Turning to the second case, if a brand has low conversion to sales from consumer attitudinal metrics, but high responsiveness to marketing, we label that scenario “ineffective marketing focus.” For example, brands that invest substantially in consideration-set enhancing advertising, may witness lifts in advertising that do not translate into sales movements. A case in question is water brand WA with respect to advertising and consideration. In contrast, for the same water brand WA, increases in advertising generate awareness lifts that convert to sales. Hence, for water brand WA, an “effective marketing focus” is on the consumer attitudinal metric of awareness rather than consideration.
Third, if the attitudinal metric has high sales conversion but does not respond well to increased marketing spending, that would result in an “ineffective marketing lever” scenario. This is the situation that shampoo brand SB finds itself in with regard to sales promotion and consideration. This also applies to shampoo brand SA with respect to sales promotion and awareness. In contrast, increases in shampoo brand SA’s advertising generate awareness that converts into sales. Hence, advertising is an “effective marketing lever” for shampoo brand SA to generate sales conversion from a lift to awareness, while promotions are not.
Finally, if the attitude metric has high sales conversion, and there is high responsiveness to marketing, we label that as a situation with “long-term potential.” For example, water brand
24
WA has sales conversion from awareness, and awareness has a high responsiveness to all marketing actions. This offers an opportunity to allocate marketing resources to move the needle on the consumer attitudinal metric of awareness and eventually to long-term sales lift.
Our research opens up several avenues for future work. One area is to examine alternative functional forms on the relationship between attitudes and sales. These could be an assessment of the relative performance of log-log models, log-linear models, as well as other forms of non-linearity. Moreover, the effectiveness of marketing actions (e.g., promotions and advertising) could be modeled as functions of awareness, consideration and liking. In addition, while we model potential as the remaining distance to the maximum for its ease with respect to elasticity definitions, future research should compare alternative operationalizations (e.g., square root of the remaining distance to the maximum etc.) as well as alternative models and estimation techniques (e.g., Bayesian Vector Autoregressive Models).
Future research should also explore category comparisons with even higher levels of consumer involvement, such as durables and high-value services, possibly using data at different time intervals (e.g. weekly, monthly, quarterly, etc.) and including that of competitors. Comparisons between brands could also be made in a more systematic way on the basis of their strategic orientation, for instance in terms of their degree of differentiation. If individual-level attitude metrics are available, these could be used in more granular response-model specifications. Moreover, data on the profits gained from better decisions would enable managers to weigh them against the cost of collecting attitudinal metrics, thus providing an ROI measure for such data. Indeed, the need for attitudinal metrics that match the transactional records is a limitation of our approach. Such attitudinal tracking data are typically survey based, which is costly and subject to sampling error. However, the digital age offers new opportunities in this