Key concepts for profiling and identity management in BlueConic
Definitions of CDP terminology you need to know when building your unified profiles with BlueConic.
Aggregated Data Manager (ADM)
In BlueConic, the Aggregated Data Manager (ADM) is a dedicated “super tenant” that enables multi-brand organizations to sync first-party data from their “sub-tenants.” This centralized dataset enables users to surface insights and perform modeling across tenants, while ownership over activation of that data can remain with individual businesses, regions, or brands. For example, each brand can manage customer segments and connections to their specific systems for their use cases, but a centralized analytics team can calculate a CLV score across all brands for an individual customer. Learn more about using the Aggregated Data Manager in BlueConic.
A profile is considered an anonymous profile if it does not contain data that uniquely identifies a customer or visitor and matches them to their behaviors. In BlueConic, anonymous user behaviors are still tracked and once that individual becomes identifiable, those profiles are merged. Ways to convert anonymous profiles to known profiles include setting up listeners to collect identification data or including forms that visitors must fill out with contact information.
Behavioral profile property
A behavioral profile property is a customer attribute that is related directly to actions and interests, rather than known facts. These values track how frequently customers are active on your site, their level of activity, the intensity of their visits, and relative increase or decrease in activity (momentum scores). Behavioral profile property scores range from 0 to 100 and are created by BlueConic automatically, making it easy to compare profiles on the same scale. You can use these profile properties for behavioral customer segmentation in BlueConic.
A BlueConic ID is a unique string of letters and numbers given to each profile that is automatically generated and used to identify individual profiles from each other within the platform. The BlueConic profile ID serves as a unique identifier.
Forrester Research defines a clean room as, “A secure platform where brands can access advertising data and use it for targeting, measurement, and analysis. The advertising performance data provided in a clean room is aggregated and has controls to ensure privacy.” BlueConic data clean rooms bring together first-party data and second-party data in a way that ensures profiles are associated only with one-way hashes of identifiers (a process also known as pseudonymization) and that any identity resolution based on those hashes is used to link profiles. This solution enables pseudonymized data sharing without requiring ongoing IT involvement to maintain and add partners over time. In a data clean room, only aggregated data (’how many people did X?’) can be queried, without exposing individual data (‘who did X?’).
Data sensitivity is a setting for BlueConic profile properties, groups, and Timeline event types that enables organizations to control access to data based on user role. For the data sensitivity setting, there are two options, PII or non-PII. The default setting for new items is non-PII, except for unique identifiers which are always set to PII. When BlueConic profile properties, groups, and Timeline event types are first set up, you can specify which items are unique identifiers and whether items will contain PII or non-PII. Once created, only user roles with access to PII will be able to see the values of those items. Learn more about controlling access to PII in BlueConic.
Demographics are a category of data that can be stored in profile properties that relates to the statistical analysis of different populations. Examples of profile properties that would fall under demographics include age, race, ethnicity, gender, marital status, income, education, and employment. Using demographic information can help brands identify their target audience and better inform marketing strategies.
Deterministic matching in BlueConic is the process of identifying and merging two distinct records of the same customer where an exact match is found on a unique identifier, like customer ID, Facebook ID, or email address.
These identifiers often come from a user that has authenticated (i.e. filled out a form or logged in) or from a system that generates a unique ID. The key here is that you are looking for an exact match on the first-party data you have the most confidence in, plus the ability to use any combination of identifiers to match on. Learn more about profile merging via deterministic matching.
Dynamic segmentation is the process of dividing profiles into groups with set criteria, providing a constant count of the people in each segment, as individuals meet the criteria and are added to the segment in real time, or stop meeting the criteria and are moved out. Because BlueConic segments are not static lists, they are not immediately out-of-date, nor do they require constant re-processing for updated counts. Learn more about dynamic customer segmentation in BlueConic.
Engaged profiles are also known as monthly active users and are defined as the number of distinct individuals who engage with your websites or apps in a given month, both identifiable and anonymous, as measured on the last day of that month. In BlueConic, active engagement is defined as having at least two requests from a unique client to one of your BlueConic channels in the previous thirty (30) days.
In BlueConic, an external tracker is a data collector that can be used to monitor customer or visitor activity outside of your domains. For example, a tracker can record whether someone has viewed a newsletter, email, or web page or whether a hyperlink has been clicked. It can also be used in social channels to track the social origin of visitors to your channels. Within one external tracker you can define several tracking elements.
In BlueConic, groups are used to organize, segment, and target profiles in a set that share a common attribute or are assigned a common profile property. Group types include households, accounts, companies, or a custom group that aligns with your plans. Individual profiles can be associated with groups when profiles include the group ID as a special profile property.
Unifying data to create a ‘single customer view’ is the foundational and most critical capability of a customer data platform (CDP) like BlueConic. Profile merging is the underlying mechanism BlueConic uses to ensure you can recognize the same person across multiple channels and devices and engage them in a consistent way. To resolve identities, BlueConic leverages two profile merging methods: deterministic matching through profile merging, and probabilistic, or fuzzy, matching using machine learning and AI with the BlueConic AI Workbench.
In BlueConic, interest ranking is the process of assigning individual profiles potential interests based on actions they take. By using an interest ranker, you can add interests to a visitor's profile based on a points-based system. Points for an interest are scored based on the visitor's behavior, which includes actions like viewing content, clicking something, coming to a website from a specific referring URL, and landing on a URL that contains a specified string. Once an interest has been added to a visitor's profile, you can create segments to target customers and visitors based on these interests. Learn more about keyword interest rankers in BlueConic.
In BlueConic, a profile is considered a known profile if it contains data that uniquely identifies a customer or visitor. For example, a profile with name, contact, and device information. BlueConic users can always measure their ratio or percentage of known profiles using the Profile Recognition Dashboard.
PII (personally identifiable information)
PII, or personally identifiable information, is information that can potentially identify individuals, such as data that serves as a unique identifier for your customers and visitors.
Probabilistic matching, or “fuzzy” matching, uses AI algorithms to score and weight the variables and inconsistencies present in customer profiles, to determine “What is the probability that these records are the same person?” A probabilistic model can determine if or when profiles should be merged or not merged, depending on whether they reach a certain threshold. Learn more about probabilisitic matching in the BlueConic AI Workbench.
Profile cleanup refers to the process of merging profiles that belong to the same individual, as well as deleting older profiles that do not match a current user or visitor. BlueConic lets you adjust profile cleanup rules to suit your use cases.
A profile database, like BlueConic, uses key/value pairs collected in profiles as the main source of data storage. CDPs create and store true unified profiles at the individual level, as opposed to just creating a chaotic graph with deconstructed events, or enforcing an arbitrary data schema on the data set. Since, by definition, CDPs must provide a “persistent” profile, a profile database provides both high-volume storage and fast read/write speeds so data is unified and actionable, such as for segmentation, personalization, and analytics.
In BlueConic, profile merging is the process of identifying two profiles that belong to the same individual and consolidating them into one profile. As customers and visitors interact with your online channels, BlueConic creates profiles for both known and anonymous visitors. Individual users might have several different profiles because they visit on different devices and browsers. Using the profile merging feature in BlueConic, disparate profiles can be combined and unified using deterministic or probabilistic methodologies and match on any combination of unique identifiers. In order to successfully merge profiles, it is important to actively collect unique identifiers so that merging rules can be created around them.
Profile origins are special BlueConic profile properties that provide detail and visibility into where and when a customer first became a profile in BlueConic. When profiles are created or imported, BlueConic registers details about whether customers first arrived over the web (mobile or desktop), via a mobile app, or from other systems through a connection. Profile origins are stored in three origin of profile properties: type, source, and detail.
A profile property is a key/value pair that stores information about a customer or visitor within a profile. Profile properties receive data from various parts of BlueConic, for example from listeners that collect data, from customer input in dialogue forms, from connections that integrate with other systems, from AI Workbench notebooks, and so on. Examples of profile properties include email address, location, username, company, consent status, CLV, CRM ID, etc.
Progressive profiling is the process of building on the same persistent profile, session after session, making your customer information progressively richer over time. When a website visitor visits channels in your domains, a profile is created for them that includes information about their activity. This allows you to, over time, gather more data about each visitor's interests and behavior, enabling richer targeting with relevant dialogues that fit their needs. Learn more progressive profiling in BlueConic.
The profile sampling feature assigns a Sample ID to each BlueConic profile. You can use this profile property to create segments for lifecycles or campaigns that deliver messages to randomly created test groups. BlueConic automatically creates the Sample ID profile property for all profiles and adds a number value to it between 1 and 10. Learn more using profile sampling and creating test groups in BlueConic.
In BlueConic, a segment is a dynamic grouping of customers or visitors characterized by a defined set of attributes, interests, behaviors, preferences, or demographic, psychographic, or technographic properties. Known and anonymous users can be segmented with a single filter or a complex set of filters so you can engage with users at the right moment with a meaningful and relevant dialogue. Customers and users can belong to multiple segments. For example, a single user might be included in the segments "under 35 years old," "interested in football," "referred by social media," and "read December newsletter." Learn more about customer segmentation in BlueConic
In BlueConic, timeline events are time-based actions that occur for a profile. Examples of events include ordering a product, clicking a page, or opening an email. A BlueConic Timeline stores events to capture information about timing and sequence of events for a BlueConic profile. A Timeline belongs to a profile and includes the set of all events belonging together for that profile. Learn more about storing event and order data in BlueConic Timeline events.
The term unified profile refers to a single source of truth where all data about an individual customer can be found in a centralized place (also known as data unification). Using BlueConic connections, you can import and export profile data to synchronize customer data across your entire marketing technology ecosystem, building more unified profiles and targeting audiences based on smarter segmentation.
In BlueConic, a unique identifier is a profile property value that distinctly recognizes a specific customer or visitor. For example, there are several types of customer data that uniquely identify customers, including email address, login name, customer ID, or mobile phone number. Note that for a profile property to be unique, only one profile should contain the same value. Learn more about unique identifiers in BlueConic
BlueConic counts a visit when a profile comes to a site running BlueConic and continues until the visitor closes the site OR until they have been inactive for 30 minutes on the site. If they are inactive for 30 minutes 1 second and then return to the site, a new visit will be recorded. The Global Listener collects data stored in the Visits profile property. For details see, What kind of information does BlueConic collect?