Posts Tagged Analytics
The times are changing and changing fast. Just like the data for these times. It comes fast. It changes often. This opens up new opportunities for savvy marketers. However, these kinds of opportunities need a structural and organizational mandate. These opportunities are big and the best competitors are capitalizing on them. What are the opportunities? Real-time automated customer interaction — customized customer interaction. That’s a mouth full but very meaningful — real-time automated customized customer interaction.
This is the peak of the big data mountain value. Its a mountain and that’s why it takes serious organizational commitment. Building or moving mountains. First, you need the vision of where you want to go. Then you have to make the mountain. That involves getting your data assets and resources properly organized. Then you need an infrastructure of BI and reporting to glean low hanging fruit value and initiate best practices organization governance with the right information flows. Then you take this data further through exploration and analytics. Finally you apply those analytics into your automated, customized framework.
In the ideal world — you can piece it together like this. But more likely than not, you must work in the context of legacy structures. You must incrementally build. You must generate short term wins, rally the troops, generate greater commitment and build into a vision. This is where the rubber meets the road for most of the folks who dive into big data.
Its a constantly evolving world with new tools and technologies. Its a challenging world where intellectual resources and manpower is constrained. Its a world where going it alone has some serious risks — risks of doing it with less effectiveness but also risks of not getting it done at all.
This article, authored by me, at CMO.COM, motivates the need to be on this journey and to plan your partners carefully.
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.”
You have probably seen highly charged atmospheres in companies where the executive team has decided that the company must have a Big Data strategy. Those tasked with then putting it together feel this pressure to prepare an impressive plan that represents a big leap and a lot of investment. It’s similar to what happened in the 90’s with CRM and then what happened right around the 2000 mark with the Internet. Faced with the rising wave of interest and investor questions, companies risk making knee-jerk reactions which all too often result in corporate and personal disaster. Big Data is on the same trajectory. We see it in our everyday conversations with others, in the press and promotional arena. It has also been shown in the Gartner “Hype Cycle” for 2012 — http://www.infoq.com/news/2012/08/Gartner-Hype-Cycle-2012.
Leveraging Big Data in your company does not have to be mysterious, intimidating or expensive. There are different ways to approach the elephant — and as the adage goes, maybe the best way it to take it one piece at a time to digest it properly and align it within the organization.
One approach I’d recommend is doing what I call the 3-V Application Value analysis. This is where you assess the specific Big Data that you have access to and then look at the differences that Big Data offers from what data, analyses and resulting applications you currently use. Do this by each of the V’s that define Big Data: Velocity, Volume and Variety. This leads to an opportunities and costs analysis that will then be the basis of a plan of action. This is a reasoned approach to getting the best value out of your investment in Big Data.
For instance, let’s take Velocity. What is it that is different about Big Data because of Velocity? And, when looking at the form of Big Data you have access to, what does that imply for the applications you could build? A very high level assessment is where you would start and it may look something like this:
- Opportunities. The opportunities that come from high velocity data include the development of real time or more immediately updating applications. These might be
- New and more relevant executive dashboards
- Tools that allow you to make adjustments to engagement campaigns while they are executing
- Development of individualized recommendation systems
- Quickly identifying product quality issues
- Better capitalizing on unforeseen benefits or uses of your product or service
- Costs. The costs of taking advantage of these opportunities would be driven by a number of factors, including:
- Instituting new layers of data connectivity
- Building machine learning and continual statistical processes layers
- Designing and implementing real time reporting and simulation tools
The benefit of this kind of approach is the creation of a rational framework for advocating specific kinds of Big Data investment. A team can examine the detailed differences between existing data being used and Big Data, link those to potential new analyses or applications, and tie them to specific investments. The contrasting and incremental nature of this approach takes the mystery out of Big Data by relating it to what you have experience with and providing a stepping-stone approach that builds on strengths and ensures investments will be made with confidence and less risk.