AU2013204719A1 - Method and system for aggregation and analysis of metrics - Google Patents

Abstract

The present invention relates to a computer-implemented method for data aggregation and analysis of electronic marketing metrics, including the steps of: collecting electronic marketing data associated with a plurality of businesses into a database; filtering and analysing the collected data, whereby the data are aggregated into specified categories; computing at least one benchmark, the benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; comparing the at least one benchmark with a corresponding performance metric based on data in the database associated with a target business; and where the target business performance metric is below the benchmark for a category, providing at least one marketing recommendation to the business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric. The present invention also relates to a computer-implemented system for data aggregation and analysis of electronic marketing metrics including: a database configured to contain data from a plurality of businesses; a data collector for collecting electronic marketing data associated with the plurality of businesses; a data analyser for filtering, analysing and aggregating the collected data into specified categories, and for computing at least one benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; a matrix builder for comparing the at least one benchmark with a corresponding performance metric based on data in the database associated with a target business; and a recommendation engine for providing at least one marketing recommendation to the target business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric, wherein the target business performance metric is below the benchmark for a category. .. .. . .. . . . . .. .. . .. . . . . .. .. . .. . . . . -................... ...... ..... ..... .... .... .... .... .... .... .... .... ...... ...... ..... ...... ..... ..... .... .... .... .... ...... ...... ..... ....... ....... ... ... ... ... .. .... .... . ...... ..... ...... ..... .... ..... .... ...... ...... ..... .... .... .... .... .... .... .... .... .................. ...... ...... ..... ...... ...... ..... ...... ...... ..... .... .... .... ... .... .... .... .... ............... w ... .. .. ... .. .. .... .... .... .... .. . .... .. .: ... ................ ... .. .. .. .. .. .. .. ... ..... ..... ...

Description

I METHOD AND SYSTEM FOR AGGREGATION AND ANALYSIS OF METRICS FIELD OF THE INVENTION This invention relates in general to aggregation and analysis of 5 performance metrics and more particularly to a method and system for providing business analytics in relation to electronic marketing activities. BACKGROUND TO THE INVENTION Businesses are always looking to increase profit, increase the number of customers/clients and ensure the business remains viable. There are many ways 10 to achieve this including improving service, offering complementary services/goods, introducing a reward program, changing prices, and changing where or how a business advertises or markets its products and/or services. However, knowing what to do and how to do it can be hit and miss and also expensive if not chosen wisely. 15 One area which is critical to any business is what form of marketing will increase sales of its products and/or services, and which marketing method provides the best return on investment. Thus, a business can decide whether a particular marketing method is worthwhile pursuing. It is possible to employ a consultant who can review a business and 20 suggest, based on previous experience and current market trends, what type of marketing that business should and can use to improve its profitability and patronage. However, this approach is not conclusive and the recommendations suggested are based on observations, experience and intuition rather than objective analysis of actual measured results, so the business owner will not 25 know if these suggested marketing techniques are the best for their business and will provide the business owner with a good return on investment. It is becoming easier to obtain statistics such as website traffic, Facebook 'likes' Twitter follower numbers etc. for your own business by virtue of free or low cost systems such as, Google@ Analytics, Facebook@ Insights or Twitter@\ web 30 analytics. These systems allow a business owner to review their own business statistics by using data gathered directly from that business. It is then left up to the business owner to interpret those results into ways in which the business can improve. Whilst these statistics may be useful to see how a business is 2 performing relative to another period of time, the business owner will not know how their business is performing compared to any other business in the market. That being the case, and based on assumptions of how other similar businesses are performing, a business may be setting an unrealistic performance target to 5 achieve. In this respect, a business owner would benefit from knowledge of the performance of marketing activities of competing businesses. However, a business is not going to suggest to another competing business to implement a particular marketing campaign which had proven successful. In this way it is very 10 difficult to know what marketing works well for a business' competitors. Traditionally in the retail and restaurant industry, to compare how a business is performing relative to another business, business owners often frequent similar businesses, as a customer, to investigate what that other business is doing. This is a simple way business owners get ideas on how they 15 can improve their own business. However, it does not provide a business owner with conclusive information on what marketing tactics perform best to increase revenue for the business. Further, what a business owner considers works well for one business, may not work the same way for another business. Another way to gain an understanding of where a business may rate 20 relative to other businesses in a specific industry is to buy business information reports. These reports are prepared from information gathered by interviewing and surveying a number of businesses as well as limited publicly available data. Most of this information used in the reports is subjective and does not provide suggestions on what sorts of marketing initiatives a business can implement to 25 improve. Further, without having actual comparative data a business owner may use trial and error of different marketing initiatives to improve performance of the business. If the initiatives implemented do not improve the performance of a business or only improve a business' performance marginally, the business will 30 not have achieved a good or maximum return on marketing investment. At present there is no system that provides information, based on actual data, to a business about how it is performing relative to other businesses and that can provide marketing recommendations that a business can implement to improve profitability, patronage and return on marketing investment. SUMMARY OF THE INVENTION In light of the above, according to an aspect of the present invention, there 5 is provided a computer-implemented method for data aggregation and analysis of electronic marketing metrics, including the steps of: collecting electronic marketing data associated with a plurality of businesses into a database, filtering and analysing the collected data, whereby the data are aggregated 10 into specified categories; computing at least one benchmark, the benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; comparing the at least one benchmark with a corresponding performance 15 metric based on data in the database associated with a target business and where the target business performance metric is below the benchmark for a category, providing at least one marketing recommendation to the business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric. 20 According to another aspect of the present invention, there is provided a computer-implemented system for data aggregation and analysis of electronic marketing metrics, the system including: a database containing data from a plurality of businesses; a data collector for collecting electronic marketing data associated with the 25 plurality of businesses; a data analyser for filtering, analysing and aggregating the collected data into specified categories, and for computing at least one benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; 30 a matrix builder for comparing the at least one benchmark with a corresponding performance metric based on data in the database associated with a target business; and 4 a recommendation engine for providing at least one marketing recommendation to the target business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric, wherein the target business performance metric is below the 5 benchmark for a category. An advantage of a method or system embodying the invention is that individual participating businesses are able to benefit from each other's successful marketing initiatives in aggregate, without sharing sensitive commercial information with competitors. 10 The computer-implemented system preferably further includes a report generator for generating reports associated with performance metrics of the target business or any other business in the database. The data used in the method or system is preferably collected from one or more of: website activity; SMS marketing; social media activity; or email direct 15 marketing. The benchmark may be calculated in a number of ways, but preferably the benchmark is calculated by taking the median of the aggregated data. The data may be aggregated into any one or more of the following specified categories: social media data; website data; e-marketing data; social 20 network data; Facebook Fans; Facebook Likes; microblogging data; Twitter Tweets; Twitter Followers; unique website visits; email direct marketing; email open rate; SMS message marketing; SMS message click rate; search engine marketing; and search engine click through rate. The predetermined criteria preferably include one or more of: location; 25 industry; sub-industry; and type of data. It is desirable for the type of data to be sourced from any one or more of: Facebook data; Twitter data; email direct marketing data; SMS data; and website data. It is therefore an object of the present invention to provide a method and system that compares and rates a business with similar businesses and provides 30 details about how that business is performing relative to other businesses. In addition it is an object of the present invention to provide marketing recommendations that a business can implement to improve profitability, b patronage and return on investment using data that has been collected from that business and other businesses. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments of the invention will now be described with reference to the 5 accompanying drawings. It is to be appreciated that the embodiments are given by way of illustration only and the invention is not limited by this illustration. In the drawings: Figure 1 is a flow chart providing an overview of a system for providing business analytics in relation to electronic marketing activities by aggregation and 10 analysis of performance metrics according to an embodiment. Figure 2 is a flow chart showing a method used by a data collector in the system shown in Figure 1. Figure 3 is a flow chart showing a method used by a data analyser used in the system shown in Figure 1. 15 Figure 4 is a flow chart showing a method used by a matrix builder in the system shown in Figure 1. Figure 5 is a flow chart showing a method used by a report generator in the system shown in Figure 1. Figure 6 is a flow chart showing a method used by a recommendation 20 engine in the system shown in Figure 1. DESCRIPTION OF PREFERRED EMBODIMENT The preferred embodiment of a method and system for providing business analytics in relation to electronic marketing activities according to the present invention will be described in relation to restaurants. However, it is understood 25 that this could relate to any business, for example, hairdressers, beauty salons, clothing shops, florists, or hardware stores. The flow chart shown in Figure 1 provides a general overview of the operation of an embodiment of the invention. Preferably this system and method are computer-implemented. 30 It is possible for a business to subscribe 100 to a system 1 that provides business analytics in relation to electronic marketing activities which measures that businesses return on marketing investment and that provides marketing recommendations that will have a high return on investment and will increase 6 revenue for the business. One way that a business can subscribe is to have its business' website hosted by the system 101. Alternatively, the business can opt to have its website tagged 102 by the system 1. Tagging the business' website allows the system 1 to collect and record only particular website activity metrics 5 112 directly related to the business. If the system hosts the business' website additional data can be collected through sources including: the business' hosted website 111, e-marketing channels (EDM) 121, social media channels 141 and tracking of various transactions 151. In addition, data related specifically to SMS marketing 131 of a business whose website is hosted by the system can be 10 collected due to using the same integrated platform to send an SMS message and to host a website, thereby integrating hosting websites and sending SMS messages in the system. Other publicly available data can also be collected, such as social media activity 122. The system and method each require the collection and input of marketing 15 data, preferably electronic marketing data. The data collected and used is critical for the integrity of the results and recommendations produced by the system. Data is continuously collected from different sources, outlined above. The components used in the process shown in Figure 1, include: 1. Data Collector 20 2. Data Analyser 3. Matrix Builder 4. Report Generator 5. Recommendation Engine As shown in Figure 1 and referring to Figures 2-6, the Data Collector 201 25 performs the step of collecting data 200, it then categorises and sorts the data and stores it in a database for use in the system. Further detailed explanation of the Data Collector is provided below. The data processed by the Data Collector 201 is used by a Data Analyser 301. The Data Analyser 301 performs the data analysis step 300 of filtering and 30 analysing thereby aggregating the data of all businesses into specified categories. The Data Analyser 301 then computes a data set which is benchmark data 360. This is called the Industry and Location Benchmark (ILB) 360. The benchmark is calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria. This is explained in further detail below. User Data 266 gathered from a target business' own data is obtained from the data collected by the Data Collector 201 stored in a database 260. A Matrix 5 Builder 401 uses the ILB 360 computed from the Data Analyser 301 and User Data 266 in a benchmarking step 400. A User Matrix (UM) 460 is created by comparing the ILB 360 and User Data 266. This is explained in more detail below. Following the step of benchmarking 400, the step of reporting 500 occurs. 10 In this step, the data contained in the User Matrix 460 is used to provide a report to the target business owner about how their business is performing compared to the benchmark and other businesses and/or the industry. The reports generated are User Reports 560. User Reports 560 are used as an input to a Recommendation Engine 600. 15 The Recommendation Engine 600 learns from top business performers and identifies what features of those top performing business' marketing (and marketing initiatives) make them a top performing business. The Recommendation Engine 600 provides a list of recommendations to the target business for optimizing that business' marketing investment and reasons why a 20 particular marketing initiative should be implemented. Where the target business performance metric is below the benchmark for a category, at least one marketing recommendation to the target business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric is provided. 25 The components of the system used to implement the method for data aggregation and analysis of electronic marketing metrics are described in further detail below. DATA COLLECTOR Figure 2 shows a process performed by the Data Collector 201 for 30 collecting the data and ultimately storing it in a database 260. As outlined above, the data gathered for use by the Data Collector 201 is from websites of businesses that are hosted on the system 1, businesses that have authorised their websites to be tagged and external social media activity 122 that is publicly b available. Data is continuously collected from different sources. The data used and gathered is critical for integrity of the systems recommendations. A business' hosted website traffic data and website performance (website activity 111) is captured by regular means. Other data sourced from businesses whose websites 5 are hosted include social media activity 141, email direct marketing activity 121, SMS marketing 131 and transaction tracking 151. The integrated platform of the system allows SMS marketing (including sending of SMS messages) to be built into the system. This, therefore, allows the one system to collect all types of electronic marketing data including SMS data. It is then possible to correlate 10 marketing conducted via SMS to actual website traffic. Information from those businesses' websites that the system tags is gathered with their permission through the use of a tracking code, although other means are possible. Some external data sources will require a business' authentication and/or authorisation. 15 Information and data that is gathered includes: * Location and postcode of the business. * Type of goods and/or services provided by the business, for example, the type of cuisine a restaurant serves. * Details of goods and services provided by the business, for 20 example, for restaurant businesses this could be menu details, price range, etc. * Statistics and analytics generated from the business' website, Facebook page, Twitter feed, SMS campaigns, email campaigns, flyers etc. 25 0 Details of the business' marketing campaigns, including social media. Examples of the specific type of social media data collected include: Facebook: " Number of LIKES on Facebook Pages. 30 0 The change in the number of LIKES over a period of time. * Percentage of the business' overall website traffic that originates from Facebook.

9 * The change in percentage of the business' overall website traffic that originates from Facebook over a period of time. Twitter: * Number of Followers. 5 0 The change in number of Followers over a period of time. * Number of Tweets (or messages posted). * Percentage of the business' overall website traffic that originates from Twitter. * The change in percentage of the business' overall website traffic that 10 originates from Twitter over a period of time. Websites: " Number of visits to a business' website. * Number of unique visitors to a business' website. " Number of page views or actions by a visitor. 15 0 Average visit duration of a visitor. " Bounce Rate. " Keywords that lead to the business' website being visited from a search conducted using a search engine. " External links / websites that link to a business' website. 20 Examples of the specific type of e-marketing data collected include: SMS Marketing: * Total number of SMS messages sent. " Average number of SMS messages sent (i.e. total number of SMS messages sent compared to the number of marketing campaigns sent 25 out). * Average Click Through Rate (i e. the total number of clicks on an SMS message compared to the total number of SMS messages sent). Email Direct Marketing: 0 Total number of email messages sent. 30 0 Average number of email messages sent (i e. the total number of email messages sent compared to the number of email campaigns sent out).

1U " Average email messages open rate (i.e. the total number of email messages opened compared to the total number of email messages sent). * Average Click Through Rate (i.e. the total number of clicks on an email 5 message compared to the total number of email messages sent). The data and type of data collected is not limited to that listed, and may be any electronic marketing data or related data. Transaction Tracking data 151 is gathered from a Transaction Engine (not shown) which monitors and records a target business' website activity and 10 analyses how well it performs compared to other business' (i.e. competitors). This is very important because by measuring the sales transactions and particularly those transactions that were caused directly by the implemented marketing, the return on investment (ROI) for the marketing initiative(s) can be measured in monetary value. 15 As an example of the transaction tracking 151, a restaurant can use an SMS marketing tool to promote an offer, contained within the message is a link or a code. Customers use this code when making an online order or when they order in store. Once an order is made and the code is entered in the online booking or quoted in store it is possible to track the code, thus, the cost of 20 revenue generated per SMS message can be calculated. This information can then be used to extract the ROI, i.e. the profit or loss from each SMS marketing message. The above data is collected by the Data Collector 201 component of the system. The data is captured continuously and recorded in such a way that 25 results in any requested period of time can be accessed for use in subsequent parts of the system, for example to run reports, look at trends over different periods of time or for marketing analysis. The Data Collector 201 filters the input data 210 into various categories. For example, as shown in Figure 2, in the data filtering step the type of data is 30 identified into i) social media data 211, ii) website data 221, and iii) e-marketing data 231. Each of the identified data is further filtered into a sub group. For example, social media data 211 is filtered into social network data 212 (such as data originating from Facebook) and microblogging data 213 (such as data 11 originating from Twitter). The data in each sub group is further filtered, for example, as shown, the social network data originating from Facebook can be filtered into data 214 including number of fans or Likes on a Facebook page, the microblogging data originating from Twitter is also filtered into data 215 including 5 number of Tweets or Followers. Similarly, the website data 221 can be filtered into data from hosted websites 222 or tagged websites 223, this data is further filtered into number of unique visits 224, etc. Also, the e-marketing data 231 can similarly be filtered into email direct marketing (EDM) 232, SMS 233 and Search Engine Marketing 234 (SEM), each of these can be filtered further into open rate 10 235, click rate 236 and click through rate 237, respectively. All the fully filtered data in the categories are then aggregated 250 and stored in a database 260 for use in the system. DATA ANALYSER The next step in the process is to analyse 300 the data 260. This is 15 performed by the Data Analyser 301. The Data Analyser 301 drills down, segments and computes a benchmark, forming the Industry and Location Benchmark (ILB). The ILB data is stored over a period of time thereby providing valuable information about the trend of a particular industry. The Data Analyser has three main tasks: 1) Data Filtering and 20 Categorisation; 2) Computing Average/Median Value; and 3) Benchmarking and Trend Analysis. It is important to filter and categorise data collected from each business correctly, because without this, it is not possible to benchmark a particular industry or business or compare a business with the benchmark. 25 The first step of the Data Analyser is to categorise a business and then filter that business' data. The four main predetermined criteria used to categorise a business data is, the business' assigned location 311, the business' industry 312, the business' industry specific category 313, and the type of data 314 (for example, data from Facebook, Twitter, EDM, SMS or website). A business 30 postcode can be used as an additional criteria. This can be used to determine a catchment area. This categorisation improves the efficiency of any subsequent process including benchmarking, matrix building or report generation.

12 It is possible to apply pre-set rules to limit how data is computed and benchmarked. For example, the location of a business can be limited to 5, 10 or 15km distance from a suburb. Each criteria can be filtered in many ways, for example: Filter options for Location category include: 1) Suburbs Only (e.g. 5 Hawthorn), 2) Range from a Suburb (e.g. 5km, 10km, 15km from Hawthorn), and 3) Catchment area (e.g. Catchment areas contain several adjoining suburbs). Filter options for Industry category may include: 1) Restaurant/Cafe, 2) Automotive and 3) Beauty, Body Health. The Industry specific category can be further filtered by a sub-category as provided in the Category filter including: 1) 10 Sub categories for Restaurant/Cafe: a) Indian, b) Chinese and c) Italian. 2) Sub categories for Automotive: a) General, b) Electrician and c) Tyre Shop. 3) Sub categories for Beauty: a) Hairdresser, b) Massage and c) Cosmetic Surgery. After the business' data is associated with criteria by Location, Industry, Industry Specific Category and Type of Data or other criteria selected, the Data 15 Analyser aggregates each data criteria, where the data is drawn from similar businesses associated with the same predetermined criteria. Preferably, this is achieved by computing the median value 320. The aggregated value, in this case, the median value of the data, generated from the analysis (e.g. median website visitors) is recorded periodically 20 (e.g. monthly). This provides a trend of how each industry is performing, forming the Industry and Location Benchmark (ILB) 360. MATRIX BUILDER Once all the data is collected, filtered and analysed, the ILB is compared with a target business' data based on certain criteria in the benchmarking step 25 400. This benchmarking step is undertaken by a matrix builder 401. The matrix builder 401 performs the step of extracting the benchmark data 411 from the ILB data 360 as well as extracting the target business' specific data set 412. The benchmark data 411 is compared in step 420 to the target business' own specific data 412. The difference between the data sets 411, 412 are calculated in step 30 430. From this a performance metric (or ranking) of the target business, in terms of marketing, to the ILB is computed. This is then compared with the target business' own data forming a User Matrix (UM) 460. The UM 460 is stored in an incremental manner so that a snapshot view of any particular period, historical 13 data or growing trend can be provided to the business owner. An example of the user matrix and measured rankings are provided below in Table 4. Essentially, the metrics of the target business are compared to the aggregated metrics (preferably median value) of other similar businesses. 5 REPORT GENERATOR The performance metric, for example, (rating or ranking) of a particular business is defined based on a comparison of the relative value to the aggregated or median value. For example: Performance Rating 50% above median value VERY GOOD 11 %-49% above median value GOOD 10% below or above median value AVERAGE 11 %-49% below median value POOR 50% below median value VERY POOR 10 Ranking can be implemented where the top performer is the lowest number in a list, e.g. in a list from 1-10, 1 is the top performer. Alternatively, a top performer may be ranked in such a way that it is allocated the higher number in a list, e.g. in a list from 1-10, 10 is the top performer. 15 Pre-defined reports are generated and stored for fast retrieval. These reports are also used by the Recommendation Engine 601. Business owners (users) can query the stored UM and generate reports for their business or about the industry in general based on certain criteria. Information such as the best performing Indian restaurant in Melbourne, the best performing restaurant in 20 Hawthorn, the biggest performing marketing initiative implemented in the last 12 months, can be obtained in a report. The report generator 501 in the reporting step 500 uses the data from the user matrix 460 and details of a rating threshold 520 to generate a report. A user report is generated for each business. 25 RECOMMENDATION ENGINE Next, the Recommendation Engine 601 uses the user reports 560 to define areas for recommendations 620, having regard to pre-set rules for recommendations 611 and a list of recommendations 612. The Recommendation Engine 601 also compares the various user reports 560 in step 630 to generate 30 competitive intelligence 640.

14 Competitive Intelligence is key to the present invention. A business is provided with a report about their performance relative to its competitors and the market. Instead of merely providing the exact number of visitors to the business' website per month, a business owner can ascertain where the business stands 5 relative to the whole market. Having results that are compared to other businesses provides a good indication whether a business' overall online marketing strategy is working or not. For instance, if average restaurants can achieve 10% increase in website traffic after an SMS marketing campaign, a restaurant should use this as benchmark and aim at achieving above 10%. 10 Assessing a business' marketing strategy in step 650 is performed by taking the recommendation information from step 620 and combining that information with the competitive intelligence gained from the industry in step 640. Following this, a list of marketing recommendations are compiled in step 660 including reasons for those recommendations. Those recommendations 700, 15 based on marketing outcomes of top performing businesses, are provided to the business owner. Because those recommendations are based on actual results, these recommendations may be identified as the business' highest return on investment marketing opportunities. If the recommendations are implemented, the business can increase its sales. 20 The system, based on the data analysis and reports generated from that data, learns from the top business performers and identifies what features of the top performer marketing makes them a top performing business. This information forms the core of the Recommendation Engine which is based on a machine learning process using rules, triggers and direct input (for example, a business' 25 existing marketing initiatives and strategy) to compile a list of recommendations for optimizing a business' online marketing investment. The Recommendation Engine looks for any pattern that can be used for drawing a conclusion and subsequently making recommendations. For example, by recording a business' website traffic data from that same business' Facebook 30 page, various correlations between the business' Facebook page and their website traffic in general can be ascertained. Similarly, a business' website traffic can be determined by SMS marketing campaigns which are initiated through the system.

1b Some examples of recommendations are provided below. Recommendation Example 1 (based on relationship between Facebook page and organic website traffic) - The finding of the recommendation engine is that for Japanese Restaurants located around Hawthorn an average of 5% of a 5 restaurant's website traffic comes from that restaurant's Facebook page. Based on this, the system recommendation is that if a new Japanese restaurant opens for business which falls into the same location, industry and category (i.e. Japanese Restaurant located near Hawthorn), the system produces a recommendation that if the restaurant created and used a Facebook page it 10 should expect an increase in its website traffic. Recommendation Example 2 (based on relationship between number of Facebook posts and website traffic from Facebook page) - The finding of the recommendation engine is that website visits that originate from a Facebook page are directly proportion to the number of messages posted on a Facebook page. 15 Based on this, the system recommendation in this example may be that if a business is using a Facebook page, it should increase the frequency of posting because this will result in more visits to a business' website and ultimately more customers. Recommendation Example 3 (based on relationship between industry and 20 bounce rate) - The finding of the recommendation engine in this example may be that on average, restaurant websites have less than 30% bounce rate. Based on this, the system recommendation may be that if a restaurant business website has more than a 30% bounce rate it is not performing as well as it could and the recommendation is to improve the website content. 25 ADVANTAGES This method and system has significant advantages and benefits. The primary advantage is that individual participating businesses are able to benefit from each other's successful marketing initiatives in aggregate, without sharing sensitive commercial information with competitors. 30 A benefit for a business owner using this system and method is that they will have a better understanding about how their business performs, in terms of marketing strategy, relative to other similar businesses. This becomes increasingly important when trying to understand the influence of factors beyond 1l the business owner's control. For example, a restaurant owner notices their website traffic and bookings dropped significantly after Christmas. The owner may be concerned about this drop in numbers, however, by using this system and method the owner can see that other businesses are also experiencing the same 5 drop off, and therefore the drop off is not due to any specific thing the business owner is, or is not, doing. This is because this system can provide statistics of other businesses' including website traffic and bookings. This method and system provides competitive intelligence to a business based on data analysed from businesses with high performance metrics and 10 successful marketing initiatives. The data captured and utilised is primary data from the business which has a direct impact on the bottom line of that business, i.e. the direct sales. Data is not sourced from secondary sources such as forums, blogs, articles, etc. This system and method is differentiated from current systems and 15 methods because it can: 0 rank a business' website based on a range of metrics against other similar businesses' websites; 0 determine relationships between various marketing initiatives including email, social media, SMS marketing, and search engine optimisation; 20 * measure the return on investment for marketing initiatives (i.e. how much money a business can generate for each dollar invested); 0 compare the return on investment for various marketing initiatives to provide insights into which marketing mechanisms are the most effective; 25 0 link origins of website visits to monetary outcomes to determine return on investment * provide a business with a benchmark of its marketing activities against average and best practice marketing activities (drawn from similar and competing businesses) 30 e identify areas of improvement for each business to increase website traffic and revenue; I/ " provide insight into the best practises for building cross media marketing initiatives, for example, using Twitter to enhance a business' Facebook page; and * determine the monetary value of individual social media interactions. 5 Presently none of the systems or methods in use aggregate all those different types of data or such information from many similar businesses. Any benchmarking or comparative data computed only using actual data from one source is not accurate. If the comparison is not relative to other businesses, the results can only be speculative and will not provide useful recommendations and 10 an accurate determination of return on marketing investment. The method and system of the present invention directly compares marketing initiatives of a target business with the aggregated information about those same initiatives of other similar businesses, without sharing sensitive commercial information. Further, this overcomes the disadvantages of those 15 methods and systems that only obtain statistics from the target business. Instead of simply making marketing recommendations based on common practise and assumptions, the present system and method makes recommendations to implement marketing initiatives based on statistic data from similar businesses in similar industries. Further, the marketing recommendations 20 suggested ensure a return on investment for that marketing initiative because there is data showing several other similar business' that have implemented that marketing initiative have increased sales or improved performance. EXAMPLE The following is an example of how the system uses the method to 25 aggregate and analyse the metrics of a business and the recommendations the system proposes to increase the business' performance. Firstly, data is gathered from a number of similar businesses, in this case, restaurants. The Data Collector processes, filters and aggregates the data as described above. The result is the data is shown in table 1 below. 30 15 TABLE 1 - Data Collection step 200 performed by Data Collector 201, resulting in data stored in Database 260 Data Collection A_____ _c________ Location Seaford Seaford Bavswater Bavswater Seaford Cuisine indian indian Indian Chinese Chinese LIK(ES 300 100 50 0 450 Increase in LIKES (per month) 15 5 3 0 25 Posts 10 5 30 0 45 Website traffic from Facebook (%) 5 20 15 0 25 Twitter~ ________________ Followers 100 300 0 100 70 Increase in Followers (per month) 5 10 0 10 5 Tweets 5 30 0 30 100 Website traffic from Twitter (%) 2 10 0 15 20 Sent 25 5 30 30 20 Open rate (%) 20 70 80 70 5 Average Facebook vVisit (%) 20 30 30 0 5 Average Twitter visit (%) 10 5 0 20 10 Average website visi (%)___5 _10__20 __ 10__ 10 Sent 0 10 10 5 20 Average click through rate (%) 0 20 30 50 5 Visit 700 500 400 1000 1500 Unique visit 500 300 350 800 800 Page views 10000 5000 4000 4000 5000 Average visit duration (sec) 120 30 45 60 40 Bounce Rate (%) 50 20 20 30 30 Number of backlinks 20 30 50 10 100 Number of keywords10 20 301510 Next this data is analysed by the Data Analyser 301 to generate an 5 Industry and Location Benchmark (ILB) data set 360. The ILB data set is generated as described above, whereby the data is filtered by particular criteria. the rnedian value calculated and benchmark data constructed. The result is shown in Table 2 below. 10 19 TABLE 2 - Data Analysis step 300 performed by Data Analyser 301 resulting in benchmark data. (ILB) 360: Data Analyser By Location By Location By Cuisine By Cuisine (Seaford) (Bayswater) (Indian) (Chinese) Median Facebook Like 300 25 100 225 Median Increase in LIKES (per month) 15 2 5 13 Median number of posts 10 1510 23 Median Site Traffic from Facebook (%) 20 8______ 15 13 Twitter Median increase in Followers (per month) 5 5 5 8 Median Tweets 30 15 565 Median website traffic from Twitter (%) 10 82 18 Median sent 20 30 25 25 Median open rate (%) 20 75 70 38 Median Twitter visit (%) 10 10 5 15 Median website visit (%) 10 15 10 10 Sent 10 8 10 13 Median click through rate (%) 5_____4________ 20_______ 28____ Website Median visit 700 700 500 1250 Median unique visit 500 575 350 800 Median page views 5000 4000 5000 4500 Median average visit duration 40 53 45 50 Median bounce rate (%) 30 25 20 30 Median number of backlinks 30 30 30 55 _Mediannumbge f keywords10252015 Next, each restaurant A, B C, D and E has user specific data extracted. 5 User specific data for each restaurant is shown below in Tables 3A-3E. TABLE 3A: User specific data for restaurant A. By Location By Cuisine Compare Benchmark (seaford) %_____ (Indian) ____ Average Facebook Like 0 0 200 2____ Average increase in LIKES (per month 0 0 10 2____ Average number of posts 0 0 0 0 Average website traffic from Facebook (%) -15 -0.75 -10 -0.66667 2U Twitter ____ ___ Average Followers 0 0 0 0 Average increase in Followers (per month) 0 0 0 0 Average Tweets -25 -0.83333 0 0 Average website traffic from Twitter (%) -8 -0.8 0 0 Average sent 5 0.25 0 0 Average open rate (%) 0 0 -50 -0.71429 Average Facebook visit (%) 0 0 -10 -0.33333 Average Twitter visit (%) 0 0 5 1 Average website visit (%) -5 -0,5 -5 -0.5 SMS _______________ Sent -10 -- 1 -10 -1 Average click through rate (%) -5 -1 ~20 -1 Site__ _ _ _ _ _ _ _ _ _ _ _ _ Visit 0 0 200 0.4 Unique visit 0 0 150 0.428571 Page view 5000 1 5000 1 Average visit duration 80 2 75 1.666667 Bounce rate (%) 20 0.666667 30 1.5 Number of backlinks -10 -0.33333 -10 -0.33333 Number of keywords 0 0 -100 -0.5 TABLE 3B: User specific data for restaurant B. By Location By Cuisine Compare Benchmark (seaford) % (Indian) % Average Facebook Like -200 -67% 0 0% Average increase in LIKES (per month) -10 -67% 0 0% Average number of posts -5 -50% -5 -50% Average website traffic from Facebook %) 0 0% 5 33% Twitter ____ Average Followers 200 200% 200 200% Average increase in Followers (per month) 5 100% 5 100% Average Tweets 0 0% 25 500% Average website traffic from Twitter (%) 0 0% 8 400% EDM Average sent -15 -75% -20 -80% Average open rate (%) 50 250% 0 0% Average Facebook visit (%) 10 50% 0 0% Average Twitter visit (%) -5______ -50% 0 0% Average website visit (%) 0 0% 0 0% 21 Sent 0 0% 0 0% Average click through rate (% 15 300% 0 0% Site Visit- 200 -29% 0 0% Unique visit -200 -40% -50 -14% Page view 0 0% 0 0% Average visit duration -10 -25% -15 -33% Bounce rate (%) -10 -33% 0 0% Number of backlinks 0 0% 0 0% Number of keywords 100 100% 0 0% TABLE 30: User specific data for restaurant C. By Location By Cuisine Compare Benchmark (Bayswater) % (Indian) % Average Facebook Like 25 100% -50 -50% Average increase in LIKES (per month) 2 100% -2 -40% Average number of posts 15 100% 20 200% Average website traffic from Facebook (%) 8 100% 0 0% Twitter Average Followers 50 -100% -100 -100% Average increase in Followers (per month) -5 -100% -- 5 -100% Average Tweets -15 -100% -5 -100% Average website traffic from Twitter (%) 8 100% -2 -100% EDM_____________ ___ _ Average sent 0 0% 5 20% Average open rate (%) 5_____ 7% 10 14% Average Facebook visit (%) 15 100% 0 0% Average Twitter visit (%) -10 -100% -5 -100% Average website visit (%) 5_____ 33% 10 100% SMS__________ Sent 3 33% 0 0% Average click through rate (%) 10 -25% 10 50% Site _ _ _ _ _ Unique visit 225 -39% 0 0% Page view 0 0% -1000 -20% Average visit duration -8 -14% 0 0% Bounce rate (%) -5 -20% 0 0% Number of backlinks 20 67% 20 67% Number of keywords 75 33% 100 50% 22 TABLE 3D: User specific data for restaurant D. By Location By Cuisine Compare Benchmark (Bayswater) % (Chinese) % Facbook Average Facebook Like -25 -8% -225 ~100% Average increase in LIKES (per month) -2 -10% -13 -100% Average number of posts -15 -150% -23 -100% Average website traffic from Facebook (%) -8 -38% -13 -100% Twitter Average Followers 50 50% 15 18% Average increase in Followers (per month) 5 100% 3 33% Average Tweets 15 50% -35 -54% Average website traffic from Twitter (%) 8 75% -3 -14% Average sent 0 0% 5 20% Average open rate (%) -5 -25% 33 87% Average Facebook visit (%) -15 -75% -3 -100% Average Twitter visit (%) 10 100% 5 33% Average website visit (%) 5 50% 0 0% Sent -3 -25% -8 -60% Average click through rate (%) 10 200% 23 82% Visit 300 43% -250 -20% Unique visit 225 45% 0 0% Page view 0 0% -500 -11% Average visit duration 8 19% 10 20% Bounce rate (%) 5 17% 0 0% Number of backlinks -20 -67% -45 -82% Number of keywords -75 -75% 25 20% TABLE 3E: User specific data for restaurant E. By Location By Cuisine Compare Benchmark (seaford) % (Chinese) % Fcebook _____ Average Facebook Like 1 50 50% 225 100% Average increase in LIKES (per month) 10 67% 13 100% Average number of posts 35 350% 23 100% Average website traffic from Facebook (%) 5 25% 13 100% Twitter Average Followers -30 -30% -15 -18% Average increase in Followers (per month) 0 0% -3 -33% Average Tweets 70 233% 35 54% Average webit traffic. from ............. Twitter......... .... 10 100%.3.14 23 Average sent 0 0% -5 -20% Average open rate (%) -15 -75% -33 -87% Average Facebook visit (%) -15 -75% 3 100% Average Twitter visit (%) 0 0% -5 -33% Average website visit (%) 0 0% 0 0% SMS Sent 10 100% 8 60% Average click through rate (%) 0 0% -23 -82% Site Visit 800 114% 250 20% Unique visit 300 60% 0 0% Page view 0 0% 500 11% Average visit duration 0 0% -10 -20% Bounce rate (%) 0 0% 0 0% Number of backlinks 70 233% 45 82% Next, the user specific data from each restaurant is compared to the ILB benchmarking data by the Matrix Builder 401 in the benchmarking step 400. Table 4 below shows the user matrix 460 and restaurant rankings for Seaford 5 restaurants. In this example, the lower the ranking, the better the restaurant is performing. TABLE 4: User Matrix and restaurant ranking for restaurants in Seaford. Matrix A Rank # B Rank # E Rank # Average Facebook Like 300 2 100 3 450 1 Average increase in LUKES (per month) 15 2 5 3 25 1 Average number of posts 10 2 5 3 45 1 Average website traffic from Facebook (%) 5 3 20 2 52 1 Twitter Average Followers 100 2 300 1 70 3 Average increase in Followers (per month) 5 1 10 2 5 1 Average Tweets 5 3 30 2 100 1 Average website traffic from Twitter (%) 2 3 10 2 20 1 Average sent 25 3 5 1 20 2 Average open rate (%) 20 2 70 1 5 3 Average Facebook visit (%) 20 2 30 1 5 3 Average Twitter visit (%) 10 1 5 2 10 1 Average website visit (%) 5 2 10 1 10 1 24 .. ....... SM S _ _ _ _ _ __.. .... ......... ........ ........ ......... ........ ........ .... .... .... ... ........ ....... Sent 0 3 10 2 20 Average click through rate (%) 0 3 20 1 5 1 2 . .... ....... ...... ..... . ....... ... .... ...... V isit 7 0 0 2 5 0 0 3 1 5 00................................................ ........................................... ............ U n iq u e visit 5 0 0 2 3 0 0 3 8 00................................ ........... ............. ......... .................................. P age view s 10000 1 5000 2 5000 2......................................... ............ ....................... ......... ............. A verage visit duration 120 1 30 3 40 2........................................................................................ B o u n c e ....................................................... ra te......... ...................... ..... 0... .... 2 0.....3 0.... N u b e o f .a k i k 2 0 .3 3 0 ................ ....................................................... ....... 1 00.......... ........... .................... N um ber of keyw ords 100 2 200 1 100 2.......................................................................................... Thrsio 70 V 2r SoGood-10 Unique __visit__Soo_2_300__30500oo P____________________ 0 Aeage vie s_1000__500__500_ Bouncerate 5 3 20 . 30g Avmer facokLinke 20% Ver Poor1.0 AvrgNumber of Postor s 1000 Poor00 Twer L7,_ L7,___ Avra ext nrasep r in Folo es(erae month e0c% Veryurnt AsaGoodlth ep r Avr etage nets B% issh wvirbeagelw Average webit tRffcrmb lowiti -% Seaford rest fo etu atB. E D M- ----------------------- --------------------------- --------------------------- ------------------------- ARaget 7% VeyPo A v e ra ge--- ---- ---- -- ------ - ------- ------ -- ----- --- ---- -- o p e n r a t -% --- -0-- V e ry-- - --- ---- - -- G o od--- ----- ----- --- Average~~~~~~ Faeokvst %y5% Go A v e a g T w itter----------------------------------------------- v isit------- ----------------- ------- P o o r-------- Averagewebsit visit(---0-Averag ------------------------------------ MS---------------- ------------------------ --------- --------------- Sent~~~ A__ V____ 0% A erg A verage click th ro u g h rate (% ) ______ __________ 300% V ery G ood--------------------------------------------------- --------------------- 2b Site_____ _ Visit 29% Poor Unique visit 40% Poor Pge vew 0 Avrge Average visit duration -25% Poor Bounce rate (%) -33% Poor Number of backlinks 0% Average Number of keywords 100% Very Good From this, the report for restaurant B is compared to the reports of other businesses, the competitive intelligence obtained from those reports enables marketing initiatives that have a successful return on investment to be 5 recommended to restaurant B. Examples of such recommendations are shown in Table 6 below. TABLE 6: Recommendation to restaurants and caf6s based on data analysed. Industry Area Recommendation Facebook Post more content on restaurant Facebook page. Restaurant and Promote Facebook page on business website, let more people Cafe know about it. Twitter Post more content and Tweets. Promote Twitter feed on business website, let more people know about it. EDM Reduce the complexity of the email. Check if emails are treated as spam. SMS Increase sending frequency of SMS. Try to shorten SMS message. Website Check Domain name length. Increase website content. 10 Example 2 An Indian Restaurant in St Kilda receives 125 visits to its website per month. The owner of the Indian restaurant decides to have the business' website hosted through the system of the present invention and implement suggested recommendations. Three months later the number of visitors to the Indian 15 restaurant's website has increased to 525 visitors per month. The business owner is provided with a report stating that the restaurant is ranked in the top 70% of restaurants in Australia, the top 80% of Indian restaurants in Australia and the top 90% of restaurants in the St Kilda area.

26 The system also provides marketing recommendations the business owner can implement to improve sales. Because the data used to formulate these recommendations is gathered from top performing businesses similar to that of the Indian restaurant these recommendations are proven to increase sales of 5 other similar businesses. The recommendations include: " The restaurant should set up and use a Facebook page, because 30% of restaurants use Facebook and they receive an extra 12% of traffic from their Facebook page. * The restaurant should commence using Twitter to advertise because 10 20% of restaurants using Facebook receive an extra 5% traffic if also using Twitter. * The restaurant should run at least one email campaign a month because on average an email campaign generates 20% more website traffic and hence sales. 15 0 The restaurant should increase and improve the content of its website. Currently, the restaurant's website contains 175 words. The average restaurant website has 775 words and generates 3 times more results from the Google ® search engine than the Indian restaurant. The recommendations are suggested because they have been determined 20 as marketing strategies that that will provide the best return on marketing investment to the business owner. The system allows the prediction of visitor traffic, allowing for a guarantee for the amount of traffic to be provided to the website, based on comparisons of websites of similar businesses and in areas with similar numbers of customers. 25 Example 3 A mechanic sends out a monthly SMS campaign to customers in a database who have not had their car serviced in the last six months. Customers are able to visit the mechanic's website and book their vehicle in for a service. 30 The mechanic flags customers in the database who make a phone call to book in their vehicles. The mechanic sends out a marketing campaign via SMS to 112 customers, receiving eight bookings online and another 12 bookings over the telephone. The average profit per vehicle service is $65, therefore with 20 2/ bookings; the mechanic can expect a profit of $1,300 resulting from the SMS campaign which cost $11.20. Return on investment (%) = Net profit ($) / Investment ($) * 100 %. Therefore the system informs the mechanic that the return on investment for the SMS campaign is approximately 11600%. This 5 marketing campaign is compared to campaigns of other businesses as well as the mechanics own business, showing that this campaign has had the highest return on investment. Variations can be made to the above-described method and system without departing from the spirit or scope of the invention as described herein or 10 as claimed in the appended claims. For example, the types of sources where data is collected from could encompass other social media such as Pinterest or other forms of e-marketing when they become available. Embodiments of the invention may be implemented by systems using one or more programmable digital computers. Figure 7 depicts an example of one 15 such computer system 750, which includes at least one processor 760, such as, e.g., an Intel or Advanced Micro Devices microprocessor, coupled to a communications channel or bus 762. The computer system 750 further includes at least one input device 764 such as, e.g., a keyboard, mouse, touch pad or screen, or other selection or pointing device, at least one output device 766 such 20 as, e.g., an electronic display device, at least one communication interface 768, at least one data storage device 770 such as a magnetic disk or an optical disk and memory 772 such as ROM and RAM, each coupled to the communications channel 762. The communication interface 768 may be coupled to a network (not depicted) such as the Internet. 25 Although the computer system 750 is shown in Figure 7 to have only a single communications channel 762, a person skilled in the relevant arts will recognise that a computer system may have multiple channels (not depicted), including for example one or more busses, and that such channels may be interconnected, e.g., by one or more bridges. In such a configuration, 30 components depicted in Figure 7 as connected by a single channel 762 may interoperate, and may thereby by considered to be coupled to one another, despite being directly connected to different communications channels.

28 One skilled in the art will recognise that, although the data storage device 770 and memory 772 are depicted as different units, the data storage device 770 and memory 772 can be parts of the same unit or units, and that the functions of one can be shared in whole or in part by the other, e.g., as RAM disks, virtual 5 memory, etc. It will also be appreciated that any particular computer may have multiple components of a given type, e.g., processors 760, input devices 764, communications interfaces 768, etc. Data storage device 770 (Figure 7) and/or memory 772 may store instructions executable by one or more processors or kinds of processors 760, 10 data, or both. Some groups of instructions, possibly grouped with data, may make up one or more programs, which may include an operating system 782 such as, e.g., Microsoft Windows ®, Linux ®, Mac OS ®, or Unix®. Other programs 784 may be stored instead of on in addition to the operating system. It will be appreciated that a computer system may also be implemented on 15 platforms and operating systems other than those mentioned. Any operating system 782 or other program 784, or any part of either, may be written using one or more programming languages such as, e.g., Java ®, C, C++, C#, Visual Basic, VB.NET@, Perl, Ruby, Python, or other programming languages, possibly using object oriented design and/or coding techniques. 20 One or more operating systems 782 and/or computer programs 784 may be described as comprising and/or using one or more "modules", "components", "engines", "libraries", and/or other similarly-named objects. It will be appreciated that the meaning of any or all such terms may be contextual and may designate function performed or capable of being formed by the software without 25 corresponding directly to any one or more distinct logical entities within the computer system. Depending on the context, any function or functions described or claimed in connection with one such object may in an embodiment of the invention be performed by multiple objects in hardware and/or software, and functions described or claimed as performed by a single object may in an 30 embodiment of the invention be distributed across multiple objects. One skilled in the art will recognise that the computer system 750 (Figure 7) may also include additional components and/or systems, such as network connections, additional memory, additional processors, network interfaces, 293 input/output busses, for example. One skilled in the art will also recognise that the programs and data may be received by and stored in the system in alternative ways. For example, a computer-readable storage medium (CRSM) reader 786, such as, e.g., a magnetic disk, a magneto-optical drive, optical disk drive, or flash 5 drive, may be coupled to the communications channel 762 for reading from a CRSM 788 such as e.g., a magnetic disk, a magneto-optical disk, an optical disk, or flash memory. Alternatively, one or more CRSM readers may be coupled to the rest of the computer system 750, e.g., through a network interface (not depicted) or a communications interface 768. In any such configuration, 10 however, the computer system 750 may receive programs and/or data via the CRSM reader 786. Further, it will be appreciated that the term "memory" herein is intended to include various types of suitable data storage media, whether permanent or temporary, including among other things the data storage device 770, the memory 772, and the CSRM 788. 15 Two or more computer systems 750 (Figure 7) may communicate, e.g., in one or more networks, via, e.g., their respective communications interfaces 768 and/or network interfaces (not depicted). Figure 8 is a block diagram depicting an example of one such interconnected network 800. Network 860 may, for example, connect one or more workstations 820 with each other and with other 20 computer systems, such as file servers 824 or mail servers 828. A workstation 820 may comprise a computer system 750 (Figure 7). The connection may be achieved tangibly, e.g., via Ethernet® or optical cables, or wirelessly, e.g. through use of modulated microwave signals according to the IEEE 802.11 family of standards. A computer workstation 820 or system 750 (Figure 7) that participates 25 in the network may send data to another computer workstation system in the network via the network connection. One use of a network 860 (Figure 8) is to enable a computer system to provide services to other computer systems, consume services provided by other computer systems, or both. For example, a file server 824 may provide common 30 storage of files for one or more of the workstations 820 on a network 860. A workstation 820 sends data including a request for a file to the file server 824 via the network 860 and the file server 824 may respond by sending the data from the file back to the requesting workstation 820.

3U Further, a computer system may simultaneously act as a workstation, a server, and/or a client. For example, as depicted in Figure 8, a workstation 820 is connected to a printer 832. That workstation 820 may allow users of other workstations on the network 860 to use the printer 832, thereby acting as a print 5 server. At the same time, however, a user may be working at the workstation 820 on a document that is stored on the file server 824. The network 860 may be connected to one or more other networks, e.g., via a router 836. A router 836 may also act as a firewall, monitoring and/or restricting flow of data to and/or from the network 860 as configured to protect the 10 network. A firewall may alternatively be a separate device (not pictured) from the router 836. An internet may comprise a network of networks 860 (Figure 8). The term "Internet" refers to the worldwide network of interconnected, packet-switched data networks that uses the Internet Protocol (IP) to route and transfer data. For 15 example, a client and server on different networks may communicate via the Internet 80, e.g., a workstation 820 may request a World Wide Web document from a Web server 844. The Web server844 may process the request and pass it to, e.g. an application server 848. The application server 848 may then conduct further processing, which may include, for example, sending data to and/or 20 receiving data from one or more other data sources. Such a data source may include, e.g., other servers on the same computer system 750 (Figure 7) or LAN 860, or a different computer system or LAN and/or database management system ("DBMS") 852. As will be recognised by those skilled in the relevant art, the terms 25 "workstation", "client", and "server" are used herein to describe a computer's function in a particular context. A workstation may, for example, be a computer that one or more users work with directly, e.g., through a keyboard and monitor directly coupled to the computer system. A computer system that requests a service through a network is often referred to as a client, and a computer system 30 that provides a service is often referred to as a server. But any particular workstation may be indistinguishable in its hardware, configuration, operating system, and/or other software from a client, server or both.

31 The terms "client" and "server" may describe programs and running processes instead of or in addition to their application to a computer systems described above. Generally, a software client may consume information and/or computational services provided by a software server. 5

Claims (13)

1. A computer-implemented method for data aggregation and analysis of electronic marketing metrics, including the steps of: collecting electronic marketing data associated with a plurality of businesses into a database, filtering and analysing the collected data, whereby the data are aggregated into specified categories; computing at least one benchmark, the benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; comparing the at least one benchmark with a corresponding performance metric based on data in the database associated with a target business; and where the target business performance metric is below the benchmark for a category, providing at least one marketing recommendation to the business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric.

2. The computer-implemented method according to claim 1 wherein the data collected is from any one or more of: website activity; SMS marketing; social media activity; or email direct marketing.

3. The computer-implemented method according to claim 1 or claim 2 wherein the benchmark is calculated by the median of the aggregated data.

4. The computer-implemented method according to any one of claims 1-3 wherein the specified categories are any one or more of: social media data; website data; e-marketing data; social network data; Facebook Fans; Facebook Likes; microblogging data; Twitter Tweets; Twitter Followers; unique website visits; email direct marketing; email open rate; SMS message marketing; SMS message click rate; search engine marketing; and search engine click through rate.

5. The computer-implemented method according to any one of claims 1-4 wherein the predetermined criteria are any one or more of: location, industry, sub industry; and type of data.

6. The computer-implemented method according to claim 5 wherein the type of data is any one or more of: Facebook data; Twitter data; email direct marketing data; SMS data; and website data.

7. A computer-implemented system for data aggregation and analysis of electronic marketing metrics, the system including: a database configured to contain data from a plurality of businesses; a data collector for collecting electronic marketing data associated with the plurality of businesses a data analyser for filtering, analysing and aggregating the collected data into specified categories, and for computing at least one benchmark calculated based on the aggregated data in the specified categories of all businesses in the database associated with predetermined criteria; a matrix builder for comparing the at least one benchmark with a corresponding performance metric based on data in the database associated with a target business; and a recommendation engine for providing at least one marketing recommendation to the target business based on outcomes of successful marketing initiatives of other businesses having a higher corresponding performance metric, wherein the target business performance metric is below the benchmark for a category.

8. The computer-implemented system according to claim 7 further including a report generator for generating reports associated with performance metrics of the target business or any other business in the database.

9. The computer-implemented system according to claim 7 or claim 8 wherein the data collected is from any one or more of: website activity; SMS marketing; social media activity; or email direct marketing. 34

10. The computer-implemented system according to any one of claims 7-9 wherein the benchmark is calculated by the median of the aggregated data.

11. The computer-implemented method according to any one of claims 7-10 wherein the specified categories are any one or more of: social media data; website data; e-marketing data; social network data; Facebook Fans; Facebook Likes; microblogging data; Twitter Tweets; Twitter Followers; unique website visits; email direct marketing; email open rate; SMS message marketing; SMS message click rate; search engine marketing; and search engine click through rate.

12. The computer-implemented method according to any one of claims 7-11 wherein the predetermined criteria are any one or more of: location; industry; sub industry; and type of data.

13. The computer-implemented system according to claim 12 wherein the type of data is any one or more of: Facebook data; Twitter data; email direct marketing data; SMS data; and website data. M4GROUP PTY LTD WATERMARK PATENT & TRADE MARK ATTORNEYS P35836AUUS