The Importance of Data Enrichment in Delivering Customer Personalization Experiences
Here we'll go through what data enrichment is, and how it helps organizations improve their customer personalization experiences.
Companies often require third-party data such as social media data to be part of their existing customer data. This allows businesses to create personalized experiences, run targeted marketing campaigns, create advanced lead generation strategies & much more. This augmentation, appending or addition of data to an existing data set is termed as data enrichment.
Data Enrichment in Large Companies with Disparate Data Sources
It’s not uncommon for retailers, banks, insurance companies and other similar consumer-facing companies to want to enrich their information. Most often, these large enterprises have disparate data sources or systems. For example, a bank’s marketing department may store customer information in a different CRM or system. The customer support system may store information in another system. If the bank wants to initiate a customer personalization service, or to understand their customer journey, they will need to append data from these sources to the main source to get a single view of the truth.
This amendment of data sources to get deeper insights is a common practice in organizations, however, disparate data sources are a significant challenge to achieving this goal. Companies have to spend a significant amount of time cleaning and matching multiple data sources to be able to build a comprehensive or, ‘enriched,’ customer view.
The first step then to data enrichment is actually data cleaning. Without clean data, you will not be able to get the insights you need to build a customer view that can be used to deliver personalized experiences.
Why Do Companies Need to Enrich Data?
Apart from fulfilling modern business goals, data enrichment also allows for:
- Defining and managing multiple levels of data hierarchies
- Creating new business rules and classifying data
- Processing and managing data more efficiently
- Using data for predictive analysis
Data enrichment also impacts operational and organizational goals as companies continue to upgrade their capacity for digital transformation, AI and machine learning systems. For these transformations to happen, companies will need to rely on high quality data and unify disparate sources to create a single version of the truth.
The underlying goal of data enrichment however lies in the need to personalize customer experiences. For instance, the bank mentioned in this example, will need to enrich their existing customer database with household information (information about the customer’s family members) to provide customers with student loans for their children. This kind of personalized service drives the need for data enrichment.
Understanding the Significant Challenges with Data Enrichment
A challenging process, data enrichment starts by examining data quality of your existing *and* of your new data.
Here’s a list of challenges that threatens the success of a data enrichment project.
- Data that is stored in disparate systems differ in terms of format, attributes and information. To derive information from these sources, the data first has to be sorted, cleansed and matched within the data source to remove duplicates.
- Even if you’re obtaining third-party data such as social media accounts or web page forms, the data will need to be profiled to check for incomplete or inconsistent information, cleansed to remove data errors and de-dupe to remove duplicated information. Once this set of data is ready, it will then need to be added to the existing data. Here comes the fun part.
- If the company has not cleansed or updated its existing data, it will have to clean this data source before it can integrate the new data. This data source may consist of millions of rows of data making it challenging to use manual cleaning methods.
- Finally, when both the data sources are cleansed…. It needs to be matched! This is the tricky part. Unlike a few decades ago, data today is overly complex in format and structure. While it’s relatively easy to catch duplicates with the same features, the real challenge lies in catching probabilistic duplicates. This means data fields that may represent the same entity but with different spelling names, phone numbers or even email addresses. Hence, the need for accuracy is a major requirement when it comes to data matching.
Data enrichment, therefore, is not a simple matter of adding new columns to an existing database. It involves deep cleansing, weeding out of duplicates and creating a view that gives an accurate picture of the customer’s profile that can be used in the execution of a customer personalization campaign.
The Role of Data Matching in Data Enrichment
One of the key processes in data enrichment is data matching. When matching data to another source, accuracy is one of the key concerns. While companies spend millions of dollars in hiring data specialists and analysts, they still fail to get even an 80% accuracy in data matching. Accuracy is just one part of data matching.
Another important part is data cleansing and data deduping.
With data repositories, the chances of duplicated information is always high.
It’s quite simple if you think about it.
Any time a customer updates their phone number, their email addresses, or their address data, it’s possible that a duplicated record is either automatically generated or is manually done.
Take for example web forms. A user can accidentally fill in their information twice, each time using a new email address or a new phone number (fields that usually determine uniqueness).
Data enrichment will only be effective when these duplicates are sorted, and records are clean. Once duplicated data is removed, the data will be matched to assign the right information to the right entity.
In the world of big data, companies are striving to get deeper insights about their customers. Data enrichment is the process that allows companies to merge multiple data sources into a consolidated customer view. To truly succeed at data enrichment though, companies must have data they can trust which is why they will need to invest data quality solutions and frameworks that will allow them to obtain clean, usable data. Take a look at the data quality of your organization. Do you think you have high-quality data to pursue a data enrichment project?