When people make decisions they rely on gathering and analyzing information. If you plan on buying a new stereo, you want to make sure you make a good purchase. You want to compare product features on different models, prices in different stores, available support and warranty, what other customers have experienced using the product, and a host of other information. In short, the more information you can base your decision on, the less likely that your purchase will be a bad one.
For small decisions people can get by with less information. The bigger the decision, the more informed you want to be. Big decisions typically involve a higher level of risk, and you want to minimize that risk. So you gather relevant data and analyze it to improve the odds of a successful decision. For businesses, whose decisions have much larger impact than individual consumers’, this is even more important. Companies need not only information, but information put together in a manner that makes sense in the context of their decisions. They need Market Intelligence.
Market Intelligence is a collective term for information about a company’s environment, which is gathered, structured, analyzed and presented to decision-makers. In this environment we find competitors and their products, customers and their experiences, strategies, websites, regulations, innovation, and a host of other factors that influence the market- and ultimately business decisions.
Companies need to be aware of their surroundings to reduce risks and improve their likelihood of success. They need to know when competitors launch new products or change their strategies, when and how government regulations will affect the industry, how customers and other groups experience interacting with the company, how the company’s new website compares to the old one and to competitors’ websites, and the list goes on.
Why do companies need to know these things? Because if they don’t they are running their businesses based on random decision-making. That doesn’t sound good at all, but the truth is that many decisions are made on gut-feeling or very limited information. This might come as a surprise in an era when information is more available than ever before.
In fact some managers would argue that they don’t use Market Intelligence because they are too busy (too busy to make good decisions?). Or they say they don’t know where to start looking, and they could waste days without finding what they are looking for. And even if they do find something, they don’t have the proper systems and the continuity required to optimize the value of the information. In short, their Market Intelligence efforts are performed ad hoc, require a lot of resources, and the results can vary.
At the same time, these managers all realize the value of good information. They need to be able to provide the necessary data and analysis to back their decisions. This is especially important when businesses are facing reduced sales revenue and they can’t afford to make mistakes. That is when the value of Market Intelligence is truly appreciated.
So, if Market Intelligence is a requirement for good decisions, and it’s difficult to get it right for companies on their own… then what should they do? Here’s an idea. Maybe someone else can do it for them. Specifically, maybe we at MACAW research can do it. We work with Market Intelligence all the time and we know how to get it right. Go ahead, check out our website and drop us a line. We won’t bite.
http://www.macawresearch.com
Showing posts with label market intelligence. Show all posts
Showing posts with label market intelligence. Show all posts
Wednesday, July 15, 2009
Sunday, May 31, 2009
Testing Wolfram Alpha
Wolfram Alpha is Stephen Wolfram’s much hyped search engine project. We decided to give it a little test run to see how it can be of use in market research, and wanted to share our experiences with you. We are probably not the only ones in the market intelligence community that are curious about Alpha: http://www.wolframalpha.com
It is important to distinguish Wolfram Alpha from Google and other text-based search engines. From the hype one might almost be led to believe Alpha has artificial intelligence (AI) and can compute an answer to almost anything. It’s definitely not an AI, but uses a series of engineered scripts to provide some interesting results in select areas. The scripting is based on Wolfram’s Mathematica software, and numbers is what Alpha is good at.
Alpha is not a universal search engine. It does not return information on for example “EU regulations” or “Adam Smith’s invisible hand”, giving you the quickly annoying null result message “Wolfram Alpha isn't sure what to do with your input”. Alpha excels at numeric data and performing calculations, though, and it can conjure up useful tables and charts in instants, but only based on facts it has stored in its databases. In some instances, Alpha utilizes external sources too, typically for frequently updated data such as stocks.
For instance, we entered “DnB NOR”, which is the largest financial institution in Norway, and Alpha returned latest trade data and returns, a chart with price history, performance comparisons and correlations with various indices in table and various chart formats, projections for future pricing and alpha, beta and R Square values for daily returns for DnB NOR compared to S&P 500. Wow. Impressive, assuming the information is correct. To verify some of the data, we went to the Oslo Stock Exchange website and checked the latest trade value and weekly, monthly and year-to-date returns. The latest trade value was identical, but the return values were a bit off, as in Alpha reported an additional +5% on 30 day-return and -10% on year-to-date return. Hmm. That’s a lot.
This led us to check out the Source data link provided on Alpha’s search result page. It gave a long list of finance-related sources ranging from academic books to sites such as Morning Star, with a disclaimer that not necessarily all sources were used in the results. This can make quoting a source for particular data difficult, as the source list seems to apply for the search result page as a whole rather than for individual elements on the page. The Oslo Stock Exchange was not on the list, by the way, but several sources for aggregated financial data were.
We decided to try one of the recommended searches Alpha provides on its front page, and thus entered “EU populations.” Alpha returned a summary with the total EU population, the country with the highest population and the lowest, a chart with historical developments for the total EU population (does not say whether it includes ascension member states), a list of top 5 and bottom 5 countries ranked by population size – which can be expanded to a full list with a few clicks, a distribution plot with unspecified data (what does it show?), along with some demographic data measures for the countries that have the highest, lowest and median values for the population, population density, population growth, life expectancy and median age. This part didn’t strike us as intuitively very practically organized. A few clicks at a More option for yielded some additional demographic variables, such as annual deaths and birthrate fractions.
Of high importance, we were unable to find references to which years the various population data stem from – in fact there seems to be hardly any meta data available. We could be comparing years-old figures for some parameters and countries with very recent data for others without knowing. The source list doesn’t provide us much help in this case either. This is a major issue that needs to be addressed if Alpha is to be used by market intelligence professionals.
Summarizing briefly, Wolfram Alpha currently can produce substantial amounts of data on select subject matters of quantitative nature. For market researchers looking for quick access to on-the-fly generated charts and tables on data otherwise obtained and structured via other sources, Alpha could be a low-cost solution. However, seeing the source references are vague and Alpha seems to lack very important meta data, it seems more like a tool for a) the general public with simple data requirements, b) students and specialists in certain fields with a need to perform mathematical and statistical computations without using desktop applications.
We’ll definitely keep tabs on Alpha and play around with it some more, but currently it doesn’t strike us as something that will revolutionize the market intelligence industry. If anything, it is a supplement to other search engines and tools. As far as search engines go, Wolfram Alpha is certainly not a replacement for Google, but then again, it doesn’t intend to be either. We look forward to seeing Alpha evolve.
It is important to distinguish Wolfram Alpha from Google and other text-based search engines. From the hype one might almost be led to believe Alpha has artificial intelligence (AI) and can compute an answer to almost anything. It’s definitely not an AI, but uses a series of engineered scripts to provide some interesting results in select areas. The scripting is based on Wolfram’s Mathematica software, and numbers is what Alpha is good at.
Alpha is not a universal search engine. It does not return information on for example “EU regulations” or “Adam Smith’s invisible hand”, giving you the quickly annoying null result message “Wolfram Alpha isn't sure what to do with your input”. Alpha excels at numeric data and performing calculations, though, and it can conjure up useful tables and charts in instants, but only based on facts it has stored in its databases. In some instances, Alpha utilizes external sources too, typically for frequently updated data such as stocks.
For instance, we entered “DnB NOR”, which is the largest financial institution in Norway, and Alpha returned latest trade data and returns, a chart with price history, performance comparisons and correlations with various indices in table and various chart formats, projections for future pricing and alpha, beta and R Square values for daily returns for DnB NOR compared to S&P 500. Wow. Impressive, assuming the information is correct. To verify some of the data, we went to the Oslo Stock Exchange website and checked the latest trade value and weekly, monthly and year-to-date returns. The latest trade value was identical, but the return values were a bit off, as in Alpha reported an additional +5% on 30 day-return and -10% on year-to-date return. Hmm. That’s a lot.
This led us to check out the Source data link provided on Alpha’s search result page. It gave a long list of finance-related sources ranging from academic books to sites such as Morning Star, with a disclaimer that not necessarily all sources were used in the results. This can make quoting a source for particular data difficult, as the source list seems to apply for the search result page as a whole rather than for individual elements on the page. The Oslo Stock Exchange was not on the list, by the way, but several sources for aggregated financial data were.
We decided to try one of the recommended searches Alpha provides on its front page, and thus entered “EU populations.” Alpha returned a summary with the total EU population, the country with the highest population and the lowest, a chart with historical developments for the total EU population (does not say whether it includes ascension member states), a list of top 5 and bottom 5 countries ranked by population size – which can be expanded to a full list with a few clicks, a distribution plot with unspecified data (what does it show?), along with some demographic data measures for the countries that have the highest, lowest and median values for the population, population density, population growth, life expectancy and median age. This part didn’t strike us as intuitively very practically organized. A few clicks at a More option for yielded some additional demographic variables, such as annual deaths and birthrate fractions.
Of high importance, we were unable to find references to which years the various population data stem from – in fact there seems to be hardly any meta data available. We could be comparing years-old figures for some parameters and countries with very recent data for others without knowing. The source list doesn’t provide us much help in this case either. This is a major issue that needs to be addressed if Alpha is to be used by market intelligence professionals.
Summarizing briefly, Wolfram Alpha currently can produce substantial amounts of data on select subject matters of quantitative nature. For market researchers looking for quick access to on-the-fly generated charts and tables on data otherwise obtained and structured via other sources, Alpha could be a low-cost solution. However, seeing the source references are vague and Alpha seems to lack very important meta data, it seems more like a tool for a) the general public with simple data requirements, b) students and specialists in certain fields with a need to perform mathematical and statistical computations without using desktop applications.
We’ll definitely keep tabs on Alpha and play around with it some more, but currently it doesn’t strike us as something that will revolutionize the market intelligence industry. If anything, it is a supplement to other search engines and tools. As far as search engines go, Wolfram Alpha is certainly not a replacement for Google, but then again, it doesn’t intend to be either. We look forward to seeing Alpha evolve.
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