Making sense of data you are using for your mobile campaigns is not an easy task. It requires a deeper insight into the methods of collecting, processing and aggregation of data for audience creation and campaign management. To make use of the possibilities offered by mobile, marketers are well aware that reliability and validity of users data is crucial for targeting and overall success of an advertising campaign.

Understanding the process of data collection is a topic, which is part  of  a series of articles we have dedicated to comprehending the realm of data. According to the IAB Data Segments & Techniques Lexicon, data can be described by different attributes such as source of data collection – online or offline, relationship between collector and user – 1st or 3rd party data, attribution of data – whether data is descriptive or predictive or by the method of providing data – declared or inferred. In a previous article we’ve already explained the differences between 1st, 2nd and 3rd party data, whereas in this article we will explain the process of how user data is generated whether data is declared – actively provided by the user or whether user data is inferred – generated through  a system. Declared/inferred data is also known as explicit/implicit data.

Declared data

Declared data can be any personal information that an individual have willingly and explicitly shared by filling out a form, completing a social media account page or taking another purposeful action. Usually declared data contains demographics about a user such as age, gender, address, phone number but it can also contain more specific type of info for an individual such as preferred time for communication, interests or hobbies.  

It is often considered the most high-quality data because it has been directly provided by the user and it also implies a permission or acceptance for future use of that information, such as for an email campaign or CRM purposes. For marketers, such information is fundamental for personalization and segmentation. However, declared data doesn’t mean that this data correctly and truthfully provided by the user so it can be wrong to assume that these data is more accurate than inferred (system generated) data. Some people may purposely provide wrongful information such as reporting exaggerated income or the size of their company, or giving incorrect birth date.

Inferred data

Inferred data is user data generated by a system and not explicitly provided by the user. It includes the characteristics assigned to people based on their activities and behaviors, often based around content consumption such as online searches, artists or playlist followings, public information shared on social media or recent purchases or subscriptions. Another critical source for behavioral data can also come from offline behaviour such as location history, that can be further leveraged by marketers for more precise targeting.

Inferred data can be matched with declared data to enrich and add to a customer profile already created based on other user information. Such behavioral data offers personalized insight to customer lifestyle interests, political and religious inclination or buying habits, which can in turn be of a very high value for marketers.

Combining the two

Aligning declared and inferred data together through creating a pattern of the user’s behaviour is necessary for building precise audience segments at scale. Managing and verifying declared data can be handled through cross-checking it with inferred data to increase accuracy. The only way to handle properly issues with declared data and to extend its value is through overlapping and cross-checking it with inferred data.