Patent Publication Number: US-2012036085-A1

Title: Social media variable analytical system

Description:
BACKGROUND 
     Given the ubiquitous nature of the Internet, the Internet has become a common vehicle for purveyors of goods and services to reach new customers and make sales. For example, online advertising is a highly-popular, Internet-based tool used by businesses to achieve their objectives, such as to increase market share. Typically, a user surfing the Internet or running a search on an Internet search engine web site or otherwise accessing a web site, may encounter an online ad. The online ad commonly includes a clickable ad displayed on the web site. The user can click on the ad, which typically takes the user to another web page describing a product or service being marketed in the ad. Then, the user may obtain more information about the product or service being advertised and may make purchases online. 
     Relatively recently, social media applications have become popular. Social media applications typically use web-based technologies to create and post user-generated content. Some examples of social media applications are social networking applications, such as MYSPACE, TWITTER and FACEBOOK. Other types of social media applications may include wikis, blogs, etc. 
     As described above, companies use online ads to reach consumers accessing web sites. Thus, companies may also seek to exploit social media applications to reach consumers and many have started doing so. For example, some companies maintain FACEBOOK pages for their popular products to globally reach consumers. Through this and other social media applications, companies can globally provide information about their products and promotions and maintain brand loyalty through a medium that has become popular with many of their target demographics. 
     As companies incorporate social media into their marketing campaigns, these companies need to justify spending on social media marketing. One way to justify spending on social media marketing is to measure the impact of social media marketing on sales. However, traditional metrics used to measure the impact of marketing on sales may not be applicable to social media marketing. For example, traditional metrics may not measure how a blog making negative comments about a product can impact sales or how a blog making positive comments about a product can impact sales. Thus, it is difficult to link the impact of social media applications to sales. As a result, it is difficult to justify spending for marketing through social media applications and to determine how best to optimize marketing through social media applications. Furthermore, even if metrics were identified for measuring the impact of social media applications, it is difficult to determine the accuracy of the metrics for estimating sales and to combine these metrics with other variables associated with other marketing channels to determine the overall impact of a marketing campaign. 
     SUMMARY 
     According to an embodiment, a social media analytical system determines aggregated social media variables, which may be used for mixed modeling. The social media analytical system includes an information identifier module determining keywords and phrases, and an aggregator, which may be executed by a computer system. The aggregator receives information collected from social media applications using the keywords and phrases, determines values for social media variables from the collected information, and aggregates the social media variables based on the values and weightings of the social media variables. 
     According to an embodiment, a method of determining aggregated social media variables includes determining keywords and phrases; receiving information collected from social media applications via the Internet using the keywords and phrases; determining values for social media variables from the collected information; and aggregating, by a computer system, the social media variables based on the values and weightings of the social media variables. The method may be performed by a computer system executing computer readable instructions stored on a computer readable medium, which may be non-transitory. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The embodiments of the invention will be described in detail in the following description with reference to the following figures. 
         FIG. 1  illustrates a system, according to an embodiment; 
         FIG. 2  illustrates an example of different phases performed by the system shown in  FIG. 1 , according to an embodiment; 
         FIG. 3  illustrates examples of a category, sub-categories, and keywords and phrases, according to an embodiment; 
         FIG. 4  illustrates different types of social media applications, according to an embodiment; 
         FIG. 5  illustrates an example of determining aggregated social media variables from social media variables, according to an embodiment; 
         FIG. 6  illustrates an example of aggregating the social media variables across subcategories and categories, according to an embodiment; 
         FIG. 7  illustrates generating time series curves for the aggregated social media variables, according to an embodiment; 
         FIG. 8  illustrates sales curves for different marketing channels that may be used in a mixed model, according to an embodiment; 
         FIG. 9  illustrates a method for aggregating social media variables, according to an embodiment; 
         FIG. 10  illustrates a method for aggregating social media variables across topics, according to an embodiment; 
         FIG. 11  illustrates a method for aggregating social media variables across subcategories and categories, according to an embodiment; and 
         FIG. 12  illustrates a computer system that may be used as a platform for the system shown in  FIG. 1 , according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     For simplicity and illustrative purposes, the principles of the embodiments are described by referring mainly to examples thereof. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent however, to one of ordinary skill in the art, that the embodiments may be practiced without limitation to these specific details. In some instances, well known methods and structures have not been described in detail so as not to unnecessarily obscure the embodiments. Also, the embodiments may be used in combination with each other. 
     1. OVERVIEW 
     According to an embodiment, a system uses econometrics to determine the impact of social media applications on sales of a product, which may include a good and/or a service. Social media applications may include web-based technologies that use the Internet to publish user generated content. A social media application may use web-based technology for social interaction. As described above, some examples of social media applications are social networking applications, such as MYSPACE, TWITTER and FACEBOOK. Other types of social media applications may include wikis, blogs, etc. 
     The system identifies social media variables that may be used as metrics to measure the impact of social media applications on sales. The variables may include time series variables to estimate the impact of social media applications over time. The system is also configured to aggregate the social media variables into a smaller subset of variables that may be provided as an input for mixed modeling. The aggregation may include using econometrics to determine weights used for aggregation. 
     Mixed-modeling is used to estimate the impact that a variety of different activities, including activities outside social media applications, may have on sales. The mixed-modeling uses variables for the different activities. These variables may include variables associated with different marketing channels, such as TV, online, radio, print, etc. The mixed modeling can include more variables than the number of observed data points. Thus, the mixed modeling may allow a limited number of additional variables that can be used for social media. The number of variables used to measure the impact of social media applications on sales may exceed this limited number of additional variables that can be used by the mixed modeling. Accordingly, according to an embodiment, the social media variables are aggregated to a limited number of variables that may be included in mixed modeling to estimate the impact of a marketing campaign across many different marketing channels. 
     The embodiments are generally described with respect to determining the impact of social media applications on sales. It will be apparent to one of ordinary skill in the art the embodiments may be used to determine the impact of social media applications on other business objectives, such as improving brand equity, maintaining customer lifetime, etc. 
     2. SYSTEM 
       FIG. 1  illustrates a social media analytical system  100 , according to an embodiment. The system  100  includes information identifier module  101 , listening tool  102 , aggregator  103 , modeling engine  104 , user interface  105 , optimizer  106 , and data storage  130 . The information identifier module  101  gathers information for multiple variables, referred to as social media variables, associated with social media applications. In the description below, first, the functions of each of the components of the system  100  are described. This is followed by examples that illustrate the functions performed by the system  100 . 
     The information identifier module  101  determines the information to capture from social media applications on the Internet. In one embodiment, categories of information to capture are identified. These categories may be categories related to a particular product. Sub-categories are determined for each category, and keywords and/or phrases are determined for each category and sub-category. For example, a category for a product may be electronic goods. A subcategory may be mobile phones. Keywords and phrases may be names of brands of mobile phones, including competitor brands, descriptions of mobile phone features, and terms related to the mobile phones. 
     The categories, sub-categories, and keywords and phrases may be computer-generated by analyzing data sets comprised of terms and descriptions related to different products. Classifiers and other known artificial intelligence techniques may be used to generate the categories, sub-categories, and keywords and phrases. Also, experts may determine one or more of the categories, sub-categories, and keywords and phrases, and this information may be provided to the information identifier module  101  through the user interface  105 . 
     The listening tool  102  captures information  110  from social media applications related to the categories, sub-categories, and keywords and phrases. In one example, topics in social media applications are identified by the listening tool  102 . A topic may include information published on the Internet, which may be available for subsequent social comment by other users. A topic may include user generated content comprised of one or more messages. A message is a publication of user generated content, for example, on the Internet. A message may including a post, such as video posted on a website. A topic may include an original message and multiple related messages. For example, the posted video is the original message and comments posted on the web site about the video or ratings of the video are related messages. In another example, an original post on a blog or personal web page or some other type of social networking application may be an original message. Any messages referencing the original message are related messages, and together they may comprise a topic. 
     The information identifier module  101  provides information  110 , including the keywords and phrases, to the listening tool  102  so the listening tool  102  can identify the topics. The identified topics may include one or more of the keywords and phrases for the subcategories. These topics are identified by the listening tool  102 , for example, by scanning social media application web sites for the keywords and phrases. 
     Conventional scanning tools may be used for the listening tool  102 . These tools are capable of scanning social media application web sites for matches with the keywords and phrases. For matches, the topic, including associated messages, is identified. Also, the messages retrieved from the web sites may have meta data that can be used to identified related messages. Topics gathered by the listening tool  102  are analyzed as described in detail below to determine aggregated social media variables that may be used in a model. 
     The aggregator  103  analyzes the identified topics and associated messages to determine aggregated social media variables  120 . The analyzing may include determining weights at the message level, topic level and subcategory level, and using the weight to aggregate social media variables. The modeling engine  104  may create a model  121  with the aggregated social media variables  120 , and then the model  121  may be used to estimate the impact of social media applications on sales or other marketing objectives. 
     The optimizer  106  may be used to forecast or estimate sales based on a set of inputs and identify optimal investments in various marketing channels based on the forecasting to maximize sales. The optimizer  106  uses models, including the model  121 , generated by the modeling engine  104  to perform the forecasting. 
     The modeling performed by the modeling engine  104  may include generating a mixed model. The model generation may include determining sales data from different marketing channels and building regression models to determine how much each activity/channel contributed to the sales. The optimizer  106  uses the mixed model to estimate the impact on sales for different investment scenarios in the marketing channels. The marketing channels may include social media applications, TV, radio, newspaper/print ads, etc. The mixed model, which is generated by the modeling engine  104 , is generated from the aggregated social media variables and variables for the other marketing channels. 
     The user interface  105  may include a graphical user interface. The user interface  105  may be accessible via the Internet or through a private intranet. The user interface  105  can receive user data used for determining aggregated social media variables and for identifying data for generating models and for optimizing marketing investments. The user interface  105  may also display information related to the aggregated social media variables, models and investment optimization. The data storage  105  stores any data that may be used by the system  100 . The data storage  105  may include a database for storing the data. 
     3. EXAMPLES 
       FIG. 2  illustrates an example of different phases performed by the system  100  shown in  FIG. 1 . The phases include define  201 , listen  202 , weight  203  and aggregate  204 . In the define phase  201 , the information identifier module  101  of the system  100  determines one or more categories, such as the category  1 , sub-categories  1 - n  for the category  1 , and keywords and phrases for each of the sub-categories  1 - n . In the listen phase  202 , information from the define phase  201  is used by the listening tool  102  of the system  100  to determine topics, such as the topics  1 - n  for keywords and phrases derived for the sub-category  1 . The keywords and phrases, categories and subcategories may be provided by users and/or determined by computerized analysis of data relating to product whose sales are being optimized. 
     In the weight phase  203 , the aggregator  103  of the system  100  determines weights  207  for social media variables  205 , such as followers, key opinion leaders, topic relevance, and topic&#39;s unique followers. Other social media variables may also be used. The social media variables  205  may include metrics for measuring an attitude or emotion of users of social media applications as directed to a topic. The topic may be related to a product, so the social media variables  205  can be used to estimate the impact on sales of a product. In the weight phase  203 , a scaling system may be used to apply the weightings, such as described with respect to  FIG. 5 . 
     In the aggregation phase  204 , the social media variables  205  are combined to determine values for aggregated social media variables  206 . The aggregated social media variables  206  describe an attitude, thought or judgment or emotion of users of the social media applications as it relates to a topic. The aggregated social media variables  206  by way of example may include positive, neutral and negative. Aggregation may include aggregating across topics and subcategories and categories to determine the aggregated social media variables. The aggregated social media variables  206  may be combined across different topics to determine the attitude towards a particular subcategory, such as subcategory  1 , or towards a particular category. For example, values for the “positive” aggregated social media variable are determined for each of topic  1 - 3  in subcategory  1 . These values are summed to determine the total “positive” value for subcategory  1 . Similarly, total “neutral” and “negative” values can be determined for subcategory  1 . Also, weights may be determined for each category, so a time series of each aggregated social media variable across all the categories is determined. Aggregation is further described with respect to the examples in  FIGS. 5 and 6 . 
       FIG. 3  illustrates examples of a category  301 , sub-categories  302 , and keywords and phrases  303 . The category  301 , for example, is “online banking service” for a company that provides these services. The sub-categories  302  are security, e-commerce, and innovation. The keywords and phrases  303  for security may include payment security, data security, fraud protection, payment data encryption, and secure online payment solution. The keywords and phrases  303  for e-commerce may include online enrollment, online application, online account transfers. The keywords and phrases  303  for innovation may include encryption, and secure international transfer. 
       FIG. 4  shows different types of social media applications. The listening tool  102  of the system  100  may be used to scan the social media applications for topics. The different types of social media applications may include communication  401 , collaboration  402 , multimedia  403 , reviews and opinions  404 , and entertainment  405 . Examples of each type of social media application are shown in  FIG. 4 . For example, communication social media applications  401  may include blogs, microblogs, social networking and events. Collaboration social media applications  402  may include wikis, social news (such as small city or town news sites). Multimedia social media applications  403  may include content sharing sites, such as photography sharing, video, sharing, and music sharing. The reviews and opinions  404  may include travel review web sites, product reviews, etc. The entertainment  405  may include online games, virtual worlds with personal avatars, and other entertainment platforms. Listening tools are available to scan the social media applications to identify topics relevant to the product or category. 
       FIG. 5  shows an example of determining aggregated social media variables from social media variables. The social media variables are weighted, and the social media variables are aggregated based on the weights. The aggregated social variables may be periodically determined over time and plotted to form a time-series plot. The periodicity for determining the social media variables may be weekly, bi-weekly, etc. Also, the social media variables and the aggregated social media variables may be determined for each topic. 
     An example of a topic shown in  FIG. 5  is “Company A&#39;s service is bad.” This topic is labeled as topic  1 . The topic  1  may include multiple messages as described above. “Company A&#39;s service is bad” may be the text from the original message of the topic. 
     The aggregated social media variables  501  determined for the topic  1 , for example, are positive, neutral and negative. Examples of the social media variables that are aggregated are message count, sentiment, key opinion leader (KOL), number of unique followers, and relevance of topic count, which are shown as social media variables  502 . Of course other social media variables may be used. The weighting performed to aggregate the social media variables  502  may include scaling one or more of the social media variables  502 . Simple scales may be used as described below or more complex scales may be used. The weighting and aggregating may also include combining the scaled variables to determine a value for each of the aggregated variables  501 . 
     Keywords and phrases from the define phase  201  shown in  FIG. 2  may be identified by the listening tool  102  in the topic  1 . In this example, keywords  503  found in the messages for the topic  1  include great, good, OK, bad, awful, and worst. Each of the keywords is associated with one of the aggregated variables  501 , such as positive, neutral, and negative. Message count, sentiment and KOL are shown as social media variables  502 . Message count is the number of messages including the keyword. Sentiment is an attitude, thought or judgment of the topic. In this example, sentiment is valued on a scale from −2 to +2. For example, the keyword “great” is valued at the highest sentiment of +2 and the keywords “awful” and “worst” are valued at the lowest sentiment of −2. KOL describes the number of people considered to be important that create a message for a topic, such as celebrities, experts, political leaders, etc. KOL values may be on a scale of 1 to 3, where 3 is the highest. Thus, as shown in  FIG. 5 , one or more of the social media variables may be given a value in a scale according to a message or a keyword in the message. Also, as shown in  FIG. 5 , each keyword may be assigned to one of the aggregated variables  501 , so the scaled values for the social media variables  502  can be used to determine a value for each of the aggregated variables  501 . 
     The values for the weighted social media variables are combined to determine values for the aggregated social media variables. In one embodiment, scaled values for message level social media variables are summed for each keyword and phrase and for each aggregated social media variable. Then, the sums are multiplied by scaled values for topic level social media variables to determine values for the aggregated social media variables. Message level social media variables are determined based on each message and include message, count, sentiment, and KOL. Topic level social media variables are based on all the messages in the topic and may include unique followers and relevance of topic. 
     In the example shown in  FIG. 5 , values for message level social media variables determined for message, sentiment, and KOL are summed for each keyword and for each aggregated social media variable. For example, the summed values for the social media variables for the keywords “great” and “good” are 6 and 1 respectively. Then 6 and 1 are summed and multiplied by values for the topic level social media variables comprised of unique followers and relevance of topic to determine a value of 14 for the “positive” aggregated social media variable. Similarly, 4 and 14 are values determined for the “neutral” and “negative” aggregated social media variables for week 1. 
     Values for each of the aggregated social media variables may be determined week-by-week based on keywords and phrases identified in each of the messages in each of the topics. For example, 4, 4, and −6 are values for the “positive”, “neutral” and “negative” aggregated social media variables for week 2, as shown in  FIG. 5 . These values are based on the keywords and phrases identified in the week 2 messages for the topic  1 . Note that the keywords and phrases may be different for each week because the messages are different from week-to-week. The values for the social media variables and aggregated social media variables are incrementally calculated from week-to-week so the social media variables are not double counted. For example, week 2 values are determined for new messages identified for the week 2 time period. As a result, three time series graphs may be generated for the positive, negative and neutral aggregated social media variables, and these values may be used for a model. 
       FIG. 6  shows an example of aggregating the social media variables across subcategories and categories.  FIG. 6  shows four stages for the aggregation. At stage  1 , values for the aggregated social media variables (e.g., positive, neutral and negative) are determined for each topic, such as described with respect to  FIG. 5 . At stage  2 , for each topic, the values for each aggregated social media variable are summed. For example, for topics  1 - 3 , all the “positive” values are summed to determine a total “positive” value for subcategory  1 . The summing may be performed per week. For example, week 1 “positive” values are summed for topics  1 - 3  to determine a total for the week. Then, week 2 values are summed and so on to generate a time series of the totals. Total values for “neutral” and “negative” are similarly determined for subcategory  1 . 
     At stages  3  and  4 , econometrics are used to aggregate across subcategories and to determine the final time series values that may be used for a mixed model. Econometrics includes applying conventional quantitative or statistical methods to analyze and test economic relationships, which in these examples may includes the relationship between sales and products. Through conventional statistical processes, at stage  3 , an aggregation weight is determined for each subcategory. The statistical processes may include testing different weights on historic sales data to determine the accuracy of the weights. At stage  4 , econometrics may include using linear regression to generate a model and testing the model with the weighted aggregation variables to determine the accuracy of the model for forecasting the impact on sales. 
     The aggregation weights determined at stages  3  and  4  are applied as follows. The aggregation weights are applied to each subcategory to determine totals for each category based on the econometrics. For example, the total values for “positive”, per week, per subcategory, are multiplied by an aggregation weight for the subcategory to determine a weighted subcategory value for “positive” per week. For each of subcategories  1  and  2 , the weighted subcategory value for “positive” are combined to determine a weighted category value for “positive” per week. Weighted category values, per week, for “negative” and “neutral” are also determined. 
     The optimizer  106  of the system  100  shown in  FIG. 1  performs analytics. Analytics measures the impact of social media applications and the impact of active social media engagement on sales. The active social media engagement is responsive to information intentionally provided to a social media application to elicit response or influence sales. The information may include viral seeds seeded by a company&#39;s marketing efforts (e.g., promotions, product information, etc.) or information provided in a debate through messages in a social media application. The analytics also measures the impact of unsolicited sentiment of users of social media applications. The analytics uses the model generated by the modeling engine  104  to estimate the impact of social media applications and the impact of active social media engagement on sales or incremental sales. 
       FIGS. 7 and 8  illustrate generating time series curves for the aggregated social media variables and using the curves in a mixed model, which may be used by the optimizer  106  to perform analytics. In steps  1  and  2 ,  FIG. 7  shows the aggregating described in  FIG. 6 . For each of the aggregated social media variables (e.g., positive, neutral, and negative), a time series is generated. In step 3, through regression analysis, the modeling engine  104  generates curves  701  for each of the aggregated social media variables. The curves may be combined to generate the social media uplift curve  702 , which may be used by the optimizer  106  to estimate the optimal investment in social media marketing efforts to maximize sales. The x-axis represents that amount of effort (e.g., monetary investment) and the y-axis represents sales. 
       FIG. 8  shows sales response curves  801 , for example, generated by the modeling engine  104 . These sales response curves  801  form a mixed model that can be used to estimate sales for multiple different marketing channels. The sales response curves  801  may be used by the optimizer  106  to estimate sales for different marketing investments in the marketing channels and to select the optimal marketing investments in each of the marketing channels to maximize sales. 
     4. METHODS 
       FIG. 9  illustrates a method  900  for aggregating social media variables, according to an embodiment. The method  900  and other methods described herein are described with respect to the system  100  shown in  FIG. 1  by way of example and not limitation. The methods may be practiced in other systems. 
     At step  901 , the information identifier module  101  in the system  100  determines keywords and phrases for subcategories and categories, such as shown in the define phase in  FIG. 2 . In the define phase  201 , the information identifier module  101  of the system  100  determines one or more categories, such as the category  1 , sub-categories  1 - n  for the category  1 , and keywords and phrases for each of the sub-categories  1 - n . The keywords and phrases are related to the categories and subcategories and may describe one or more products. 
     At step  902 , the system  100  receives information collected from social media applications via the Internet using the keywords and phrases. The listening tool  102  may scan social media applications on the Internet using the keywords and phrases to identify information such as topics including the keywords and phrases. 
     At  903 , the system  100  determines values for social media variables from the collected information. Examples of values for social media variables are shown in  FIG. 5 . For example, the social media variables may include message count, sentiment, key opinion leader (KOL), number of unique followers, and relevance of topic count. Values for each of these social media variables are shown in  FIG. 5 , and may be determined through weighting/scaling. 
     At step  904 , the system  100  aggregates the social media variables based on the values and weightings of the social media variables and weightings of subcategories and categories. The aggregation may include aggregating the social media variables by topic, such as shown in  FIG. 5 . The aggregating may also include aggregating subcategories and categories, such as shown in  FIG. 6 . The aggregating is further described below with respect to  FIGS. 10 and 11 . 
       FIG. 10  illustrates a method  1000  for aggregating social media variables across topics, according to an embodiment. At step  1001 , aggregated social media variables are determined. In the examples described above, the aggregated social media variables include positive, neutral and negative. However, other types of aggregated social media variables may be used. 
     At step  1002 , from the keywords and phrases determined at step  901 , a set of keywords and phrases assigned to each of the aggregated social media variables are determined. 
     At step  1003 , values for the social media variables are determined based on the sets of keywords and phrases assigned to the aggregated social media variables. Examples of values for social media variables associated with keywords are shown in  FIG. 5 . For example, for the keyword “Great”, values are provided for each of the social media variables including message count, sentiment, key opinion leader (KOL), number of unique followers, and relevance of topic count. One or more of the values may be weighted, for example, through the scaling described with respect to  FIG. 5 . Note that the step  1003  may be performed as part of the step  903 . 
     At step  1004 , values for the aggregated social media variables are determined using the values for the social media variables from step  1003 . For example, as shown in  FIG. 5 , the “positive” aggregated social media variable value for week 1 is 14 and is calculated from the values of the social media variables as shown. Values for each of the aggregated social media variables are also determined for each week. Thus, a time series set of values by topic for the aggregated social media variables is determined. The aggregator  103  of the system may perform one or more of the steps of the method  1000  and store the values for the aggregated variables in the data storage  130 , and this information may be retrieved for aggregating across subcategories and categories. 
       FIG. 11  illustrates a method  1100  for aggregating social media variables across subcategories and categories, according to an embodiment. At step  1101 , values for aggregated social media variables for each topic are determined. These are the values from step  1004 . 
     At step  1102 , the values for each of the aggregated social media variables for each topic in each subcategory are summed. For example, as shown in  FIG. 6 , subcategory  1  includes topics  1 - 6 . All the values for the “positive” aggregated social media variable are summed for topics  1 - 6  for week 1. Also, all the values for week 2 are summed and so on to determine a time series of “positive” values for subcategory  1 . This is also performed for the “neutral” and “negative” aggregated social media variables for subcategory  1  to obtain a time series of values for each of the aggregated social media variables for subcategory  1 . Similarly, a time series of values for each of the aggregated social media variables for each of the other subcategories is determined. 
     At step  1103 , aggregation weights for the subcategories are determined. Econometrics may be applied to determine the aggregation weights. Econometrics includes applying conventional quantitative or statistical methods to analyze and test economic relationships, which in these examples may include the relationship between sales and products. Through conventional statistical processes, an aggregation weight is determined for each subcategory. The statistical processes may include linear regression to determine the weights based on historic sales data. 
     At step  1104 , the summed values for each subcategory are combined using the aggregation weights to determine aggregated social media variable values for each category. For example, as shown in  FIG. 6 , subcategories  1  and  2  are under category  1 . The values for each aggregated social media variable per week in subcategories  1  and  2  are multiplied by their corresponding aggregation weights. The weighted aggregated social media variables are then summed per week to determine a time series of weighted aggregated social media variables for category  1 . This process may be performed for each category to aggregate the social media variables across categories. 
     At step  1105 , the values for each category are combined to aggregated social media variables aggregated across categories. Weights for each category may be determined using regression analysis and simulation or provided by a user. The weights are applied to each respective category and used to determine final aggregated social media variable values. The values may be represented in a curve, such as shown in  FIGS. 7 and 8 . 
     A model is generated using the time series aggregated social media variables. The model may include a mixed model such as shown in  FIG. 8 . For example, the sales response curves form a mixed model that can be used to estimate sales for multiple different marketing channels. The sales response curves may be used by the optimizer  106  to estimate incremental sales for different marketing investments in the marketing channels and to select the optimal marketing investments in each of the marketing channels to maximize sales. 
     The methods and system described above may be used to aggregated variables other than social media variables. For example, information is collected for the variables. Values for the variables are determined from the collected information, and the variables are aggregated based on the values and weightings determined for the variables. The aggregated variables may be used for model generation. 
     The embodiments described herein provide technical aspects beyond statistical processing. For example, the system  100  may generate a model including sales curves, such as shown in  FIG. 6 . The sales curves may be displayed via the user interface  105  to provide a user with a convenient visualization of estimated incremental sales given a particular investment. A user, from a displayed sales curve, can easily identify a point on the sales curve where sales may not be improved or where sales may be minimally improved if investment in the marketing channel is increased. This point may be considered a point of diminishing return and an investment may be selected at this point or just before this point. Thus, the embodiments may decrease the mental and physical effort required from a user in order to perform a task of identifying optimal investment in a marketing channel. In addition, another technical aspect is that the generation of the model using the aggregated social media variables allows for faster processing by the optimizer  106  in the system  100  to determine the optimal investment for different marketing channels. For example, through use of the sales curves in the model, optimal investment points for each marketing channel can be quickly identified by a processor. Furthermore, the models may be stored in the data storage  130  and easily updated based on newly gathered social media information for topics as well as based on new information for other marketing channels. This allows for fast generation of more accurate models and more accurate determination of optimized investments in the marketing channels. Additionally, the system transforms data so it may be used for the mixed modeling and so it can be used to generate the sales curves. The transformation includes the aggregation of the social media variables. 
     One or more of the steps of the methods described herein and other steps described herein and one or more of the components of the systems described herein may be implemented as computer code stored on a computer readable medium, such as the memory and/or secondary storage, and executed on a computer system, for example, by a processor, application-specific integrated circuit (ASIC), or other controller. The computer readable medium may be a non-transitory medium, such as a storage device. The code may exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats. Examples of computer readable medium include conventional computer system RAM (random access memory), ROM (read only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory. 
       FIG. 12  shows a computer system  1200  that may be used as a hardware platform for the system  100 . The computer system  1200  may be used as a platform for executing one or more of the steps, methods, and functions described herein that may be embodied as software stored on one or more computer readable storage devices. The computer system  1200  includes a processor  1201  or processing circuitry that may implement or execute software instructions performing some or all of the methods, functions and other steps described herein. Commands and data from the processor  1201  are communicated over a communication bus  1203 . The computer system  1200  also includes a computer readable storage device  1202 , such as random access memory (RAM), where the software and data for processor  1201  may reside during runtime. The storage device  1202  may also include non-volatile data storage. The computer system  1200  may include a network interface  1204  for connecting to a network. It is apparent to one of ordinary skill in the art that other known electronic components may be added or substituted in the computer system  1200 . 
     While the embodiments have been described with reference to examples, those skilled in the art will be able to make various modifications to the described embodiments without departing from the scope of the claimed embodiments. For example, the systems and methods of the embodiments are generally described with respect to aggregating social media variables. However, the embodiments may be used to aggregate variables for other marketing channels or to aggregate non-marketing variables.