At Factori, we collect a variety of data sets from leading publishers, data platforms, online services, and data aggregators globally, linked to consumers, places, and businesses. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify, and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavioral, geography, etc.

Audience Data Categories: The below-mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. -Brand Shoppers Methodology: This category has been created based on the high intent of users in terms of their visits to brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. -Place Category Visitors: Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. -Demographics: Methodology: This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital status, education, party affiliation, and state residency are available in the US. -Geo-Behavioural: Methodology: This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. -Interests: Methodology: This segment is created based on the user's interest on a specific subject while browsing the internet, when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and determine whether the information is valid. The recency of data can range from 14 to 30 days, depending on the topic. -Intent: Methodology: Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the e-commerce sector, and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days, depending on the product category. -Events: Methodology: This category was created based on the high interest of users in terms of content related to specific global events—sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. -App Usage: Methodology: The mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. -Auto Ownership: Data Type: Consumer Profiles, Available for US and Australia Methodology: This audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. -Motorcycle Ownership: Data Type: Consumer Profiles - Available for US and Australia Methodology: This audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. -Household: Data Type: Consumer Profiles, Available for US and Australia Methodology: This audience has been created based on users' declaring their marital status, parental status, and overall number of children via a survey or registration. These audiences are currently available in the USA. -Financial: Data Type: Consumer Profiles, Available for US and Australia Methodology: This audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior and wealth and declaring their estimated net worth via a survey or registration. -Purchase/ Spending Behavior: Data Type: Consumer Profiles, Available for US and Australia Methodology: This audience has been created based on their behavior in different spending behaviors in different business verticals available for the USA. -Clusters: Data Type: Consumer Profiles, Available for US and Australia Methodology: Clusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. -B2B Audiences; Data Type: Consumer Profiles - Available for US and Australia Methodology: This audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations.

Customizable Audiences Data Segment: Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness.

Here are some examples of custom audiences you can create: Customer Locations: Target people who live, work, and shop in specific areas, localities, or postcodes. Customer journey: Target people based on their online and offline purchase journeys. Geo-personas: Target people using geo-demographic personas based on their gender, age group, affluence, and geography. Business Locations: Target people based on their business locations and their competitors.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a monthly basis.