Identity resolution is the process of creating a trackable customer profile by analyzing and resolving data across multiple touchpoints, attributes and systems. Attributes might include email addresses, cookie identifiers, device identifiers, mailing addresses, social media handles and more. These attributes can have both personally identifiable information and anonymous identifiers.
Why is identity resolution so important?
50 years ago, people chose which car to purchase by watching a TV advertisement on one of three basic network channels, doing a test drive… and that was that. Now, a customer can engage with companies via email, website, events, print subscriptions, social media, etc. While this generates a treasure trove of useful customer data, marketers struggle to activate all that data to their best of their ability.
That’s because each marketing channel stores data independent of one another. And since each tool is usually run by different cross-functional teams, it’s hard to transfer and standardize that data independently.
That doesn’t just hurt productivity. It also makes the customer experience clunkier and less impactful.
Because unless you proactively merge all of a customer’s data into a single place, you’re not getting a full picture of their interests, behaviors, preferences, etc. Instead of giving them a single cross-channel experience that speaks to all of their needs, you’ll give them different messages across channels. That’s confusing at best and a dealbreaker at worst.
Unifying your customer profiles help you deliver more cohesive customer experiences.
Identity resolution unifies all of an individual’s interactions with your company into a single profile. This way, you can easily view all their past purchases, website behaviors, content downloads and know exactly what action to take in response. Customer data platforms resolve identities through two different automated matching processes: deterministic and probabilistic matching.
Deterministic v. probabilistic matching
Deterministic matching looks for an exact match between two pieces of data. This seems simple in theory, but if your data labels aren’t standardized or a customer uses a different name or email address on two different channels, you won’t be able to resolve this person’s profiles via deterministic matching. You’ll still have multiple profiles for one person.
Probabilistic matching takes a different approach. This method uses a statistical algorithm to estimate the likelihood of two profiles belonging to the same person — this is called a match score. If the match score meets a certain threshold, you can reasonably assume that the two profiles belong to one person and you can merge them. (On Omeda, we recommend merging two profiles if the match score is over 95.)
Because it doesn’t depend on an exact profile match, but the existence of many similarities, probabilistic matching is a better fit for companies with larger and more complicated tech stacks.
How can Omeda help resolve customer identities?
Omeda has developed an award-winning Identity Resolution solution that uses a combination of deterministic and probabilistic matching to develop a match/confidence score for determining unique and common records. Match fields include name, company name (including salutation and suffix), address, title, email, and phone fields.
Our Match Evaluation feature allows you to easily match on any number of characters of any text data field(s) against an external file, giving you more ways to merge customer profiles and eliminate duplicates. For example, users can match on the first three digits of the zip code, five digit zip code, company name(s), etc.
