Here they are:

Big Idea #1: Beyond the Database

I alluded in the book to the fact that we have moved from a world where information is scarce to one where it is abundant and that this has huge implications for MR. One consequence is the idea of “the river” of information. Another is so-called “Big Data”. And yet another is the primacy of screening, sifting and analyzing over collecting. So far, so good. Any reasonably clued in observer gets this.

But, there are two very big problems we’re going to get hit with shortly that I don’t hear enough about.

Problem #1 is that the amount of information we have is going to explode in a way that will astonish even the experts. RFID tags and their progeny will be in EVERYTHING. Smart phone ubiquity will make user-generated content explode. Location-based data will explode. The “river” is going to be more like an ocean. Assuming privacy concerns in some quarters are navigated (and I think they will be), “Big data” is going to be very big business.

Problem #2 grows out of the success that we will have in solving the first problem. We will harness this information. We will make life better because of this – in targeted products, in better efficiency, and in wellness applications. But, the weak link is going to be our knowledge management systems. There is data. It becomes information. Then we turn it into knowledge. The challenge is going to be banking, and organizing this knowledge in ways that allow us humans to build on what we know. I think that three things will flow from this challenge:

1. We will spend a considerable amount of time developing insight or knowledge taxonomies (I spent the last year developing one for StrategyOne).

2. Insight Management will play a critical role in the coming decades.

3. We will build systems in which the insights in an insight management system are programmed to find and communicate with the right people in the organization. You’ll have an insight management dashboard capable of displaying insights by domain and by category (building blocks, gamechangers, outliers, etc.), but the insights will also find you. Individuals within the organization will be tagged for alerts of new insights in their focus areas. This will come to you in video-game format as an avatar contacting you with new knowledge you should absorb.

We talk about “databases” today. We will need to develop insights management systems and the language to describe them. Just as we use the term “database” today, we will have common words for this insights system in the future. The more success we have in uncovering new knowledge through “big data”, the more we will need to strengthen our insights management systems.

Big Idea #2: “Rateocracy”

Everyone recognizes words like “technocracy” (rule by a technical elite), “democracy” (rule by the people), “theocracy”, etc.

But freedom, ubiquitous personal computing, and social networks are ushering in “rateocracy” – rule by raters. Today you can rate restaurants (Yelp!), employers (Glassdoor.com), bosses (eBossWatch), eBay sellers, college professors (ratemyprofessors.com), and other people (honestly.com).

At some point everything and (almost) everyone will have a rating.

Don’t believe me?

Think about Facebook “likes” today. Now think about what can be done as Facebook expands this to other “verbs”.

Rateocracy will give marketers a very tight feedback loop. I suspect that it will drive the growth of RIMEing (rapid, in market experimentation). But, it will also drive the use of in the moment, follow-up research clarifying why a consumer rated a product or experience as X. This will make the current net promoter score look rather quaint.

RPM

These are:

Asking Epoch
Observational Epoch
Co-Creative Epoch
Anticipatory Epoch

At present, MR is trying to move out of the Asking Epoch and into the Observational Epoch.

But, its the co-creative evolutionary stage I want to focus on today.

Recently a number of “crowdsourcing” firms have created an industry group – Crowdsortium.

I actually believe that this is a fairly big deal.

The creation of a trade group, sharing of best practices, and development of shared language are all signs of a maturing industry.

Although crowdsourcing threatens the traditional market research paradigm, I view this kind of collaboration as (a) inevitable as society uses technological platforms to create more inclusive systems, (b) desirable within a Western framework of individual liberty and self-expression, and (c) incredibly threatening to large hierarchical systems. As a pro-market libertarian it is difficult to be a neutral observer of this phenomenon. I have a very clear bias in favor of distributed, evolutionary systems, agreeing with Virginia Postrel on what drives the future.

What is interesting about Crowdsortium and the idea behind it is what a game changer it can be beyond market research.

As Kevin Lonnie points out, there are very significant challenges to crowdsourcing within large organizations. Big organizations want stasis, not mass change. This is one of the reasons that they quit evolving and are replaced by stronger competitors.

Still, I remain optimistic. Market research can harness crowdsourcing and help provide better products and services as a result. Businesses can adapt to a much more open-ended relationship with the customer. After all, weren’t we all taught in High School economics that “the customer is king?”

“I think that in 10 years if you ask a question on a social network and you get an answer you will not know if a computer or a person has answered you.”

While my first reaction to reading this was to think back to my recollections of the so-called “Turing Test” and the “Chinese Room”, it occurred to me that the evolution of chatterbots will have an impact on market research.

These so-called Artificial Conversational Entities (chatterbots) could be deployed across social networking sites much like the old door-to-door interviewers of the early Elmo Roper and George Gallup era.

If a scenario like this comes to pass, research participants wouldn’t be able to distinguish between a real (human) interviewer and an AI interviewer asking them questions.

At present there is quite a bit of experimentation with “web scraping” technologies. Nielsen was famously criticized by a Wall Street Journal article for scraping sensitive consumer data. Despite the privacy and analytical challenges I expect web scraping to continue. But, will APING (AI chatterbots conducting “interviews” on social networking sites) replace SCRAPING (web scraping programs)? It is possible? Yes. Is it probable? I’m skeptical.

I suspect that chatterbots as research tools will have several challenges in their own right:

1. Chatterbots will need to disclose that they are conducting research.
2. Chatterbots may eventually be required to clearly identify online as chatterbots (and not humans).
3. Chatterbots could be deployed so easily that they could quickly (a) become a nuisance and (b) crash respondent participation levels.

These strike me as significant hurdles.

But, with all these challenges in mind I can think of at least two (2) likely and appropriate uses for chatterbots in research:

1. Chatterbots could be utilized within large MROC environments to keep niche discussions vibrant and help the moderator manage the site on a 24-7 basis. After all, a community manager has to sleep at some point.

2. Chatterbots could be used for inbound, online, customer satisfaction feedback. They could be programmed to get to the bottom of a customer’s complaint or praise in a conversational online format (like instant message) and could relay information in real-time using text analytics.

Even these scenarios seem to be years away.

In any event, it will be interesting to see this unfold.

On the flight from Washington I had some time to think and finish reading Rachel Botsman’s new book on “the rise of collaborative consumption.”
Botsman paints a sweeping landscape of consumers cooperating in their purchase and use of products. It’s an excellent read.

Her book makes me think that one way to look at the change coming to market research is to segment these changes by endogenous change coming from within marketing research and exogenous change coming from the outside (something Smith and Raspin in their book Creating Market Insight call the “remote environment”.

As an industry we have quite a bit to be proud of, especially when it comes to recent innovation (MROCs, mobile, neuromarketing, the stuff BrainJuicer seems to cook up daily, etc.), but it certainly does feel as though a significant piece of the change is coming from outside.

And, when it comes to external change, the world Botsman paints suggests some extreme levels of change in product innovation (crowdsourced), branding (from status to purpose and me to we), product usage (shared) and product lifecycle (longer). It’s no exageration to say that the future she paints would turn marketing and marketing research upside down.

If Botsman is right, then those of us in marketing research are more likely than ever to run/manage/moderate insights communities (MROCs) and design communities (see HYVE). We’ll track the discussion of purpose-driven community clusters in real time, tightening the feedback loop.

If she’s right, brands will become much more about shared purpose and passion. And, they will be built or destroyed by a corporation’s reputation. But, this reputation element will no longer be ethereal. As I’ve noted before and as Botsman states in her book, by the end of the decade we are likely to have a kind of online reputational currency for both people and corporations (she calls these “reputation bank accounts”).

This creates in us a sense of urgency and we want to point out how these futures are already happening (in a small way) today.

In this regard, Leonard Murphy at the GreenBook Market Research Blog does us all a favor with his piece titled:

The Future Is… Now

Here Murphy outlines how many of the things Ian Lewis (research in 2020) and I are thinking about as future trends already can be found in small efforts, experiments, and new tech ventures today. For example, I believe that “serious gaming” will become a serious research tool, and Murphy points out how BrainJuicer is already experimenting with serious games for MR — today.

The challenge that we all have in thinking about the futures is that while we see the weak and strong signals for a future today, we are often at the lowest phase of the S curve looking up. As many have pointed out, the S curve appears to be a dominant pattern in all kinds of physical phenomena, including changed states and this applies especially so to technology adoption. At the beginning, adoption is slow and increases at an almost arithmetic rate, then it hits a critical mass and the adoption of that technology increases at a lightning pace. Once adoption hits near ubiquity, it levels off. Our challenge today is that in analyzing the futures of market research, we are looking at weak and strong signals for the future in the very early stages of the S curve. We expect change to come quickly, but the futures may keep us waiting longer than we would like.

Murphy does us all a favor in identifying the weak and strong signals today that will lead to change tomorrow. His piece gives us concrete examples of how the things we are talking about are not as far-fetched as they sound, and he helps us better understand where we are on the S curve.

1. Shared bicycle services or short term home-apartment rental sites or freecycling
2. The viral spread of marketing messages via social media
3. The idea of social currency based on reputation (eg “Whuffie”)

They have something in common.

They all are aspects of “collaborative consumption.”

The idea, espoused in Rachel Botsman’s new book (What’s Mine is Yours: The Rise of Collaborative Consumption), is a shift from our current consumption patterns where assets are owned individually and used infrequently to assets that are shared via repetitive lease or fractional ownership and used heavily.

One insight here is that (a) this shift is far more efficient than the existing paradigm and (2) it could only be fostered in a heavily networked, peer to peer world.

Those subscribing to the idea that this shift is occurring foresee a transition from credit to reputation (social currency), advertising to community and individual ownership to shared access.

I think this shift is happening and will intensify over time.

If this is the case, there are several implications for the insights field:

1. Reputation-based business models using some form of social currency will make every consumer a prosumer, one that cares as much about an actor’s market behavior and purpose as their pricing and product. This is why I have become so interested in consumer boycotting and “buycotting” (selecting a product to support the seller’s values) practices. If the collaborative consumption shift occurs, we can anticipate networks of consumers buying and sharing products matching their values. This leads to point #2.

2. As researchers and marketing strategists we may want to design products that our core collaborative consumers can share and want to share. Some marketers might encourage this sharing. Others may want to find ways to avoid this sharing via personalization, for example.

3. Sharing-renting may become an avenue to purchasing later.

4. If consumers begin forming collaborative consumption networks, why not just crowdsource the product in the first place?

5. And finally, there are some categories that are far more susceptible to this type of consumption than others. For example, personal care products are not likely to be shared. Only products that (a) have high rates of infrequent use and (b) are easily accessed will fit the bill. In this case, possible candidates are: bikes and other personal transport, 2nd homes, lawn mowers and other yard tools, some kitchen appliances, churches and movie theaters, books, and even luggage (for infrequent travelers).

As I’ve noted in a previous post, I think the word is being used in a general way to mean “thinking ahead of the curve” and not in the specific sense used by professionals working in “strategic foresight.”

I do think that market research will become increasingly forward leaning and future-focused and anticipate that “anticipation” will be the next buzzword in this area.

But, where will MR need to go to truly harness foresight?

Method:
In terms of method, I think MR will adopt something like the framework developed by Peter Bishop and Andy Hines in their book Thinking About the Future. MR will do this by integrating existing research techniques with the creation and analysis of possible, probable and preferred futures developed through a STEEP analysis.

Tools:
Here I anticipate the mass adoption of prediction markets, realtime Delphi panels and crowdsourced futures engines like the ones being tested by the Institute for the Future.

Conclusion:
This may ultimately result in what I’m calling “futures dependent strategy”. FDS creates multiple futures scenarios based on the social, technological, economic, environmental and political drivers operating in the present. Then it requires that these futures are mapped in two dimensions – expected likelihood and potential impact. Once this is done, strategies can be developed to (a) achieve preferred futures, and (b) avoid or mitigate unfavorable futures.

But, this will challenge market research, because as many have noted “there are not future facts.” Stretching our thinking from the now into the future will move MR into a different space, well beyond our current comfort zones defined by surveys and focus groups.

Today at ARF Susan Whiting with Nielsen surfaced “privacy” as a core challenge to the industry. She’s right.

Given the recent stories regarding privacy and web “scraping”, I think we’re very close to the boiling point on this issue.

If doors are closed based on societal privacy concerns (eg some forms of listening), what new doors do we need to open?

Proprietary insights communities and prediction markets may be one set of answers that steps around some of these privacy concerns.

Although we will always need to ask consumers questions, the trend toward “passive research” is fairly obvious.

But, what about gaming?

Could gaming enter into the MR toolbox in a serious way?

I think so.

Gaming has some intrinsic strengths that the other methods lack. First, gaming is actually fun for the participants. If we built games or simulations for consumers to play to better understand their likely behavior, people may be more likely to participate. Next, gaming can be observed. In this manner it is less intrusive. And finally, gaming sidesteps the privacy concerns that are surfacing currently in social media listening.

There are really two types of game designs for MR purposes.

The first has human players. Here I can imagine shopping games, travel games, etc. Players will be given scarce resources and will have to allocate those resources within the game format. If this type of research takes off, then I expect someone like Jane McGonigal at Institute for the Future to lead the way.

The second type will utilize digital agents in what is called “agent based computational modeling.” Here the players are actually programmed entities within a game format. The person I expect to lead this type of research is Joshua Epstein at Johns Hopkins. The author of “Growing Artificial Societies: Social Science from the Bottom Up”, Epstein has claimed that by 2050 agent based computational modeling will be how social science is done. He may be right. Already his simulations have been used for better understanding how populations might react under pandemic scenarios. In the MR world, I could see this type of simulation being used for casinos, theme parks, churches and the travel industry.

Gaming won’t become a dominant MR tool, but it could become a very useful one.

By this I mean the application of memetics to marketing campaigns utilizing research and testing techniques.

Memetics is the application of the viral analogy to the world of ideas. The idea is the virus and the brain is the host.

Consider that the world of communications is transitioning from a vertical communications environment (top down, advertising, telling-yelling) to a horizontal - peer-to-peer model. This transition from mass advertising, mass market to niche marketing, the long tail and peer-to-peer will have major impacts on the market research industry in the future.

And this begs the question of what model we use in this new communications era. The vertical communications era created a stimulus-response research design. The horizontal communications era may require a different set of tools.

One approach is to apply the ideas in memetics and epidemiology to message testing.

What would this look like?

First, it would require multiple monadic cells with each cell being a slightly different meme. Each meme would be tested for stickiness, repeatability and selfishness.

Another approach might be running a number of parallel viral experiments among multiple monadic cells and measuring the spread of the meme over time. This would be a purer test, but difficult to measure. One measurement alternative would be listening to participants across their social media “lifestreams” and charting the spread of a meme via repetition of key phrases. This would require, among other things, very strong text analytics. The first market researchers to do this will be considered applied memetic engineers.

But, there are plenty of challenges. One challenge is evolution. One would expect these experiemental test memes to evolve over time, with word and meaning changes along the way. Tracking these would require the creation of a solid analytical framework and powerful text analytics.

Will this happen?

I think so. We already are seeing numerous experiments tracking Twitter traffic to box office sales and Presidential approval scores. This kind of work should lead naturally into the work I described above.

This is why I was pleased to see Nigel Hollis at the Millward Brown blog write on the topic of “yelling, story telling, and selling”. Here he writes about Seth Godin speaking at Foro Mundial. It’s worth a read, and it highlights how others are thinking of a meme-driven communication mix in a horizontal, peer-to-peer world.