Category Archives: Pulsar

Does social media drive sales? A research review

As social media research matures, the big question on everyone’s lips is “How can we connect this to other data?” More particularly, how can we connect it to what really matters to our business: sales?

Last week I gave a webinar on exactly this topic, sharing the results of our research study mapping social media buzz for 3 music events against ticket sales. You can find that presentation here on Slideshare, and download the full recording from this link.

In this blog, I want to put our work in context and map the wider industry thinking on this issue by summarising 5 other key social-to-sales research studies. There are a number of different ways that social media activity and sales can be compared, and I hope it’s useful to provide a summary and outline some of the key differences:

1. Buzzkill: Coca-Cola Finds No Sales Lift from Online Chatter
March 2013

Presenting at the Advertising Research Foundation’s Re:Think 2013 conference, Coke’s Eric Schmidt reported that “We didn’t see any statistically significant relationship between our buzz and our short-term sales.” (AdAge.com)

Note that Coke are still big believers in social media’s effectiveness as part of an integrated campaign: said Wendy Clark, “It’s the combination of owned, earned, shared and paid media connections – with social playing a crucial role at the heart of our activations – that creates marketplace impact, consumer engagement, brand love and brand value.”

IEDGE-cocacola-social-media-strategy-2

[image created by Coca Cola, via iEdge.eu]

But this study is evidence that overall social media buzz – the number of brand mentions – doesn’t necessarily correlate with sales. What might?

2. McKinsey Finds Social Buzz Can Affect Sales — Negatively, Anyway
June 2013

“The consulting firm initially couldn’t find any connection between social-media buzz and sales, either when looking at overall data changes or even by applying an algorithm to assign sentiment to the buzz. But McKinsey found the relationship between negative buzz and a decline in sales when it “hand tabulated” sentiment in social-media comments.” (AdAge.com)

This negative sentiment hurt signups by 8%, “offsetting their entire TV spend,” McKinsey principal Jonathan Gordan said at the Advertising Research Foundations Audience Measurement 8.0 conference in New York. Why? Because the negativity was primarily driven by complaints about the sign-up process and call-centre workers at the telecom provider. 

This shows how a relationship between social and sales can become visible when you drill down into more specific aspects of social data. Brand volumes didn’t impact telecoms sign-ups - but complaints about the sign-up process did.

3. Eventbrite: Facebook Drives More Ticket Sales Than Twitter And LinkedIn Across US And UK
April 2012

A different metric here – not social media volumes (aka the number of messages mentioning a brand), but the number of shares:

“The company says that Facebook is the king of all social networks when it comes to ticket sales. In the UK, if a person shares an event on Facebook, it generates an average of £2.25 ($3.60) in additional gross ticket sales. A share on Twitter, meanwhile, drives an average of £1.80 ($2.90), and an event shared on LinkedIn generates an average of £1.24 ($1.99) in additional event revenue.” (TechCrunch.com)

Eventbrite’s reason for why Facebook is bringing in more sales is good sense: “The connections we have on Facebook most closely represent the people we actually know and spend time with offline,” its researchers write.

Eventbrite facebook

4. Why Twitter Buzz ≠ Movie Ticket Sales
December 2012

“140 Proof looked at 25 major Hollywood films released in 2012, compiling data on each movie’s social media activity (mentions and hashtags) two weeks before, and two weeks after the release. It found that the number of overall Twitter mentions is a poor predictor of box office sales (unlike tweet volume and TV ratings). What did correlate to box office success was the number of tweets from influential tastemakers” (Readwrite.com)

Again, the relationship between social and sales doesn’t show up when you just look at raw volumes – but it is still there. Pulsar’s range of influencer metrics such as visibility and Klout filters can enable deeper analysis of how influence relates to sales, going beyond “number of tweets from tastemakers” to understanding how influence levels and sales-power scales.

5. Vision Critical: “From Social To Sale”

A totally different methodology – they’re not mapping activity in social media, or measuring clickthroughs from social channels, but rather surveying 5,657 people asking them to report whether they’d ever bought anything they’d seen on Twitter, Facebook or Pinterest.

This is worth doing because, “68% of Facebook users  are “lurkers” who post only rarely, so the influence of  social on their purchasing will not be visible from social  media analytics alone.” It’s a good reminder to think about social media users as much as an audience as content-creators - and that the path to purchase is more complex than old-fashioned sales funnel models, or simple ‘last-click’ attribution.

social-media-selling-for-un-sexy-brands

[image via Digital Information World]

Five studies, two key take-aways for understanding how social and sales connect:

1. Think about your user journey. How do people make a decision to buy your product – who or what might influence them? How  do people consume your product – is it particularly social, like something you would want to do with friends, or something worth boasting and sharing on Twitter and Facebook? Is it something people can purchase quickly online, or a more considered purchase?

2. Think about what aspect of social media to measure. It may not be simple volumes of brand messages that correlate with sales, but something more specific – such as influence, sentiment, or specific topics. Or perhaps it’s not messages at all but behaviours such as sharing. Most of all, remember to measure all of social media, not just owned channel activity: you’re looking for consumer behaviour, not just reactions to your own!

As these studies from a diverse range of brands show, social media does often connect to sales – not all of the time, but often with some statistical smarts & a deep knowledge of social, a link can be found.

Note that we’re saying “connect”, not “cause” - correlation can be assessed using relatively simple stats such as R-squared tests, but unpicking causation (Was it social media activity that made someone buy, or a price promotion, or TV advertising?) is a challenge for regression analysis and a bigger topic than we can discuss here.

And sometimes the relationship between social and sales can go both ways – not only “I buy a concert ticket because I saw the news on Twitter”, but also “I bought a concert ticket for my favourite band and I’m so excited, I want to tell everybody!” Perhaps brands can even hope for a virtuous circle of social driving sales, which drives further social activity, which drives even more sales… Fingers crossed!

Found this interesting? Read our social to sales study: get the presentation here on Slideshare, or download the webinar recording from this link. Thanks!

FACE’s ALS Ice Bucket Challenge

This week the inevitable happened. FACE became the next in a long line that has been nominated for ALS’ Ice Bucket Challenge. Yes, the social media phenomenon reached us in our London Office; we have Brightsource to thank for that!

ALS is a disease that many had never heard of or understood before the Ice Bucket Challenge; it has brought a huge amount of awareness to the charity and many similar non-profits who are trying to fight the cause.

The challenge represents more than just throwing iced cold water over your head. It shows us the incredible power of social media, and how quickly content can go viral.

We would like to thank Brightsource for the nomination. Our nominations go to Extendi, Sennep and Sensum. You have 24 hours – considering it’s a Friday we’ll give you until Monday.

Please donate what you can to the following links:

ALS in the US here:
https://secure2.convio.net/alsa/site/…

Or to the UK’s Motor Neurone Disease Association here:
http://www.mndassociation.org/news-and-events/Latest+News/the-mnd-ice-bucket-challenge

Find out more about ALS (known more commonly as Motor Neurone Disease in the UK or Lou Gehrig’s disease):

http://www.alsa.org/about-als/what-is…

The Samsung vs. Apple court case shows the value of social media research

An excellent case study demonstrating the value of social media research has just emerged from an unlikely source: the Apple vs. Samsung patent dispute.

Apple-Samsung-Trial

Documents shared as part of the court case reveal some fascinating information about how the two companies were thinking about social data in 2013.

It shouldn’t still bear saying in 2014, but the messages seems slow in getting though: social media data isn’t just about “looking back” at campaigns or the last quarter’s KPIs. Samsung recognised the power of social data for “thinking forward”, for understanding customer needs strategically to feed into product innovation and early-stage comms planning. Here at FACE, we think this is an incredibly valuable and under-used use-case.

Here’s how it works:

1. Samsung used social data strategically: to attack Apple

From Neal Ungerleider in FastCo: Networked Insights Reveals How Samsung Used Social Media to Hack the iPhone:

“Samsung took on a company with the arguably most successful consumer product ever created,” Networked Insights CEO Dan Neely told Fast Company. “Samsung asked us how to use analytics to attack Apple.”

[...] Using aggregated online posts and machine learning techniques, Samsung found several specific weak spots where they could outperform Apple. Customers specifically complained about the iPhone’s comparatively poor battery life, the inefficiencies of Apple Maps, how small the screen was, unhappiness with the Lightning cable, the lack of customization, Siri, and the iPhone’s fragility. Samsung felt that it could compete with Apple on most of these points–and, importantly, that they hard data to back up these consumer preferences.

When working with Networked Insights, a big part of Samsung’s strategy was to vacuum up any information on the iPhone 5 that was posted to social media. This meant using the dashboard they licensed to obtain every iPhone-related post on Tumblr, Twitter, Disqus (a popular commenting platform), WordPress, and YouTube, as well as new hits on Google. This information was then classified, as Neely put it, “15,000 different ways.” A big part of the problem for Samsung and others, Neely said, was the difference in extracting relevant information when they needed it versus finding erroneous information on other aspects of individual customers that were irrelevant to the task at hand. That meant a lot of data processing and fine-tuned analytics.

Importantly, Samsung used the dashboard to find what people were posting online about the iPhone–rather than just looking for posts about Samsung’s own products. They then identified specific complaints about the iPhone where their own products outperformed Apple’s products, and tweaked marketing campaigns to emphasize these Samsung strong points.

So: social media research isn’t just about tracking your own brand activity.

It’s incredibly powerful when you search for unmet needs and pain points – what are the gaps where consumer desires aren’t being fulfilled? Do this across a category (e.g. smartphones) or a competitive set (Apple, Samsung, HTC, Sony Xperia, Nexus, Motorola) to identify the “whitespace” opportunities that  aren’t currently being met.

As such, social media has just as much of a forward-looking role to play in innovation and NPD as it does “looking back” at campaign performance and the past quarter’s KPIs. Use it to shape campaigns and communications, not just to measure their impact.

2. Apple thought it was “nuts” to pay for social media monitoring tools. Their loss

Business Insider’s Jay Yarrow spotted something else interesting in the court documents:

Jay Yarow quote

Apple famously don’t do research, you say? No, Apple do do research – but they don’t necessarily do it well, as Tom Ewing recently illustrated.

You’d see the occasional interesting message if you just look at mentions of “iPhone 5″ through Twitter search… But also an awful lot of noise, at a million mentions per day kind of scale. It’d only be through luck that you might stumble across a message that’d spark any strategic consideration.

You want to understand the relative dissatisfaction with battery life, screen size, and poor signal reception? You need a social data research platform. Social media monitoring tools make this data analysable as a whole  in a way that free online tools simply can’t. For example our platform Pulsar (pulsarplatform.com) collects over 1MB metadata around each tweet, making big datasets like this powerfully segmentable by sentiment, channel, hour, influence level, profile bio and other demographics – allowing for a really fine-grained analysis of not just what people are saying, but who and why.

Technology and data augmentations enable the unmet needs to be identified, quantified and ranked. Use a tree graph to visualise the most common words and phrases that follow “I love…” and “I hate…”. Use semantic analysis to aggregate topics, and compare the top topics across the range of positive, negative and neutral sentiment scores. Start coding tweets into clusters, and use machine learning to extend this across the whole dataset.

Through structured analysis, the depth of insight that can be gained from social data is vast – Samsung realised this, Apple didn’t.

3. What we’ve done

This story was met by us at FACE with a nod of recognition – we have been using social data beyond reputation management for many years now.

Here’s a couple of examples of previous work:

i) Mapping the 4G mobile launch

EE Launch Event..Mandatory Credit Tom Oldham/Tom Dymond

Like Network Insights with Samsung, we also dug into what people were saying around 4G to identify complaints and pain points. What topics were driving discussion – signal, pricing, contracts/tariffs, or the iPhone? For each we identified the specific customer pain points our client needed to address in both comms and their product offer.

“WHAT EVEN IS 4G THOUGH I DON’T UNDERSTAND” – tweet, Sept 2013

But it turned out the biggest unmet need was understanding – a high share of discussion came from people expressing their total bewilderment at the new, high-speed mobile spectrum band.  We used social data to identify and categorise people’s questions, helping our client (a mobile operator) recognise and simplify the messages they needed to communicate to help people understand the new proposition.

ii) “Designing Relevance” for Nokia

Here at FACE we’ve been using social data for strategic insight for years. Back in 2010, Francesco D’Orazio and Esther Garland presented at ESOMAR alongside Nokia’s Tom Crawford on how social media research can be used alongside co-creation to produce a better innovation process:

Innovation should not be so much about ‘creation’, but more about ‘emergence’. Defining the boundaries of possible futures means creating the conditions for fostering the emergence of ideas that are already taking shape in the social space, but have not filtered up to the top or are not formed enough to bubble up yet. In a connected real-time ecosystem where the consumer can be as creative as the designer, the new model of innovation should be listening, reducing complexity, decoding the signal from the noise, collaborating with consumers and only then defining the boundaries of possible futures.

The project started with a “download” from social media to gather the widest possible range of themes and scenarios for this project:

The project kicked off with a two week Social Media Monitoring and Trends Analysis programme using netnography, semantic and network analysis across forums, social networks, blogs, news sites, microblogs, video and photo sharing sites from the United States. Using Face’s social media analysis platform Pulsar we tracked more than 100, 000 ‘sources’ (where Twitter counts as one source) and harvested almost 1.5 million items of content. These were analysed to gather insight into how key consumer segments in North America talk about smart-phones and which key themes, topics and angles were most resonant with them. 

Analysing conversations amongst users talking to each other rather than responding to researchers yielded a huge amount of richness. Furthermore, this helped develop clear learnings on language, tone of voice and attitudes to the brand and the category. It allowed for a different kind of research landscape, one which subverts the traditional question and answer format and replaces it with something far more natural and intuitive. By working in a more natural communication mode we also ended up expanding our research agenda to challenges we didn’t even know existed or that we wanted to investigate.

For the full story, read the full whitepaper up on Slideshare here, or check out the presentation:

Or get in touch if you’d like to talk forward-looking social research – I’m at Jessica@Facegroup.com

A Social World of Whisky Part 1: Big Drinkers, Small Talkers?

Winston“The water was not fit to drink. To make it palatable, we had to add whisky. By diligent effort, I learned to like it.” — Winston Churchill

Amongst all spirits, whisky holds a very particular place. From teenagers to world leaders, from whisky and soda to $460,000 bottle – a 1946 Macallan in a Lalique decanter was auctioned at this price in 2010, whisky proves being more than simply a category of alcohol, but a potent landmark of social and economic belonging.

The whisky market is diverse, but can be divided in two main categories: Scotch (i.e. distilled in Scotland and matured for a minimum of three years in oak casks) and non-Scotch whiskies. Both have experienced continuous growth, with some particularly dynamic markets in the last couple of years in emerging countries, especially India and China. Scotch whiskies represent around 85% of Scottish food and drink exports and nearly a quarter of the British total, according to the Scottish Whisky Association.

Such a success in the context of our digital era questions us about the way this phenomenon echoes on social media, how consumers take part into the whisky related social discussion around the world, and what insight can social media bring for the whisky industry.

This blog is the first of a series about the whisky industry that will demonstrate several ways we, as social media researchers, can investigate a broad social dataset and make sense of it thanks to the use of different research techniques and integration of other data sources like sales data.

In this first blog, we’ll have a look at the big picture: identifying how whisky-related social discussion is naturally featuring, and how whisky in social media differs from actual consumer behaviour.

Simply looking at raw social data volumes can be misleading since it doesn’t take in consideration the actual population size of each country, and the proportion of its population using social media. In order to balance the countries’ weight and get a better idea of the countries where whisky discussion is getting more traction, we weighted each country to its population:

Average whisky related social posts per 1000 capita 

Screen Shot 2014-05-11 at 19.30.42

Content posted between August 15th to August 31st,
including “whiskey”, “whisky”, “whiskeys” or “whiskies”.
Collected  by Pulsar, our social media monitoring tool.

What patterns do we see, and why?

Whisk(e)y as a share of British and Irish identity - Ireland is the country eliciting the most social discussion per capita, demonstrating the vitality and weight of the whiskey topic in this country. The second place of United Kingdom in both overall social volumes and discussion per capita, also highlights the importance of the whisky industry and the passion towards this spirit, as home of Scotch whisky – at least for the moment!

The home of Bourbon trails behind Ireland and UK – The United States remains a major country for whisky discussion, especially considering the impressive overall amount of content originating from this territory. But the volumes per capita put this domination in perspective, suggesting that Irish and British are more passionate about whisky.

Whisky proves a healthy topic of discussion in South America and Oceania - A few less populated countries, especially in South America and Oceania, elicit a comparatively high level of whisky conversation, proving their attachment to this beverage, namely Uruguay (6th), New Zealand (7th), Venezuela (8th), and Australia (9th).

Now we’ve drawn a map of social media whisky discussion, getting the most of this landscape implies connecting it to the reality of whisky consumption.

To do so, we are using Euromonitor whisky consumption country data per capita.

Annual whisky consumption/capita (in liters)

Screen Shot 2014-05-11 at 19.35.25

Source : Euromonitor, Worldbank

This data offers us a ranking of the biggest whisky drinkers that we can compare to the ranking of the biggest whisky “talkers”, giving us a new perspective over the whisky market opportunities in terms of social strategy.

Whisky Drinkers versus Whisky Talkers

Screen Shot 2014-05-11 at 19.41.43

* Searches didn’t include words in Hindi, Japanese or Chinese
alphabets, 
so these ranks are likely to be higher in reality

A correlation between whisky consumption and whisky social discussion

Out of the top 10 countries with the higher consumption of whisky per capita, 7 also feature in the top 10 countries with the more whisky related social discussion per capita. However the ranking is quite different…

Less social verbose, more drinking?

Two groups of countries emerge:

On the one hand, countries that feature higher in the consumption ranking than in the social discussion ranking. Including Uruguay, Australia, India or South Africa, this group bears a high potential for social marketers: healthy markets with a lack of social media structure, thus an opportunity for whisky brands to own the category with targeted efforts. The emblem of this group is France, that ranks at the first position for whisky consumption, but only 19th for whisky related social discussion. Some could think that French people drink too much whisky to be able to post their experience on social media. Being well placed to answer this exaggerated statement, I tend to consider that the reason is more likely to lie within cultural and media habits, both in terms of whisky consumption and social media use. This will be the topic of a future blog.

On the other hand, countries that feature higher in the social discussion ranking than in the consumption ranking. And this comprises almost all main whisky producers, namely United Kingdom and Ireland: in addition to a healthy discussion around the whisky consumption itself, distilleries, associations, news websites and organisations contributes to the fact that whisky also feature as a business and economy related topic.
This first glance at the whisky social landscape opens quite a few doors that we will enter in the next couple of months, and that will lead to how we dig more qualitatively into social discussion:

  • Scotch/Bourbon fracture: how is it tangible on social media, and which is winning the social battle?
  • Booze vs Nectar: whisky’s duality
  • A whisky connoisseur social audience
  • The French enigma: understand the specificities of the French social whisky environment
  • Whisky brands: what is their place within the social conversation, and which ones are stealing the show

Stay tuned!

*

anthony

Anthony Fradet is a social media research manager in FACE’s London office. Since gaining a Masters degree from Sorbonne University, Anthony has spent 5 years working for French market research companies, with quantitative, qualitative and social media focus. Before joining Face in 2013, he was responsible for a unique partnership between a top 5  ’traditional’ market research agency (CSA) and a social media research agency (linkfluence). Get in touch with Anthony via LinkedIn or Twitter.

Pulsar update: Visibility 2.0

Today we are introducing a new updated version of the Visibility algorithm that’s powering the Pulsar platform: Visibility 2.0.

The main reason why Pulsar is called Pulsar is that the whole platform is built around the idea of making it easier for anyone to sift through vast amounts of social data by making “important” social media content more “visible”.

One of the key ways Pulsar does this is through its proprietary Visibility algorithm. The algorithm defines “importance” as the ability of a piece of content to reach a larger then average audience and engage a larger than average crowd. The algorithm weights every content on the platform and applies a Visibility score to each post which is then available amongst the metadata used to index and filter the data.
Slide7

Since we launched Pulsar the Visibility Algorithm has been one of the pillars of the platform allowing you to slice any data view (e.g. trends, influencers, topics) by Volume of data or by the Visibility of the content analysed. Below a series of comparative screens that show how different the same social data looks like when analysed by Volumes vs Visibility:

Posts per Day VS Visibility per Day

Slide1

Sentiment Volume per Day VS Sentiment Visibility per Day

Slide2
Top Posts by Volume vs Top Posts by Visibility
Slide6
But the web is an ever-changing ecosystem: new channels are born, new behaviours are introduced, old behaviours evolve to a new scale or disappear and new ways of measuring them are introduced on a weekly basis. In an effort to keep up with the evolution of the web and continue to deliver effective measures of reach and engagement, over the last three months we have been working hard updating the Visibility algorithm.

The new algorithm takes into account:

  • New sources of engagement data, which are now factored in the calculation of reach;
  • New sources of online viewership data which are now factored in the calculation of reach;
  • New sharing and engagement metrics introduced by the new channels we have integrated, such as Tumblr;
  • Raising levels of engagement across all channels resulting in a need for new engagement and reach benchmarks;
  • New behaviours introduced by new channels like Tumblr, where for example the “weight” of a reaction (a re-blog) is completely different from the weight of a reaction on Twitter or Facebook.

Overall, the new algorithm introduces three key improvements:

  1. More accurate audience size estimates for all channels, particularly for News, Blogs, Forums and Review sites;
  2. More accurate engagement figures across all channels;
  3. A more balanced cross-channel view of reach, to enable effective comparisons between the reach of top down and bottom up media (eg. news vs. tweets).

The new visibility weighting applies from April 10 onwards. However, should you want to re-analyse historical data you can extend the reach of the algorithm to historical data from the Data Management interface in the Results View.

We think the new Visibility algorithm is going to help you run better analysis and make more effective decisions and we look forward to hearing your feedback as you start seeing the new data coming through on the Pulsar platform.

If you are not yet using Pulsar and want to know more about Visibility and Pulsar get in touch here.