Over the last five years, the technology landscape has changed dramatically. Today nearly every organization is collecting both first-party data and third-party data to power analytics. However, one of the core challenges across all industries is data enrichment.
With all of the advancements in the modern data stack, most companies are extremely capable at capturing raw data and generating insights to make informed decisions. Turning insights into action is quite challenging though, and this is the exact reason that data enrichment is so important.
What is Data Enrichment?
A good way to view data enrichment is through the lens of an operational system. At a basic level, a CRM tool like Salesforce or Hubspot provides high-quality data on various objects like contacts, companies, deals, etc. Typically these objects have a set of sub-fields like first name, last name, deal size, email, phone number, headquarters, address, etc.
Business teams like marketing, sales, support, product, finance, etc. all use a different set of SaaS tools. In order to do their jobs effectively, they often have to hop back and forth between tools to attain a 360-degree view of the customer. This same analogy can be applied to pretty much any SaaS tool, with the only difference being that the objects and fields differ depending on the data that is stored.
In its simplest form, data enrichment is the process of enhancing existing datasets and tools with information that is generated from external data sources. The end goal is to create an enriched customer view. With data enrichment in place, business teams can access the customer data in near real-time in the tools they are comfortable with to improve customer personalization. Emphasizing data enrichment processes improves data accuracy which translates into a better customer experience.
Why is data enrichment important
To realize why data enrichment is so important, it’s first relevant to understand the pieces of a modern data stack. For most organizations, the end goal is always to create an end-to-end flow between, data acquisition, data integration, and data analytics.
In most scenarios, data is generally collected through a variety of different data sources (ex: Salesforce, Hubspot, Marketo, Amplitude, Zendesk, Braze, Marketo, etc.) This raw information is then ingested into a cloud data platform (ex: Snowflake) using a data integration tool (ex: Fivetran).
After the data is loaded into the warehouse, the next order of business is to transform and model it for analysis using a tool like dbt (data build tool). Once this is done, business stakeholders can consume the data in a dashboard or reporting tool like PowerBI or Tableau.
The problem is, reports and dashboards only provide business direction; they don’t make data actionable. Even worse, reports only show a zoomed-out view of the customer because the data is not typically associated with individual users.
When data only lives in a dashboard it’s not actionable by anyone except data teams or technical users who know how to write SQL. Business teams like marketing, product, sales, support, etc. cannot access the customer data that lives in the warehouse, and this makes it impossible to answer questions like:
- Who is the most active user in an account?
- Which users are active?
- Which customers have downloaded X marketing resources?
- How many messages did X account send in the last 30 days?
- What pricing plan should customer ABC be on based on their usage?
Every tool is limited to the data it captures. Data enrichment de-silos information and democratizes it to everyone, not just the technical users who know how to write SQL. When more information is available, various teams can ask and answer more questions than ever before.
Data enrichment creates a single unified view of the customer across the entire organization. This gives every team access to the same information so they can leverage it on a day-to-day basis to drive meaningful business outcomes and create better customer experiences.
Types of data enrichment
There are generally three main types of data enrichment:
- Behavioral data enrichment focuses on adding behavioral patterns to existing user profiles.
- Demographic data enrichment focuses on information about the customer.
- Geographic data enrichment focuses on information around the customer.
However, businesses in every industry struggle with creating a 360-degree view of their consumers, so the emphasis should be placed on defining the common behavior attributes for ideal customers, even if it is something as simple as income level, marital status, physical address, etc. to create a more valuable customer dataset. With that in mind, there are four data subsets where data enrichment can provide immense value.
Product data refers to all information about the customer that is captured directly through the product. Some examples could be:
- Workspaces created
- Number of users
- Signup date
- Messages sent
- Last login date
Sales data refers to all of the information about the customer that is captured in the sales process and pipeline. Some examples could be:
- Active deals
- Companies in POC/trial
- First meeting
- Product demo date
- Deal stage
Marketing data refers to all of the information that is captured in the customer journey. Some examples could be:
- Web pages viewed
- Resources downloaded
- Links clicked
- Session length
Finance data refers to all of the information that is captured throughout the payment process. Some examples could be:
- Contract size
- (ARR) Annual recurring revenue
- Last payment date
- Subscription type
By combining the customer data sets from disparate systems, a single golden customer record is created.
Data enrichment use cases
The core use cases for data enrichment are usually centered around marketing and sales teams because these teams are often searching for a centralized customer profile.
Consider this scenario. Product-led-growth companies like Slack and Grammarly give users the ability to sign up for a free version of their products. Both of these companies offer additional features in the premium version of their products. The typical adoption path begins with a single user and expands when additional team members see the value in the product.
Once enough users are leveraging the tool, management will purchase an enterprise license to cover the entire organization. This is a fantastic go-to-market strategy because it amplifies sales and marketing efforts to spread awareness and increase adoption. Obviously, this model only works with a strong product, hence the name “product-led-growth.”
However, converting free users to paid customers is a major challenge, so in most cases, the role of marketing and sales in PLG companies is to accelerate the adoption cycle and shorten the time the customer spends in the sales funnel. This means doubling down on outreach efforts and delivering highly personalized content, messages, and offers.
When nearly all of the information about the customer is captured “in-product”, it makes it really difficult to leverage the information because it doesn’t exist in native business systems like Salesforce or Hubspot where marketers and salespeople live on a daily basis.
Knowing exactly where a prospect or customer is in their journey is absolutely crucial for sales and marketing teams because conversion is usually directly correlated with personalization. Being able to associate product usage, emails opened, integrations installed, links clicked, pages visited, resources downloaded, etc. is priceless. With data enrichment, business teams can access this information at a moment’s notice.
How to implement data enrichment
Although many companies offer third-party data enrichment services. There are generally three main options when it comes to data enrichment tools in the context of the modern data stack:
CDPs give users the ability to consolidate all of their customer data into a centralized platform for analysis where it can then be sent to different destinations. The main advantage that CDPs provide is the fact that they integrate automatically with other 3rd-party APIs.
This makes it very easy to push data into the hands of business users in their preferred tools. All CDPs have a few core problems though. For most companies, all of the customer data already lives in the warehouse, so adopting a CDP simply creates a second source of truth.
On top of this, CDPs are expensive and time-consuming to implement. They are also built around rigid data models and make it so users can only send data on specific objects like users and accounts. This is quite problematic as no two businesses are alike.
iPaaS platforms move data between apps and external sources with little or no transformation. They typically act as point-to-point solutions moving data from point “A” to point “B” (ex: sending Salesforce data to Braze for lifecycle marketing). Since data is simply being moved from one system to another, it’s nearly impossible to create a 360-degree view of the customer.
All iPaaS tools are based on event triggers. A trigger represents an event that takes place in an individual system. That event is then transmitted to the integration platform through an API call or Webhook that performs predefined actions set in place by the user.
The main drawcard of iPaaS solutions is that they give users the ability to build intuitive workflows to move data. However, for anything other than simple use cases, these workflows become an absolute nightmare to manage and build, and in many cases, custom code is required to get them operational.
Reverse ETL is a better option when it comes to data enrichment. The major advantage of Reverse ETL lies in the fact that it integrates natively with the data warehouse. With Reverse ETL, users can leverage the existing data models in their warehouse or simple SQL to sync data to their end destination.
Reverse ETL tends to be a much simpler and more efficient solution compared to the alternatives because it establishes the data warehouse as the single source of truth and integrates within the modern data stack. Users simply have to define the data and map it to the appropriate columns and fields in the end destination.
The benefits of data enrichment
Every company captures data. However, very few leverage data for anything more than high-level decision-making. With data enrichment, business teams can access the data they need in the tools they use to create better customer experiences and drive outcomes that can positively affect the bottom line.
When data is accessible to everyone, it’s actionable by everyone, and this means that different teams will always be working towards the same goals. At its core data enrichment improves data accuracy and enables organizations to offer better customer experiences.
The first integration with Hightouch is completely free, so you can start enriching your data today.