Aggregator Model – Definition

Cite this article as:"Aggregator Model – Definition," in The Business Professor, updated July 29, 2019, last accessed November 26, 2020,


Aggregator Model Definition

An Aggregator Model is a networking E-commerce business model where a firm, known as an Aggregator, collects (or aggregates) data pertaining to goods and/or services offered by several competing websites or application softwares (commonly known as apps) and displays it on its own website or application software. Typically, an aggregator does not possess any manufacturing or warehousing capability, but instead, relies on its ability to create a domain that  allows visitors to conveniently match prices and specifications of products and/or services.

A Little More on What is the Aggregator Model

From the perspective of a consumer, it is often a time-consuming as well as a confusing endeavor to visit several different websites or install different application software in order to find the most suitable products or services at the best market prices. Similarly, finding news articles on general or specialized topics might also prove to be an arduous task. This is where the aggregator model comes in handy. An aggregator provides instant access to several competing products and services all at one place. This not only reduces the search time but also offers the consumer an instant and often, customizable list of similar products/services to compare.

An aggregator usually forms partnerships with several product/service providers who agree to sell their products or services under a common umbrella, i.e the brand name of the aggregator. The aggregator leverages its vast marketing network to provide product/service providers with a pool of customers in return for a commission on every confirmed sale of a product or service. Similarly, a news aggregator collates syndicated web content such as online editions of newspapers, news blogs, video blogs (or vlogs) and podcasts in a common forum, known as a news feed, for the benefit of its visitors. Ride hailing/ride sharing companies such as Uber and Lyft can be considered cab aggregators, although the term Transportation Network Company (TNC) is usually preferred nowadays to refer to this type of business entity.

Attributes of an Aggregator Business Model

Notwithstanding the diverse business sectors that different aggregators cater to, they all share a few common attributes. These are:

  • Customers: Any aggregator business model has two customer bases – (1) the consumers and (2) the goods/service providers who act as customers for the aggregator.
  • Industry: All goods/service providers associated with a particular aggregator belong to the same or similar industries.
  • Partnerships: None of the goods/service providers are employed with the aggregator. On the contrary, they are business partners of the aggregator and are free to make independent business decisions. These partnerships are formed through partnership agreements that typically obligate goods/service providers to conform to acceptable levels of quality, while entrusting the aggregator with the responsibility of marketing and creating more sales opportunities for their partners.
  • Brand Image: Brand image is one of the most important attributes of any business. As such, aggregators allocate a large proportion of their investments in brand-building exercises such as emphasizing on high quality of products/services, setting practical and attractive price bands, and offering delivery on demand.




Types of Aggregators

There are several different types of aggregators. Below are some of the most common types:

  • Search Aggregators are classified as metasearch engines since they simultaneously aggregate results from several search engines on topics specified by their users. A search aggregator typically searches parameterized RSS feeds that are published by various sites. Examples include Scour and WebCrawler.
  • News or Content Aggregators gather news, updates, insights or general web content from various online sources and display them at a single location. Examples include Metacritic and PopUrls.
  • Review Aggregators are similar to news aggregators. However, they typically collate user or expert reviews of films and television shows, video games, books, restaurants, automobiles, software, etc. Examples include Rotten Tomatoes (films and television), OpenCritic (video games), iDreamBooks (books), Yelp (restaurants), Motor Trends (automobiles) and Software Advice (software).
  • Poll Aggregators collate individual opinion poll results conducted by various organizations in order to estimate public opinion on important matters. Poll aggregators such as Votamatic and Frontloading HQ offer polling analysis and election forecasting of major US elections.
  • Social Network Aggregators are also known as real-time feed aggregators. These are typically websites that aggregate content from multiple social networking sites,such as Facebook, Twitter, Instagram, Flickr, LinkedIn, etc. and present them in a single domain. Examples include Hootsuite and FriendFeed (defunct).
  • Video Aggregators aggregate content from different online video sites and organize them in categorized lists. Examples include uVouch, Aggrega and VodPod.
  • Shopping Aggregators collate results of several shopping engines and offer price, product and ratings comparisons. Shopping aggregators are some of the most popular sites on the web, especially since they usually provide the best value, most reliable results. Examples include Amazon and BizRate.
  • Real Estate Aggregators are websites or software applications that collect and display information pertaining to real estate and MLS listings from various sources. Real estate aggregators primarily target home hunters, especially first time home buyers, by displaying home prices, property details and available deals as listed on various popular property websites. Examples include Zillow and RealEstate.
  • Job Aggregators are websites or software applications that aggregate job postings from various career sites, employer job listings, and other job posting sites. Examples include LinkedIn and Google Jobs.





References for Aggregator Model

Academic Research

E-hubs: the new B2B marketplaces, Kaplan, S., & Sawhney, M. (2000). E-hubs: the new B2B marketplaces. Harvard business review, 78(3), 97-106. The paper reports results of Theil entropy index, shift-share, and regression analyses of county variations in the change of manufacturing employment in the United Kingdom between 1971 and 1976. The research identified a very marked and consistent urban–rural shift in the relative distribution of manufacturing employment during this period, associated primarily with nonstructural influences. Regression tests of specific hypotheses derived from previous work, concerning the possible impact of government regional policy incentives, residential space preferences, and female labour availability (the ‘restructuring hypothesis’), failed to add significantly to the statistical explanation achieved. The paper concludes with a brief discussion of possible implications for UK regional policy in the 1980s.

An aggregator model of Canadian export supply and import demand responsiveness, Lawrence, D. (1989). An aggregator model of Canadian export supply and import demand responsiveness. Canadian Journal of Economics, 503-521. The aggregate Canadian technology is modelled by a GNP function with four export and four import components included by the use of aggregator functions and a two-stage estimation process. The recently developed Symmetric Generalised McFadden functional form is used at both the aggregator and GNP function levels. The aggregate export own-price elasticity is found to be 1.67 in 1970, while the import own-price demand elasticity is — 1.62. Increases in the prices of both imports and labour are found to decrease the supply of exports, while exports are found to be complementary to the output of domestic sales supply. /// Un modĂšle de la sensibilitĂ© de l’offre canadienne d’exportation et de la demande canadienne d’importation utilisant la procĂ©dure des fonctions agrĂ©gatrices. L’auteur dĂ©veloppe un modĂšle de la technologie canadienne agrĂ©gĂ©e qui utilise une fonction de PNB avec quatre composantes d’exportation et quatre composantes d’importation construites par la procĂ©dure des fonctions agrĂ©gatrices; une procĂ©dure de calibration en deux Ă©tapes est utilisĂ©e. La forme fonctionnelle symĂ©trique gĂ©nĂ©ralisĂ©e Ă  la McFadden est utilisĂ©e Ă  la fois au niveau des fonctions agrĂ©gatrices et de la fonction de PNB. L’Ă©lasticitĂ© des exportations agrĂ©gĂ©es par rapport Ă  leur prix est de l’ordre de 1,67 en 1970; l’Ă©lasticitĂ© de la demande d’importations par rapport Ă  leur prix est de l’ordre de — 1,62. Il appert que des accroissements dans les prix des importations et du travail entraĂźnent une chute dans l’offre d’exportation; on trouve que les exportations sont complĂ©mentaires Ă  la production offerte sur le marchĂ© domestique.


Export and output supply functions with endogenous domestic prices, Newman, J. L., Lavy, V., & De Vreyer, P. (1995). Export and output supply functions with endogenous domestic prices. Journal of International Economics, 38(1-2), 119-141. This paper deals with the estimation of export supply functions which has been neglected by the literature in favor of the export demand function. Reliable estimates of export supply elasticities are essential for the evaluation of trade policy interventions. The paper estimates short-run export and output price elasticities in CĂŽte d’Ivoire. We attempt to improve on previous approaches by modeling exported goods and goods intended for domestic consumption as imperfect substitutes, incorporating the domestic demand for domestic output and endogenizing the domestic price. This should capture important feedback mechanisms missed by previous models.

A two‐stage market model for microgrid power transactions via aggregators, Kim, H., & Thottan, M. (2011). A two‐stage market model for microgrid power transactions via aggregators. Bell Labs Technical Journal, 16(3), 101-107. In this paper, we propose a market model where microgrids sell their surplus power to a utility via aggregators. This is a scalable model where a utility does not directly interact with a large number of microgrids. Thus, aggregators collect power from microgrids and resell it to the utility. From the microgrids’ perspective, aggregators are buyers. From the utility’s perspective, aggregators are sellers. In this context, based on the two‐stage Stackelberg game, we show how to achieve efficient market equilibrium using the tatonnement process and supply function bidding. We also show that the participation of aggregators may significantly affect the market depending on the supply elasticity of microgrids, which in turn depends on the cost structure of microgrids. For example, when the cost function of microgrids is roughly linear, the aggregators may not make a profit. However, if the cost function of microgrids has a higher order term, aggregators may accumulate a large profit, which potentially raises the issue of the regulator’s role in the market. © 2011 Alcatel‐Lucent.

Firming renewable power with demand response: an end-to-end aggregator business model, Campaigne, C., & Oren, S. S. (2016). Firming renewable power with demand response: an end-to-end aggregator business model. Journal of Regulatory Economics, 50(1), 1-37. Environmental concerns have spurred greater reliance on variable renewable energy resources (VERs) in electric generation. Under current incentive schemes, the uncertainty and intermittency of these resources impose costs on the grid, which are typically socialized across the whole system, rather than born by their creators. We consider an institutional framework in which VERs face market imbalance prices, giving them an incentive to produce higher-value energy subject to less adverse uncertainty. In this setting, we consider an “aggregator” that owns the production rights to a VER’s output, and also signs contracts with a population of demand response (DR) participants for the right to curtail them in real time, according to a contractually specified probability distribution. The aggregator bids a day ahead offer into the wholesale market, and is able to offset imbalances between the cleared day-ahead bid and the realized VER production by curtailing DR participants’ consumption according to the signed contracts. We consider the optimization of the aggregator’s end-to-end problem: designing the menu of DR service contracts using contract theory, bidding into the wholesale market, and dispatching DR consistently with the contractual agreements. We do this in a setting in which wholesale market prices, VER output, and participant demand are all stochastic, and possibly correlated.

Explaining the relative decline of agriculture: a supply-side analysis for Indonesia, Martin, W., & Warr, P. G. (1993). Explaining the relative decline of agriculture: a supply-side analysis for Indonesia. The World Bank Economic Review, 7(3), 381-401. The relative decline of agriculture in growing economies is a central feature of economic development and a major influence on agricultural policies. The literature on the causes of this decline has focused on the relative price effects arising from demand factors, especially Engel’s law, rather than on supply-side influences, such as changes in relative factor endowments and differential rates of technical change. This article develops a simple structural model of the transformation of the Indonesian economy, applying an error correction mechanism to capture the dynamics resulting from disequilibria and the costs of adjustment. The decline in agriculture’s share of gross domestic product is found to be caused much less by the relative price effects typically emphasized in the literature than by capital accumulation and rapid technical change in agriculture.

Export supply and import demand elasticities, Lawrence, D. A. (1987). Export supply and import demand elasticities (Doctoral dissertation, University of British Columbia).

The adjustment of Canadian import demand to changes in income, prices, and exchange rates, Deyak, T. A., Sawyer, W. C., & Sprinkle, R. L. (1993). The adjustment of Canadian import demand to changes in income, prices, and exchange rates. Canadian Journal of Economics, 890-900. The paper provides estimates of the sensitivity of Canadian import demand to changes in income, prices, and exchange rates. The model specification allows for the consideration of long-run elasticities as well as the dynamics of short-run adjustment of imports to changes in these variables. The estimated long-run elasticities obtained are similar to those in previous studies. It is also shown that Canadian imports react quickly to changes in either foreign or domestic prices, but that the reaction to changes in exchange rates is much slower.

Net exports, consumption volatility and international business cycle models, Raffo, A. (2008). Journal of International Economics, 75(1), 14-29. Conventional two-country RBC models interpret countercyclical net exports as reflecting primarily the dynamics of capital. I show that, quantitatively, theoretical economies rely on counterfactual terms of trade effects: trade fluctuations, on the contrary, are driven by consumption smoothing, thus generating procyclical net trade in goods. I then consider a class of preferences that embeds home production in a reduced form: consumption volatility increases so that countercyclical net exports reflect primarily a strong relation between consumption and imports, as in the data. The major discrepancy between theory and data concerns the variability of international prices.

Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers, Boait, P., Ardestani, B. M., & Snape, J. R. (2013). IET Renewable Power Generation, 7(6), 689-699. The ability to influence electricity demand from domestic and small business consumers, so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signalling to consumers to indicate when electricity use is desirable or undesirable. However, simply signalling a time-dependent price does not always achieve the required demand response and can result in unstable system behaviour. The authors propose a demand response scheme, in which an aggregator mediates between the consumer and the market and provides a signal to a `smart home’ control unit that manages the consumer’s appliances, using a novel method for reconciliation of the consumer’s needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, the authors show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement.

Managing the human cloud, Kaganer, E., Carmel, E., Hirschheim, R., & Olsen, T. (2013). Managing the human cloud. MIT Sloan Management Review, 54(2), 23.

Mobile money: The economics of M-PESA, Jack, W., & Suri, T. (2011). Mobile money: The economics of M-PESA (No. w16721). National Bureau of Economic Research. Mobile money is a tool that allows individuals to make financial transactions using cell phone technology. In this paper, we report initial results of two rounds of a large survey of households in Kenya, the country that has seen perhaps the most rapid and widespread growth of a mobile money product – known locally as M‐PESA – in the developing world. We first summarize the mechanics of M-PESA, and review its potential economic impacts. We then document the sequencing of adoption across households according to income and wealth, location, gender, and other socio‐economic characteristics, as well as the purposes for which the technology is used, including saving, sending and receiving remittances, and direct purchases of goods and services. In addition, we report findings from a survey of M‐PESA agents, who provide cash‐in and cash‐out services, and highlight the inventory management problems they face.

Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part I: Theory, Bessa, R. J., & Matos, M. A. (2013). Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part I: Theory. Electric Power Systems Research, 95, 309-318. This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.

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