The Top 21 Emerging Research Methods of 2016 (A GRIT Sneak Peek)

 

Editor’s Note: The GRIT Report for Q3/Q4 2016 will be published in early January and it is chock full of good stuff. In this wave we covered a variety of topics, some new and some that we track normally. They were:

• Adoption of emerging methods/technology
• Usage of traditional Qual
• Usage of traditional Quant
• Sample Quality and Respondent Engagement
• “Buzz Topics”: Automation, AI, Marketplaces, Big Data, Storytelling, Nonconscious Measurement, & Attribution Analytics
• Projected Research Spending/Budgets
• Use of non-traditional data sources for insight generation
• Trends Impacting Corporate Researchers: Organizational fit, impact, insourcing vs. outsourcing, internal innovation
• Supplier Satisfaction levels and drivers of satisfaction
• Hiring trends
• Training resources
• Information sources used by the industry
• Financial outlook for 2017
• Client side spend and average projects per year

Per our tradition, we’ll be doing some “sneak peeks” of certain sections prior to the publication, and we’re beginning today with the perennial favorite of our tracking of adoption of emerging methods, which industry Guru Ray Poynter always writes in the report.

Enjoy and Happy Holidays!

 

By Ray Poynter

In looking at what research approaches/methods are in use or under consideration, it is important to remember that the GRIT sample is not a representative sample of the market research population. The GRIT sample tends to be drawn from those more engaged with the future of research, so the ‘in use’ figures will tend to be higher than for the wider MR population. The GRIT report’s key usefulness lies in the relativities between the approaches, the trends over time, and the differences between key groups (such as the buyers and sellers of research and insight).

The Overall Picture

Table 1 shows the 21 approaches included in the GRIT study ranked in terms of how many people said they were already using these techniques. Remember, using a technique does not necessarily means using it heavily, it may mean it has gone from consideration to occasionally ‘In use’.

 

	Rank	Labels	In Use	Under Consideration	Total Interest
Mainstream	1	Mobile Surveys	75%	16%	91%
	2	Online Communities	59%	23%	82%
Wide Adoption	3	Social Media Analytics	52%	24%	76%
	4	Text Analytics	46%	30%	76%
	5	Webcam-Based Interviews	43%	22%	65%
	6	Mobile Qualitative	42%	26%	68%
	7	Big Data Analytics	38%	31%	69%
	8	Micro-surveys	35%	25%	60%
	9	Eye Tracking	35%	21%	56%
	10	Mobile Ethnography	33%	27%	60%
	11	Behavioral Economics Models	29%	25%	54%
Niche	12	Research Gamification	25%	29%	53%
	13	Prediction Markets	24%	23%	47%
	14	Facial analysis	24%	21%	45%
	15	Crowdsourcing	16%	21%	37%
	16	Neuromarketing	16%	19%	35%
	17	Virtual Environments/Virtual Reality	14%	24%	38%
	18	Internet of Things	14%	26%	39%
	19	Biometric Response	12%	19%	31%
	20	Sensor/Usage/Telemetry Data	11%	19%	31%
	21	Wearables Based Research	10%	27%	37%

Table 1 – Base = 1580

The results can be broadly divided into three categories:

1. The Mainstream Group. Mobile Surveys (75% ‘In Use’, 16% ‘Under Consideration’, making a ‘Total Interest’ score of 91%) and Online Communities (59% ‘In Use’, ‘Total Interest’ 82%) are mainstream and well ahead of all other ‘new’ research approaches.
2. The second group runs from Social Media Analytics (52% ‘In Use’, ‘Total Interest’ 76% – which makes it almost mainstream) down to Behavioral Economics (29% ‘In Use’, ‘Total Interest’ 54%). These techniques are in wide adoption, but no single approach from this group can be considered essential. However, having some of them in your toolbox is likely to be necessary for most organizations.
3. The bottom group runs from Research Gamification, which is just below the second group (25% ‘In Use’, ‘Total Interest’ 53%) down to approaches with very low scores, such as Wearables (10% ‘In Use’, ‘Total Interest’ 37%). This group tends to be a mixture of the very specialized and those approaches that have not yet broken through in terms of usage.

Stability Over the Last Two Years

Table 2 shows the ‘In Use’ data for the last five waves, dating back to August 2014, a period of just over two years. The data shows that there are changes, but few of them are large. Given the nature of the data, sampling variation etc, we suggest that you ignore anything smaller than plus or minus 5%.

 

Table 2	2014 Aug	2015 Feb	2015 Oct	2016 Feb	2016 Nov	Change
Mobile Surveys	64%	67%	68%	74%	75%	11%
Online Communities	56%	59%	50%	61%	59%	3%
Social Media Analytics	46%	44%	43%	48%	52%	5%
Text Analytics	40%	38%	38%	45%	46%	6%
Webcam-Based Interviews	34%	38%	33%	42%	43%	9%
Mobile Qualitative	37%	43%	34%	44%	42%	5%
Big Data Analytics	32%	32%	34%	39%	38%	6%
Micro-surveys	25%	30%	25%	35%	35%	10%
Eye Tracking	34%	28%	28%	36%	35%	2%
Mobile Ethnography	30%	35%	31%	34%	33%	3%
Behavioral Economics Models	25%	27%	21%	31%	29%	3%
Research Gamification	23%	21%	20%	27%	25%	1%
Prediction Markets	19%	21%	17%	22%	24%	5%
Facial analysis	18%	17%	18%	22%	24%	6%
Crowdsourcing	17%	19%	12%	20%	16%	-1%
Neuromarketing	13%	14%	15%	17%	16%	3%
Virtual Environments/VR	17%	15%	10%	14%	14%	-3%
Internet of Things	12%	11%	9%	14%	14%	1%
Biometric Response	13%	10%	10%	11%	12%	-2%
Sensor/Usage/Telemetry Data	—	—	7%	11%	11%	5%
Wearables Based Research	7%	7%	8%	12%	10%	3%

 

The two approaches that show the largest movement, +11% and +10%, are Mobile Surveys and Micro-Surveys – both of which tend to fit well with the trend towards agile research that has been a feature of many blog posts and conference presentations over the last two years.

Many people will be disappointed and/or surprised to see the scores for Social Media Analytics and Virtual Reality have not risen by more over the last five waves. At the bottom of the table, techniques such as VR and biometrics seem to be trapped in the bottom part of the table. We suspect this is due to the current scalability limits (and hence cost implications) of these approaches, and like others that have seen growth when scale is achieved with concomitant cost reductions, more growth will occur.

We also checked the open-ended suggestions for emerging techniques that were not part of the existing survey, and this process generated four suggestions. Three of the techniques were Automated Research, Geolocation, and Artificial Intelligence. The fourth suggestion was Implicit Research, something which could be seen as a sub-category of Neuro, but seems to be emerging in its own right. In future editions of GRIT we will monitor those emerging methods more closely as well.

Buyers and Suppliers

There are a couple of good reasons why Suppliers might say they are using more research techniques than research Buyers/Users:

1. Suppliers typically work with many companies, and may use a different range of techniques with different clients. Of course, it is also true that large clients use many researcher suppliers.
2. Suppliers need to know all of the details of the research they are providing, such as whether Research Gamification was used in the design and what proportion of the surveys are completed via mobile device. A research buyer may want to know this too, but in many cases the buyer of the research is less interested in these details.

Table 3 shows the ‘In Use’ data for Buyers and Suppliers of market research, and the right-hand column contrasts the results.

 

Table 3	Buyer %	Supplier %	Seller – Buyer
Mobile Surveys	65%	77%	12%
Online Communities	58%	59%	1%
Social Media Analytics	64%	49%	-15%
Text Analytics	42%	47%	5%
Webcam-Based Interviews	31%	46%	15%
Mobile Qualitative	31%	45%	14%
Big Data Analytics	47%	35%	-11%
Micro-surveys	37%	35%	-3%
Eye Tracking	37%	35%	-2%
Mobile Ethnography	26%	35%	9%
Behavioral Economics Models	24%	30%	6%
Research Gamification	17%	27%	10%
Prediction Markets	26%	24%	-3%
Facial analysis	21%	24%	4%
Crowdsourcing	18%	16%	-3%
Neuromarketing	16%	16%	0%
Virtual Environments/VR	11%	15%	4%
Internet of Things	16%	13%	-4%
Biometric Response	14%	11%	-3%
Sensor/Usage/Telemetry Data	13%	11%	-3%
Wearables Based Research	8%	11%	2%

 

The pattern of techniques and approaches in use is similar between Buyers and Sellers (not surprisingly) with an r-squared value of 82%. However, there are some interesting differences.

In several of the technical areas of research the percentage of suppliers using them is considerably higher than the buyers, for example: Webcam Interviews (46% Sellers, 31% Buyers), Mobile Qual (45% Sellers, 31% Buyers), Mobile Surveys (77% Sellers, 65% Buyers), Research Gamification (27% Sellers, 17% Buyers), and Mobile Ethnography (35% Sellers, 26% Buyers).

However, the more interesting cases are those where the Buyers are more likely to be using an approach than the Suppliers. The two key ones being: Social Media Analytics (Buyers 64%, Suppliers 49%) and Big Data Analytics (Buyers 47%, Suppliers 35%). This finding is consistent with earlier waves of GRIT and we believe it indicates that for these two services many clients are buying from non-MR suppliers – which is consistent with the findings from some of the other findings from this wave of the GRIT study.

Differences by Region

There are some interesting differences by Region, and if you have a chance to dive into the data you will find some interesting differences by country too. However, the main message is that the advanced market research world is essentially a similar place – comparing North America to the other three groupings gives an r-squared of 85% or higher for each region.

Table 4 shows the data for North America, Europe, APAC, and Other – regions that have been determined by sample size and geography.

 

Table 4	North America	Europe	APAC	Other
Mobile Surveys	76%	75%	74%	67%
Online Communities	56%	62%	60%	58%
Social Media Analytics	53%	52%	44%	55%
Text Analytics	46%	41%	47%	47%
Webcam-based Interviews	48%	41%	34%	31%
Mobile Qualitative	43%	44%	34%	34%
Big Data Analytics	43%	33%	30%	34%
Micro-surveys	33%	41%	32%	45%
Eye Tracking	32%	44%	32%	28%
Mobile Ethnography	33%	35%	28%	21%
Behavioral Economics Models	26%	35%	31%	20%
Research Gamification	24%	32%	15%	16%
Prediction Markets	23%	26%	25%	24%
Facial Analysis	22%	27%	20%	19%
Crowdsourcing	17%	18%	16%	13%
Neuromarketing	15%	18%	10%	18%
Virtual Environments/Virtual Reality	16%	14%	7%	8%
Internet Of Things Data	13%	11%	15%	19%
Biometric Response	12%	11%	9%	12%
Sensor/Usage/Telemetry Data	11%	13%	11%	9%
Wearables Based Research	10%	10%	10%	14%
Base	761	430	149	98

 

The top two items, Mobile Surveys and Online Communities are the same in each region, but after that there are a range of variations. In terms of the number of approaches being used, the averages are higher in Europe and North America (33% and 31%) and lower in APAC and Other (28% for both).

And the Big Picture?

The two main messages are A) over the last couple of years things have been relatively stable, and B) that the advanced research world is pretty similar globally (yes you can find differences, but the overall pattern is similarity).

The stability message is of particular interest to those championing the exciting approaches that have yet to take off, for example Biometrics, Wearables, Virtual Reality, and Neuromarketing. When and if we see these techniques becoming more mainstream, we will see them moving up the GRIT league table – but there is no sign of that yet.

If you are running a middle-sized organization then the data suggest that unless you are an outlier, you should be using Mobile Surveys and Online Communities, some of the techniques in the middle group, and perhaps one of the emergent techniques in the bottom group.

The main worry for market research providers is the suggestion from the data that many research buyers are turning to non-market research sources for their Text Analytics and Social Media Analytics – something the GRIT report has been showing for some time now.