Shopping Bot Definition
Online shopping bots for electronic commerce – ResearchGate
Shopping bots are software applications that assist consumers with online comparison-shopping by searching for, identifying, and comparing products offered by numerous e-tailers. This paper examines the output of nine comprehensive shopping bots that were employed to conduct multiple searches for forty books, twenty CDs, and twenty DVDs. The results produced by each bot were analyzed in order to determine bot effectiveness based on accuracy, consistency, and repeatability of recommendations, using product price as a key measure. It was concluded that there is no best shopping bot available, most bots offer very limited product information to the end users, and all bots often present inaccurate information in terms of the actual product price or product availability. Based on the findings, several recommendations for shopping bot developers and researchers are gures – uploaded by Khaled W. SadeddinAuthor contentAll figure content in this area was uploaded by Khaled W. SadeddinContent may be subject to copyright. Discover the world’s research20+ million members135+ million publications700k+ research projectsJoin for free
nt. J. Electronic Business, Vol. X, No. X, xxxx 1 Copyright © 2002 Inderscience Enterprises Ltd. Online shopping bots for electronic commerce: The comparison of functionality and performance Khaled W. Sadeddin Faculty of Business Administration, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada Fax: +1-807-343-8443 E-mail: Alexander Serenko Faculty of Business Administration, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada Fax: +1-807-343-8443 E-mail: James Hayes Faculty of Business Administration, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada Fax: +1-807-343-8443 E-mail: Abstract: Shopping bots are software applications that assist consumers with online comparison-shopping by searching for, identifying, and comparing products offered by numerous e-tailers. Based on the findings, several recommendations for shopping bot developers and researchers are presented. Keywords: electronic commerce; intelligent agents; e-business; shopping bots; functionality; performance; Internet; price. Reference to this paper should be made as follows: Sadeddin, Serenko and Hayes (2007) ‘Online shopping bots for electronic commerce: The Comparison of functionality and performance’, Int. Y, pp. 000-000. Biographical notes: Khaled Sadeddin is a graduate student at Lakehead University pursuing a Master of Management degree. He received an undergraduate degree in Photonics Engineering from The University of Hull, United Kingdom. He has worked as a management and ecommerce consultant for a number of years. His research interests are in the area of business and corporate strategy, ecommerce strategic management, and knowledge management. Alexander Serenko is an Assistant Professor of Management Information
deddin Systems in the Faculty of Business Administration, Lakehead University, Canada. He holds a in computer science, an MBA in electronic business, and a Ph. D. in Management Information Systems. Dr. Serenko’s research interests pertain to user technology adoption, knowledge management, and innovation. Alexander’s articles appeared in various refereed journals, and his papers received awards at Canadian and international conferences. James Hayes is a Master of Management student in the Faculty of Business Administration, Lakehead University. James holds an Honours Bachelor degree in computer science from Lakehead University and has worked in IT and TQM for several years. His research interests include MIS and econometrics. Corresponding author: Khaled. W. Sadeddin, E-mail: An earlier version of this paper was presented at the Seventh World Congress on the Management of e-Business, Halifax, Canada, July 13-15, 2006. 1 Introduction 1. 1 What are shopping bots? Shopping bots are “automated tools that allow customers to easily search for prices and product characteristics from online retailers” [1, p. 446]. They are available on the Internet and act as electronic commerce search engines. Bots accept user queries, visit e-shops or websites of online merchants that may have a specific product, retrieve search results, and present them in a consolidated and compact format for visual comparison. The purpose of this paper is to examine the functionality and performance of various shopping bots (or price comparison engines) for electronic commerce. There are various shopping bot services. In general, they can be divided into two types: server-based and client-based solutions. A server-based shopping bot performs price comparison on a Web server. Some examples include,, and For a client-based bot, a special software application needs to be installed on the client-side. This system can be configured to check specific item prices from known vendors or search engines on a regular basis. Some examples include Copernic Shopper1 and Best Price2 [2]. Technically, there are three ways to provide shopping bot services: a centralized database, broker agents and mobile agents. In the centralized database approach, each shopping bot has its own product information database. Sellers submit their offerings and update the database regularly, either manually or automatically. Essentially, the bot provides advertising services for the sellers. In the broker agent approach, shopping bots are used to extract product information from different sellers’ web sites. Mobile agents can be utilized to visit each seller’s website to compare the price of the product of interest. Besides searching, a mobile agent 1 2
Online shopping bots: The comparison of functionality and performance can in fact be employed to complete a purchase which is the last step in the buying process [2]. 1. 2 Motivation for the development of shopping bots A theoretical framework that leads to the development of shopping bots can be found in the economics of information theory, where Stigler [3] argued that consumers who value time will stop searching when the marginal benefits of search no longer outweigh the marginal search costs. Hence, the usage of a shopping bot is not limited to simply typing in a few keywords and waiting for the results. Consumers need to decide how the information generated by a bot adds to the entire purchase decision-making process. To be effective, time spent searching with shopping bots needs to be minimized. This is particularly important since the use of a shopping bot is only one stage in the product acquisition process. Peterson, Balasbramanian, & Bronnenberg [4] emphasize that for some categories of goods, consumers are likely to search both the Internet and conventional retailing channels. The theoretical framework mentioned above was the driver for the early stages of shopping bots design and implementation and continues to fuel the efforts of improving the performance and functionality of shopping bots. In the past, shopping bots were often referred to as agents, intelligent agents, software agents or intelligent assistants. In this paper, they are treated as regular software-based applications. It is noted that a discussion of whether shopping bots actually belong to the field of intelligent agents is out of the scope of this project. As such, this study concentrates on the performance aspects of this technology rather than on its theoretical or philosophical issues. 3 History of shopping bots As early as 1995, researchers envisioned shopping bots as a solution for finding products under the best terms from online vendors when price was typically the most important feature [5]. A shopping agent queries multiple sites on behalf of a shopper to gather pricing and other information on products and services. Client-based shopping bots that appeared in the beginning of 1997 achieved that by allowing consumers to comparison-shop online without actually visiting merchants’ sites to locate best prices [6]. The first shopping agent (BargainFinder) was developed by the consulting firm Andersen Consulting in 1995 [7]. It let users compare prices of music CDs from Internet stores. However, some retailers blocked access because they did not want to compete purely on price, and BargainFinder ceased operations. PersonaLogic, another comparison-shopping bot, let users create personal profiles to describe their preferences. This approach allowed the bot to identify products with features that users considered most important. However, vendors had to provide interfaces that explicitly disclosed product features so that PersonaLogic could match them with user profiles. AOL (America Online) acquired PersonaLogic in 1998, and the technology disappeared soon after that. Ringo was a bot that recommended entertainment products, such as CDs and movies, on the basis of collaborative filtering by using opinions of like-minded users [8]. Collaborative filtering implies making automatic predictions (filtering) about the interests of a user by collecting preference information from many users (collaborating). An
deddin underlying assumption of collaborative filtering approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for music preferences could make predictions about which music a user should like given a partial list of that user’s tried before (likes or dislikes). Such predictions are specific to the person, but use information gleaned from many users. This differs from a more simple approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes. This became one of the earliest commercialized bot technologies when it evolved into FireFly [9]. Microsoft acquired FireFly Network Inc. in 1998, and the FireFly bot ceased operation shortly thereafter. However, collaborative filtering has become a common technique nowadays; for example large commercial vendors such as Amazon use it, although in simplified ways. ShopBot, another price comparison engine, could submit queries to e-commerce sites and interpret the resulting hits to identify lowest-price items [10]. ShopBot automated the building of “wrappers” to parse semi-structured HTML documents and extract features, such as product descriptions and prices. The process would entail wrapping treatments learners (programs used to find rules that change the expected class distribution compared to some baseline) in a preprocessor that would search to make subsets from the current set of attributes. The attribute subset would continue to grow until the accuracy of the model was no longer more accurate. Parsing transforms input text into a data structure, usually a tree, which is suitable for later processing and which captures the implied hierarchy of the input. The overall method when applied to data sets from e-vendors’ websites would yield an HTML documents with the specified attribute set extracted from such website. Despite the usage of wrappers, the ShopBot technology’s fate was similar to those of PersonaLogic and FireFly. Excite acquired and commercialized it under the name Jango but soon replaced it with a biased vendor-driven agent [9]. Tete@Tete was a bot that integrated product brokering, merchant brokering, and negotiation [11]. A start-up called Frictionless Commerce applied the technology to business-to-business rather than to business-to-customer markets. Most of the comparison-shopping agents available to consumers such as MySimon, DealTime and RoboShopper, present results only from partner companies who pay service subscription fees. Most current business models are based on vendor rather than buyer revenue, because users are reluctant to pay fees for these services. However, a vendor-based revenue model still produces hidden costs such as higher prices, limited choices, and poor service. In this context, the established vendors’ reluctance to shopping bots is certainly understandable [9]. 4 Current state of research on shopping bots Based on a comprehensive review of academic literature in the fields of Management Information Systems, Human-Computer Interaction and Computer Science, three distinct approaches to study shopping bots were identified. The first line of research focuses on the engineering of technical design and functionality aspects of shopping bots. As such, the scholars investigate various design specifics and technical algorithms that can be developed and utilized to enhance shopping bot performance. Such enhancement would yield better functioning systems with increased accuracy of information gathered from
Online shopping bots: The comparison of functionality and performance vendors, and a more adaptive and customized shopping assistance for online consumers [9]. Other aspects of the engineering approach are the design and performance assessment of other models of shopping bots such as mobile shopping bots and the investigation of the effectiveness of their functionality [2]. The second research approach focuses on the economic effects of bots. In this type of research, academics analyze the impact of shopping bots on various economic problems, such as price dispersion (defined as the distribution of prices across sellers of the same item, standardized for the item’s characteristics) in the online environment [12], economics of information theory [3, 13], value of information in online markets [14], and price range and consumer intentions [12]. The final approach to shopping bots research is the impact they have on marketing issues, such as consumers response to the presence of shopping bot services [1]. Researchers explore the role of service quality as an important product attribute even for otherwise homogeneous goods [15]. The influence of shopping bots on consumer research behavior [16] and many similar marketing issues related to shopping bots are also studied. Overall, the area of research presented above is in its embryonic stage of development. Most documented works offer theoretical discussions and conceptual overviews of the field, or the technological solutions for bot implementations. Based on an extensive and exhaustive search of all major indexes, journals, and online resources conducted by the authors, there have been only a few attempts to study the performance and functionality of shopping bots from the end-use perspective. Even though the popularity of shopping bots has been continuously growing, there have been very few attempts to empirically evaluate their performance. Such evaluation would be the true test of their abilities at gathering unbiased and thorough product-related information and presenting it in a useful fashion that would reduce search costs and facilitate an efficient decision making process. This study suggests and attempts to answer a number of research questions that have not been covered before through a quantitative approach. The expected contribution of this paper is two-fold. First, this will be one of the first documented attempts to empirically investigate the performance of shopping bots. Second, based on the findings, a number of suggestions for shopping bot service providers, electronic commerce companies utilizing this technology, and online consumers will be provided. Unfortunately, no such guidelines are presently available. The following section offers more detail on the theoretical background and research questions. 2 Theoretical background and research questions There is a general consensus that a consumer buying process can be divided into three phases, namely searching, comparing and executing [2]. For consumers, online shopping may greatly facilitate the collection of item-related information and price comparison. Online shoppers may adopt a number of strategies when looking for a product. The most straightforward approach is to visit various vendor websites; for each one, a person searches for a particular product.
deddin This simple approach has several drawbacks. First, because no single site caters to all shopping needs, a user’s search time increases for each new product category. Second, getting acquainted with individual non-standard vendor interfaces slows browsing and hinders impulse shopping. Third, this approach likely favours only the largest vendors (e. g., because of name-branding), which reduces the market’s efficiency by providing fewer competitive choices to consumers [9]. There are several widely employed online tools that assist shoppers. For example, some vendors allow individuals to sign up to receive price alerts that notify them when a product’s price changes or falls below a specified amount. Some of these services require shoppers to fill out lengthy surveys, and most of the websites offer little or no personalization. Even though it is possible to offer personalized shopping experience by creating user profiles, this shopping approach has attracted much criticism because it threatens people’s privacy [9]. Another option involves the compilation of voluntary user ratings and reviews of vendors and products. Such recommendation systems might reduce the marketplace’s size and introduce bias, because obtaining a sufficient number of ratings for every vendor and controlling the sources’ reliability are difficult to achieve for a single shopper. Overall, shopping bots offer a good alternative to further automate the search process that has been gradually gaining recognition among online shoppers. Specifically, shopping bots, or price comparison engines, may alleviate some of the shortcomings of the solutions above. Several theories exploring the impact of shopping bots on various aspects of electronic commerce were proposed since the inception of this technology. For example, some of these theories discussed economic factors such as online price dispersion, and marketing factors such as marketing mix needed by retailers in response to shopping bots. Other theories addressed consumer behaviour such as people’s response to shopping bots’ information and services. While such research addressed some of these issues that have presented in the previous section of this paper, many questions remain unanswered or partially covered, and further exploration is needed. It follows from the extant literature that the degree of price dispersion and consumers’ reactions to price dispersion are a very important investigation area [4]. Managers must be aware of macro forces (such as price dispersion) to deal effectively with variables within their control (such as pricing). Many conjectures have been made in the business literature about a lower degree of price dispersion that should emerge due to the Internet [12]. Since Internet presence has virtually become a necessity [17], most managers have to deal with Internet pricing issues at some point – and thus with the forces of price dispersion. Therefore, various effects of price dispersion, including the average item price, number of competitors in the marketplace selling a specific product (or a number of vendors reported by the shopping bot), and retailer quality need to be examined carefully. In one of the first attempts to empirically investigate the functionality of shopping bots, Rowley [18] compared search facilities and outputs across ten different shopping bots using three recent best selling books as a product group. She found that there was a significant variability in the search facilities and search outputs among different shopping bots. Most bots offered searches by title, author, and ISBN. For the most part, search mechanisms were found to be rudimentary. Searches on title fragments and parts of author names produced long lists of items that led to information overload. Rowley’s use of search facilities and the accuracy of search outcomes in terms of book title and author name provided a measure to compare the functionality
Online shopping bots: The comparison of functionality and performance (effectiveness or accuracy) of any shopping bot. However, to further our knowledge and understanding of the functionality of shopping bots, other indicators can be used as forms of measurement to asses the performance of a shopping bot and allow comparing it with other bots. Since price dispersion results can be used as a performance indicator, the following research question is suggested: Research Question 1: Do different shopping bots produce similar price dispersion results (high, low and average price) for identical product searches? Early electronic commerce studies hypothesized that online retailing would spiral into a never-ending price war [15], while more recent projects discovered that price is not the only factor because many customers tend to pay higher prices to superior quality online retailers that they trust. This explains why more than 50% of the dollars spent online go to the top 30 retailers [19] and points out that price alone is not the only dimension of competition in the online retail environment. For example, Collier and Bienstock [20] argue that product delivery has a very strong influence on customers’ satisfaction and future purchase intentions. Rowley [18] found that the various outputs of shopping bots varied considerably; some offered only item price, whereas others showed delivery and shipping arrangements. Both delivery options and price can be influential factors in consumer purchase decisions. Rowley concluded that shopping bots are likely to play a useful role in profiling the e-market place in future, but their functionality should be improved. Users require various output information generated by shopping bots. These include variations in shipping and handling information, customers’ feedback on vendors, product reviews, tax charges, delivery time, product views, and return policies. Therefore, it follows that another measure of a shopping bot overall functionality can be the provision of supplementary information that can aid users in making a rational decision about a purchase: Research Question 2: Do different shopping bots produce similar supplementary information, such as shipping and handling, customers’ feedback on vendors(vendors’ reviews), product reviews, tax charges, delivery time, product views (i. e., pictures), and return policies? Accuracy, defined as information integrity, is another factor that may dramatically influence the usefulness and future adoption of shopping bots. For instance, if there is a difference between the product price presented by a shopping bot and the actual price that the vendor charges the purchaser, it is unlikely that this user will ever utilize this specific bot, or even any other bots, in future. To further enhance an understanding of bots functionality, another measure can be employed as an indicator of performance. As such, the integrity of the information provided by the bot is believed to be highly important, and a third research question is suggested:
deddin Research Question 3: How accurate is the information and recommendations provided by shopping bots? (i. e., is the item in fact available from each reported online vendor for the quoted price? ) The last, but not the least measure that can be useful to bots’ users is the number of options it provides in terms of the number of potential vendors who sell the required product, allowing for a wider range of price/product/supplementary information available to customers. Hence, e-merchant coverage may be a very useful measure of bot functionality, and the following research question is suggested: Research Question 4: Do different shopping bots produce similar e-merchant coverage results? 3 Methodology and results 3. 1 Experiment description In order to examine the effectiveness of shopping bots as shopping tools, an experiment was conducted. Nine shopping bots were randomly selected from an exhaustive list available at the Web site after excluding specialized bots. The intention of this study was to focus on general shopping bots with wide product coverage, therefore bots that specialized in particular product groupings were excluded from consideration. All of the selected bots were server-based solutions. The following shopping bots were randomly selected:,,, m1,,,,, and For products, 40 books were randomly selected from the New York Best Seller list covering four different topics: fiction (entertainment), non-fiction (general interest), business, and children books. Twenty CDs and twenty DVDs were also randomly selected from the New York Best Seller list. All products were searched either by ISBN, ASIN, or UPC number. Only new items were considered. This enabled the explicit identification of identical products for searching using each shopping bot. This prevented the need to utilize keyword and title searches provided by the shopping bot search facilities, which was not included in the scope of this study. The following sub-sections outline the results. 3. 2 Price comparison To answer the first research question of whether different shopping bots produce similar price dispersion results (high, low and average price) for identical product searches, the high, low and average prices of products were compared using ANOVA. This data analysis technique was chosen because it allows keeping the significance level constant 1 It is noted that the service by was discontinued soon after the completion of this study.
Online shopping bots: The comparison of functionality and performance when analyzing data produced by different bots. In the present case, the employment of MANOVA was not recommended because of variable interdependency (i. e., average price is influenced by both high and low prices). The overall goal was to test price dispersion of the shopping bots under investigation for three sets of products: 1) books; 2) DVDs; and 3) CDs. Only those products that were actually available on the vendor’s website were considered. Table 1 offers the results. All values statistically significant at the 0. 001 level indicate that there are differences in this product category for a specific price (i. e., high, low or average). For example, in terms of an average price, a difference for CDs but not for books and DVDs was observed. Table 1 Price comparison High Price Low Price Average Price F-value P-value F-value P-value F-value P-value Books (n=40). 273(8;346) ns 1. 590(8;346) ns. 128(8;346) ns DVDs (n=20) 6. 598(8;168) <. 001 12. 202(8;168) <. 001 1. 258(8;168) ns CDs (n=20) 14. 423(8;161) <. 001 16. 997(8;161) <. 001 26. 940(8;161) <. 001 3. 3 Supplementary information comparison The goal of the second research question was to study the comprehensiveness of supplementary product information such as shipping and handling, customers’ feedback on vendors, product reviews, tax charges, delivery time, product views (i. e., pictures), and return policies. To answer this question, each shopping bot was individually analyzed. Table 2 offers the results.
deddin Table 2 Supplementary information Shipping/ handling Vendor Reviews Product Reviews Taxes Delivery Time Product Views Return Policy ActiveShopper yes yes no no no yes no BizRate yes yes yes yes no yes no DealTime yes yes no no no yes no Dulance no no no no no no no MySimon yes yes yes no no yes no NexTag yes yes yes yes no yes no PriceGrabber yes yes yes yes no yes no PriceScan no yes yes no yes no no Shopping yes yes yes yes no yes no Based on the findings, it is concluded that no shopping bot offers comprehensive supplementary information. As such, none of them informed users about product return policies. Only one (PriceScan) offered delivery timeline, and four bots (BizRate, NexTag, PriceGrabber, and Shopping) either calculated or allowed people to calculate tax charges. At the same time, a majority of bots had shipping/handling information, product views, and customer reviews on vendors. 4 Information accuracy and online vendor coverage The third research question concentrated on the accuracy of obtained results. To investigate this issue, each case when the advertised product was not actually available on the vendor’s website was counted. For example, after obtaining a search list for a particular book, the researchers visited each vendor to verify whether the book was actually available for purchase. The first row of Table 3 portrays the accuracy of each shopping bot investigated by listing the number of times a product was not found on a vendor’s website. The fourth research question focused on e-merchant coverage. In order to answer this question, price searches were performed, using each of the nine shopping bots, for each of the eighty items. Each time a new, unique vendor was encountered in a search result, it was assigned a unique vendor code to be used throughout the experiment. In the process of conducting these searches, the data was compiled in tables that indicated, for each item searched, each of the vendors returned by each shopping bot. These data were summarized to indicate the average number of unique vendors returned by each shopping bot. Table 3 offers the findings. As such, it presents the number of times each product was not available, the average number of unique vendors, and their ratio.
Online shopping bots: The comparison of functionality and performance Table 3 Information accuracy and online vendor coverage ActiveShopper BizRate DealTime Dulance MySimon NexTag PriceGrabber PriceScan Shopping Product not available 8 11 12 20 14 6 19 43 9 Avg. # of unique vendors 3. 10 8. 99 3. 21 8. 95 4. 23 4. 00 9. 54 11. 51 3. 39 Ratio 2. 58 1. 22 3. 74 2. 23 3. 31 1. 50 1. 65 4 Discussion, conclusions, and directions for future research 4. 1 Answers to research questions The overall purpose of this study was to empirically investigate the functionality and performance of online shopping bots. For this, an empirical experiment was conducted. Based on the extant literature, four research questions were proposed. Three categories of products were selected, and 80 items were randomly chosen: books (n=40), DVDs (n=20) and CDs (n=20). Web-based searches on nine shopping bots were performed during one day. The goal of the first research question was to analyze price dispersion of shopping bots. There are three points that need to be addressed. First, no statistically significant differences were discovered for book prices. This implies that the overall high, low, and average prices are similar for the nine bots under investigation. At the same time, a visual inspection of the dataset demonstrates that, in each case, there was at least one price that was dramatically lower than those of other bots. This reveals that online shoppers may potentially find a real bargain for a specific product if they utilize each bot and compare the results. Second, in the case of DVDs, significant differences in shopping bot performance were found for high and low prices; BizRate and PriceGrabber had the lowest prices, and PriceScan had the highest ones. Third, for CDs, high, low and average prices were different; for example, NexTag was the lowest price leader. Based on these observations, it is suggested that, in general, there is no ‘best’ or ‘parsimonious’ shopping bot in terms of price advantage. Indeed, the performance of shopping bots depends on the overall product type as well as on a particular product. Therefore, it is argued that in order to locate the best deal on the Internet, shoppers should obtain information from a variety of bots. A possible alternative for shopping bot
deddin vendors may be to develop a meta-shopping bot that would work similar to meta-search engines. As such, a meta-bot would obtain product information from several shopping bots, summarize it, and present it to the user. The objective of the second research question was to analyze supplementary information offered by shopping bots. The analysis indicated that no shopping bot provided comprehensive supplemental information of this nature, and in general, supplied information was only found to be satisfactory. Breitenbach and Van Doren [21] suggest that online price comparisons represent a very complex process. As such, in the intensely competitive environment of the global e-commerce marketplace, e-merchants will attempt to differentiate themselves by offering additional benefit to their consumers, such as favourable delivery options, attractive return arrangements, flexible payment options, and superior service. It is suggested that these factors affect the consumer’s overall satisfaction and perception of the value obtained through their purchase. If these criteria are important to consumers and are likely to influence their purchase
What is a Shopping Bot? – Definition from Techopedia
A shopping bot is an online price comparison software tool which automatically searches the products of many different online stores to locate the most affordable rates for customers.
Generally, these shopping bots rank items by price and let buyers link directly to the website of an online merchant site to actually buy their preferred product.
Shopping bots, short for “shopping robots, ” can find the best online deals for products, including films, books, video games, computer devices, TVs, etc. Using shopping bots, buyers can get quotes from multiple retailers for the same product instantly, without spending extra time searching for each retailer’s price.
When buyers search for a product using shopping bots, they find the products and report back with prices, descriptions, etc. However, in reality, there are many bots with slightly different features. So, shopping bots can be websites, plugins, browser-based, price-comparison-only, etc. Also, some find online products only while others search mail-order catalogs or brick-and-mortar shops.
Some popular shopping bots are:
mySimon: This is the most popular shopping bot, with top ratings from reviewers. It queries 1, 700 plus merchants in various categories.
DealPilot: This is a browser-based bot, which presents price comparison on bars toward the bottom of a browser. It performs the online search and reports back with the details on where the customer can get the best deal, in addition to information on availability and shipping.
iChoose: This one sends an advisor along with the customers (it is a free download) as they browse and shop through their preferred sites. When the customers find a preferred item, it informs them if there is a better deal to select from, along with info on the pricing, shipping as well as taxes.
This site provides the customers with the latest information on sales and special offers at the brick-and-mortar stores near their location.
This site helps customers find services and products form 1, 000-plus brands. Then, it is up to the customers to decide whether they need to buy online or make use of the Shop Local feature to locate the best rates in the neighborhood.
Best 30 Shopping Bots for eCommerce – Ada Support
SnapTravel
For those who love traveling, SnapTravel is one of the best shopping bot options out there. Customers can get up to 30 to 50% off hotel and travel deals. Prestigious companies like Sabre, Amadeus,,, and so much more partnered with SnapTravel to make the most out of the experience.
All you have to do is enter your city, preferred accommodation, and the date you want it to be booked. Once all of this information is entered, your bot will automatically scan the web to find the perfect exclusive deals for your trip. Customers can use either WhatsApp or Facebook Messenger to confirm your bookings. SnapTravel offers 24/7 customer chat support and exclusive VIP packages.
Advanced shopping bots like is a self-service support system that studies the algorithm of retailers and provides solutions on how to improve it drastically. As a frontliner in modern analytics, the chatbox can also automate business processes in different departments like Airlines, Hospitality, Real Estate, eCommerce, Broadcast TV, and Human Resources without breaking a sweat.
Operator
Operator is the first shopping bot built explicitly for global consumers looking to buy items from U. S. based companies. The app allows the users to browse product lists and make a purchase without it being too overwhelming. But in cases that it does get complicated, the app provides human experts to help guide them with the process of importing.
is a free and easy to follow eCommerce platform that customers can install directly on their own messenger app or the brands website. The perks of using the app is that customers can connect to over 2, 000 brands and local shops; there are more than 40 categories with over 8, 600, 000 products and 40, 000 exclusive deals you can find in their webpage.
Birdie
There is a tendency that customers immediately purchase the product they’re looking for in the first shop they see and regret it afterwards because there was a cheaper version of it somewhere else.
Birdie helps you minimize these situations by providing you detailed product reviews and their ranking online. The client’s personalized profile allows the bot to suggest products and brands that fit the preference of each user’s shopping habits.
SMSBump, a Yotpo Company
SMSBump is a good self-service portal that makes the functionality of SMS Marketing extremely easy. This self-servicing IT has the biggest automation library in the market. Choosing the best automated message that suits the users market and potential leads is a piece of cake with the help of this self-service software.
ChatShopper
If you want a personal shopping assistant, ChatShopper provides a 24/7 personal shopping bot named Emma. Just like advanced AI solutions similar to Siri and Alexa, Emma will help you discover a wide variety of products on Android, Facebook Messenger, and Google Assistant.
Customers will be given a ton of options from different categories that vary from clothing and accessories. All the user has to do is type in the name or keyword of the item you’re looking for and Emma will provide a list of items that are the perfect fit for the query.
Letsclap
Letsclap utilizes voice and conversational solutions that allows merchants and customers to enjoy the advantages of two different things. It offers mobile messaging, voice assistance for business owners and clients, and chatbots that are ready to assist them 24/7.
Shopping bots will take the requests of their clients and help guide them throughout the process of selecting and purchasing the leading match. Should there be any problems the bot can’t solve, human experts will interfere right away.
RooBot
IT self help applications is an extremely competitive market and this inspired RooBot to take it a notch higher with their online self-service app. Similar to Amazon Alexa, RooBot empowers enterprise self-service shopping apps with an AI-driven personal shopper that answers any customer query through voice detention.
To make eCommerce a lot easier for business owners and their customers, this shopping bot also personalizes every customer’s shopping profile to provide better product recommendations.
Kik Bot Shop
For meme lovers, Kik Bot Shop should be on your top 10 list of web self-service apps online. This playful shopping bot elevates the overall conversation and shopping experience of the customers with a variety of eCommerce shops. Businesses are given the freedom to choose and personalize entertainment bots that share memes to engage and connect with their users.
The competitive edge has against the competitors is that it’s a monetization platform. This shopping bot allows merchants to personalize or construct product recommendations that customers will not only love, but also be persuading enough to be a potential sale conversion in the end. This can be installed and accessed either on a mobile phone or eCommerce platforms such as Telegram, Slack, Facebook Messenger, and Discord.
Yellow Messenger
Connecting to enterprises shouldn’t be complex. AI experts that developed Yellow Messenger were inspired by Yellow Pages in general. Yellow Messenger gives users easy access to a wide array of product listings that vary from plane tickets, hotel reservations, and much, much more.
Yellow Messenger drastically enhances employee productivity and lessens time spent on tedious tasks. Applications like Microsoft teams, Slack, and Hangouts are platforms that power self-service and instant connection.
5Gifts4Her
5Gifts4Her has impeccably intelligent self-service solutions that simplify buying gifts for the special women in your life. This shopping bot makes gift buying easier by showcasing a weekly catalog designed specifically for women.
Users who are having a hard time choosing a gift for women can now freely browse and purchase the perfect gift directly from your Facebook Messenger.
Cybersole
Getting your hands on the latest sneakers without having to compete with a crowd is impossible and mostly frustrating. Cybersole is a shopping bot that is specifically designed to satisfy the needs of every sneakerhead. This shopping bot’s lightning fast features are multi-threaded to ensure the finest and most reliable service there is.
Cybersole supports a variety of retailers including Finish Lane, Supreme, Mesh, Footsies, and 270 more stores to choose from. With this app, you wouldn’t have to worry about missing a drop ever again.
Luko
Get the most bang for your buck with Luko’s advanced features. This feature-rich shopping bot tracks the cheapest Amazon product deals there could probably be. Once the price of the item you’ve had your eye on for a long time drops, Luko makes sure that you will be the first one to find out about it.
All you have to do is notify which item you want to price-track and let Luko do it’s magic.
Dashe
Manually checking out will never be your problem again once you subscribe to Dashe’s shopping bot. Shopping bots like those under Dashe are auto-checkout tools that help the user check out immediately without delay. This self-service software scans the wide internet world for hard to resist deals for just a monthly fee of $50. (note that they only accept PayPal as their mode of payment)
Beauty Gifter
Similar to the 5Gifts4Her shopping bot, Beauty Gifter’s services also revolved around finding the best gift for women. The main difference between the two is that Beauty Gifter can use personal profiles as a reference for their gift ideas, whereas the latter doesn’t. The bot collects information from the receiver by asking a series of questions.
Data gathered from the profile programs the shopping to create the perfect list that is bound to exceed the expectations of the user.
CelebStyle
What Bretman Rock, Rihanna, and Kim Kardashian all have in common is their unorthodox and hip fashion sense that never fails to wow the world. If you want to have the same wardrobe as them, CelebStyle is the perfect shopping bot to help you.
CelebStyle helps their users find the exact clothes celebrities are wearing and the merchant that sells them online. New celebrity profiles are uploaded to give customers more options to choose from. With CelebStyle, anyone can now dress up like their favorite A-List superstar.
H&M
From joggers and skinny jeans to crop tops and to shirts, as long as it’s a piece of clothing, H&M shopping bots have got you covered. Customers can connect directly to the customer service portal to get access to the company’s clothing gallery to find items that suit your style.
Note that this app is designed to only feature H&M products. Users will be given limited edition product deals and exclusive information on how to build an outfit style that anyone can rock during night outs.
Madi
Hair color junkies listen up. The famous Madison Reed hair coloring company launched a 24/7 shopping bot that acts like your personal hair stylist. Madi is like having your own professional colorist in your pocket.
The customer service portal helps clients find which hair color works best for any skin tone and eye color. You wouldn’t have to worry about using the wrong shade of hair color ever again.
Francesca’s
Finding high-quality clothes and accessories for women are Francesca’s specialty. The simple design of the bot makes it one of the best self-service websites that can answer different questions like the availability, shipping, and sizing options without using rocket science to do so.
Readow
Self-service solutions provided by Readow caters to those who are book lovers. The bot scans the wide web for the best book recommendations and high-quality reads that will satisfy the need of the user.
To make the recommendations more personal, the bot engages in a conversation with the user first and asks specific questions like which genre they prefer reading and which author they love the most.
Botler Chat
Botler Chat is one of the self-service options independent sellers like startups and small marketing agencies can use to grow their market. Engaging in a conversation with the shopping bot provides the user solutions and detailed strategies on how to sell their products and services to different market categories online.
Dropshipping Assistant
Self-service businesses take advantage of Dropshipping Assistant’s ability to follow different product trends in the market. The users will be given exclusive access to eCommerce topics that can help expound their businesses in different terms.
NexC
NexC is a self-service platform that provides their users an extraordinary shopping experience in four easy steps:
Elaborate to the bot what specific products you want. The bot will then scan the web using AI technology to find the best match for your needs. Once the bot finds a list of possibilities, it narrows it down to the top three products that are the perfect fit for your request. Lastly, personalized recommendations will be provided that weighs the products pros and cons to help the users decide which product to buy.
WeChat
WeChat is a self-service company app that allows businesses to communicate freely and build a relationship with their customers by giving them easy access to their products. It makes product inquiries, easier and more manageable for both ends.
Magic
The name says it all. Magic provides users with supernatural self-service applications that provide AI-solutions and human experts to assist each customer with anything. From placing an order online to booking a ticket to the beach, Magic gets the job done.
BlingChat
BlingChat caters to millennials that are looking to buy engagement rings or an assistant in planning their wedding. This shopping bot also provides merchants to use the app to present their ring designs and get discovered by a larger market.
Frequently Asked Questions about shopping bot definition
What is meant by shopping bots?
A shopping bot is a self-service automated system that scans thousands of website pages around the world once a product inquiry has been made. Once it finds the best deal, it will immediately alert the user without wasting a second.
Is it illegal to use a shopping bot?
Are sneaker bots illegal? At least in the U.S., the answer is no. While using automated bots to buy goods online often violates the retailer’s terms and conditions, there are no laws against it at the current time for sneakers.Feb 1, 2021
How much is a shopping bot?
You can go online and buy a bot from anywhere between $10 to $500. But it’s risky. “Some experts say, ‘Yes, get yourself a bot because you are not going to be fast enough using your fingers,’” Tomlinson says.Nov 13, 2019
