they have a poor experience. Mining social media to learn more about your customers gives you a more honest view of how they feel about your brand (and your competitors).

Consumer insights platforms like Meltwater can collect these insights on your behalf by monitoring for keywords and analyzing the context behind them.

Leveraging industry-specific third-party market intelligence and research allows you to learn more about demographics, attitudes, and general market shifts. This data can be useful in learning about customer buying patterns and preferences within specific demographics, such as income brackets or age groups.

You can also perform your own market research with your own customers. Focus groups, case studies, surveys, and monitoring online reviews can all reveal helpful insights about your customers’ sentiments.

Tip: Learn more about market research and learn how to conduct sentiment analysis.

When customers use your website, their interactions are tracked by your website analytics.

Things like session durations, number of page views, bounce rates, exit rates, and dwell time are all logged by analytics tools. These data points can prove useful in improving your website experience and ensuring customers get what they came for.

Collecting intelligence on your target consumer is just the first step. Once you have the data in hand, you also need a strategy to apply it to get the results you want.

After collection, the next step is to categorize the consumer data. Consumer intelligence platforms can do this on your behalf. You might choose to organize the data based on the data source (online reviews, social media, website analytics, etc.), current client vs. lead or prospect, or qualitative vs. quantitative data.

Consumer Intelligence: Definition & Examples

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

Earlier this year we added a new feature to Radarly, our Consumer Insights solution, called Discovery. This AI tool is capable of automatically detecting unusual trends and patterns within a Radarly workspace, and then highlighting these changes to the user to make them aware of a potentially important new insight that should be further explored. 

Now we’re pleased to announce that we’ve improved this feature by adding Discovery Explainer, which uses the power of generative AI to describe the context of its discoveries in easy-to-understand natural language. This means that it’s easier than ever before to find and understand new insights as they arise from the data.

The animation below shows you exactly how the feature looks in Radarly, and should give you a clear picture of just how easy to use this new innovation is.

It’s not just about saving time. Because Discovery Explainer isn’t human, it doesn’t always think like a human, and that means it’s capable of spotting changes and patterns in data that a person might miss, so it has the potential to help our customers surface new kinds of insight. 

Another key benefit of this new feature is that it opens up Radarly to non-analyst users, who might not have the skill-set to make sense of the raw data. This empowers a wider range of users to take advantage of Radarly’s advanced Consumer Insights capabilities, even in situations where an analyst is not available to work on the project.

How does it work? When the AI detects something interesting happening in the data, it analyzes all of the content, and contextual information around each item of content (made possible by Radarly’s unique data structuring) including keyphrases, hashtags, affects, emotions, sentiment and content classification. This is then processed by a generative AI tool which is able to extract meaning from all of the data and describe the event in natural language, including English, Chinese, French, Japanese, and German.

At Meltwater we’re working hard to find new ways that AI can make our tools even more powerful and easier to use for our customers, this is just one example of many AI-powered features we’ve recently added, and there are many more on the way. We hope you find it useful.

Not a Radarly client yet? Get a free tour by filling out the form below.

Discovery Explainer - Surfacing Insights with AI

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

The Meltwater Blog

Consumer Intelligence: Definition & Examples

The Fundamentals of Deep Learning

Discovery Explainer - Surfacing Insights with AI

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

Neural Networks: Ultimate Guide

Why Are Your Brand Mentions Spiking? AI Can Explain

Why You Need a Marketing Communication Strategy

50 Content Marketing Metrics to Measure Your Campaign

Transactional Marketing vs Relationship Marketing: What's the Difference?

How to Pitch Your Press Release to Journalists

Consumers' Most Recommended Cold and Flu Remedies

When is the Best Time to Post on Instagram in South Africa?