The Socratic Brand Power Rating™ System

Historical and Theoretical Background. The quest to deliver a stable model that links a firm's marketing actions to a measurable return on investment has been the goal of marketing departments for at least the past three decades. Many theorists have attempted to link advertising, promotion, communications, public relations, sales strategies and other direct market actions to replicable and predictable outcomes that have a direct correlation with financial performance.

Since the mid-1990s a model that has shown a great deal of promise is the "sales funnel" concept.

The sales funnel model utilizes the "Awareness-Interest-Desire-Action (AIDA) framework and other planning concepts…[and has been particularly well] adapted to fit high tech services." (Dunn & Probstein, 2003, p 7.) In essence, this framework measures the power of a firm's brand—through its marketing activities—to directly influence the proportion of people who, once aware of the brand's presence in a market, are eventually converted to loyal, repeat customers. At each node of the sales funnel, brands tend to lose share. Precisely at what point the losses take place in the funnel are elements of the model that provide great diagnostic power for managerial action [See Figure 1].

Figure 1: The Historical AIDA Framework

Historically, the AIDA framework has been built on theories relating to the relationship between the customer and firm. The sales funnel model borrows from work that establishes that the stronger the relationship between the firm and the customer, the greater the loyalty due to higher barriers to switching brands.

An early theorist, Ford (1980) put forward a relationship development model that consists of five stages that directly relate to the AIDA framework:

• The pre-relationship stage - or the event that triggers a buyer/supplier to seek a new business partner.
• The early stage - where experience is accumulated between the two parties although a great degree of uncertainty and distance exists.
• The development stage - where increased levels of transactions lead to a higher degree of commitment and the distance is reduced to a social exchange.
• The long-term stage - that is characterized by the companies' mutual importance to each other.
• The final stage - where the interaction between the companies becomes institutionalized. (quoted in Honeycutt, Ford & Simintiras, 2003, p. 256)

Another way of stating the "institutionalized relationship between companies," is loyalty, which in turn, has been shown to have a direct correlation with reduced costs and greater market share. As described by Frederick Reichheld (The Loyalty Effect, 1996), satisfied and loyal customers are less costly to serve, are less price sensitive, and tend to allocate more of their category dollars to the brand.

The Socratic Brand Power Rating™ (BPR) System

Since 1999, we have studied many versions of the sales funnel form of measurement and have synthesized an improved version of brand power modeling with very strong correlations with current market share, but also has shown to track successfully against directional changes in future share.

The Socratic BPR system modifies the AIDA framework to measure four strong components common to most market conditions (Awareness-Consideration-Preference-Purchase Intent), and creates a single index number that indicates the overall efficacy of a brand to move customers down the sales funnel. A representation of the Socratic BPR is shown in Figure 2.

Similar to the AIDA framework, the BPR measures the drop-out of potential customers at each purchase decision node within the funnel. The degree of drop-out from start-to-finish indicates the efficiency with which the brand maintains control of the purchase process. The strongest brands are well known and convert the majority of the customers aware of the brand's presence into repeat buyers. Conceptually, the purchase decision conversion process can be described as follows:

Figure 2: The Socratic Brand Power Rating™ System

• If a customer is not aware of a brand (in the relevant market segment), he or she cannot consider it for purchase
• If the brand is not considered, it cannot be preferred as one of the short-list of acceptable competitive substitutes
• If the brand is not one of the preferred brands, it is highly unlikely to be purchased on a loyal basis.

The BPR calculation itself is based on two market-proven realities:

1. The higher a brand's initial awareness, the stronger its general position vis-à-vis lesser known brands that must struggle (with both time and money) to make the market aware of their entry; and
2. The more people that are converted from simply "being aware of a brand" into being loyal customers, the stronger the brand's long term prospects for holding onto a share leadership position.

The BPR, therefore, is the average of the initial total % awareness and the conversion rate (% of those aware who are converted into customers).

Socratic Brand Power Rating Calculation

The Brand Power Rating for any brand always falls on a 0 to 100 scale, where 100 means that 100% of the people in the market (based on a scientific sample) are aware of the brand's products and/or services and 100% of them have a strong purchase intent for those products and/or services. This would represent a virtual monopoly and rarely, if ever, exists in the real world; however, scores for some very strong brands frequently do reach the 85 to 90 mark. A BPR of "0," on the other hand, represents a brand for which there is no awareness, nor is there any purchase intent. We frequently see weak brand BPRs in the 10 to 20 range, and only very rarely below 10.

In order to quickly communicate the meaning of a particular BPR score within a specific market, a qualitative scale has been created [See Table1] to describe the competitive power associated with various levels of BPR.

Table 1: BPR Point Interpretation

This process can also be depicted as a waterfall chart that shows the amount of "leakage" at each node [See Figure 3]. This brand is quite strong with a BPR of 78, indicating that it falls into the "Dominant" category of brand.

Figure 3: ACPP Component Trend Declination of the Socratic BPR

Analyzing the Trend Declination

Simply understanding the overall BPR is not enough for management to take appropriate action, because the cause of a lower-than-expected BPR can come from many sources. As customers pass through the sales funnel, "brand bottlenecks" may occur (Chatterjee, Jauchius, Kaas & Satpathy, 2002). These bottlenecks are represented by large jumps or gaps in our waterfall chart. At each node of the funnel, the actions needed to correct a large drop-off of customers on their way to becoming loyal purchasers differ.

As the ACPP funnel progresses from Awareness to Purchase, the level and types of actions change from more strategic to more tactical actions [See Figure 4]. Generally, the strategic actions tend to take longer and cost more to implement than the more tactical actions. For example, establishing Brand Awareness usually requires a large advertising investment and takes a long time, particularly if there are other more well-established brands in the market.

Figure 4: General Trend Declination of ACPP and Associated Brand Actions

This should not be interpreted as meaning that tactical programs are either cheap or fast to implement. If Purchase Intent is being hampered by pricing problems or distribution issues for example, the degree to which actions must be taken to influence the final purchase decision can, in fact, be very expensive.

Commonly Observed Problems

Over time, we have seen that brands operating within a niche technology market (either B2B or B2C), display any number of common issues within the ACPP sales funnel.

Low Initial Awareness

As mentioned previously, low Awareness is a major factor in depressed BPR scores. Unfortunately, it is also one of the more difficult, expensive and time-consuming problems to correct. The standard prescriptives include any number of communications programs, such as broadcast or direct advertising, public relations work, word-of-mouth campaigns and outreach through institutional channels in order to raise the general awareness and create positive associations with the brand.

Figure 5: Trend Declination of ACPP: Low Awareness

Loss of Inclusion in the Consideration/Preference Set

Another commonly observed bottleneck is the drop-off between initial Awareness and Consideration. Consideration is defined as a brand cohort that would be generally acceptable as a substitute for other brands in the market. If people are aware of a brand, but still would not consider it, there is usually something wrong with the brand's reputation. Here, prescriptive activities include fixing quality, performance and/or value perceptions and communicating the "new and improved" brand-promise to the market.

Consideration problems can also be linked to "Preference Inertia" (MacElroy & Wydra, 2004), in which the market is "frozen" in loyalty to an existing brand that is "good enough" so as to not induce shopping for new alternatives. In this case, programs to induce trial (or re-trial) designed to demonstrate the improved and/or unique benefits of the brand, can help move customers (usually those with low levels of involvement in the category) from simple Awareness of the brand to its active Consideration.

In many cases Consideration and Preference are closely associated (if there aren't many brands in a niche market, the brands that would be "considered" are often the same ones as those "preferred.") If there is a bottleneck in Preference, however, corrective actions may often include activities that further segment and target specific needs and desires, so as to raise the brand's relevance with target groups and to increase those customers' bonding with the brand.

Figure 6: Trend Declination of ACPP: Low Consideration or Preference

Major Bottleneck at Point of Purchase

In some instances, the bottleneck in the funnel occurs at the final steps of securing a purchase. There are myriad possible reasons for this fall-off, including channel partners being influenced to promote other brands, price shock, competitive promotional activity, difficulty in promoting the benefits through the packaging, and so on. Most of these problems are addressed with tactical programs rather than strategic initiatives.

The types of programs that seem effective are as diverse as the problems they seek to correct. Examples include: Key city competitive funding of merchandising and local promotional advertising, channel promotions (spiffs), enhanced merchandising and point-of-sale collateral, improved packaging for increased shelf impact and findability, and the use of periodic promotional or discount configurations to drive short-term sales.

Figure 7: Trend Declination of ACPP: Low Purchase Intent

Calibrating the Model's Predictive Capacity

The Socratic BPR index has been calibrated using more than 150 brand ratings collected through interviews with more than 25,000 individual ratings. The results have shown that a strong positive correlation exists between the BPR and the current market share for brands in their respective market categories.

The general model includes thousands of brand ratings from niche technology markets within both B2B and B2C applications, including office equipment, computer peripherals, consumer packaged goods, food and liquor producers, retailers, airlines, quick service restaurants, mobile technology, personal computing devices, software and e-commerce sites.

The mathematical model providing best fit to the data is not linear, but rather curvilinear, showing that the greater the starting levels of BPR, the faster the gain in market share for further increasing BPR ratings [See Figure 8].

Figure 8: Relationship between Brand Power and Market Share

This also indicates the converse, that powerful brands have far more to lose if they do not defend their strong positions.

• In the Weak Range (BPR < 40, Nescient through Weak) the curve is inelastic; with each 5-point increase in BPR yielding a predicted average market share gain of only 1%.
• In the Mid-Range (BPR = 40 to 69, Entry through Influential), the curve is unitary elastic; with each 5-point change in BPR yielding a corresponding 5% average change in market share.
• And at the Strong Range of the scale (BPR ≥ 70, Dominant through Monopoly), the curve becomes highly elastic; with every 5-point change in BPR yielding a corresponding average change in market share of more than 12%.

While the general model has a normatively high correlation coefficient (R2 = 0.8623); the individual niche markets tested have shown an average correlation of more than 0.900. This means that while BRP is generally applicable to the strength of brands across categories, it is even more helpful for understanding the competitive value of the sales funnel conversion rates within specific competitive environments.

Limits of the BPR Model Applicability

Although this model has shown to be remarkably robust—applying equally well in both U.S. and European consumer and business technology markets—there have been several instances where problems have been associated with being able to accurately link the BPR to share estimates. These instances have been most profound in emerging markets (particularly in Asia) where several local issues appear to be at play.

First, the income gap between economic classes in many emerging regions appears to create a disconnect between the BPR and the actual share figures. This appears to be largely a function of the social desirability of owning relatively expensive Western brands, but without the wherewithal to fulfill those desires. In this case, people in some cultures will express positive attitudes towards a brand, leading to a very high calculated BPR, but much lower real market share than the model would predict.

Second, distribution problems for a brand's products outside of the regions where they are traditionally the strongest, can lead to lower-than-predicted share data due to the fact that in some areas people simply can't find the products of a brand that they would otherwise purchase. There are several instances where the brand activities to stimulate the sales funnel have worked extremely well, creating high levels of ACPP ratings, only to wind up losing share to other, less desirable, brands only because alternative brands are immediately available.

A final delimitation of the use of this model has to do with the concentration of competitors within a niche market. The model has an extremely high predictive capacity in markets where there are a few, very well-known competitors (oligopolistic markets) with a few lesser-known brands. However, when the markets are chaotic, with numerous lesser known brands in low-involvement categories (usually regional in nature), the BPR for the best known brands of the cohort tends to overstate the degree of share they actually possess. We attribute at least some of this phenomenon to brand confusion and poor memory regarding actual brands purchased.

Other Corroborating Sources

Other relevant work, from which the Socratic BPR has evolved, includes a number of studies and published works that have helped establish the basic underpinnings for our model and provide validation for the various applications of analysis. A few of these sources, which we would like to acknowledge, include the following references.

Scaling for the Sales Funnel Questions

A benchmark study of customer attitudes toward steel and branded steel products was conducted in 1996 by Wirthlin Worldwide. Four main goals and accompanying performance measures were defined and provided early scale validation on key components of a "sales funnel" measurement system:

1. Awareness:Increase consumers' general awareness of steel, its uses, and advantages.
2. Favorability:Increase overall positive perceptions of steel and steel products.
3. Attitude:Increase positive perceptions of steel in comparison to the competition.
4. Behavior: Translate changes in attitude to increased purchase of steel products, tracking key markets (automotive/housing). (Cook, 1999, p. 59)

Interpreting the Impact of Trend Declination for the ACPP Component

Work on interpreting the relationship between consumer psychology during the purchase process and the role of the ACPP cycle, was explored by Chatterjee, Jauchius, Kaas & Satpathy (2002). The focus on "how people buy" illuminates a common thought process that applies to many product and service categories.

Studies have shown that consumers move through the purchase process predictably. In choosing a car, for instance, they typically start by considering five or six models, adding some and dropping others as they proceed. The number of vehicles narrows as consumers move from awareness to familiarity to consideration to the test drive and, finally, to purchase. Brands pass through a "purchasing funnel" in which products are subjected to new requirements at every stage of the selection process. By crafting the brand-management effort to deal with these requirements as they unfold within each market segment, companies can overcome obstacles to purchase (p. 136).

In addition to establishing the brand bottlenecks (or areas of steep trend declination in our model) they also linked the diagnostics to elements of market action, which they refer to as "active brand management" exercises.

Consumer behavior may be strongly emotional, but influencing it takes data and discipline. The purchasing funnel is a source of information about consumers and a device for interpreting it. Four phases of active brand management--the targeting of high-potential consumer segments, the isolation of purchase bottlenecks, the expansion of the range of consumer benefits, and a concentration on consumer touch points--rely on this data. (Chatterjee, Jauchius, Kaas & Satpathy, 2002, p 136)

Calibrating the Link between Sales Funnel Efficiency and Market Share

Working with another similar model (Millward Brown's BrandDynamics™ Pyramid), Hollis (2005) found that results from measuring the efficiency of this version of a "sales funnel" model have demonstrable return-on-investment implications:

Importantly, other research has demonstrated that the attitudinal equity measures reviewed here do relate to both behavioral and financial outcomes. Farr provides evidence that how well a brand converts consumers up the five levels [Awareness to Loyal Purchase] compared to other brands in the category has a relationship with market share change in the year following the survey (Farr, 1999). Muir builds on this analysis to show how this measure of brand momentum also relates to revenue growth, profit growth, and shareholder value (Muir, 2005). That the framework does relate to behavioral and financial outcomes implies that the ability of online advertising to change the attitudinal relationship with a brand is not just nice to know, it has real implications for the bottom line.

Tying the results from sales funnel data to even broader measurements, like market capitalization of the brand's parent company has also been helpful in determining the overall applicability of this approach. Many studies and superb documentation have been offered by authors such as Gregory & Mcnaughton, (2004), discussing the models developed by the CoreBrand group.

Knowing the values of familiarity and favorability in the absence of corporate brand equity, we can determine minimum expected market capitalization at these base levels. To do this, we use our cash flow multiple model to determine how changes in familiarity and favorability affect the multiple. We again do multivariate analysis and include the remaining factors influencing stock price--cash flow growth, financial strength, price stability, earnings predictability, etc. This equation determines the cash flow multiple, the stock price, and the subsequent market capitalization in the absence of corporate brand equity. Corporate brand equity is the difference between the current market capitalization and market capitalization at this base level.

References

Chatterjee, A., Jauchius, M.E., Kaas, H.W., Satpathy. A. (2002.) Revving up auto branding. The McKinsey Quarterly. p. 134.

Cook, W. A. (1999.) Ogilvy winners turn research into creative solutions. Journal of Advertising Research. Volume: 39. Issue: 3. p. 59.

Dunn, D.T., Probstein, S.C. (2003.) Marketing high tech services. Review of Business Journal. Volume: 24. Issue: 1. St. John's University, College of Business Administration. p. 10.

Ford, J. B., Honeycutt, D., Simintiras, A. C. (2003.) Sales management: A global perspective. Routledge. London.

Gregory, J. R. & Mcnaughton, L. (2004.) Brand logic: A business case for communications. Journal of Advertising Research. Volume: 44. Issue: 3. p. 232.

Hollis, N. (2005.) Ten years of learning on how online advertising builds brands. Journal of Advertising Research. Volume: 45. Issue: 2. p. 255.

MacElroy, W. & Wydra, D. (May, 2004). Hidden barriers to new product acceptance: preference inertia. Quirk's Marketing Research Review. p. 52.

Reichheld, Frederick F. (1996.) The loyalty effect: the hidden force behind growth, profits and lasting value. Bain & Company, Inc. and Harvard Business School Press. Cambridge, MA.