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Customer data is imperative to success.
Successful companies utilize data throughout the customer lifecycle - from acquisition to engagement, to repeat purchase.
The challenge is how to effectively combine data. Customers interact with brands on many channels. Customer Data Platforms aim to solve this by unifying data, and making it easily accessible to marketing teams.
This article explores what customer data platforms are, and how best to utilize them to drive business results. To skip straight to customer data examples, click here.
What is a Customer Data Platform (CDP)?
A customer data platform (CDP) is a technology that unifies customer data from multiple sources to create a single customer profile. Unlike other systems, customer data platforms are built for accessibility with simple interfaces.
Less-technical team members like marketing and customer service are expected to interact and use data through the Customer Data Platform - without the use of IT resources.
Lastly, customer data platforms make this data actionable. Some data platforms focus solely on the data, and rely on integrations to accomplish this. Others, like Barilliance, equip clients to use this data directly in their applications.
While a CDP sounds simple in conception, it solves a series of deceptively difficult questions: who are your prospective and current customers? When should you reach out to them? Which offer should you use?
Customer data examples
Customer data refers to collected information about a customer. Customer data can include traits, behaviors, and demographic data collected by the business. It is best to organize customer data around the customer.
Below we outline examples of customer data, and highlight what data eCommerce stores should use to create better customer experiences.
1. Customer identity data
The first type of customer data is identity. Customer identity is data that enables you to distinguish one customer from another.
Most prospects being relationships with brands as an anonymous shopper. Platforms such as Barilliance track anonymous user actions, and ultimately connect them to a known customer record.
This is most often done when the anonymous visitor takes a self-identifying action. This can be prompted by brands via signup bonuses, when a prospect logs into an existing account, or creates a new account as part of the checkout process.
Below is a customer identity example from Bookings.com.
Above, Bookings.com recognizes potential returning visitors via IP Address. They create a popup encouraging the anonymous visitor to identify themselves by logging in.
A more typical example of customer identity comes from Pampers. Here, an anonymous visitor is greeted with a welcome pop-up. The pop-up offers an incentive in exchange for creating an account, identifying the customer and opening up the ability to attach customer data to the individual.
Above, Pampers uses welcome pop-ups to transform anonymous visitors into known prospects.
2. Basic behavioral customer data
Behavioral data is the highest signal customer data a brand can gather. It demonstrates a customer's intent more than other types of data, and is crucial to analysis techniques like behavioral segmentation and eCommerce cohort analysis.
Basic behavioral data refers to typical actions a customer takes on an eCommerce site. This includes actions like viewing an item, adding an item to a cart, removing an item from a cart, and completing a purchase.
Above is an example cohort analysis dashboard, which combines basic behavioral customer data by customers who made their first purchase in a certain month. Cohort analysis allows brands to make better assessments of LTV, payback periods, and helps in resource allocation.
3. Checkout customer data behaviors
Shopping cart abandonment remains a significant issue in eCommerce. The average cart abandonment across industries is a staggering 78.65%.
This makes the checkout process the highest ROI opportunity for brands to collect customer data around. Brands should look at when the checkout process was started, which checkout steps are completed, if customers entered a payment method, and ultimately if an order was completed or abandoned. From this data, a customer data platform can trigger cart abandonment campaigns to recover sales.
There are many ways to recover sales with cart abandonment campaigns. Above is an example from Thrive Market. We put an entire guide on abandoned email templates.
4. Post purchase customer data
In our latest analysis on email marketing statistics, we found post purchase campaigns to be incredibly effective, with over a 7% conversion rate.
Beyond fueling post-purchase campaigns, this customer data is vital for customer success teams. Post purchase data can include if an order is updated, what updates are made, if an order is cancelled, and whether a customer left a review or not.
Above, Amazon uses customer data in combination with post purchase data to suggest specific product recommendations and create repeat purchases.
5. Customer browsing behavior data
Most potential customers never reach the checkout pages. To maximize conversions, brands should expand their abandoned cart triggered campaigns to also include measures further up the purchasing journey.
To do this, customer data such as products searched, viewed, and filtered should be gathered. This data can then be used to personalize content in browse abandonment campaigns.
Above, Fashion Nova utilizes Facebook Messenger to bring customers back after browsing an item. Using customer data within triggered campaigns creates relevant, personal offers.
Customer Data Platform Use Case
As mentioned, customer data platforms have many use cases, spanning acquisition, engagement, conversion, and maximization.
To help illustrate, I pulled a few examples from our clients that showcase how they are using a CDP to improve sales.
Use Case #1: Making Facebook Custom Audiences More Effective with CDP
You should use customer data platforms to enhance Facebook Custom Audiences.
Custom Audiences allow companies to target a specific customer list on Facebook, Instagram, or the Audience Network.
The effectiveness of these ads depend on the quality of your list. This is where customer data platforms come into play.
CDPs give you the power to segment your customers, creating unique messages to each type.
To illustrate, I will use a few anonymous examples from our clients.
Targeting Recent Buyers
Recent buyers are highly engaged with your brand and much more likely to make a second purchase.
Research shows that making even a small improvement in retention creates massive returns.
Unfortunately, relying purely on the FB Pixel undercuts your ability to target this group.
With Barilliance, our client is able to append customers who bought on other channels, including their physical stores, to create a complete customer list.
Above, the screenshot above, they define recent buyers as someone who has made a purchase less than 90 days ago, and whose order value was $100 or greater.
You can synch this audience continuously with Facebook. Whenever a prospect makes a purchase, they will be added to this audience automatically. Likewise, when their last purchase extends beyond 90 days, they will be removed.
Targeting First Time Buyers
One significant variation of recent buyers are first time buyers.
These customers have less affinity to your brand than loyal customers, and many top brands invest heavily in motivating return visits.
Above, our client makes an added specification - limiting the total number of orders to 1, and ensuring that the first order was less than 1 day ago.
This creates a revolving audience of first time customers who bought 24 hours ago or less.
Use Case #2: Using Customer Data Platforms to Crate Relevant Engagement
Relevant messaging depends on good data.
You should leverage purchase history, current session behavior, demographic data, and more to create better offers.
Customer data platforms gives you access to this data. Above, our client Skandium is able to engage clients in real time based on a number of factors, including device type, location, and behavior.
In this case, a pop-up displays when a prospect is highlighting a product name. This behavior is normally followed by a search looking for comparative prices.
To address this concern, we create a price match guarantee. We add credibility and relevancy by dynamically changing the messaging to reflect their current location, in this case the UK.
You can read a full case study on how Skandium uses Barilliance here.
Are you successfully using data? Or are you treating most customers the same?
We've written a guide on important customer segments for eCommerce here. It's a great primer for identifying high impact customers, and understanding the need for different messaging and offers.
If you're ready to make a choice in technology, I recommend you take a look at our guide on How to Select a Personalization Vendor.
Lastly, if you would like to learn more about how Barilliance helps companies unify their data for increased sales, schedule a demo here.