Business Consulting, Marketing Services, Market Research & Market Structure Studies in Houston, TX

Dr. Clark's Education and Blue Chip Experience Led to A. B. Clark Marketing's Statistical Services Expertise that Analyzes Operating Data and Sheds Light on Improvements

Dr. Clark took many doctoral level statistics classes at three universities and taught advanced statistical analysis courses at two universities. While teaching such courses, he also published many refereed academic articles dealing with analysis issues.

Just as importantly, Dr. Clark built the artificial intelligence, credit scoring, direct mail, distribution efficiency, fuzzy logic, geoclustering, neural network, prospecting, sales forecasting, spill-and-recapture, and other systems for Fortune 500 corporations. Some of these yielded "canned software" products, sold by companies like A. C. Nielsen, R. R. Donnelley's Metromail, and Fair Isaac, with the latter being the case due to a joint venture between Fair Isaac and Metromail.

Before starting A. B. Clark Marketing, Dr. Clark used univariate, multivariate, parametric and nonparametric techniques to determine how 50 million worldwide purchasers were influenced by 1,700 variables. The knowledge gained enhanced market shares on major product lines by 8-10%. Similarly, Dr. Clark improved demand forecast accuracies by 15-20% for domestic and international markets with log-linearized, LOGIT models.

Because of Dr. Clark's Fortune 500 experience, A. B. Clark Marketing conducts questionnaire, panel, focus group, mystery shopping, exploratory, descriptive, causal, correlation, and multiple regression statistical studies. These often provide Returns on Investments exceeding 1000%.

Moreover, A. B. Clark Marketing even develops systems that predict the sales of never made products for every store, so that manufacturers can better prioritize their New Product Development activities. Likewise, A. B. Clark Marketing determines the optimal media allocations for advertisers and such efforts have in the past increased the response on coupon mailings by more than 300%.

Using cross-tab, Chi-square, Kolmogorov-Smirnov, Mann-Whitney, ANOVA, MANOVA, Wilcoxon and other statistical tests, along with factor, cluster, discriminant and multidimensional techniques, A. B. Clark Marketing segments markets and helps companies develop highly successful product lines. In fact, such techniques enabled one service provider to grow its business from $100,000/day to $800,000/day sales volume and another client to increase its $80 million retail sales by more than 30%.

Since A. B. Clark Marketing has a VERY high level of expertise, it is uniquely qualified to handle your statistical business needs. This is true whether they are for an analysis of your databases to spot profit-enhancement opportunities or for Six Sigma Quality Assurance programs. A. B. Clark Marketing builds Management Information Systems and more sophisticated Decision Support Systems that tell management when to offer promotions, run advertisements, manufacture products, order raw materials and schedule shipments. As a result, shareholders in these companies have seen the value of their stock increase.

Specific Problem Handled by A. B. Clark & Beneficial Results

Problem: One of the largest battery manufacturers in the United States developed an extended life video camera battery pack. However, management determined that it would be too expensive to get the product into stores due to there being slotting fee costs of approximately $100 per store.

Solution: The remedy that A. B. Clark came up with was to obtain a list of individuals who owned a video camera. This list, prepared from warranty card information, had three million households on it. Based upon the information on the database, it was determined that it would be better to mail to a sample of the three million households, rather than all three million. Then, after determining that the final model would likely have five variables (i.e., based upon the quality and types of data), it was determined that the proper sample size was 30,000. Thus, promotional literature was sent to a random sample of 30,000 households drawn from the database with three million video camera households. After a six-week period, some of these 30,000 households responded positively, while others did not respond to the offer.

Since this sample of 30,000 households had been divided in half before the offer had been mailed, half of the sample was used to build a predictive model. The model was then tested by predicting the results for the other 15,000 households and comparing the prediction to the actual results for this holdout group.

Once the scoring model was built and tested, each of the remaining households on the three-million-household database was then given a probability score of buying the video camera battery pack, based upon the mathematical model. The significant variables in the model included things like presence of small children (as well as children of marrying age), travel (i.e., especially international), income, ethnicity, and lastly whether or not the household had home exercise equipment. This latter variable was highly significant since it captured households who had enough money to belong to a gym, but who instead purchased exercise equipment due to a desire to spend more time at home.

By mailing to households with "breakeven and better" probability scores, the battery company made $2.7 million profit. However, if it had mailed promotional materials to all the video camera households on its database, it would have made $1.7 million, and if had sold battery packs in stores it would have lost money for at least two years. The result of A. B. Clark's effort was that the extended life battery manufacturer made $1 million more than it would have otherwise.