Follow EmergingSpaces on Twitter

Insight, news and opinions from Starcom MediaVest Group London.

Subscribe by RSS or by Email,
and follow us on Twitter.

Measuring mobile effectiveness with POEM

Michael Vitello's picture

For retail clients with the objective of increasing sales, it can be a challenge for media agencies to prove the contribution of media to their overall sales, and therefore the ROI of their media investment. Unless there is a way to connect offers through media (for example, unique promotional codes) directly to the tills, econometric modelling of sales results can be as elusive as the Holy Grail.

However, it can also be a barrier to innovation — why take the risk with new media channels when we know that digital display is yielding a strong ROI? For our client Pizza Hut, we wanted to ensure that their mobile activity could be tied back to a return on investment measurement without falling back on standard econometric modelling which may not fully reflect the value of mobile.

But innovating to stand out from other casual dining restaurants is important in this category. So, in order to tap into the opportunities mobile advertising can provide, we needed to give Pizza Hut the confidence to try out mobile media at minimum risk to their bottom line.

PHpoem1.PNG

Starcom’s bespoke modelling technique – Paid Owned Earned Modelling (POEM) – lets us build a holistic view of each element of a campaign, showing not just a direct measurement of each media channel, but how different elements work together with one another. This provided the means of testing Mobile’s overall contribution to sales, which secured Pizza Hut’s buy-in.

POEM.png

We knew that:

  • Our Pizza Hut audiences (Mums and Sociables) are increasingly connected through their use of mobile
  • These audiences actively seek out restaurants from their mobile
  • These audiences are also heavily influenced by offers, even when on-the-go. Therefore, providing these consumers with a relevant Pizza Hut Restaurant offer via their mobile is something they will not only react positively to but act on and go in-store!
  • This understanding of consumer behaviour identifies Mobile as a crucial communication and engagement device for Pizza Hut

Instead of letting customers search for restaurant offers and locations, we brought Pizza Hut Restaurant offers and locations to them via SMS. Our strategy was two-pronged:

  1. Making sure our audiences are aware of Pizza Hut’s offers relevant to them on key dates for Pizza Hut.
  2. Capitalising on the opportunity to convert low hanging fruit – O2 customers within 250m of a Pizza Hut restaurant. Messages were time-targeted to promote the most relevant offer

By building a partnership in the truest sense with O2, we were able to minimise risk to Pizza Hut and remove the barrier to Mobile.

PHpoem2.PNG

The mobile element of the campaign proved to be was 142% more efficient in delivering incremental sales revenue than the campaign average, with the majority of mobiles impact in directly driving sales (90%).

ICO to change cookie policy to recognise implied consent

The ICO has recently announced that, contrary to their previous position that insisted on explicit consent for cookies dropped on a user’s computer, it will now consider implicit consent. On Jan 28th 2013 they announced the cookie dropping policy on its own website will change to aid the collection of "reliable information to make our website better".

The EU ePrivacy Directive requires websites gain the consent of users before placing cookies on their machines, whereas previously websites only needed to provide the means for users to opt out of receiving cookies. However, websites still have the responsibility to provide a clear and user friendly mechanism to opt-out from any data collection and ensure that identifiable information is anonymised. As the data watchdog, the ICO has lead by example. However, its strict application of the EU rules last year led to an immediate 90% drop in measured visits. Now, the following changes to the ICO’s site will be made:

  • Cookies set on arrival to the site
  • New cookies banner displayed which shall explain that the website uses cookies and that cookies have been set, tells users they can change their cookie settings (via a new cookies page), or continue to use the site
  • New ‘Cookies’ page (separate from our Privacy notice, but linked to and from it) to increase prominence of the information
  • Users given clear, detailed information about the cookies set and how to manage them, and new buttons allowing users to delete or allow non-essential cookies
  • Limit the geographical information collected by their analytics cookies

The desire for the change in this decision is to do with awareness. They felt explicit consent was appropriate in 2011, since users weren't aware of cookies and what they were used for. The landscape has changed since owing to websites complying and being more apparent in how they display opt-out options. PWC conducted an online survey of over 1,000 individuals in February 2011 and the results illustrated that only 13% fully understood how cookies work while 41% were unaware of the different types of cookies. New factual evidence supporting this theory about growth in awareness hasn’t been provided yet and even the industry findings on this are dated.

The ICO stressed that those who seek to rely on implicit consent should not see it as an easy way out or use the term as a euphemism for “doing nothing”. Implied consent can be used provided a website owner is satisfied that users are able to understand that their actions will result in cookies being set; “Without this understanding, you do not have their informed consent,” the ICO have said. Additionally, website operators will need to be vigilant that when they collect sensitive personal data such as information about individual’s health, the data protection law may require them to obtain explicit consent.

The key point is that when taking action the individual has to have a reasonable understanding that by doing so they are agreeing to cookies being set. The provider must ensure that clear and relevant information is readily available to users explaining what is likely to happen while the user is accessing the site and what choices the user has in terms of controlling what happens. Exactly how or if this will affect advertisers, use of the AdChoices icon or the steps needed to take to comply with the “new” directive is still unclear, but the coming months promise interesting developments.

Why would the TV industry want Twitter ratings?

Scott Thompson's picture

On Monday, Twitter announced a deal with Nielsen to provide an official ‘Twitter TV’ rating for the US. This news came just over a month after Nielsen announced the acquisition of SocialGuide, an analytics company who measure – you guessed it – engagement with TV programmes on Twitter.

The new ratings aren’t expected to launch until later next year, and for the time being at least it looks like they will be US-only. But the creation of an ‘official’ ratings system in the US is bound to lead to an increased interest in something similar in the UK.

Over the course of 2012, we have been looking at the relationship between TV and Twitter (including, amongst others, a project around the Olympics), and trying to get a clearer idea of what can be learnt from social media data about television viewing. From this, we have a few ideas of what a ‘TV Twitter Rating’ might be able to offer that existing social listening/buzz monitoring tools alone can struggle to provide.

New Television Behaviours

With smartphone penetration in the US having passed the 50% mark and one fifth of US households having tablets, Nielsen’s own research shows that typical owners of these these devices are using them while watching television several times a week at least – with around a quarter doing this several times a day. At the Future TV Advertising Forum recently, Dan Biddle (head of TV partnerships at Twitter) revealed that 40% of tweets are about television shows during peak TV hours.

Alongside the growth of social media over the last 5 years, this indicates a significant change in the way that audiences are watching television. While before, TV viewers might have also been reading books or magazines (or maybe even talking to other people in the room), this combination of broadcast programming with 200 million active Twitter users regularly talking about the same thing at the same time represents something different.

Predictability of Social Media

The idea of planning for social media conversations means knowing what people are going to talk about before they start talking about. Obviously, this presents a challenge – not unlike asking someone to predict what the front page headlines will be next Thursday, or what the weather will be like in a couple of weeks time.

But one thing that is predictable and regularly drives online conversation is television. We know that the average Brit is spending about 4 hours a day watching television, mostly in the evening, and we know what programmes are going to be on. So this is an area where, even if we can’t predict exactly what people are going to be saying, we can make a good guess about what it will be about - which presents an opportunity for social media marketing.

Social Engagement Ratings

The most obvious starting point of a ‘TV Twitter rating’ is to identify where TV and Twitter are coming together, and seeing which shows are generating the most buzz. If you are a Twitter user then you will no doubt have a view on this already – you will probably have seen how Saturday night’s XFactor broadcast can quickly dominate your Twitter timeline.

But the benefit of typing these metrics in with TV audience measurement is not just seeing how much ‘buzz’ is there around a particular television programme, but how it relates to the size of the audience. This is where television ratings will come in.

From our own work at SMG this year, we saw that there are the predictable big programmes that create a big buzz (for example, major sporting events and award shows like the MTV Music Awards or Eurovision Song Contest), but when you start to look at the relationship between Twitter volumes and audience size, things get a bit more interesting.

Looking at something like a ‘tweets per rating point’ measurement, you start to see that there is a greater intensity of activity coming from certain shows with smaller audiences – and not always the ones you might expect. For example, last years Take Me Out in the UK saw significantly less activity on Twitter than some of the bigger programmes – but compared to the size of the audience, it had a far greater intensity of social activity per audience member. A full integration of TV and Twitter data will create real opportunities to make the most of smaller but highly engaged audiences like these.

Predicting Social Engagement

So, from the first step of an ‘activity per audience member’ measurement, you can then start to look for the kinds of patterns that you can’t get from looking at buzz volume alone. What genre of programmes are leading to high social engagement? Are there particular days or times when people are more likely to be tweeting about television programming?

Perhaps more interestingly, this will start to uncover the patterns of how different types of audiences are ‘socially engaging’ with programmes. In our analysis earlier this year of a selection of programmes with larger TV audiences, we expected to see that larger younger audiences would be more likely to be tweeting about the programmes they were watching. Interestingly though, a more significant factor turned out to be the proportion of younger to older viewers (under 35s to over 55s)– in other words, fewer older viewers seemed to be a better predictor of tweet volumes than more younger viewers. (An explanation of this might be that the audience composition tells us something about the content of the programming – a younger audience might feel more connected to programming that feels like it is ‘for them’, and therefore more likely to tweet about it.)

Better TV audience understanding

But assuming that a ‘TV Twitter’ metric will be all about Twitter and social media might be an oversight. While Twitter might be relatively small (when compared to usage of Facebook, email, text messages and other social/communications platforms), it is very public – which makes it a useful proxy for evaluating audience behaviours, rather than simply activity within Twitter.

Existing TV ratings measure the size of the audience, typically based on TV viewing meters and/or self-completion diaries. But what they don’t report is on the way that people are watching – the difference between watching one of your favourite programmes and being in the room while someone else watches their favourite programme obviously means quite different viewing behavior.

So while there is an obvious application for the kind of advertising that seeks to start online conversations (or ‘Likes’ or retweets), there is a broader point that this measurement gives us a new dimension in understanding viewer behavior. At one end of the spectrum might be the viewer who is more interested in the online conversations about the programme than the programme itself. At the other end would be the viewer who is utterly glued to the screen – who might want to tweet about the programme before and after, but wont want to interrupt a moment of the viewing experience. Putting social media actions aside for the moment, different types of advertising might be better suited to different types of behaviours.

Or to put it another way, simply assuming a low ‘Twitter TV rating’ means a low value television experience could be a mistake.

Will it come to the UK?

Billions of dollars in the US (and billions of pounds in the UK) change hands based on the accepted currency measurement of television viewing, which makes changes in measurement methodology a challenge at the best of times. In the US, the measurement system is owned and run by Nielsen. In the UK, we have BARB; a JIC (Joint Industry Committee) which represents the interests of both television broadcasters and advertisers, and aims to provide a universally accepted measurement system.

What this means is that while it is relatively straightforward for Nielsen to implement innovative changes to the way its measurement works, for BARB to formally integrate this kind of reporting in the UK would require the agreement of all partners – not just on how it would be measured and reported, but also for funding of the research. So it would probably make little sense for BARB to try to offer something similar in the UK – at least, not on an exclusive basis.

At first glance, this might seem like bad news for innovation in the UK’s television industry, but in this case the opposite is more likely to be the case. While nobody is likely to provide an ‘official’ TV/Twitter metric, anybody with access to BARB’s viewing figures and Twitter data (some of which is made freely available by Twitter through a number of APIs, or alternatively can be bought by their data partners) would be able to build a “Twitter TV ratings” system of their own.

But the seal of approval that SocialGuide’s data will carry next year – from the US TV measurement currency on one hand and Twitter themselves on the other – will set their data apart from competitors. And with Twitter’s data being readily available through 3rd party partners, while TV listings are made similarly public, there are – and will no doubt continue to be - competitors (such as BlueFin Labs, Trendrr and Networked Insights.)

So while this latest announcement might be bad news for competitors in the US, this might well lead to increased interest and open up new opportunities for them in countries like the UK, where TV measurement is owned by the industry rather than the measurement provider.

British Comedy Awards tweets split by two hashtags

Scott Thompson's picture

Last nights British Comedy Awards saw thousands of tweets around the programme as viewers took to Twitter to discuss the live awards show. But was there a missed opportunity for the producers?

During the build-up to the annual awards show, the prominent hashtag being used was the official #BritComAwards, being promoted by the official Twitter account. But shortly after the broadcast began, despite the official hashtag being promoted on screen, it was quickly overtaken by the much more easily guessable #BritishComedyAwards.

BritishComedyAwards.PNG

The split between two hashtags means that people seeing and clicking on the more prominent unofficial hashtag would not see any of the 'official' tweets – and vice versa. So instead of a single conversation around the programme, there would have been two different groups, following two different conversations. As can be seen above, the broader conversation consolidated around the unofficial tag, while the broadcast continued to promote the official tag.

Furthermore, users of the Zeebox application (which saw a significant upgrade this week, adding the ability to change channels on Sky+ and Virgin Media TiVo set-top boxes) would only have seen tweets around the hashtag chosen by Zeebox – which was the unofficial #BritishComedyAwards.

Perhaps most prominently, some of the most popular tweeters around the programme were using the unofficial hashtag;

Including a disappointed Sarah Millican – nominated for Best Female Television Comic (which was won by Jo Brand);

Twitter's own Hashtag best practices stresses the importance that hashtags should be obvious - using the example of XFactor USA promoting the #XFUSA hashtag, while five times as many people used the non-promoted #XFactor hashtag.

One of the benefits of conversations within social media is the amplification that it offers – the more people talking, the more people can become aware of the conversation and join in – leading to greater visibility. And although it is debatable whether social media is effectively driving significant numbers of TV viewers, the benefits for 'second screening' viewers on a platform like Twitter come from a simple, shared experience – rather than a fragmented collections of disparate conversations running across the network.

iPad is now the biggest source of non-PC internet traffic in the UK

Scott Thompson's picture

With Apple's latest reported figures of over 98 million iPads sold by the end of September, and 3 million sold in the 3 days following the launch of the iPad mini last week, Apple have now sold over 100 million iPads in two and a half years. Considering the 80 days it took to sell the first 3 million iPads, it is safe to say that demand is still increasing – with the 3G/4G network enabled models yet to go on sale. With the iPad mini now offering lower cost and greater mobility than the 'full sized' iPad – and 7" tablets from Google and Amazon competing strongly on price, it is hard to see Tablet growth slowing down for some time yet.

Figures from a new report from comScore show that tablets are accounting for a growing amount of internet traffic. In fact, in August 2012, iPads accounted for more internet traffic than either iPhones or Android devices, making them the largest single source of non-PC internet traffic in the UK.

Tablets2012b.png

Apple don't break out their sales figures for different iPad models or sales in different countries, but comScore estimate that in August 2012, there were about 4.7 million iPads in the UK, out of a total of 6.26 million tablets – showing a 155% growth in the UK market in 12 months.

The comparison of just 4.7 million iPads accounting for more internet traffic than around 12 million Android phones might be surprising, but it highlights one of the challenges that we have as researchers in really understanding the behaviours around this new class of devices. While tablet operating systems are essentially the same as smartphones (ie. designed for a single user to have access to email, applications etc.), there is a great deal of shared usage between devices, making it hard to attribute particular usage patterns to individuals within a household. Understanding the nuances of how Tablets are being used differently – both patterns of different uses by different types of people, as well as different types of devices – is a significant and important challenge facing media researchers as the growing market develops.

Obviously, communication is still the primary use of a mobile phone, but with 27m smartphone owners using them as 'connected media' devices, 11m watching mobile video and 7.9m using them to find store locations, mobile media is an increasingly important space.

Tablets2012a.png

Although Google's Android platform accounts for the largest share of devices in the UK with 44% of smartphones, iPhones account for a greater amount of internet usage with 25% of non-PC internet traffic going to Apple phones (compared to 23% to Android phones.)

NRS launches ‘PADD’ – Print and Digital Data: A step towards holistic measurement?

NRS PASS.png

The launch of PADD (Print and Digital Data) has been heralded by the NRS as a major step towards understanding the holistic reach of newspaper and magazine brands. Here, SMG asks what implications these developments may have for traditional print titles, and moreover how it will benefit media planners and advertisers in the long-run.

Methodology

NRS PADD brings together two leading measurement tools: (1) the highly regarded NRS and (2) UKOM - the most robust source for online audience information. These data sets have been fused to breakout combined print-online reach across period intervals of a day / week / month. Whilst fusion techniques are not ideal, they are at present the most accurate way of getting cross-media measurement of this type. Fusion techniques have already proved invaluable in the production of media planning tools such as the IPA Touchpoints survey.

However, there are some limitations, the biggest being the absence of any data for the rapidly growing Tablet/ Mobile market. Unlike Press and Online that have established measurement tools, there are still a number of technical and political issues that prevent there being a consistent, standardized measurement approach for these devices.

Implications…

What does this mean for print media brands?

Many of the industry headlines have focused on those winners that have come out of the first wave of NRS PADD data:

Such stories may make for great PR – but what other implications will this have for print brands? These figures go someway to counteract the negative headlines about declining circulations and show that these brands are actually increasing their overall readership through digital channels. As a result we expect to see increased focus from these titles to improve their online products and try and compete for higher ‘net cross-platform’ reach. One of the likely outcomes will be a greater impetus on sales teams to deliver cross-platform opportunities. These will be sold by (1) adding digital/print to an existing plan in order to increase overall net reach or (2) giving advertisers the opportunity to reach a user across multiple touch points. The key benefit for advertisers is that with an independently verified and consistent measurement methodology we will have a greater understanding of the value of cross-platform solutions. With such a resource available, media owner proposals will need to be backed up with more robust statistical data.

What else does this mean for media planners and advertisers?

As an industry we need to understand cross-media relationships and NRS PADD gives us an additional layer of insight into this. However, it is important for us to remember that these brands have distinct online and offline audiences. Furthermore, unlike traditionally loyal print readers, online audiences have been shown to be far more promiscuous when it comes to consuming editorial. Consequently we also need to have an understanding of how users discover and share content through Search, SEO and Social, and the key to cross-media planning will be through ‘joining-the-dots’ across all these disciplines.

Final Thoughts

Digital was traditionally viewed as a threat to the Press market, when actually it is already evident that both can coexist and even complement each other. What is important to recognize is the different ways in which users engage with print brands. What people want from an online version of a newspaper might not be the same as what they expect from a hard copy (ie. they may read a newspaper for the editorial/ and the website for news).

At SMG, we design Human Experiences by understanding the whole picture; the context, the why, when and where of how users are consuming content. We do not design Experiences at a singular media brand, platform or device level.

The important thing for us is to understand how different media are working alongside other media and their digital counterparts. Research tools like IPA Touchpoints, NRS PADD and BARB's forays into combining online video and television measurement are going to be vital for brands to understand how this develops as consumer behaviour changes.

The Twitterlympics

Scott Thompson's picture

Along with much of the country – if not the world – we have been watching the London 2012 Olympics with one eye on the TV and the other on Twitter, watching the reactions in what has been called the first "Social Media Olympics."

But as well as watching what's going on in our own timelines, in the SMG London Research team we've also been using our own ECHOscreen analysis tool to track the wider mentions and conversations about the Games.

We have been sharing a few of our findings with The Wall, Brand Republic and Ad Week – this is a selection of some of the most interesting things we have seen over the course of the Games so far.

This is a snippet- read the full post.

Researchers beware of an aging population

Steve Smith's picture

On Monday, the Office for National Statistics released its first wave of findings from the 2011 census for England and Wales (Scotland, you will have to wait until later in the year).

The census data shows how rapidly the population of England and Wales is aging. Why is this important for researchers?

Have a look at the age distribution for England Wales:

population.png

The chart shows a long ‘tail’ of people aged 70 and over, so much so that they form 12% of the population. What this does is to skew upwards any averages we want to make when age is included in our analysis. For example, the average age of people in England and Wales is 39.3 years. Yet when we exclude people aged 70 and over we get a lower age of 34.5, which is much more representative of the bulk of the population.

To see how this can affect our research, let’s look at the proportion of internet users in the UK who are likely to discuss online a programme they are watching on TV. Across all internet users, it’s 25%. Yet when we look at age groups, this doesn’t look right.

skew.png

If each of these segments had the same number of people in, the average would be 31%. The reason for this six point difference is that there is a long tail of people aged 55+ in the internet user population. Only 12% of these people discuss programmes online whilst watching TV, but because there are so many of them, they skew the average down by six percentage points.

What is the implication of this? That an aging population means we need to be much more careful when using averages, because they can conceal the behaviour or characteristics of the bulk of the population. When once we could use averages to create simple pictures of the population, often this will no longer be the case.