Why Screeners Must Go Away
By Pala Kuppasamy
I was sitting at the Dulles airport in Washington DC after missing my connection to attend the Market Research Innovation Summit organized by Research Now with a salmon fishing event in Alaska, when this unrelated thought struck me. Why are we screening out people so badly and madly in research studies? Especially in this era of mobile and big data where you can avoid these screeners mostly if not totally?
Someone told me sometime that the screener questions came into existence when the pen and paper studies and telephone studies migrated to online studies. As you had no clue who is responding, what gender or age the person is, and since some people in some families shared (?!) email addresses, asking these ‘screening and demographic’ questions every time became important.
But why anymore? All big research and panel companies are profiling their members, keep these info fairly updated, and yet ask people the same questions they already have the answer for, and screen them out mid way. This is counter intuitive, euphemistically speaking.
Have you met some of these panel respondents in real life and asked them about what they think of these screeners? I have. They tell me that the screeners are the worst thing that keeps them away from taking surveys and the single most reason that disengages them with the panel. They are less disturbed about longer surveys and smaller rewards. They do derive some satisfaction in responding to surveys. They DO NOT derive pleasure from being told that they don’t qualify : definitely not when they notice that their odds of getting qualified is becoming meager compared to the odds of getting disqualified.
This is why screeners must go away.
But what can make it go away? Its (1) Mobile and (2) Big Data.
Mobile is perhaps the most personal device that we use. iPhone and iPodtouch are the most personal devices of my wife and daughter respectively, that they wouldn’t allow me to get hold of. Until the iPhones and iPodtouch, it was their hair brushes / combs that were most personal to them.
The point is, when mobile becomes a mainstream research data collection channel, the profile information provided by the users (say via the survey app) will reliably relate to a specific individual and can be used for pre-screening them to determine if they would qualify for a study.
Combine this with the range of behavioral data that you can collect from the mobile devices – Eg., location data, app usage data, site visit data etc., you can now avoid a large set of behavior based screeners too by prescreening with these information.
Screening questions like the ones below can be avoided and by replaced with a behavioral targeting.
- Did you fly in the last two weeks?
- Did you dine out this weekend?
- Were you at a movie hall in the last two weekends?
- Do you read CNN on your mobile device?
- Do you use banking apps on your device?
- What kind of music do you listen to?
Big data will be extremely useful if we make those small connections amongst them.
In the market research world, we collect various data points from the respondents and its not uncommon to ask the same questions to the same respondent multiple times. Let alone the possibility of connecting the dots and making inferences. There are various reasons why things are the way it is today in the industry, but I don’t think we should accept it as the best practice going forward. Especially in the era of big data.
In a broader context, the level of data we deal with in the research industry may not really qualify for being called as the ‘big data’, but it will get there very soon when we start to include and accumulate the behavioral / passive data we gather about respondents. There is a big opportunity to think and use this data for the respondent benefit too, besides using it to meet the client needs.
Although the term bid data has become a cliché of late, the power of analytics – both derivative and predictive – that the big data wave is enabling, has interesting applicability to what we are doing in the market research industry – at pre and post data collection stages.
Think about making meaningful connections out of all the ‘non client specific’ data that we collect from respondents, and using them to address the biggest annoyance of the panel members by eliminating the screen-outs by means of intelligent targeting. Oh boy, we have hit on something really big, really valuable for the research industry.