Quick Tutorial: Marketing with IP Address Matching including Use Case
IP Address Identification
Today’s technology allows digital reporting systems to keep track of every individual who visits a website, based on Internet Protocol (IP) addresses, allowing access to a complete and permanent record of activity-per-visit. While marketers may strive to have website visitors fill out a form, or self-identify via the “contact me” button, the reality is that potential buyers are researching companies and products far in advance of their willingness to identify themselves. Studies show that up to 75% of a buyer’s research is done prior to self-identification.
However, it’s not necessary to wait for prospects to identify themselves before marketers start tracking their actions or identifying them using IP recognition techniques.
IP Address Matching
In marketing applications, IP addresses can often be tied back to an email address. However, IP addresses can have a one-to-many relationship with an email address. For example, a family or roommates that access the internet from a single router will result in more than one email address associated with a single IP address. An IP address is similar to a landline telephone number for internet access. More than one party can have the same IP address or landline telephone number.
However, IP address matching to email address is highly useful in online targeting or remarketing, especially when coupled with a time stamp or basic location information.
Beyond identification of email addresses, IP address information is also useful to determine overall demographics of an online visitor. Once an individual is identified through IP address matching, additional behavioral attributes can be appended to further identify specific buying behaviors or lifestyle information of the individual. This information is used in personalized ad retargeting, obtaining multi-channel marketing information, and personalizing a consumer’s online experience.
IP Address Matching Use Case: Mobile Banking
Situation: Online mobile banking company needs to identify web visitors by their IP address and associate users with demographics behavioral attributes.
Solution: The online bank provided input IP address, zip code, and time stamp information. This information was matched to Infutor’s database containing almost 400MM IP addresses. Information was subsequently matched to a comprehensive database of demographic attributes and automobile ownership.
Results: The client’s analysis demonstrated a 55 – 60% correlation of historical purchasing activity to appended Infutor attributes. This highly correlated information provides strong insight into consumer behavior where actual purchase history is unknown. This allows the client to use the demographic data for modeling tied to the IP address. This model allows for relevant product advertising with no Personally Identifiable Information (PII) involvement.