Social networks, big data and the semantic web are changing the practices of competitive intelligence and marketing research. These two professional practices can learn from one another to adapt and thrive in the face of these changes.
Social media represents both a challenge and an opportunity for both MR and CI.MR are not used to unstructured, non-proprietary data.CI are not used to considering the customer as anything more than an abstraction.MR is also being asked to provide predictive insights. CI has always been asked to be predictive, and this is a challenge with which we are very familiar.Two emerging technologies will challenge us both and upend both of our professional practices:Big data analytic tools will make new kinds of insights possible from new information sourcesThe semantic web (more on what that is later) will change how we interact with unstructured expressions of customer sentiment.
Today CI and MR are very separate, and it’s rare (based on my experience) that these two functions interact with one another. There is a lot of benefit to this cooperation, and there are some themes around which that cooperation should be pursued for both immediate and long-term professional benefit.
So what does the CI professional do? I often feel like a corporate “MythBuster” who challenges assumptions about the industry, trends and competition. Another analogy I use a lot is that I am the Devil’s Advocate.I am constantly asking “Is that REALLY true?” “So what?” and “What if?
All good competitive intelligence is predictive: much of the value we deliver is telling decision-makers what can happen or what is likely to happen. I generally also tell them what they can do to mitigate risk, prepare and respond to contemporary and likely future events.
Competitive Intelligence is systematic, deliberate and ethical. The intelligence cycle is an oldy but goodie, and it describes at a high-level what we do and how we do it. Every step of this process is well-structured, though there is great opportunity for creativity in the actual execution of CI.We have to know what it is the company needs to understand. I take a very decision-focused approached to competitive intelligence. I focus on projects that are going to help senior executives make the decisions they need to make that are going to be most impactful to the top and bottom line.CI professionals will conduct secondary (Internet & print) and primary (interviews and field research) in varying degrees. Big telcos tend to focus mostly on secondary research. Pharmaceutical companies, by contrast, conduct a lot of primary research. All of our methods and tools are structured and our collection methods are ethical.We choose analytical tools and frameworks that will help us understand the information we are capturing in our research in order to put the information into the correct context. There are over 300 recognized analytical tools, many of them very specialized to a given industry or situation. A bit more on two that I find most relevant in a second.We are very deliberate in how we communicate the results of our intelligence. Often we are delivering news people may not want to hear. Doing this in a way that people will understand and agree with you is very difficult.Through each project we evaluate our successes and our failures. Even a wildly successful project generates more questions. So the process begins again.
One of the analytical frameworks I use is Porter’s Five Forces. Most of you who have your MBAs will be familiar with this model. This concerns itself with the balance of power among the players in a given market to determine the intensity of the rivalry among industry players. This is also a good illustration of where the CI professional focuses their attention: on competitors, supply chains, disruptive competition and a general macro perspective on customers.This macro perspective on customers is a big difference between MR and CI. It’s actually somewhere MR can add value, because often the CI pro would benefit from remembering what and how customers’ priorities and preferences are more than our current models accommodate.
Another analytical model I use is STEEP, which stands forSocialTechnologicalEconomicEnvironmentalPoliticalI wanted to show this to you in part to illustrate that CI is not simply COMPETITOR intelligence as some might perceive. We review very broad trends that impact the firm and the industry and are concerned with much more than just the direct competition.
This is an example of one day in my life as a CI professional. My typical day includes activities that are both strategic and tactical as well as a fair amount of administrative work to manage the program.I try to keep all of our stakeholders informed with industry developments. This includes a lot of analysis of past and present developments to give the real “so what” behind what each development means for the company. I also talk a lot about what is likely to happen next in these briefings.The market & competitive intelligence program within my business unit consists of a small team of specialized professionals, a decent library of syndicated industry analysis and custom market sizing and forecasting. For 2012 we’re looking to re-incorporate new custom intelligence projects into what we’re doing. Recently I’ve been spending a lot of time figuring out what we can trim from our current activities and where we can get additional funding to drive those new initiatives.The team spends a lot of time working with sales teams involved in competitive bids to develop strategies and messaging to win. I don’t believe in FUD. What I think works best is developing messaging that convinces customers that our strengths are the most important things they need in a solution, and motivating them to ask questions that will reveal the competitors’ comparative weaknesses.I also advise the company on strategic issues. On this day I was advising the team that does our capital planning on how to prioritize spending on new product development.
Marketing Research and Competitive Intelligence
Marketing Research andCompetitiveIntelligence<br />
3 Things Make the Semantic Web Possible<br />Data standards that enable representation of complex relationships<br />Frameworks to explicitly represent knowledge of subject domains<br />Linguistic tools that enable machines to translate human language into data structures that can be processed (“understood”)<br />
Big Data Tools will Give us Ways to Use “Data Exhaust”<br />Big Data is data of volumes and complexity beyond standard analytical tools, software and hardware<br />Huge volumes of data coming from search, mobile and social media<br />Most firms do not make adequate use of “data exhaust” from existing processes and transactions<br />
MR and CI Working Together<br />Build analytical practices that balance quantitative with qualitative<br />Broaden our perspectives<br />CI needs to understand customers<br />MR needs to consider broader trends<br />Seek near-term opportunities for cooperation<br />CI working with MR to refine and analyze NPS<br />Look for software and vendors that will help use use social media and big data analytics to extract predictive insights<br />
Thank you!<br />August Jackson<br />http://augustjackson.net<br />@8of12<br />Mosi Systems<br />http://www.mosisys.com<br />@mosisys<br />Break down your data silos to unleash your competitive advantage!<br />