Aggregator Model - Explained
If you still have questions or prefer to get help directly from an agent, please submit a request.
We’ll get back to you as soon as possible.
- Marketing, Advertising, Sales & PR
- Accounting, Taxation, and Reporting
- Professionalism & Career Development
Law, Transactions, & Risk Management
Government, Legal System, Administrative Law, & Constitutional Law Legal Disputes - Civil & Criminal Law Agency Law HR, Employment, Labor, & Discrimination Business Entities, Corporate Governance & Ownership Business Transactions, Antitrust, & Securities Law Real Estate, Personal, & Intellectual Property Commercial Law: Contract, Payments, Security Interests, & Bankruptcy Consumer Protection Insurance & Risk Management Immigration Law Environmental Protection Law Inheritance, Estates, and Trusts
- Business Management & Operations
- Economics, Finance, & Analytics
What is an Aggregator Model?
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 software (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.
This type of service is common among financial lenders and insurance companies.
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.
Academic Research on the Aggregator Model
- 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.
- 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.