Do you know of any 2nd or 3rd generation social media listening platforms?

example of displaying insightsMarket research companies do not really have a reputation of being a very innovative bunch; for years there has been no revolutionary innovation in market research tools.

Social media listening is a discipline that, as we wrote many times before, was elusive for market research. Both the corporate and agency researchers did not see the value; they did not trust it, it was not “representative”…yes, they used the 'R' word…ooouuuuuuuuuhhhhhhhhh. The first generation of social media monitoring tools (which are still in full use today) are not deemed useful for market research by the corporate researchers; rightly so if you ask me. Their sentiment accuracy is less than 60% and usually only capable of dealing with a specific language. There is no capability of drilling down into topics and sub-topics, when they provide sentiment it is only at the brand or search term level, and the percentage of noise (irrelevance to the topic) that a user search query returns is 80-90%.

I had not realised that our own listening247 is a 2nd generation social media listening tool until Lenny Murphy (@lennyism), Editor-in-Chief of the GreenBook Research Industry Trends Report and blog,  called it that during a phone conversation a couple of weeks ago. I guess what 2nd generation means is a platform that was specifically developed for consumer insights- one that addresses all the shortcomings of 1st generation tools that are mentioned above. Social media research is now a very concrete discipline with new tools such as listening247 and online community tools (such as communities247) that complement it. We believe that the biggest shortcoming listening247 addresses is multilingual sentiment accuracy - the result of almost 3 years of hard work and relentless focus of the DigitalMR R&D team is being able to consistently reach over 85% sentiment accuracy in any language.

I was very pleased to hear Lenny go on describing what he thought 3rd generation tools will be like; he said they will link sentiment to customer behaviour and customer profiles, there will be more focus on analysing images, voice, and video for sentiment and more granular emotions. He also spoke about using text analytics to deal with sources of unstructured text other than social media e.g. email databases, instant messaging, call centre conversations etc. In case you are wondering why I was pleased, you can reach out and ask me on @DigitalMR_CEO.

Some of these new market research methods will disrupt traditional market research as we know it even further. The do-it-yourself aspect of these new platforms will help democratise the space and allow current non-users to convert to online market research, mainly because these platforms will be affordable, efficient and effective.

End clients of market research have already started realising that 1st generation social media monitoring tools have very low sentiment accuracy, and even if they are captive to these tools, they still ask DigitalMR to score their posts for sentiment. In most cases, the sentiment accuracy difference between listening247 and the other tool is over 30 percentage points. If you would like to know how we define and measure accuracy please ping me on Twitter and I will be more than happy to provide definitions and examples.

At DigitalMR we have a couple of other rabbits in the hat, not sure if they will help us qualify as a 4th generation social media listening tool, but we are certainly happy that we are currently considered 2nd generation going on 3rd :) since the majority of the current players are still gen 1.

Communities plenty for 7...or 8: The DigitalMR Christmas community

communityDigitalMR prides itself on its multilingual social media listening platform - listening247. Funnily enough, the multilingual aspect is also an integral part of the DigitalMR team. We are a multi-ethnic company, either from different countries or with different backgrounds, from machine learning scientists to psychologists, researchers and software engineers, you couldn't put together a more diverse bunch if you tried; well recently we discovered that our musical interests are very different as well – which comes as no surprise.

During our Christmas party in our office in London, our CEO Michalis asked whether someone would like to go to an opera concert with him (to hear the British Tenor Alfie Boe) as he had a spare ticket, to which everyone responded with something along the lines of “ehh, hmm, umm, no thank you”.

Taken by surprise, he responded “what, no one wants to go and listen to Alfie sing?!' and then attempted to woo us by interrupting the Christmas Carols playlist to play a song by the tenor he was going to see (at the O2 arena). Although we all admitted that the music sounded fine (..ish), classical songs were just something that none of us would like to endure. This discussion prompted the idea for everyone to play their favourite song/music, so that we could all get a better understanding of what the others listen to; little did we know, we were about to start our own market research community, about music.

We transitioned from Death Metal, to Reggae, to House/Electronic, Opera, Funk Rock, Croatian Pop, Chinese Folklore, Greek Rock; let's just hope the office next door wasn't listening in.

Although our music was very diverse, we all listened to a significant amount of each other's music and shared our thoughts; despite the fact that we didn't want to admit it to each other, we had a suspicion most were dreading having to listen to one another's music.

teamwork makes the dream workSurprisingly, the whole “activity” was not unpleasant, each person explained what their song of choice was about and what it meant to them and in a way, it brought us closer together as a team.

After that, we all worked together as a (co-creation) community and decorated a fantastic looking tree, if we may say so ourselves. Just imagine all these people in the picture above, working around one tree at once, you would think that chaos would ensue, but it didn’t, each one of us worked with the other to build this tree. (Finishing touches like a red Ethernet cable garland by Chris the Digital Systems Director are what make our tree unique.)

This dynamic is not dissimilar to what takes place in online communities; not only does it not matter how diverse an online community is, as long as they are gathered for one common interest it makes it even better - in this case, it was a Christmas party that turned into music exploration. Members of online communities are more engaged and motivated when they are united by a common purpose and when their size is less than 300 members; connecting as humans is much more powerful than connecting for online market research alone.

Happy holidays from the most diverse market research company we know!

Beautiful tree with ethernet cable garland

PS. “I find the opera quite boring to watch” – Alfie Boe : )

The other 5 reasons why every organisation should build a community online

An online communityIn our previous blog post we mentioned the top 5 reasons why every organisation should build an online community. There are of course more than 5 reasons, so in this blog post we will describe the other 5 , and who knows, maybe we can come up with another 5 in the future :).

Here are the other 5 reasons why every organisation should build and operate private online communities with its stakeholders:

1. Open a window into your customers’ lives

An online community of customers can make the necessary marketing-related information available on tap. It is now possible for all levels of your organisation, not just the marketers, to connect with customers and get first-hand information from them – see them on video and hear them express their thoughts.

2. Increase the number of marketing resources/employees without having them on the payroll
Imagine having an online community of 200-300 customers who can participate in any marketing decision you will ever make. Instead of sitting in a meeting room brainstorming with a handful of colleagues in the marketing department, you can now involve another 300 people in your decision-making process without adding headcount on your payroll.

3. Impress your Board/CEO
Board Directors and CEOs, especially in B2C, love to see and hear a consumer talk about their needs and their products and brands. They are many degrees away from talking to consumers on the street, so bringing the consumers into their boardroom elevates their understanding of the ultimate client and helps them make more informed decisions.

4. Reduce your market research cost
Especially when the target customer is difficult to recruit due to low incidence; owning online communities of customers and being able to invite them to participate in research projects can provide a dramatic saving. For any incidence rate of customers below 10%, a substantial saving can be achieved in the case of needing sample for online surveys and online focus groups.

5. Increase the speed of accessing insights (reporting)
Having a few hundreds of customers on private online communities first of all eliminates the need to find them; that alone takes days if not weeks out of a traditional research project. On co-creation communities, where customers can participate in research as many times as we want them to, not only are we not worried about expert respondents but on the contrary, we want them to become really good marketing employees honing in on their creativity and other skills. Frequent participation and especially designed data collection tools (e.g. short polls) can get surveying time down to 24 hours with a real time observation of the progress. Also, employing the advanced text analytics methods used in listening247 such as machine learning, unstructured discussions on the community wall or a bulletin board can be analysed in real time.

I hope the 5 other reasons make as much sense to you as the 5 main reasons described in the previous blog post. We are very keen to hear your views on other good reasons why an organisation should make it a priority to engage with customers on bespoke online communities.

5 Reasons why every organisation should build online communities

An online communityThis is not just a catchy headline, it is a strong statement, that every organisation should have its own online community

Finding out officially that the online community concept has not yet been embraced or considered by everyone, at least in developed markets, is a bit surprising.  According to the latest Greenbook Research Industry Trends (GRIT) report for Autumn 2014, 56% of all respondents said they are already using online communities. Another 26% stated that a community for their organisation is under consideration, 14% said they’ve had no interest to date or that they are not sure, and  4% claim that they will never use one.

DigitalMR has been developing its platforms for social media research since early 2011; one of these platforms is communities247, with version 3.0 currently in use and V4.0 scheduled to be released in production mode early next year.

Here are 5 main reasons why literally every organisation should build and operate private online communities with its stakeholders:

  1. Agile Engagement with stakeholders is hot!
    ‘Agile’, ‘lean’, and ‘energetic’ - these are image attributes that you definitely want your brand associated with. Being able to open a window into your customers’ lives and inviting them to join the inner circle of your marketing department is extremely powerful. This idea does not have to be limited to customers but can also be used to engage with your employees, shareholders, opinion leaders, journalists, and other stakeholders.

  2. New online collaboration tools open up new possibilities without breaking the bank
    A few years ago, if an FMCG company wanted to conduct ethnography they had to send a camera team to the households of their consumers in order to observe how they use their products. Nowadays, the video & photo diaries tool enables consumers to easily upload video clips or images from their smartphone, tablet or computer. The benefit of online ethnography (or ‘netnography’) is not only financial; the quality and quantity of information gathered has dramatically improved at the same time. This is due to the fact that consumers can upload multiple video clips during a period of time, whereas previously we could only afford to send the camera team to their homes once. Also, the recording is more realistic since  nobody is intruding in their home to record the video clip.

  3. For co-creation and unique insights otherwise not attainable
    Co-creation essentially means, that a company is working with an extended marketing arm by reaching hundreds of customers (or other stakeholders) who operate as motivated employees without having to be on the payroll. The most valuable element that a co-creation community can help produce is digital content to be used for inbound marketing. Connecting the dots by using all possible research activity tools that an online community carries increases the odds  of producing unique insights.

  4. To build a brand ambassador programme
    Amplified customer advocacy or systematised word of mouth are only possible because online communities exist. The co-created content can be shared by the same people who created it as well as other product category influencers on social media. A positive comment about a brand is more believable coming from a “consumer like me” rather than in a TV commercial; amplified customer advocacy is a new mass medium.

  5. More than half of your competitors are already doing it, they must know something.
    We are well into the technology adoption bell curve. It is no longer only the early adopters that are using communities, not even the early minority; we are looking in late majority territory and you are at risk to be labelled a laggard if you are among the 44% who do not have an online community for insights, co-creation and customer advocacy yet. It will soon be seriously uncool to not have your own online community.

To be honest I could’ve easily called this blog post ‘10 reasons why every organisation should build  an online community’;  I will save some reasons for the next blog post. If you are among the 56% of current online community users, please do share your experience. We know there is a steep learning curve and it is not always all rosy;  if one is not careful there can be many pitfalls, and if you know of any or if you have any best practices to share, we are all ears. Stay tuned; more to come on online communities soon.

Is there anything left unsaid about social media research and marketing?: Part 3

Sentiment and Semantic analysis

Part 3: Sentiment And Semantic Analysis

It took a bit longer than anticipated to write Part 3 of a series of posts about the content proliferation around social media research and social media marketing. In the previous two parts, we talked about Enterprise Feedback Management (December 2013) and Short -event-driven- Intercept Surveys (February 2014). This post is about sentiment and semantic analysis: two interrelated terms in the “race” to reach the highest sentiment accuracy that a social media monitoring tool can achieve. From where we sit, this seems to be a race that DigitalMR is running on its own, competing against its best score.

The best academic institution in this field, Stanford University, announced a few months ago that they had reached 80% sentiment accuracy; they since elevated it to 85% but this has only been achieved in the English language, based on comments for one vertical, namely movies -a rather straight-forward case of: “I liked the movie” or “I did not like it and here is why…”. Not to say that there will not be people sitting on the fence with their opinion about a movie, but even neutral comments in this case, will have less ambiguity than other product categories or subjects. The DigitalMR team of data scientists has been consistently achieving over 85% sentiment accuracy in multiple languages and multiple product categories since September 2013; this is when a few brilliant scientists (engineers and psychologists mainly) cracked the code of multilingual sentiment accuracy!

Let’s dive into sentiment and semantics in order to have a closer look on why these two types of analysis are important and useful for next-generation market research.

Sentiment Analysis

The sentiment accuracy from most automated social media monitoring tools (we know of about 300 of them) is lower than 60%. This means that if you take 100 posts that are supposed to be positive about a brand, only 60 of them will actually be positive; the rest will be neutral, negative or irrelevant. This is almost like the flip of a coin, so why do companies subscribe to SaaS tools with such unacceptable data quality? Does anyone know? The caveat around sentiment accuracy is that the maximum achievable accuracy using an automated method is not 100% but rather 90% or even less. This is so, because when humans are asked to annotate sentiment to a number of comments, they will not agree at least 1 in 10 times. DigitalMR has achieved 91% in the German language but the accuracy was established by 3 specific DigitalMR curators. If we were to have 3 different people curate the comments we may come up with a different accuracy; sarcasm -and in more general ambiguity- is the main reason for this disagreement. Some studies (such as the one mentioned in the paper Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews) of large numbers of tweets, have shown that less than 5% of the total number of tweets reviewed were sarcastic. The question is: does it make sense to solve the problem of sarcasm in machine learning-based sentiment analysis? We think it does and we find it exciting that no-one else has solved it yet.

Automated sentiment analysis allows us to create structure around large amounts of unstructured data without having to read each document or post one by one. We can analyse sentiment by: brand, topic, sub-topic, attribute, topic within brands and so on; this is when social analytics becomes a very useful source of insights for brand performance. The WWW is the largest focus group in the world and it is always on. We just need a good way to turn qualitative information into robust contextualised quantitative information.

Semantic Analysis

Some describe semantic analysis as “keyword analysis” which could also be referred to as “topic analysis”, and as described in the previous paragraph, we can even drill down to report on sub-topics and attributes.

Semantics is the study of meaning and understanding language. As researchers we need to provide context that goes along with the sentiment because without the right context the intended meaning can easily be misunderstood. Ambiguity makes this type of analytics difficult, for example, when we say “apple”, do we mean the brand or the fruit? When we say “mine”, do we mean the possessive proposition, the explosive device, or the place from which we extract useful raw materials?

Semantic analysis can help:

  • extract relevant and useful information from large bodies of unstructured data i.e. text.
  • find an answer to a question without having to ask anyone!
  • discover the meaning of colloquial speech in online posts and
  • uncover specific meanings to words used in foreign languages mixed with our own

What does high accuracy sentiment and semantic analysis of social media listening posts mean for market research? It means that a 50 billion US$ industry can finally divert some of the spending- from asking questions to a sample, using long and boring questionnaires- to listening to unsolicited opinions of the whole universe (census data) of their product category’s users.

This is big data analytics at its best and once there is confidence that sentiment and semantics are accurate, the sky is the limit for social analytics. Think about detection and scoring of specific emotions and not just varying degrees of sentiment; think, automated relevance ranking of posts in order to allocate them in vertical reports correctly; think, rating purchase intent and thus identifying hot leads. After all, accuracy was the only reason why Google beat Yahoo and became the most used search engine in the world.