RATINGS-DRIVING CONTENT? INCLUDE EVERY LISTENER!

Posted by: Admin
Categories: pp1 • pp2 • program directors • radio content • radio programming • radio stations
28 Dec
0

Brainstorming interesting topics is just one half of the equation; finding mass-appeal angles is the most creative part of successful storytelling.

How to find and present compelling radio content is one of the topics program directors should know how to engage audiences with great content from a radio programming (rather than pure journalistic) perspective. They should always try to take it to the largest common denominator.

“WE COULD ASK A DOCTOR WHAT TO DO AGAINST HAY FEVER”

BE CRITICAL ABOUT CONTENT

What makes radio different from print & online is that it’s linear, so audiences cannot skip items, as opposed to a newspaper or website. Every listener has to get through every bit, and has the ability to jump off when it’s not interesting. Think carefully about what to put on air. What we already do with music scheduling — playing songs that appeal to a large audience, based on research — do the same for radio content. Instead of asking, “who will be interested in this bit?”, ask which percentage of the people who are tuned in right now, is not interested?

CHOOSE ENGAGING CONTENT ANGLES

Interest does not even depend so much on the chosen topic. It depends more on the chosen angle, and how a bit is being introduced. The percentage of people who are interested should be greater than those who are not, meaning: in your choice and approach of topics — and in everything you do on air, for that matter — you want to include listeners, rather than exclude them. Appeal to as many as possible.

FOCUS ON ORIGINAL PERSPECTIVES

The real creative challenge is to come up with original ideas for implementing a topic. An expert sharing ‘how to’ tips is an often-used angle. The challenge of this approach is that 85% of the listeners would not care about (this way of covering) the topic.

“THERE ARE MORE CONSUMERS EXPECTING A PACKAGE THAN THERE ARE PEOPLE WORKING AT AMAZON” 

APPEAL TO LARGE(R) AUDIENCES

Try to take it to the largest common denominator. People’s perspectives are differently, so pull several different ‘approaches’ out of a hat, which can lead to a funny bit, which hopefully entertained a larger part of the audience. To simply cover the topic by interviewing an expert, would not only rule-out 85% of the audience, it won’t be really interesting for the 15% either. Perhaps they probably have the issue for quite some time, and know how to handle it.

KEEP MASS-APPEAL TOPICS BROAD

It makes sense to keep a topic that everyone cares about also interesting for everyone, because a too segmented approach, such as ‘this is how victims of burglaries suffer from breaking and entering’, would only speak to people who have had this experience.

INVOLVE ALL LISTENERS ALWAYS

“The European Football Championships? Talk about how fans are celebrating, rather than sports facts. Those with a keen interest in sports know more than you can ever cover on air. When there’s a strike at a local distributor of Amazon, rather tell people if their package will arrive in time. There are more consumers expecting a package than there are people working at Amazon. Otherwise we would run the risk of making radio for specialists; today for hay fever sufferers, tomorrow for robbery victims; constantly talking to small fractions. Stay away from “The Big Addition Deceit”; the idea that when we have spoken to all of these segments one time, we have reached everyone. The opposite is true, because with every single specialized bit, you’re neglecting a huge amount of not-interested people, who may tune out.

FIND UNIQUE LOCAL STORIES

It’s not just about what you want to do, but also (and maybe even more) about what you want to do with it; finding a story angle that will interest a large part of your audience. When you have good stories, you can get very far as a station. In a search for good stories, and in light of Munich already having the world’s largest Volksfest and Germany’s leading football team, they got the idea to highlight less-known local achievements, well-branded as Die Gong 96.3 München-Rekorde (The Gong 96.3 Munich Records).

MAKE USE OF CROWDSOURCING

They did not do the obvious; just presenting some nice stories, but actively involve listeners in and reward them for finding stories instead. Whoever brought in the ‘record of the week’ story, got a € 10.000 (then about $ 11.200) cash reward. It was a successful radio promotion because of its local character, its extended purpose – it was not only engaging and entertaining listeners, but also giving them monetary value.

MEASURE CONTENT ALONG CRITERIA

Although you can research a lot, you find the best stories when you activate your community. The city’s tallest person is 24-year-old Jannis: 2 meter and 22 centimetres! Another extraordinary case is the biggest age difference in a relation; Lia (33) is living together with her husband Heinrich (84). Jaw-dropping stories are also clickable & sharable via social media that are great distribution tools in addition to the station website. Gong 96.3 tries to feature surprising, current, regional, unique, emotional and valuable stories. These 6 points should — as much as possible — be included in every piece of content.

 

 

BIG DATA ANALYSIS OR LEGACY RADIO RESEARCH?

Posted by: Admin
Categories: mobile data • portable people meter • ppm • radio programming • radio stations
28 Dec
0

In this era of Big Data and user profiles from mobile & smart devices, is there really still a place for traditional ways of radio research?

Stephen Ryan (Photo) sees audience research as helpful instruments for creative programming.

Today’s research for radio stations is based on a legacy of tried and tested methods. In a time of Silicon Valley-driven technology evolution, where everyone with a connected device is monitored and analyzed in the tiniest of detail, is there really still a place for these traditional methodologies?

  “It’s not simply about size; it’s more about complexity”

Most radio research data are less complex than Big Data from online giants!

Much is made these days on the use of Big Data and companies such as Amazon, Facebook and Google, using complex data and predictive analytics to profile users and personalize experiences. Recent market valuations of these companies illustrate the payback for the investment in developing their sophisticated algorithms. Closer to our industry, music streaming services like Spotify, Deezer and Pandora spend vast amounts of time, money and resources on using Big Data to fine tune the listener experience and strive for ultimate personalization.

This all begs the question: where does Big Data fit with radio, and is it relevant? More importantly, if these data are so valuable, is it time to say goodbye to some of our traditional research analysis methodologies, and focus on Big Data processing instead? I will argue that while programmers should use every available resource to identify listeners’ desires and needs, including Big Data analysis from the likes of Twitter, we should still use legacy research methods to quantify and understand exactly what our listeners are doing – and more importantly, why they are doing it. Our emphasis is on music research, but the same applies to other forms of established radio research.

There is an important distinction to make. The availability of detailed data sets (such as through Portable People Meter analysis) is not the same as Big Data analysis in its truest sense. Big Data depends on the ability to analyze complex interactional and transactional behavior in an attempt to discover patterns and trends. It’s not simply about size; it’s more about complexity. The vast amount of these data is unstructured. Their analysis requires advanced computational power and methods that traditional data analysis simply cannot cope with. Legacy data analysis relies on a structured approach. Research methods that we have become so dependent on tend to be based on relational database models. Big Data analysis takes wildly unstructured and complex data, and attempts to make it structured and understandable through patterns and trends; taking random individual behavior, and trying to identify commonalities.

PPM data and music playlists don’t show us the reason why people like or dislike a certain song!

When we talk about monitoring Shazam or Spotify trends, we are of course looking at third-party generated analysis. Should radio develop its own Big Data sets? Such data and analytics are both resource and time intensive. It may be an opportunity for large radio groups with sufficient financial resources, but it’s likely to be well beyond the bounds of smaller groups and single stations. Also, while our listeners are complex as individuals, how they consume our service is simple. In pure transactional terms, the interaction is far from complex, and it is complexity where Big Data analytics come to the fore.

Even if resources do allow, how much information do you really need and what exactly are you going to do with it later? Research should help to generate actionable results, eliciting views and opinions that either support parts of a strategy or flag issues that need attention. In a Big Data world, we might be able to know not only that person X does most of his radio listening in a car, but also that the car is a blue Volvo, that its average speed is 50 miles/hour, and that the average occupancy of the vehicle is 2. Great, but do you have the time and resources to analyze the value of all this, and if yes, exactly what is your benefit? There is a large number of innovative web- and app-based techniques for identifying listener behavior. However, for the most part, these techniques illustrate what happens, but not necessarily why.

As an example, there are a number of analytical tools available that can match Portable People Meter data to the reconciled schedule play-out logs. Cross-referencing allows us to track the audience’s behavior as each song or segment plays across the day. By identifying people’s behavior each time say a certain song plays, we could see a particular trend where when that song is played, the audience dips. However, in a similar way to radio ratings, it tells us what potentially happens when the song plays, but it doesn’t tell us why. In order to spot a consistent trend, a song needs to be sufficiently exposed by having a reasonable rotation. If the song turns out to be turkey, hasn’t the damage already been done?

        “The nuances that an experienced programmer can spot are simply not there”

Listener-driven music voting apps often offer just a Like and Dislike button to vote on songs!

While the US have pushed through the adoption of the Portable People Meter, there are more countries still reliant on diary and yesterday recall methods. This includes the UK, where RAJAR has retained diary methodology, while they investigate some concerns on methodology and cost. However, stations in non-PPM territories can still get minute-by-minute data using logs from their streaming output and/or the use of a station app for listening. Again, cross-referencing the logs with the reconciled schedule allows us to spot trends. But the issue remains the same: cross-referencing tells us what happens, not why.

Music research, such as callout, allows you to follow the life cycle of a song. If there is an issue, perhaps it’s unfamiliarity or (in the latter part of the cycle) burn. On numerous occasions, I’ve seen a new song with a high unfamiliarity – which may have been prematurely tested – in combination with a negative score. An experienced program director can see the nuances. If a new song is allowed to be further exposed, that negativity often dilutes as it becomes more familiar. If a decision was based on what listeners did when the song was initially played, a lot of songs with potential could be ripped from the playlist!

We’ve come a long way from listener requests and dedication letters being the only feedback channels. Now we have sophisticated interaction systems to use through websites and apps. Listeners can preview songs, vote on songs, and potentially influence the upcoming playlist at a click of a button. However, this voting is usually confined to an absolute choice of ‘like’ or ‘dislike’. Once again, the nuances that an experienced programmer can spot with music research are simply not there.

“Tried and tested radio research methods still remain relevant”

 

Stephen Ryan argues that AMTs and callouts are still important radio research instruments!

Listener behavior tracking through analysis of Portable People Meter and Internet radio streaming data (or the interactive voting results through the station’s website or app) are valuable tools for any PD. However, in a similar way that we should use focus groups, the results should only be used as a potential flag for further research, rather than an end in themselves. Further investigation could be done through callout research or auditorium music testing.

While mobile and smart devices continue to increase in sophistication and speed, there’s a growing array of tools for the modern radio programmer to understand more about their audience. To truly quantify and qualify the listeners’ desires, tried and tested radio research methods still remain relevant. We just need to ensure that the ability to capture and gather our sample data continues to evolve with (and remains compatible with) the use of mobile and smart device technology.