Imagine this: you’re a retail brand who has recently acquired a new online customer, Samuel, who ordered a pair of sunglasses from your website. After his order was placed, Samuel was funneled into your designated welcome journey for new buyers. He’s sent a thank you note for his interest and a few links to your most popular blog content. That same week, Sam, a pure in-store shopper, reaches the seven-month mark without interacting with your brand. You enroll him in a win-back campaign, nudging 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? Rather than one online newbie and one offline churned shopper, you now have one unique multichannel repeat customer. Suddenly, the welcome and win-back campaigns don’t seem that relevant anymore for him, and you may have actually confused a brand loyalist with inconsistent messaging.
Today, the customer journey is multifaceted. People move between different channels, devices and store locations. Brands are constantly inundated with more data and records of each customer interaction. Most digital marketers will apply a blind exact match on the email address. But what about those customers who don’t provide an email address? Or those who make a typo? Or those who share an email with a family member? Or those who use different emails? Or those who change emails? Or those who provide a dummy email? Or those who enter the email of the person that they are buying the product for?
All these hypotheticals can quickly build up into a significant share of your contact base that we would prefer not to leave on the curbside. That is 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 picture of how your business is evolving. Whether your customers are tied to different IDs in siloed systems (e.g., POS, e-commerce, customer care center, email or SMS execution systems) or whether a customer has created two different profiles in the same system, identity resolution will provide you with the clearest understanding of your customers. Acquia Customer Data Platform (CDP) is equipped with a built-in Identity Resolution Engine (IRE) that continuously monitors every customer interaction to accurately resolve customer identities.
At Acquia, we see that leveraging identity resolution on our customers’ databases increases the customers’ share of repeat buyers by 23% on average. This number underlines how likely it is that a large part of your contact base may not be receiving the appropriate messaging until you address the identity challenge.
Acquia CDP’s Identity Resolution Engine unifies siloed customer data to create a 360-degree 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 Your Data So You Can Trust It
As with everything data-related, you need to start with data that you can trust.
Consider the addresses below:
Currently, your system may view these as three different locations due to variations in how they were entered. This means your database will count three different customers, despite the fact there is just one person. Without catching this error, marketing teams will send three catalogs to one address ― wasting resources and frustrating the customer. A proper identity resolution engine will correct these addresses to follow the national address standard.
Acquia CDP also applies standardization to email addresses, phone numbers, names and any identifiable fields that you want to leverage.
Once you have standardized the data, you can validate values. This step will help you operationally: you can avoid having your call center agents try to reach out to invalid numbers, you can remove addresses that are not valid delivery points from mailing campaigns and you can reduce the amount of emails sent by suppressing invalid domains.
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 are evaluated based on profile completion.
Finally, the validation should help you remove dummy values. For instance, two different people could both have said that their emails were [email protected] You don’t want to match them, as they are likely different people. This will prevent having hundreds of records improperly rolling up to 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 Williams, you also consider Bills. For addresses, you can leverage change of address registries.
Deduplicate Your Records with Flexible Matching Rules
Once your data is clean and enhanced, it is 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. Hence, 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 are concerned about home phone numbers being shared, you may also want to check the first name when the phone number matches between two records. The tool that you use should give you the flexibility to combine fields when defining the matching rules.
Finally, typos and variants happen and standardization will not catch them all. Having some room for fuzziness is important. As illustrated below, customers can make a mistake 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 deduping 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 is a match or not a match.
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 fields
As time goes by, you may want to strengthen or loosen matching rules. The tool that you use should be flexible enough to add, delete and edit rules because your data and your 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.
Once the profiles are linked, it is 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 are set to do accurate reporting and engage with your contacts relevantly.
Acquia’s Identity Resolution Engine offers multiple layers of identity resolution and allows you to configure rules to meet your needs and 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, check out our e-book: Working with Customer Data: From Collection to Activation.