Data Intelligence, Business Analytics
Inform your marketing team as to which set of features to highlight, within a product, on a product conversion campaign for current customers or prospects. This design can be extended for copy and image influence before campaign targeting.
When we are done you will be able to simulate the response rate to a campaign as a function of how you mix together emphasis on brand features, as copy, image, etc. This is a great opportunity to partner with creative types and marketing managers in a cost efficient research design.
In this example we will use the term “product”, but if your brand is the product, or a feature of the product you are selling, which it is more often than not, then you can simply work that into the design.
We are not changing the product features, but rather using marketing intelligence to construct a compelling presentation of which features to sell based on one’s likelihood to respond to a product campaign.
The impact of this method can be reflected in innovative dashboard techniques .
If you do not present to executives, there will always be someone willing to proselytize your research if it is worthwhile, so make it easy for them by a clear and concise study pitch, sell the benefits, keep the cost low. If you are a behavioral scientist, this will simply add to your mystique and may justify your decision to wear black all the time.
The method discussed here is useful because some product features, such as car insurance, mobile pricing plans, checking accounts, and health insurance, are more often than not the result of some serious considerations. This makes it unrealistic to adjust product features on a campaign-by-campaign basis.
So the solution is to choose which specific product features to highlight directly in ad copy and graphics, or perhaps more subtle and indirectly through various persuasion techniques designed to bypass critical thinking.
We are going to highlight brand features depending on the scored probability of a response model. Those more likely to convert in a campaign might have different needs and wants than those least likely to respond.
Building out products with varying features often includes intense market research, which has to reconcile with a firm’s ability to actually deliver customer needs and preferences in a profitable manner.
It is truly a unique optimization problem to maximize profit within the constraints of a complex network of resources while also maximizing customer attractiveness. We will address this in another research brief, but this lesson is necessary in order to make more advanced maneuvers.
Intense research efforts can result in a combinatorial explosion of different choices for consumers, which pose a validity problem for researchers . Various techniques have been developed to deal with these situations in Conjoint Analysis (CA) research, but they have fancy names that hard to pronounce.
No matter what CA method is used, the method discussed here assumes that the brand features are orthogonal or unrelated. This can be confirmed using the data from the brand feature study. The levels of the features can, however, be correlated. For example, if a deductible is reduced as the price for a policy increases that is less problematic if minutes on a mobile plan, in the eye of the consumer, are always as equally important as number of texts per month. CA forces these choices, but since we are modeling choices as a function of probability to respond, two correlated choices might be a compelling alternative explanation for the variability in choice ranks rather than the probability to respond.
Oddly enough, this is a nested CA when you consider it as a whole, but since all the work has been done and product specifications have been considered, you only have to complete the final leg.
You could do this as a nested part of an original CA to optimize cash flow, but we will build on this as part of another research brief because there will be constraints per your organizational infrastructure assets. If you are thinking constraint-based optimization-ish, please get out of my head because I am about to type the method below.
Questions, Comments, Smart Remarks? Always welcome.
Ramirez, Jose Manuel (2009). "Measuring: from Conjoint Analysis to Integrated Conjoint Experiments". Journal of Quantitative Methods for Economics and Business Administration 9: 28–43. ISSN 1886-516X.
Flanigan et al. , 2012 “Targeting Utility: Penalizing Response Models As a Function of Practical Bias In Multivariate Survey Response To Increase Customer Relevance For Targeting Applications In Digital and Offline Advertising”
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 M. Biderman University of Tennessee at Chattanooga (Genius, Mentor, Life Coach at 5 year intervals,-Personal Communication) -