Introduction

Audience behavior is becoming increasingly complex. There is more content than ever before, across more channels than ever before, and more devices upon which to consume that content than ever before.

And, because of this, audiences are more demanding than ever before.

Some quick stats...

3h 46m

Average time a US adult spends on their smartphone each day

86%

The % of US adults who get news on a smartphone, computer or tablet

41%

The churn rate for OTT services in the US Q1 2020 – up 6% YoY

2.2 Trillion

The number of global on-demand audio song streams in 2020

$47

Average monthly US household spend on streaming services in Dec 2020

495m

The estimated worldwide eSports audience in 2020

Audience Analytics in Practice

Here’s how the Philadelphia 76ers used advanced analytics to understand their audience and how it has changed during COVID-19.

Read more

 
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What is Audience Analytics?

Simply put, Audience Analytics is the process of integrating audience data from multiple sources, building a detailed picture of your audience and audience segments, and then analyzing that data to reveal insights and inform strategy.

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Is this new?

No, at least not in principle. Understanding audiences has always been crucial for M&E businesses. What’s new is the complexity of audience behavior, the amount of data that is available, and the sort of detailed insights that can now be gleaned.

What sort of insights?

Well, by developing a detailed understanding of your audience, you can develop better, more targeted content, deliver it at the best time, and across the most effective channels, to increase engagement, enhance acquisition, and ultimately drive revenue.

1. Boost Acquisition

Understand how, why, and when your audience is subscribing to your platform, consuming content, or purchasing tickets – use this knowledge to boost acquisition strategies.

2. Enhance Content

Access huge amounts of audience data to understand what content resonates best and feed this intelligence into your content development strategy.

3. Hyper-target by Segment

Go one step further and target specific audience segments with one-to-one content developed just for them.

4. Improve Scheduling & Channel Optimization

Understand when, where, and how your audience consumes content and use this to optimize your scheduling and channel strategy.

5. Boost Engagement

Enhance audience engagement and build an accurate picture of how they feel about your content with in-depth tracking of social media channels.

6. Increase Revenue

Optimize advertising conversion rates and provide detailed audience feedback for sponsors to attract and retain higher revenues.

7. Reduce Churn

Understand how, why, and when you are losing subscribers or viewers and use this knowledge to reduce audience churn.

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Want to know more?

Check out our blog post on data-driven marketing for the media & entertainment industry!

Read blog

 
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Connect with the right data

The M&E industry has access to a huge amount of audience data. But, this needs to be harnessed correctly. One of the first steps is to master data governance.

To even start to understand how your audience interacts and engages with their content, you need to connect to the right data.

Which data will depend on your business, however, it helps to group your data sources into the types of data they are providing.

Advertising Platforms

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Google Ads

 

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Facebook

 

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AdRoll

 

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Criteo

 

Social Media Platforms

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Twitter

 

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Facebook

 

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LinkedIn

 

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Instagram

 

Customer Engagement Platforms

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Google Analytics

 

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MailChimp

 

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HubSpot

 

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Drift

 

Content Platforms

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YouTube

 

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TikTok

 

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Outbrain

 

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Taboola

 

Data Less Ordinary

Don’t only look to the most obvious data sources. By connecting with different, less ordinary, datasets you can draw out some truly valuable insights.

For example, if you have live events, integrate weather data to see how this is impacting ticket sales. Or, by connecting to platforms like Brandwatch, Sprinklr, or Hootsuite, you can start opinion mining.

See all connectors

 
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Establish appropriate Metrics & KPIs

Once you have the right data, you can start identifying the right metrics and KPIs to track.

Broadly speaking, you can split these between metrics for tracking performance on the one hand, and KPIs for tracking engagement and revenue on the other.


Performance Metrics

ADS

Impressions, Clicks, Costs

 

SOCIAL

Total Reach, Follower Growth

 

PRINT

Circulation, Unsold Copies, Readership

 

WEBSITE

Users, Sessions, New Users

 

SEO

Impressions, Clicks, Referring Domains

 

TV

Audience Reach, Total Viewers

 

CONTENT

Page Views, Organic Traffic

 

LIVE PERFORMING

Number of Attendees, Ticket Pricing Variations

 


Engagement KPIs

ADS

CPM, CTR, CPC, CPA, CVR

 

EMAIL

Subscriptions, Open Rates, CTR

 

WEBSITE

Pages per Session, Avg. Session Duration, Bounce Rate

 

TV

Average Viewing Time, Audience Share, GR

 

SEO

CTR, DA, Average Ranking Position

 

PRINT

Readers per copy

 

CONTENT

Time on Page, Scroll Depth, Social Shares, Asset Downloads

 

SOCIAL

Interactions (Likes, Shares, Comments), Sentiment, CTR

 


Revenue KPIs

ADVERTISING REVENUE

Total Ad Inventory Revenue, Average CPM, Page RPM

SPONSORSHIP REVENUE

Team Sponsorships, Media Rights, Product Placement

SUBSCRIPTION REVENUE

Annual Revenue per User, Total Customer Value, Churn Rate

EVENTS REVENUE

Ticket Revenue per Event, Total Annual Ticket Sales, Event Sponsorships

Learn more about KPIs

To learn more about KPIs, check out our blog on the top KPIs for News and Media Companies!

Read blog

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Build reports to measure your business performance

With the right KPIs, you can build reports. Reports help you visualize your KPIs, compare them, and track your audience more efficiently. To be most effective, reports should be linked to core business areas or functions. Here are the top reports you should be looking at to measure business performance.

Content performance

Content performance reports will allow you to track the performance of various content types (clustered by topic, length, keywords…) against different audience segments (age, location, gender…) to understand which types of content are resonating with the audience and feed this intelligence back into content development strategies to hyper-target specific audience segments.

 

Customer subscription

Develop audience journey reports by finding out what channels audiences came from, what content converted them, and what their next engagement steps were. For example, combine CRM data with social platform platforms and ad channels data. Use cohort analysis reports to determine key acquisition or churn moments and compare audience segments with each other.

 

Social media performance

Social media engagement reports will display the total engagement with your audiences on social channels, from basic metrics (Likes, Shares, Comments), through Clicks driving traffic to your website or other properties, to complex analysis that displays Audience Sentiment and other parameters showing levels of awareness and advocacy among key target audiences.

 

Advertising & sponsorship

Deconstruct the structure of your advertising revenue by analyzing the performance of each channel, or even individual inventory. Build detailed ROI and conversion rate reports for your sales team that demonstrate the value of your inventory and help attract and retain more advertisers and sponsors.

 


Cohort analysis reports (and why you need them)

Cohort Analysis Reports are an extremely powerful tool for understanding your audience. Download the full guide for more information on what they are and how to read them.

Download full guide

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Audience Analytics and Predictive Models

Once you’ve integrated all your data, determined your KPIs, and built various reports to track your success, you need to start drawing some insights from it all – and this where data analytics comes in.

What is Data Analytics?

Data analytics is where you can extract the most amount of value from your data and start generating insights.

For example, you can:

Understand which channels are bringing the most customers – and optimize your marketing budget accordingly
  • Learn which groups of customers spend the most and what they are spending their money on – and target them with specific offers and sales
  • Reveal which customers are least likely to make a second purchase and why – and develop an appropriate customer retention strategy
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This can be done manually – many M&E businesses have teams of data analysts whose job it is to do precisely this. Or you can let a computer do it for you, which is the next step – artificial intelligence.

What is AI Analytics?

AI, or Augmented, Analytics is where you enhance your data analysis by utilizing artificial intelligence to sift through it and draw out insights.

One of the key benefits is that AI can analyze data, find patterns, reveal anomalies, and generate insights at a scale, speed, and level of detail impossible for individual human analysts.

For example, with AI analytics you can:

Spot a drop in conversion rates among a specific customer segment that would be missed if you are only looking at aggregate data
Identify a dip in ROAS on a specific campaign that again might be missed by human monitors

Once making the decision to utilize AI for your data analysis, this also opens the door to the next step – predictive analytics.

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What is Predictive Analytics?

Predictive analytics is the process of using historical data to find patterns and make assumptions to predict future developments.

While strictly speaking this can be done by human analysts, the excruciating complexity of predictive analytics is AI’s playground. By using AI, marketers can draw out incredibly sophisticated predictive insights that would be extremely costly and time-consuming using human analysts, if not completely impossible.

For example, with predictive analytics you can:

Predicting the impact of content for specific audience segments including high-potential markets and demographics they should target, or forecast and reduce churn rates
Forecasting media budget allocation at a total brand/campaign or channel level based on what is the most effective at achieving your KPIs
Identifying potentially costly problems as early as possible before they become a major issue
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When it comes to understanding increasingly complex audience behaviors, this is a game-changer – the difference between only ever looking backward at what has already happened and instead, looking forwards and being proactive about what audiences are going to do.

Excited by AI?

So are we! Learn more about AI-powered Augmented Analytics in our in-depth guide.

Read the full guide

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Show the data

“Above all else show the data” – Edward Tuft

Most M&E businesses are already working out how to improve their audience analytics.

The question remains though, what is the best method for doing so? Every business is different so it is important to understand the data analytics market to know which approach will work best for your business.

There are three core components to establish a data-driven approach to your audience:

  • Data Integration – collecting and harmonizing the data from all your different data sources
  • Data Visualization – displaying that data in a visual data dashboard
  • Augmented Analytics – using AI and machine learning to draw out insights and predict behaviors
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What types of tools are available?

 

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Some tools specialize in data integration and transformation, and can feed data into business intelligence, data warehouses, reporting or visualization applications.

 

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Then there are solutions that focus solely on data visualization and are purchased separately from data integration tools.

 

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Lastly, there are end-to-end marketing analytics platforms that offer data integration and transformation, dashboard visualization, and analytics.

 

End-to-end marketing analytics with Adverity

Adverity is in this third category, as a comprehensive data integration, visualization and analytics platform.

This means that businesses can use a single vendor for their entire audience analytics set-up, or pick and choose the modules they need and combine them with their own in-house systems.

This is especially useful if today you only need data integration, but later you’ll need to scale up to include additional modules.

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Check out how we helped live entertainment business AnalytixLive

When COVID-19 stalled operations for all of their clients, analytics consultancy AnalytixLive managed to reinvent their business with the help of Adverity.

Download the case study

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