Posts Tagged market research
Now nearly five years since Omnicom Group, Inc. acquired The Modellers, LLC, founder Dr. Chris Diener has started a new company, The Analytics Team, Inc. (www.analytics-team.com). When Dr. Diener originated the idea and co-founded the prior firm in 1998, the aim was to provide advanced analytics services to other market research, advertising and consulting firms. That was the main roadmap for the first eight years. It was a “wholesale” analytics approach. It then grew into a full service market research firm creating potential channel conflict with its original clientele. Now, it has been merged with Hall and Partners, Inc. leading to further channel concerns.
The Analytics Team, Inc. is returning to this pure “wholesale analytics” service model, giving service providers a new analytics option, yet based on over 20 years experience in the industry and having done it before. On the market research side, It provides high quality multivariate analytics from maps and driver models, through chaid and segmentation, to MaxDiff and choice modeling or choice-based conjoint. But it also combines these more traditional approaches with database, social media, and marketing analytics helping internal and external MR teams to integrate all the data at hand.
Though similar to the first company, The Analytics Team, Inc. has several unique elements shaped for the needs of today’s market. Dr. Diener, the President and founder, describes the positioning of the company,
“When I first started nearly 20 years ago our choice modeling approaches were leading edge and we thus positioned the company as the one that could solve your toughest problems — sort of the pinnacle of advanced analytics. We invested heavily in very custom approaches, because there were no alternatives for what we were aiming to do. We also acquired a level of exclusivity to our brand. This also required premium fees for our work. Most research firms don’t need the ultimate complex solution. And many of the existing needs are now more standard.”
“Our partners don’t want to differentiate on complexity or handling the hardest projects. Rather they want to differentiate based on unique, innovative solutions and custom-fit insights. They want to build their own brands and potentially design their own signature or hallmark services. They want to build their own internal analytics resources across time. They are looking for a partner who can help build their business with branding, and attracting the type of work and type of client best suited for profitable growth.”
“The Analytics Team, Inc. acts as an integrated partner — consulting with them to provide reliable, predictive, custom, even white-label branded, analytics products and services. We can help them sell their services through assistance with design and in the sales process. We also serve our clients as an internal resource for their teams, training them to competently use analytics tools and think through design and application. We grow with them, but there is a time when they will use us less and rely more on their internal resources.”
You can contact Dr. Diener at firstname.lastname@example.org for more details.
In 2013, 20% of surveys were taken on a mobile device. That is double from 2012 .. and the pace of change increases into 2014. And yet, over 60% of researchers avoid surveys on the mobile platform. One of the reasons for this low use by researchers is that they don’t trust mobile surveys to be able to handle the complex kinds of questions that they employ in regular surveys.
What they don’t realize is that with 60% of American adults owning smartphones, most of these folks are doing complex tasks on their phones already. Cell phone owners have the capability and capacity to do complex surveys on their phones.
One of the most complex of survey tasks is Conjoint — also more specifically referred to as Choice-Based Conjoint or Choice Modelling. Last year, with the help of the AbsolutData team, I designed and tested conjoint on mobile. The results showed that both in the US and internationally, people are ready to do complex surveys on the mobile platform. Other than specific sampling needs, there is no reason to avoid mobile and many reasons to embrace it more consistently.
See the presentation I gave at the 2013 Sawtooth Software conference on this topic that details the findings of our international study.
If you are in market research (MR), you may debate the which is tortoise and which is hare, but you probably do see a race and competition for the prize. Or, more realistically, you may fear that others see it as a competition. The prize may be budget, recognition as the source of valuable customer insights, or winning jobs. This may be a limiting perspective. If you are in the Big Data space could see it as a race but MR is probably not on your radar or you consider the outcome a foregone conclusion. You probably believe that MR could be a valuable appendage but ultimately discount MR when viewing the main role and impact of Big Data. There may be some blind spots. However, the two areas do overlap and treating it emotionally or functionally as a competitive situation will be counter-productive and lead to lost opportunity.
Some say that focus group and survey-based market research has reached the end of its life-cycle and will be replaced by Big Data analytics. Those who fully promote this position usually have an agenda to push or they are not well informed about market research. However, it does seem that these two areas of market intelligence, customer insight, and decision support both offer valuable and maybe even critical decision support. The million, or billion, dollar question is how to integrate them and leverage them together. Will this integration be the basis for the next round of killer market research applications or the means of getting the most value from your big data investments? My thoughts in this area were triggered by the linked article that contains some comments, including one application that combines the two areas of MR and Big Data.
In both areas, those who wish to capitalize the most will figure out how to combine them to leverage value. In Big Data, folks will come to realize that there are many holes in the data, many missing links, many areas where current or past data will not suffice in predicting the future, and many areas where the value of the insights can be magnified through the leaven of custom MR.
On the other hand, MR folks either have or will come to recognize that the in-context reality and pure organic validity of Big Data adds immensely to the precision inquiry possible in MR. Big Data integration will increase MR value by bridge limitations or biases created by interviewer/researcher subjectivity, sampling frames, respondent behavioral self-report inaccuracies, perceived insights credibility, perception of frequent lack of tangible real-world action-ability, to name a few.
Seeing or specifically identifying the areas of combined value will be the first and most important step to making this happen. The next step, and arguably the more difficult one, is to actually combine the functions related to Big Data and MR to bring about a profitable union. Most corporations have these functions silo-ed separately, based on their historical origins and historically separate functions, skill-sets and processes. In fact it goes even deeper in that these two areas may compete for the same budget and in fact may feel quite competitive with each other for recognition in the organization. Executives not only have an organizational issue to resolve but likely an emotional one as well.
On the supplier side the difficulties are multiple for being able to combine these areas. First is that most MR firms do not have an understanding of or a captive skill set that encompasses Big Data analytics. They don’t have the mindset for what it is, how to get it, and what to do with it. Next, most emerging Big Data firms come from a programming, IT, data mining, or highly technical area of strength. They do not have the insights background or training and skills. The foundations of marketing strategy and consumer behavior and psychology is lighter than it needs to be. The softer, more granular or individually based understanding of motivations, attitudes, drivers, etc. and how they mix together to generate action and commitment are usually missing with folks who have been devoted to algorithmic programming or econometric modeling and mining.
The future in this area belongs to people and organization who can bridge these domains. This is a process and the sooner it begins the better and the more likely you can capitalize on it for competitive advantage. Identify the needs, find the relevant gaps in addressing the needs, prioritize and act on filling the holes and showing tangible progress through a series of quick wins.
To motivate these changes, we need to see more examples of how the two domains combine for added value. The referenced article (noted below) provides one lightly sprinkled example. I believe we will begin seeing more.
How will it play out? Will Big Data analytics be the hare? Will the hare win? Maybe fundamentally, is there a race? Maybe, we see the races either being set up or already in process and the question is how do we change the nature of the race so that instead of competitors, the tortoise and the hare are teammates?
Referenced article: http://smartdatacollective.com/sandrosaitta/94181/guest-post-mark-zielinski
“The general consensus seems to be that “big data” is never going to replace traditional research – that is, specific research methods like surveys and focus groups that deal with particular topics will always be around. These specific research methods answer the question of “what” – that is, they are concerned with empirical details. For example, an online survey may indicate that compared to 2011, this year 5% of soda drinkers no longer drink Coca-Cola on a regular basis. Where “big data” aims to change research is in the “why” – that is, the broad trends and underlying reasons why certain results have been obtained. Using our previous example, “big data” may be able to tell us that key nutritional influencers have recently been saying that carbonated sugary drinks reduce life expectancy by an average of 4 years in healthy individuals. By having both the “what” results and the “why” results, researchers can use this combination of data to have a much clearer picture of a particular situation, and potentially be able to advise their clients on how to act to obtain the results they wish to achieve.”