Panel providers, unite – the speech at the ASC

On the 9th of November, the ASC invited some panel providers to attend a discussion on panel harmonisation. The discussion was orchestrated by Tim Macer.

Here was my speech – the written version at least as I may have ad-libbed a few unscripted things.

ASCPanel

Market Research is changing. You have heard it a million times – not in the way that Ray Pointer announced. There will be more surveys in 10 years than ever. That’s the good news. The bad news is that most of them won’t be run by MR institutes. The goose with the golden eggs is dead – client now run their own surveys which means MR companies – just to stay in business – have to be more competitive.

Goose with the golden eggs (before / after)

before-after

They started to delocalise in India, in Romania or in Ukraine. But that was not enough. To save more money, they have started to use automation.

This has its advantages – of course the surveys were a little bit more formatted… but Millward Brown had done that successfully for years. But once the bugs are eradicated, it’s efficient, fast and most of all cheap. And no blockade by disgruntled employees – although that’s more a French problem.

PresentationBrands

The problem is that end-clients are following up the trend – they can do automation too! They are using Zappi Store and Wizer… and SurveyMonkey and SurveyGizmo and ConfirmIt (and Askia). And ToLuna. And SSI self-serve. And Lucid. And Cint.

I have mentioned it at the ASC’s last conference: we have entered a golden age. The age of the API. A golden age for geeks like me at least: the internet is changing into a gigantic API where information is exchanged through web services. Everything is interconnected and uses the same interfaces.

IoT

I do not know if any of you have used IFTTT – If This Then That. It’s an app where you define a condition and an action. If I get near the house, put the lights on. If the temperature gets below 17 at night, put the heating on. If I enter the kitchen in the morning, put the radio on and start the coffee machine. If I have no milk in the fridge, order some. The IoT – the internet of things – is happening through one common interface through web services… and all industries are playing ball because they want their share of that big cake of a connected world.

oil-rig

I know we all have panel providers on stage so they might disagree with me. But panel data is no longer the only oil on planet Research. Customer databases are increasingly used because they can be energised by communities. And there is all sort of big data available at large – aggregated or not. It could be a loyalty card data, www foot prints or mobile phone data.

wine-glass

And just like for a good Bordeaux wine, to get quality you need to master the art of Blend. The merlot a bit dry and earthy – that will be your panel data. There is some cheap Merlot and very good Merlot too. And the Cabernet Sauvignon with its fruity flavours – that will be your behavioural data.

But unlike the IoT industry, Market Research providers have not decided to play ball. There are the ones who do not facilitate automation because they are afraid of losing control and burning panel. And there are the ones who do but work in isolation.

I do not believe there can be one company that will fill all the needs in Panel data. ToLuna is posturing itself as a one stop for all MR needs: the software, the panel and the behaviour. SSI is doing something similar and the merge with ResearchNow is going to be very interesting. The Leonard Murphy analysis about that on GreenBlog was great btw. And it won’t be scraps left for the others – because the need for data is growing – the need for specialised quality data will be growing too.

babel

But we need a common language. A common grammar. What is a social grade? How do I define national representativity? And how do I trigger a soft launch? How do I notify that a quota is full?

But there is another side to this discussion. If we let anyone access a survey which is tedious, long, repetitive, with grids, 2 max-diff exercises and one 20 minute trade-off, how do we reward the dedicated weirdos that filled that nightmare of a survey? How do we warn them that they are in for the long run? Because we might lose another goose with golden eggs. How can we stop the cull of panellists and the ever drop in response rates?

tediousness

I suggest we build metrics: number of questions, number of responses in a question. And then number of words per question, number of similar questions, number of mandatory open-ended questions… and then build a model.

$(Survey) => (Length(Survey) x TotalTediousness(Survey))-1

And then remunerate the panellists (and their providers) accordingly.

While I was preparing this discussion with all of you, most of you mentioned of how slow moving our industry was. It’s not just that: it’s protective, short-sighted and technologically unaware. And that’s everything the ASC is not. It’s at the ASC that triple-S, a format to exchange survey data between competing survey software was created and promoted. It’s two of my competitors, Steve Jenkins and Keith Hughes, who patiently showed my errors and taught me how to write a proper triple-S file. Let’s all be a little bit more like them and a little bit less like Apple who introduces a new plug and a new format with each new version.

chinese-propaganda

That’s my manifesto – a call for arms… please discuss and let’s move it forward.

Panel providers of the world, unite!

The short story

The industry is demanding more streamlining and automation… the only way that can happen is via standards – what are the Panel providers doing/proposing to do in this respect? We would like better visibility on their APIs and the differences between them… possibly talk about harmonising some key variables. We think there should be an automated standard evaluation of surveys in terms of length and complexity to better pre-evaluate the cost of sample.

We would like panel providers to explain their position – and their added values – in a (wait for it) panel discussion on Thursday the 9th of November in London – ORT House, London NW1 7NE as part of the one day ASC conference.

The very long story

I have always wanted to join an English gentlemen’s club. If I moved to the UK, I was going to be Phileas Fogg: travelling the word after a drunken boast and a wager over a bridge game. Last month (after 22 years in the country), it finally happened; I was asked to join the Association for Survey Computing.

I expected a standard acceptance ceremony: arriving blindfolded in a dark room, greeted by men in togas, a solemn oath with my hand on the 15th century preserved skull of the founder of the organisation, uttering something in Latin, maybe “Nam melius quaestiones”.

I was not disappointed. It was a Thursday morning Webex call to agree the subject of the November one-day conference. After the usual rambling about the weather (it was a cold September morning with a forecast for rain in the afternoon), roles were assigned. “You’re French”, they said, “you’re good at starting revolutions” they said “write a manifesto!”

And in truth, a revolution is needed. In previous years, the only way to have a lucrative MR business (not that I know about that) was to delocalise. The new trend is to automatise: you standardise a survey (want an ad test?), select the target (nat. rep. sir?) and you have your dashboard with your data ready just as your PayPal account is being debited. For this to happen, you need an automation platform (Zappi Store and GetWizer for instance) or a survey platform with an API… and you need a sample provider.

And that’s where it gets complicated.

A short digression into the real world

Let’s imagine you have built the perfect automated survey solution… it works nicely and you get results for every wave in exactly 2 hours 47 minutes. But for a given survey, you want to use a different panel provider to reach a very niche B2B target. You contact that specialist panel provider and explain your needs. They are enthusiastic about the idea and Adam, your contact there, wants to test your survey first – their panellists are special, you don’t get to burn their community like that. After 48 hours, Adam calls you back with a price, it’s on the expensive side but you agree right away because you want the data now – well you actually wanted it 45 hours and 13 minutes ago. Now he sends you a list of the internet parameters you need to accept in your survey… what was called SG with panel provider 1 is now called SocialGrade and GE becomes Gender3b… of course you already know why it’s called Gender3b; they introduced an “other” (and a “prefer not to say”) to the gender question. Your survey scripter says he needs a day (or two) to impact the changes… but he can only start after the week-end because it’s Friday and the web designer who did the icons for the gender question has already gone snowboarding for the week-end.

Here comes Monday, the designer damaged his knee and you decide to scrap the icons. The client checks the survey on Monday afternoon (they are based in the East Coast) and they want the gender icons back to verify the sample… so you add (early next morning) a nice routing to exit the survey if they say “other”. No soft launch, we don’t have time for that. Quickly (but not quite quick enough) you realise you have screened out 99% of respondents – your scripter wrote the routing the wrong way. You call a very unimpressed Adam to stop sending sample. Your guys finally correct the routing but unimpressed Adam has gone for the day. You eventually get through to him late morning the next day and he agrees to send more sample.

The data fills your automated portal nicely… you start to relax. You shouldn’t, your client has had a look at the data and he has noticed something very weird with the student segment. How is that possible? You’ve changed nothing there… until you decide to call Adam who reluctantly agrees to take you on. He explains calmly that although the internet parameter is indeed SocialGrade, the value 23 does not indicate “Students” but “Deep sea divers”… Did you not read the explanatory document he attached to his email on Thursday last week?

Now you know you are going to have an interesting conversation with both your client and your boss. But you may as well leave it until tomorrow.

And that’s how automation got scrapped in what you must now call your previous job.

The quest

So let’s get back to my personal quest – how can I make automation and surveys better? The answer is simple: by getting panel providers talking to each other.

That’s never going to be easy. Some of them are already panel aggregators and they feel they have already done the hard job. Others feel commoditising panels is not in their interest and will drive prices down. Some say it’s simply not possible because their own data is too rich. And all agree that sending sample to a broken or boring survey is the one reason that response rates – along with data quality – are dropping.

And they are right. Data is precious. We need to treat interviewees with respect and that’s not what we do when we send them a 40 minute conjoint survey (and tell them it will last 10). For panel providers to evaluate pricing properly, they need to know how good (and more likely how bad) our survey is.

We need to build metrics on the length of a survey (a lot of data is available there) but also on the boredom index of a survey: number of grids, number of responses per question, number of words per question text, number of questions with similar text, number of mandatory open-ended questions… and prices should vary accordingly.

Another option would be that the price could be fixed by the soft launch data. At the end of the survey, we measure interview interest and fix the price of the panel accordingly – with a rebate if the full survey data is actually below the early measure.

And how do we harmonise panel data? Should we break down questions in categories and sub-categories (demographics, lifestyle, political leaning) and incorporate that in the naming? Can we have the same break-down across different countries? For which questions? Should the naming convention clearly indicate the number of responses to avoid coding errors?

Be our panelist for a day

We’ve so many things to discuss… and we thought it’d be best if we did it in public. You, the panel providers, could tell us what you think… explain what’s special about your company, detail your API or your choice not to have one. And the ASC audience – rather technical but friendly – could tell you what they want and stand witness to your promises. The result could be a standard, (national or international), an API router or just an Excel spreadsheet, depending on the uptake… but independently managed – by the MRS, Esomar, ASC or SampleCon.

So please come to ORT House in London, on Thursday the 9th of November. Tell me who from your company is ready to speak and take part in the panel’s panel discussion, and in a few lines, give me an outline of how you’d respond to our challenge on harmonising panel data and panel interfaces by Monday 2 October. We’re looking for original thinking, fresh ideas and practical answers.

Panel Providers of the World, Unite!

Welcome to the machine

The following is a transcript of a talk given by yours truly and Chris Davison from KPMG Nunwood at ASC’s One Day Conference on the Challenges of Automation in Survey Research on May, 11th 2017.

Introduction

We have entered the golden era of automation –in other word: make machines do things. At first it was repetitive and simple things – find duplicates in a sample list, copy that survey and substitute the word Coca-Cola by Pepsi and send the results to all the executives of the relevant company – not mixing up companies is the perilous thing that you do not want to get wrong.

Automation is for lazy people – and I have always considered laziness to be a quality! Lazy people [programmers] look to avoid doing things they don’t really have to, and when they do finally have to, they look to get it done with the least amount of effort.

Now if we are to believe claims automation can post-code open-ended responses (yeah right Tim!), write reports, win at Go (mark my words – that one is never going to happen) and soon Skynet is going to enslave us (it will be ok as long as we don’t choose the red pill… or the blue pill.. damn I have to remember which one).
Automation is not new. Software is automation. But not everybody is a programmer – well this is less true at the ASC.

For every-one to benefit from the work done by the best programmers in their sector, Application Programming Interfaces (APIs) were invented. An API is a pile of code (usually documented) inside a box accessible by another software without having to understand its inner working (or have access to the source code). Here’s a brief timeline:

Timeline

By 1999 it was cool to be a programmer (and not just at the ASC). Every web designer was now a programmer – writing awful code in JavaScript so they could animate their poorly designed website while growing and grooming their facial hair and sipping their Frappuccino soy latte.

XML was no longer the cool kid on the block JSON (JavaScript Object Notation) was – more compact, more elegant and directly usable in JavaScript. jQuery – a JS library, quickly replaced by Angular and then React – made it super easy to query any website and people started calling me a dinosaur because I am a C++ developer.

Automation was always possible before: to make software interact you needed a database accessible by both parts. Or start an executable from the command line… Or a file drop on an FTP server. All these are huge security risks. I am not saying that web services are not security risks – the risks are just less understood so easier to sell to your CEO.

So what was new in the survey world? Well everything.

Panel providers created APIs. First Cint then Lucid which lead to an explosion of DIY research. Software providers opened-up their APIs and some even documented it. I will not give you the list but these days, even SurveyMonkey has an API.
And for me the revolution came with CRM system – like SalesForce, Microsoft Dynamics or ZenDesk – opening up the Enterprise world. You could interview any customer after any touch point… understand what’s happening and adapt quickly, it’s called by one of our competitors the experience gap.

Behaviour is now captured outside of surveys. The “What and When” is known. Surveys can concentrate on the “Why?” and the “What if?”. Verve is managing insights for Walgreens, the owner of Boots. Thanks to the loyalty cards, they know you have bought paracetamol on the Monday, Ibuprofen on the Tuesday and nothing on the Wednesday (with an app and iBeacons they also know where you have walked) – and they can sample you accordingly and interview you to understand your buying pattern.
So the game is about asking the relevant question at the right time. In my opinion nobody does it better than KPMG Nunwood and that’s because – automation looks like magic when it’s well done – there is a wizard in the backroom somewhere in Leeds…

Case study: Nunwood (Chris Davison)

So I’d like to tell Nunwood’s automation story, how it started, the obstacles we faced and where we are now before later coming onto some of the ideas that we implement to take us further…
How did we start? Well I wish I could say we had some far reaching vision but in all honesty this is what kicked things off for us…

In case of emergency, panic.

Sadly there wasn’t much long term vision associated with our first foray into automation, that came later. What got us moving was necessity. We had several UK projects using bits and pieces of automation techniques but they still required manual intervention at key points to move the process along.

As we started to expand globally, we were faced with the challenge of processes needing to run at any point in the day – including when our UK team were tucked up in bed. Our first attempt at a solution was a successful failure – we met the needs of the project, but the mish-mash of command line, SQL scripts and Askia was very complicated and wasn’t very accessible to the entire team.

If we were going to extend the automated approach to other projects, it was clear we would need different tools and this is what brought us to LoadIt. While a simple tool to use, it allowed for a great deal of complexity meaning new starters could get to grips with it quickly but the more seasoned DPers could still deal with our most demanding projects. Later, its extensibility would allow us to integrate with our in-house developed systems such as our Fizz online reporting platform.

robot

Given LoadIt’s existing integration with Askia and the automation capabilities within Askia we soon developed the long term vision that we were missing – the Zero Hours Project.
Given certain conditions – mainly stability in the project’s design and outputs – could we automate all elements of data collection and delivery?

It was an ambitious goal and one that had to have some compromises – there would always be some elements that would need manual intervention, so “zero hours” really meant “very few hours” but that didn’t quite have the same ring to it.

Discussing these kinds of developments with the whole team raised justifiable concerns that this would mean people’s jobs, but automation does not necessarily mean reducing head count and it certainly wasn’t our goal.
The tasks that lend themselves well to automation are the ones that don’t change – removing these from the team’s workload would free up time for things that required the skills for which they were hired (primarily their problem solving abilities), the skills required for automation were also different from the typical work, meaning it provided an opportunity for people to learn more broaden their skillset. We could also expand the remit of the team – in particular we took on more responsibility for the configuration of our reporting sites. From an operational perspective it would mean we could go some way towards flattening out the peaks and troughs that had developed in our working patterns – driven by the fact that most of our work was tracking studies that came out of and went into field at very similar times.

Framed like this, it was a very positive message for the team and I’m pleased to say everybody got behind the idea.
The main challenge from the wider business was around quality control. If machines were doing all the work, who was checking it?
It was a valid concern but one that could still be approached with automation at the forefront. All surveys are based on a set of rules, however complex – each question has criteria that must be met before it is asked, so it’s reasonably simple to check that the rules have been met.
We created VB scripts that could test the rules and give a pass or fail to a set of Excel tables, this meant that the same files we were using for automated checks could also be verified manually passed to our Insight team should they want to double check things.

Quality stamp

So back to the original question – could we run a zero hours project?
The answer wasn’t simple – Yes, if you considered the caveats – when things didn’t change and we considered the standard elements of the project we could produce the outputs through automated scripts. No, because an unexpected consequence of the changes was a change to the way our Data Team worked with our Insight Team. With many of the repetitive, standardized tasks removed, we found we had more time to work on ad hoc requests and deeper analysis of the data – meaning we could add more value to projects.

We had seen many of the improvements we had hoped as well as some unexpected ones: operational improvements, better working practices and we’d started to extend our capabilities.

Paradata (Jérôme Sopoçko)

One of the most exciting areas of using automation and APIs is during (or just after) the collection of the survey. Paradata is often just the date and time of the start of the interview. But more generally it’s about storing any information about the way the interview was conducted. You can find out the name of the browser, the operating system and the language used in the HTTP request header.

If the interviewee is using Internet Explorer 5 (or more generally any version of Internet Explorer), do not bother asking technical questions. Similarly, if the operating system is Linux, forget these ask technical questions because you won’t understand the answer.

If IE is brave enough to ask to be your default browser...

Beyond the interview, you can find information about the world. If you interview someone who is boarding a Eurostar train, it’s interesting to check the volume of #Eurostar hashtags in the Twitter API: it’s a strong indication of problems on the line.

Now let’s talk about the IP address – this identifier assigned to you by your Internet Service Provider. Of course your ISP knows who you are and has been allowed to sell (in the US) your browsing history.
There are a number of companies out there who specialise in transforming an IP address in a geographical position: www.freegeoip.net, www.maxmind.com, www.digitalelement.com, …

But you can have a much better definition of the geo-location by authorising the browser. Google thanks to their StreetView vans was fishing for the Wifi network information so they can improve the location which is now at scary levels of accuracy.

As mentioned to have accurate geo-localisation, you need the permission of the user. The idea is not to gather information in a sneaky way. Tell the user what you are doing with this information, explain that you are reducing the number of questions you ask… Because there are so many resources available: openweathermap.org will give you the current weather in any location, developer.zoopla.com will find right away the average price of the house in the vicinity. And then you have the open data government sites. Data.gov.uk have put on 185,000 datasets. Call me old fashioned but I still think we are in Europe, the EU Open Data portal has 10,700 dataset with a full API to access them. For free.

So what can we do with this data? Does that help to know that my interviewee is in Cardiff where 60% of the people voted remain? Linking big data and survey data is one of the greatest challenge of #MRX – and if you are not one of GAFA (Google Amazon Facebook Apple) you are at a real disadvantage.

Let’s use a concept developed for advertising planning: the Average Issue Readership (AIR)
We ask a significant amount of people (very significant in the case of the TGI survey) these questions: “How often do you read this newspaper?” or sometimes rephrased in “When did you last read this newspaper?”. There is still a lot of discussion to find out which is the best way to ask these questions – they are usually called probability questions.

Grid question

So you get a very classic grid question like the above. Thank to these lovely people who do research on research, we get the following probabilities of reading, based on the “recent reading” question.

Recent reading - NRS survey

From there we can infer, for each interview, the probability that he has read or not any given issue of a newspaper… and the great thing is that we can use that information in crosstabs and crossing that by their likelihood of buying Corn Flakes and plan our advertising campaign according to this.
In a normal survey, if we simply ask people what newspaper they read and we cross that by their gender. When we do a cross-tab, for each interview of gender who has given brand Y, we add 1 in the corresponding cross-tab. With probability question, we add a value between 0 and 1 indicating the probability of the person having seen the brand.

AskiaAnalyse table with randomised data 01

AskiaAnalyse table with randomised data 02

OK, the data looks a bit weird because the counts have decimals but once you move them into percentages, nobody cares anymore. And of course you can still use weighting if your panel data was not balanced.
So what does that mean for you: although you never asked for the vote that fateful referendum, you can cross the NPS of your brand with the vote for leaving Europe who (from what I have seen at the MRS) increasingly used as a segmentation tool.
Of course it’s not just the election you can cross by: the level of crime, the amount of subsidy, the likelihood of rain…

Beyond paradata, you can also create additional information with the questions you actually ask. We have worked on a project where the conjoint analysis utilities were computed in real time – that meant automating R (like Ian showed earlier) to get the results a few screens further, show the best concept for a given user and validate it.
Beyond that – the revolution is also around open-ended question analysis: you do not write open-ends anymore.

You will speak to your device, your computer, but also your phone, tablet, Alexa, your fridge…any IoT device – which have ways of recognising you. MyForce, our sister company, works on Bison – a revolutionary platform of not just speech to text – but identifying people by their voice (who’s talking), classifying the tone and talking speed (how are we talking) and the content (what are we talking about).

It’s not just Bison – look at what APIs Microsoft offers (Microsoft cognitive services) and R is integrated in SQL Server…

Microsoft APIs

Google and Facebook are also on the bandwagon (the gravy train).

One of our clients, through our other sister company Platform One – Nuaxia – has a panel of 1,000,000 doctors (not all in the NHS). These guys are in a hurry but they have interesting things to say. So, Nuaxia lets pharmaceutical labs survey these guys but only ask them 10 questions. And instead of asking them to type, they film them.

Platform One interface

This is the interface to create the survey – this is kept simple – it’s for pharmaceutical people. From there a survey file is created through the API of a well-known software vendor, they debit the PayPal account of the white blouse guys, invite the doctors and the data pours in.
After that, the video data is nicely sent to a speech to text algorithm, the text data is classified with Artificial Intelligence (a la CodeIt but not CodeIt yet) and all of it sent to a dashboard.

Text-driven surveys (Chris Davison)

So we know what the typical survey is structured like and most have not moved on that much from the sort that would be posted to someone in the distant past. Linear structures and the logic dictated by closed questions. Technology gives us an opportunity to flip this paradigm on its head.
Imagine a survey more like this…

What we’re looking do is use open end questions to determine the route the customer takes through the survey, asking things that are relevant to them and providing a much more tailored survey experience.

Removing the structure from our surveys is, for me, an exciting proposition and live text analysis can be used to do just that.

Create a pool of open-ended questions and as one is asked, apply live text analysis to determine which would be the most appropriate follow up and continue until either there are no more relevant questions or some constraint, such as time limit or number of questions has been reached.
From a respondent’s perspective, they should have greatly improved experience – far less asking them questions that do not seem relevant, the questionnaire is steered by the issues they want to talk about.
From the analysis side, the data quality should be much greater – in theory you’re asking questions of the respondent that are relevant to them and their experience. Consequently the ability to understand the story behind the data should also improve.

We can also start to tackle some of the issues facing us such as falling response rates – when an invite says the survey will last 10 minutes we can guarantee that – once the time limit is reached stop picking new questions. Or take a different approach, state the number of questions you’re going to ask and don’t ask anymore.
You can always ask the participant’s permission to ask more when you get to the limit, but because you’re asking the most relevant questions to them you hopefully have got the most interesting feedback up front.

There are clearly some analysis considerations – by only asking people about topics they’ve expressed an opinion about could introduce some bias, but nothing about this approach precludes randomly selecting questions or sections to provide balance. But you know when you’re doing that – you know the context in which the question was asked when it comes to analysis – you can even tailor the way it’s worded…
“We know you didn’t mention anything about your experience at the checkouts, but we’d like to ask you about it…”

To take this a step further you can then allow participants to upload photos / videos and do the same real-time analysis and base the survey route from that.

So while this is a specific example, the key principle for me is that we start to utilise the technological landscape we have available to us to start to challenge some of the fundamentals of project design. Connecting through the myriad of APIs helps us to create a combination of services that moves our industry forward and opens up new horizons.

More engaging surveys with ADX Studio

ADXStudio is an open-source Integrated Development Environment (IDE) for people who want to create Askia Design Controls or Askia Design Pages easier and faster. This application supports AskiaScript / JavaScript / HTML / CSS and more.

ADX Studio app icon

We designed this application in order to provide a dedicated tool for survey authors who want to take their surveys one step further: interactive survey controls (Geo Maps, touch-friendly drag and drop, …) or custom layout (mobile first survey design). It therefore allows you to easily set the parameters for your survey controls or layouts, use script (AskiaScript and JavaScript) to push the boundaries of your assets and a preview in order to provide real-time feedback.

ADXStudio user interface

You can download ADX Studio or even contribute!

ADX Studio was built with Electron and is based on NodeJS. Furthermore, we have included CodeMirror (already used in DesignVista) in order to provide a complete text editor with syntax highlighting and code completion.

If you want to learn more about ADX Studio, we added two articles in our Help Centre:

Big Data with just one digit

I know some of you think I only attend conferences for the free food, the drinks and the social scene. They are right – no point in me denying. But in-between parties, I tend to heal my hang-overs in the semi-darkness of conferences.

Coming back from the ASC and ESOMAR, there are a few new tendencies in the Autumn/Winter MRX fashion. Forgotten MROC, gamification, mobile research, Big Data – that’s so last year… it’s main stream, dude.

These days the cool kids talk about Automation, Data fusion, Artificial Intelligence… and the Tinderisation of research.

Automation – if you’re an assiduous reader of this blog, you know it’s coming and fully available at a software provider near you. I am not going to ramble anymore about this for now but watch this space.

Artificial Intelligence is the next big thing in Research. It has been successfully used to post-code and (less successfully) to measure sentiment in open-ended questions and tweets. It’s also good at recognising logos and objects on pictures and films, building accurate predictive models and beating me at Go (well the latter is not news and not strictly research)… but now AI is also used to merge data. There is an inconvenient truth about convenience panels… and MR data in general. If your survey is 40 minutes long (or 20 minute on a mobile device), the resulting data will be awful: the participants are either too unusual to be trusted or they don’t care because they are not incentivised.

Although there is no evidence of the length of surveys diminishing (according to SSI), every-one agrees that it needs to happen. One way is to… well… make up data. You do not ask all the questions to every-one and you copy the data around for similar looking interviews – this is called ascription (it has been around for some time). For you stat geeks out there, it’s traditionally done using the Mahalanobis distance. The new thing is to use machine learning to infer missing data. Mike Murray and James Eldridge from the Seo toronto agency had a great paper about automating the splitting of surveys in chunks from their XML definition. Annelies Verhaeghe from Insites and John Colias from Decision Analyst also presented two great papers about enriching surveys with open big data.

And finally after the uberisation of research which has seen the arrival of monkeys, gizmos, nuts & limes, the new trend is the tinderisation of research. Millenials (there were boos whenever the term was used – and that was every 47 seconds) take decisions with their index. Left means no, right means yes… and survey research should follow. It’s easy to understand, fast to answer and it’s your system 1 talking… And the index is not just for decisions… the navigation of a survey should be done through flicks of the index. Almost being a millennial myself (the NSA has the names of those who are laughing), I see the attraction… and we are soon to release something code-named Jupiter that might just turn (or keep) Askia the best software for the Generations Y and Z.

Playing with D3js and AskiaVista

As you may already know, we added support for the Highcharts visualisation library as of version 6.0 of askiavista; and while it provides all the necessary visualisation needs for our online data analysis application, we had designed askiavista’s API to be agnostic when it comes to charting libraries.

From our initial use of NevronDundas Charts in version 5 to our use of Google Charts in one our initial demos of askiavista’s AJAX capabilities, we have always wanted to provide our users with the flexibility to chose their preferred charting library.

One such library we have always wanted to play around with is the amazing open source D3js visualisation components. This JavaScript library that produces dynamic, interactive data visualisations in web browsers. It makes use of the widely implemented SVG, HTML5 and CSS standards. While, D3 provides all basic types of charting components (bar, column, pie, area, …), it also provides many more advanced visualisations.

We have therefore played with both of those types of charts; from simple visualisations:

Classic bar chart

Diverging Stacked Bar Chart

… to more complex and advanced charts:

Sunburst Chart

Bubble Cross Tab

These Lab type demos are hooked up from a classic Askia survey via askiavista’s API to the D3 components. While D3 is not integrated by default in our API tooling, it is a simple proof-of-concept of such an implementation.

To view all the components we played around with, check out the full demo! Props to Brice de la Brière for making this possible!