Online marketing research

IBM Journal of Research and Development, Sep-Nov 2004 by Agrawal, A, Basak, J, Jain, V, Kothari, R, Et al

The net result of active learning is that each of the groups formed is of compact size, relieving the downstream processing load and reducing the total time required to obtain business intelligence. When an incentive is offered to the participants (such as an e-coupon or a discount on a future purchase, say for participation in a survey), active learning also minimizes the total amount of expense incurred (in terms of the cumulative total of the discounts). Further, it minimizes the number of users that are exposed to change in the marketing variable. This localization ensures that OMR can be conducted with minimal impact to the normal operation of the site.

Implicit and explicit experiments

Implicit experiments do not disturb the normal shopper flow and rely on the observed response to a change in a marketing variable for inferring the relationship between the marketing and response variables. On the other hand, explicit experiments disturb normal shopper flow and require the explicit participation of the shopper. Consider, for example, the task of estimating the demand as a function of price. An implicit experiment may create multiple matched groups and expose each matched group to a different price (by offering coupons of different face value to each matched group). The difference in the response (user acceptance of the coupon and subsequent redemption) can then be used to construct the relationship between price and demand. An explicit experiment, on the other hand, can be based on a survey in which the users are asked to indicate the likelihood of their purchasing the product at different price points. Implicit experiments are less distracting and often more accurate, since they do not make the shopper conscious of a question being asked and have a greater probability of capturing the shopper's true intent. To the greatest extent possible, OMR should use implicit experiments.

These key innovations serve as cornerstones for systematic, rapid, and accurate online marketing research. Clearly, aspects such as matched groups are difficult to create in traditional forms of marketing research, but they can be constructed online, thus improving accuracy. Similarly, the use of implicit experiments (to the greatest extent possible) ensures greater accuracy, while the use of active learning minimizes the cost (especially if a coupon or other price-reduction mechanisms are used) and increases the speed. Besides enabling marketing research for businesses with budgetary constraints, online marketing research provides an opportunity for continual adaptation of the operational and strategic aspects of business to enterprises as well as small and medium-sized businesses.

4. Example of actionable business intelligence from OMR

The concepts presented in the previous sections are surprisingly powerful in the range of actionable business intelligence that can be provided to a merchant. We provide a small sampling of the possibilities:

* Determining price sensitivity: The price sensitivity of a product can be measured with matched groups, with each group being offered a variable discount based on offering e-coupons of varying face value to the individual groups. The response can be used to approximate the (unknown) relationship between price and demand. Segment-specific price sensitivity can be similarly determined, with all of the individuals in each group being restricted to the specific segment for which the price sensitivity is desired.