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29 Mar 2010 16:36 UTCMon 29 Mar 2010 - 4:36 pm UTC
I'm going to a job interview at a market research firm. They have people their who make hierarchical Bayesian models. I'd like to know, in short and simple terms, what they are useful for. I don't need to know the details. I certainly don't need to understand the maths.
In short I'm just looking for a paragraph or two on hierarchical Bayesian models and their use for market research. Specifically:
- what they're useful for
- why they're better than other techniques
- any flaws or limitation
I have a layman's working understanding of a few basic concepts (sampling, correlation, statistical significance) but that's about it.
A few sentences I can understand is fine, but links to 10 articles I can't understand is not helpful! Please note that I've already tried Googling this, and found plenty of articles but not been able to understand them. Terms like 'priors', 'Monte Carlo' etc are beyond me.
You're welcome to either answer by writing a helpful paragraph or two, or by finding an article that I can understand!
Finally, I need this by 10am GMT Wednesday (31st) morning.
30 Mar 2010 11:25 UTCTue 30 Mar 2010 - 11:25 am UTC
HIERARCHICAL BAYES MODEL
"Hierarchical Bayes models free researchers from computational constraints that allows researchers and practitioners to develop more realistic models of buyer behavior and decision making. Moreover, this freedom enables exploration of marketing problems that have proven elusive over the years, such as models for advertising ROI, sales force effectiveness, and similarly complex problems that often involve simultaneity." (Reference 1)
Whenever there is a lack of data or different sets of data from multiple Reference s, scientist uses to predict future event with more confidence. Actually, these researchers take whatever data is available to them and carry out experiments to strengthen the results from original set of data. As these experiments continue to, evidence becomes stronger. As evidence becomes stronger, the lack of data effectively transform into a reliable data set. Loaded with new information, researchers predict future probabilities.
Here is the process:
1. Researcher have small sets of data from variety of different sources
2. Researchers conduct experiments to uncover more data from the original small set of data
3. Overtimes, data becomes large and is supported by evidence.
4. Based on new data set, researchers make future probabilities.
WHAT THEY’RE USEFUL FOR?
What is the Problem and How Bayesian Method helps solve the problem?
"Bayesian method is a useful tool for modeling multi-faceted, non-linear phenomena such as those encountered in marketing.
The problem with marketing data is that it is characterized by many "units" of analysis (e.g., many respondents, households or customers), each with just a few observations. The lack of data at the individual-level, coupled with the desire to account for individual differences and not treat all respondents alike, results in severe challenges to the analysis of marketing data. There often is not enough data to infer about a specific respondent's preferences, or their sensitivity to variables like prices, without making some educated guesses (e.g., people would rather spend less than more, holding all else constant) or building up a model that bridges the analysis of respondents to each other." (Reference 1)
WHY THEY’RE BETTER THAN OTHER TECHNIQUES?
"In application after application where respondents provide multiple-observation data, HIERARCHICAL BAYES MODEL estimation seems at least to match and usually to beat traditional models. Conjoint analysis is a prime example of an application that benefits from HIERARCHICAL BAYES MODEL estimation.
HIERARCHICAL BAYES MODEL permits estimation of individual-level models, which lets marketers more accurately target/model individuals. More specifically, HIERARCHICAL BAYES MODEL permits estimation of models too demanding for traditional methods: even when estimating more beta coefficients per individual than there are individual observations.
Aggregate estimation models confound heterogeneity and noise. By modeling individuals rather than the average, HIERARCHICAL BAYES MODEL can separate signal (heterogeneity) from noise. This leads to more stable, accurate models whether viewed in terms of individual- or aggregate-level performance.
The draws (replicates) for each respondent provide a rich Reference of information for more accurately conducting statistical tests and, for example, estimating nonlinear functions of parameters such as shares of preference." (Reference 2)
FLAWS OR LIMITATIONS?
There are very limited programs that can do complex HB calculations.
Most software will need to be customized for each new model.
Complex codes are required to program HB models.
Even after large calculations, variance in parameters may still be large.
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