Optimize Personalized Customer Journeys

Established In 2016, the Customer Data Platform Institute defines the CDP this way, “A Customer Data Platform is a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.”

That’s quite a mouthful, so let’s break it down.

 

Marketer Managed

Organizational data systems are managed by different entities: CRM by sales; data management platform by IT; accounting by finance; web sales by digital marketing and so on. These systems have three things in common. They are:

1. Managed jealously by and for the entity that controls it.
2. Formatted differently from each other, as dictated by the controlling entity.
3. Not a good exclusive source of customer information for marketing.

While none of these databases should serve as an exclusive source of customer data for marketing, taken together (along with other available databases) they provide a wealth of information. As the system managed by marketing, a Customer Data Platform provides measurable value by curing the wasteful issues of duplications, typos and different formats.

 

Persistent

As a persistent database, the records in a CDP survive the initial transactions or interactions that got them placed in a database in the first place. New data becomes a part of each customer’s growing customer record, painting an accurate picture of each customer’s relationship with the organization.

 

Unified

As a unified database, the CDP brings together all data from every source and stores it in one format defined by the marketing data scientists who create the rules and parameters of the CDP. For example, let’s say that in a single organization, the format for customer addresses differs as follows:

• CRM — Fully spelled out, as in Street, Avenue, etc.

• Accounting — Abbreviated, such as St, Ave, etc.

• CRM from a newly acquired firm — Abbreviated, but with the address number in its own field instead of tied to the street name

Data scientists create one unique address format for customers and write rules that automatically unify the information in the Customer Data Platform. The address example used above is just one of many formats (and one of the simplest) unified by rules written by data scientists.

 

Accessible

As a persistent and unified database, the Customer Data Platform presents a Single Customer View that is to be shared with and refined for each distinct marketing group.

 

What Makes a CDP Intelligent?

Any CDP should be able to provide a persistent and unified view of your data. But, if you want to fully leverage your data and make it actionable with deep personalization, you need an Intelligent CDP, one that allows you to tap into the power of Artificial Intelligence and Machine Learning. One example of a free, open source CDP is Pimcore.

 

Customer Data Platform (CDP) vs. Customer Relationship Management (CRM) and Data Management Platform (DPM)

CRMs and DPMs have been around for quite a while, so why was it necessary to crate the CDP in the first place? To answer that question, here’s a bird’s eye view of the three different data collection systems. The need for the CDP should become obvious.

• CRM. Born in the ‘90s as an essential part of 1:1 marketing (part 1), CRMs only track the actions a customer has taken intentionally, such as making a purchase or inquiry

• DMP. DMPs were popularized by online advertising in the 2000s, as they focus on information that is not personally identifiable, like IP addresses, cookies and devices

• CDP. With the rise of omni-channel marketing and the need to personalize campaigns, Customer Data Platforms were introduced in the mid-2010s to store every interaction with both anonymous and known individuals, including personally identifiable information such as names and addresses

Unlike CRM and DMP, CDP handles data from a large number of sources and keeps that data in order to:

• Analyze customer lifetime value

• Be the data source for win-back campaigns to reactivate lost customers

• Match anonymous first-party data to customer records with personally identifiable data

No wonder the Customer Data Platform has become an indispensable tool for marketers in a relatively short period of time.

One unexpected feature of the CDP is that it has become the single best way for organizations with a lot of data to comply with new privacy regulations, including Europe’s GDPR and the California Privacy Act (CCPA). Marketers have more responsibilities than ever in terms of how they collect, protect and use first-party data.

As a persistent and unified database that provides a single source of truth for each customer, the CDP is ideal for complying with the new regulations. Here’s why.

• Consumers covered under GDPR and CCPA now have the right to request the information a company is storing about them. They also have the “Right to Be Forgotten,” which means to have all the information a company stores about them purged from their records. These are arduous tasks when data is stored in multiple systems, but easy to accomplish in the CDP, because there is only one record for each customer.
• Some databases rely on third-party data to perform identity resolution. A CDP can perform the same privacy resolution from first-party data.
• With a unified customer profile, the CDP can store and meet customer privacy preferences across all channels.

Customer privacy is a real concern and requirement, and CDP now plays an integral role in privacy compliance.

Popularity of the CDP has risen rapidly, as shown by the number of workers employed in the CDP industry in just its first year and a half.

• December 2016 — 1,991 workers
• June 2017 — 3,170 workers
• December 2017 — 4,248 workers
• June 2018 — 5,584 workers

In 2019, 19 new CDP vendors joined the field, bringing it to 96 vendors with 9,000+ workers. Tech giants Oracle, Salesforce and Adobe have announced plans to release their own CDPs in 2019.

CDP is expected to be a $3.2 billion industry by 2023.

In its infancy, CDPs were deployed primarily by retail and media companies. Since then, B2B companies also have embraced CDPs. According to a 2019 survey conducted by the CDP Institute, 54 percent of B2B respondents said they have already deployed a CDP or have one under way, while another 19 percent report that they plan to deploy in the next 12 months.

The CDP industry is growing quickly because companies are yielding tremendous value from them.

In delivering the right offer (WHAT) to the right prospect/customer (WHO) via the right medium (HOW) at the right time (WHEN), the term “customer journey” (or “buyer’s journey”) refers to the HOW and WHEN for each customer.

Customer journeys are slightly different for each industry, and sometimes for each business within a given industry. This table shows a fairly typical customer journey and the different touchpoints that can be effective at each stage of the journey.

As the single point of truth for each customer, the Customer Data Platform arms marketers with the information necessary to map each customer to where they are on their customer journey.

This basic mapping enables a rudimentary form of customer personalization.

How important is personalization in marketing? According to these 10 stats about personalization, it’s very important.

Percentage of consumers who …

1. … want tracking across all touchpoints: 73 (Unified Commerce)
2. … don’t mind retailers saving purchase history and personal preferences if that increases personalization: 64 (Unified Commerce)
3. … bought an item they didn’t intend to buy due to a personalized recommendation from the brand: 49 (Forbes)
4. … spent more than they’d intended due to personalized service: 40 (Forbes)
5. … say they probably will buy again after a personalized shopping experience: 44 (Forbes)
6. … said it’s important for brands to automatically adjust content based on their current context:  67 (CMO by Adobe)
7. … said they get annoyed when their content isn’t personalized: 42 (CMO by Adobe)
8. … said that irrelevant content would stop them from making a purchase: 66 (CMO by Adobe)
9. … identify a bad user experience as the #1 reason they lose faith in an app or service: 55+ (Chief Marketer Report)

Marketers that used personalization …

10. … experienced average sales growth of 19 (Marketing Profs)

State of the art personalization that improves customer satisfaction and significantly improves sales growth was popularized by online giants like Amazon and Netflix. They don’t just recognize someone’s name, they present relevant offers based on previous behavior, traits and characteristics. That’s what customers look for and expect now, and that requires a Customer Data Platform made intelligent by Artificial Intelligence and Machine Learning.

1:1 marketing is the holy grail of marketing. The intent of every technological advance in marketing has been to move us closer to true 1:1 marketing. The Customer Data Platform with built-in Artificial Intelligence and Machine Learning moves us closer to that goal than ever before. Here is one extraordinary example.

Thanks to AI and ML, Netflix not only suggests relevant titles based on your previous viewing habits, it also knows enough about what you like and don’t like to customize the artwork they present for each suggestion. Like Tom Hanks but not crazy about Matt Damon? Then the artwork you see for Saving Private Ryan will feature Tom Hanks, not Matt Damon.

And they do that for each of their more than 100 million offerings.

For the typical mid-market or small enterprise, that level of personalization might seem a bit mind boggling. But AI and ML make a big difference for businesses of all sizes. especially through predictive analytics and lookalike modeling.

With predictive analytics, AI goes beyond rudimentary customer journey mapping and segmentation by using individual consumer behavior and preferences to create models that predict which customers are most likely to buy which particular products and services. Creating these models would take weeks of time for marketing teams — if they could do them at all — but only hours for AI.

As more data is collected in the CDP, the more ML improves the number of cases for which predictive analytics will be correct. What’s more, data analysis that proves accurate can be used by ML to find lookalike buyers based on traits and characteristics they share with actual buyers. This expands the pool of likely buyers who can be reached with personalized messages.