Imagine this: You’re a retail brand that's recently acquired a new online customer, Samuel, who ordered a pair of sunglasses from your website. Afterwards, Samuel was funneled into your designated welcome journey for new buyers. Your marketing automation platform or email provider sent a thank-you note for his interest and a few links to your most popular blog content.
That same week, Sam, an in-store shopper, meets your criterion for an automated win-back campaign: He’s gone seven months without interacting with your brand. Your win-back campaign nudges him with a few “Don’t forget about us!” texts and an exclusive discount code.
On the surface, these approaches make sense, but what if Samuel and Sam were actually the same person? Instead of having one online newbie and another offline churned shopper, you actually have a unique multichannel repeat customer. Suddenly, the welcome and win-back campaigns don’t seem that relevant to him, and you may have confused a brand loyalist with inconsistent messaging.
That example highlights how today's customer journey is multifaceted.
People move between different channels, devices, and store locations. Brands are inundated with mountains of data and records of customer interactions. Most digital marketers will apply a blind exact match on an email address. But what about those customers who don’t provide an email address? Or the ones who make typos, share the same email address as a parent or significant other, or have a few different email addresses?
These hypotheticals can build up into a significant share of your contact base that you don't want to leave by the wayside. That's why identity resolution should be at the core of your customer data strategy.
Identity resolution is the key process that will help you improve the quality of your customers’ engagement and get a more accurate sense of how your business is evolving. Whether your customers are tied to different IDs in siloed systems — e.g., point-of-sale (POS), e-commerce, customer care center, email, SMS execution systems — or a customer has created two different profiles in the same system, identity resolution gives you the clearest picture of who they are.
One example of a solution that offers that functionality is our Acquia Customer Data Platform (CDP). It's equipped with a built-in identity resolution engine (IRE) that continuously monitors every customer interaction to accurately resolve customer identities.
Acquia CDP’s identity resolution engine unifies siloed customer data to create a 360° profile that encompasses a customer’s full journey over time. The IRE accomplishes this through a configurable four-step process: data standardization, data validation, stitching, and deduplication.
Let’s look at how you can apply an identity resolution engine to your own customer databases to unearth hidden lifetime value.
Clean Data Is Trustworthy Data
As with everything data-related, you need to start with data that you can trust. Consider the addresses below, for instance:
Your system may view these as three different locations due to entry variations, which means your database will count three different customers even though the records point to just one person. Marketing teams that fail to catch this error will send three catalogs to one address, thereby wasting resources and annoying Ms. Monroe.
A proper identity resolution engine will correct these addresses to follow the national address standard. Acquia CDP applies standardization to email addresses, phone numbers, names, and any identifiable fields that you wish to use.
Once you've standardized your data, you can validate values. This step will help you operationally: Your call center agents won't reach out to invalid numbers, you'll remove addresses that aren't valid delivery points, and by suppressing invalid domains, the number of emails sent will be reduced.
There will also be some validation unique to your business. For instance, you may have recurring values due to unofficial resellers. You may also have agents that fill in clients’ profiles with the address or phone number of a store, if they help a customer get a delivery for an out-of-stock product, or if they're evaluated based on profile completion.
Finally, validation helps you remove dummy values. For instance, two different people could say that their emails were [email protected] You don’t want to match them, because they're likely different people. Validation avoids rolling hundreds of records improperly into a single contact. Your identity resolution tool should be able to catch those and apply the appropriate treatment.
You can then enhance the data. For names, you can apply nickname dictionaries so that when you look at records with the name "William," records with the name "Bill" are also considered.
Deduplicate Records with Flexible Matching Rules
Once your data is clean and enhanced, it's time to deduplicate records. Not all of your different data sources will capture the same information. Typically, an e-commerce website will have a high postal address capture rate while a call center is more likely to capture phone numbers. A customer may also have created two accounts with different emails but with the same loyalty ID or postal address. A good identity resolution tool will enable you to combine several matching rules.
While the likelihood of two people sharing the same email address is low, the likelihood of sharing a home address is high. You don’t want to match on name only; you want to combine the first and last name with the address fields. Likewise, if you're concerned about home phone numbers being shared, you may want to check the first name when the phone number of two records match. The tool that you use should give you the flexibility to combine fields when defining the matching rules.
Finally, we all know that typos and variants happen. Standardization won't catch them all. Leaving room for fuzziness is important. Customers can make mistakes in the street number, misspell a word, or choose to give their full name.
Your CDP should support fuzzy matches. Acquia’s IRE leverages probabilistic matching, a sophisticated deduplicating process that uses exact matching and fuzzy matching algorithms. Therefore, each matching rule is a combination of attribute-to-attribute comparison and weighting thresholds to define what's a match versus what isn't.
The default identity resolution rules that we use to get our clients started with Acquia CDP are:
- Exact match on email
- Similarity-based match on name and address field
Where To Go From Here
As time goes by, you may want to strengthen or loosen matching rules. Whatever tool you choose should be flexible enough to add, delete, and edit rules because your data and needs will change. For instance, you can add a similarity-based rule on name and phone number or an exact match on a loyalty ID.
After linking profiles, it's time to select the best personally identifiable information (PII) values (e.g., most recently opted-in email address, primary address) and compute the business intelligence values (e.g., total revenue, best channel to reach out, recommendations). Now, you can conduct accurate reporting and nurture relevant engagement with your contacts.
Acquia’s identity resolution engine offers multiple layers of identity resolution and allows you to configure rules to meet your needs as well as promote the best values to build a master record for all of your customer profiles.
For more on how Acquia CDP prioritizes data quality and drives customer lifetime value, download our free e-book Identity Resolution 101: What It Is and Why You Need It.