BUS 6523 Module 3 Brand Equity Under Algorithmic Intermediation Example

Ranked, Reviewed and Reordered by Voice: Brand Equity When Algorithms Stand Between Brand and Buyer

Student Name

American College of Education

BUS6523: Global Marketing in the Digital Economy

Module 3 Assignment

Instructor Name

May 7, 2029

Introduction

Brand equity theory assumes that consumers know brands and that this knowledge shapes their choices. The first module of this course proposed that in markets mediated by algorithms, brands reach consumers along two additional paths: by influencing the algorithm and by being presented, or not, by it. This paper examines what happens to brand equity under that arrangement. It restates the theory, analyzes how marketplace rankings, review systems and voice assistants each change the role of brand knowledge, applies the analysis to a composite kitchenware brand selling through one marketplace in three countries and draws implications for strategy.

Two Views of Brand Equity

Two theoretical views anchor the analysis. Keller (1993) located brand equity in consumers' minds: awareness and favorable, strong and unique associations lead consumers to respond more positively to a brand's marketing. Erdem and Swait (1998) offered an information economics view in which brands act as signals that reduce uncertainty when product quality is hard to judge. A credible brand lowers perceived risk and the cost of gathering information. The two views make different predictions under algorithmic mediation. If equity lies in associations, it may weaken when consumers see fewer brand cues. If equity is a signal, it may be partly replaced by other signals that platforms supply, such as ratings.

Rankings: The New Shelf Space

On large marketplaces, most consumers choose from the first page of search results, so the ranking algorithm functions much as shelf placement did in physical stores. Unlike a store buyer, the algorithm weighs signals such as sales velocity, conversion rates, ratings and fulfillment performance. A well-known brand may still rank highly because its name draws searches and clicks, but a lesser-known brand with strong performance signals can outrank it. Zhu and Liu (2018) found that Amazon tended to enter product spaces where third-party sellers had demonstrated strong demand, which shows that the platform is not a neutral intermediary but a competitor with its own interests and private brands.

Reviews: Signals the Brand Does Not Control

Review systems replace part of the signaling role of brands with the reported experience of other buyers. Chevalier and Mayzlin (2006) found that improvements in a book's reviews on one site led to relative increases in its sales there and that negative reviews had a stronger effect than positive ones. For brands, this means equity is increasingly co-produced by customers and displayed by platforms. In signaling terms, ratings can substitute for brand credibility where products are standardized and reviews are plentiful, but brand matters more where quality is hard to judge from reviews, such as durability over many years, or where reviews can be manipulated.

Voice Assistants: The Disappearing Interface

Voice commerce goes further. Dawar and Bendle (2018) argued that as consumers delegate routine purchases to assistants, the assistant's choices can override brand preferences, especially for low-involvement goods, and that the platform's own brands may be favored. A consumer who asks an assistant to reorder dish soap may not hear any brand name at all. In Keller's terms, brand recall weakens as a driver of choice because the consumer no longer recalls; the assistant does. Brands can retain equity by being explicitly requested, which requires strong associations formed elsewhere, or by becoming the assistant's default through performance and agreements.

A Revised Model of Brand Equity

Combining these observations, brand equity under intermediation can be modeled as three linked stocks. Consumer equity is the familiar knowledge in consumers' minds, which drives explicit brand requests and clicks. Algorithmic equity is the brand's standing in the signals platforms use, such as ratings, return rates and conversion. Relational equity with the platform covers agreements, advertising spending and data sharing. The stocks interact: consumer equity produces searches and sales that raise algorithmic equity, and strong algorithmic equity produces visibility that builds consumer equity. A brand weak in any one stock is exposed.

Application: A Kitchenware Brand in Three Markets

Harbor and Pine is a composite brand; its details are illustrative. Based in Portland, Maine, it sells cast iron and enameled cookware, mostly through one large marketplace in the United States, Germany and Japan. In the United States, its consumer equity is moderate and its algorithmic equity strong, with ratings near 4.7 and low returns. In Germany, consumers search heavily by product specifications and trust independent testing, and the brand has little consumer equity, so it depends on rankings and reviews. In Japan, where buyers often research products in detail and value craftsmanship stories, the brand's heritage carries more weight, but reviews are fewer, so a small number of negative reviews has a large effect.

What the Application Shows

The three markets show that the balance among the stocks differs by country. In the United States, the brand's main risk is platform entry: if its category proves profitable, the marketplace may introduce a private brand that competes on price with prominent placement, as Zhu and Liu's findings suggest. In Germany, the priority is algorithmic equity reinforced by independent test results that consumers trust, which can become associations over time. In Japan, consumer equity built through storytelling matters more, but the thin review base calls for careful service to prevent negative reviews from dominating.

Strategic Implications

Four implications follow. First, measure all three stocks, not only consumer awareness, because a brand can be well known and still invisible on the platform. Second, build associations strong enough that consumers request the brand by name, which protects against voice defaults and private brands. Third, manage the signals algorithms read, including ratings, returns and fulfillment, as brand assets. Fourth, reduce dependence on a single platform where possible, for example through direct sales and retail partners, because the platform's interests may diverge from the brand's.

Measuring the Three Stocks

Each stock can be measured. Consumer equity can be tracked through branded search volume, unaided awareness surveys and the share of sales from customers who searched the brand name. Algorithmic equity can be tracked through average ranking position for core search terms, ratings, return rates and conversion. Relational equity can be tracked through advertising terms, access to platform data and participation in programs such as the marketplace's brand registry. Tracking all three by country would let a brand see which stock is weakening before sales fall.

Limitations

The analysis relies on research from particular platforms and periods. The review study concerned books, a category with many reviews and frequent purchases, and the platform entry study concerned one marketplace in one country. Voice commerce has grown more slowly than some predicted, so its effects remain more argued than measured. The composite brand illustrates the model but does not test it. A fuller test would require panel data on brand searches, rankings and sales across countries.

Conclusion

Algorithms change how brand equity works. Rankings act as shelf space governed by performance signals, reviews supply signals brands do not control and voice assistants can remove the moment of brand recall. A revised model treats brand equity as three linked stocks held with consumers, with algorithms and with platforms. Applied to a kitchenware brand in three countries, the model shows different priorities in each. The next module turns to the ethics of the data-driven personalization on which these algorithms depend.

References

Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354. https://doi.org/10.1509/jmkr.43.3.345

Dawar, N., & Bendle, N. (2018). Marketing in the age of Alexa. Harvard Business Review, 96(3), 80-86.

Erdem, T., & Swait, J. (1998). Brand equity as a signaling phenomenon. Journal of Consumer Psychology, 7(2), 131-157. https://doi.org/10.1207/s15327663jcp0702_02

Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22. https://doi.org/10.1177/002224299305700101

Zhu, F., & Liu, Q. (2018). Competing with complementors: An empirical look at Amazon.com. Strategic Management Journal, 39(10), 2618-2642. https://doi.org/10.1002/smj.2932