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The Role of a Customer Data

Platform in Personalization

Published by: Sponsored by:

212 Elm Street, Suite 402 Somerville, MA 02144 www.evergage.com

Copyright 2017 Customer Data Platform Institute. All rights reserved. www.cdpinstitute.org

There’s no question that today’s customers demand personalized

treatment. One recent survey found that 75% expect a consistent

experience across all touchpoints, 65% say personalized offers increase

their loyalty, and 52% will stop using a brand that doesn’t personalize

communications.1

Fortunately, personalization does more than keep customers happy:

studies have shown that companies using personalization grow two

to three times faster than companies that don’t2 and 88% report a

measurable lift in business results, particularly conversion rates3.

So while it may be unnerving to realize that customers are holding

your company to the standards set by personalization experts like

Netflix or Spotify, it’s also reassuring to know that meeting those expectations will yield significant financial rewards.

Whether they see personalization as a burden or opportunity, most

marketers have long ago recognized it must be one of their goals.

Their real challenge is converting ambition into reality. Obstacles include

budgets, organizational silos, staff levels and skills, limited management

support, inadequate delivery systems, and fragmented customer data.

It often seems that management support is the essential starting place:

it can reduce organizational resistance and produce bigger budgets,

which in turn yield more staff and better technology. But technology

can also play an independent role: modern systems can make it faster,

cheaper, and easier to assemble and interpret customer data, create

effective personalized experiences, and measure the results. By reducing

investment and improving results, technology makes it easier for

managers to commit to a personalization program and for the rest of the

organization to participate. So while technology by itself cannot remove

all obstacles to personalization, it can make them easier to overcome.

This paper explores the technology you need to support your own

company’s personalization efforts, and in particular shows how a

Customer Data Platform can play an integral role in doing so.

Introduction: Personalization is Worth the Effort

1. State of the Connected Customer, Salesforce Research, 2016

2. Profiting from Personalization, Boston Consulting Group, 2017

3. Trends in Personalization, Researchscape International, 2017

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Personalization can be broadly defined as delivering tailored, relevant experiences based on individual-level data. The data can include more-or-

less static attributes such as name, company, location, customer status,

etc. and frequently updated items such as products purchased, content

consumed, time spent, and actions taken. This data is generated by

interactions with touch points including Web sites, email responses, call

centers, mobile apps, in-store kiosks, order processing, customer support,

online chat, social media, Internet-connected devices, and more.

To be used for personalization, this data must be associated with an

individual. This individual may be anonymous, such as Web site visitor

known only through a cookie ID, or identified because they have provided personal information or a system has inferred their identity from

behaviors or other data. Combining data from multiple sources to build a

comprehensive view of an individual usually requires the individual to be

personally identified so the identity itself can be used to decide which data belongs to the same person.

Once data related to an individual is brought together, there is often a

process to identify and resolve inconsistencies, such as different ways to

spell a name or different addresses. Some data elements can legitimately

have multiple values – for example, many people have several email

addresses. Other elements, such as birthdate, can have only one correct

value. Still others, such as primary address, may have only one correct

current value but can change over time. Personalization systems often

rely on a separate process to analyze the input data and classify it

correctly. This lets the personalization system use the data without

continually attempting to identify relationships or assess its quality.

The simplest form of personalization is to insert customer information

into a message. This could be a name, account balance, or most recent

purchases. Such personalization may have some value in making

customers feel they are being recognized as individuals, assuming the

inserted information is correct. (If not, it can do more harm than good.)

But if personalization is about using data to guide customers in a unique

and relevant way, then inserting customer information while treating

everyone the same is not enough to qualify.

Information insertion aside, real personalization involves data-driven

decisions. The decisions may be based on user-specified rules (“if the customer had a recent complaint, offer a $10 coupon; otherwise, suggest

an early renewal”) or predictive analytics (“recommend the movie they are predicted to be most likely to watch”). Rules and predictions are often

combined: for example, rules may determine which offers the customer

is eligible to receive and predictive analytics may choose the specific offer from that collection. Personalization systems vary greatly in the details of

how such decisions are made.

The final step in the personalization process is delivery of the chosen message. Some personalization systems handle the actual delivery

themselves – for example, by sending an email or issuing an on-site or

in-app message. Others hand off delivery to a separate customer-facing

system such as a Web content manager or call center agent interface.

This is another area where details vary. Some key differentiators include

which channels are supported, prebuilt integrations with specific systems, what data is actually delivered to or received from other systems, the

flexibility and relevance of rule-based and predictive analytics decisioning, and the ability to react to customer behaviors in real time.

MULTIPLE SOURCES A major enterprise can easily have dozens of systems that capture customer data which could be relevant for personalization. Candidates include

customer-facing systems such as Web sites and Web or mobile apps; operational systems such as call centers and order processing; customer

communication systems such as CRM, email, and marketing automation; and external sources such as social media, data compilers such as Experian

and Neustar, data management platforms (DMPs), and ad networks. The customer database may connect to such systems via APIs, SDKs, direct

queries, file transfers, or integration platforms. Data formats may be structured records, such as purchase transactions; semi-structured data such as Web logs; unstructured data such as chat transcripts; or non-text formats such as audio or video. New sources are constantly being added and existing

sources are constantly evolving as data elements are added, dropped, or modified. The ability to accommodate such changes quickly and with minimal manual effort is especially critical given the rapid change in today’s marketing technology environment.

As we’ve already seen, unified customer data is a critical resource for effective personalization. Creating this unified data is a major task

itself. Key issues include:

Personalization Basics

Importance of Customer Data

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␣ DATA PREPARATION

extract an element that wasn’t considered important when the data was collected. For data streams that are too voluminous to store, the system may

need to extract only selected items.

␣ IDENTITY RESOLUTION

Data from systems dealing with known customers often include customer IDs such as an account number, which make it easy to associate the inputs

It may also do more sophisticated analysis to identify likely matches, such as names or postal addresses that are similar or mobile devices that are

␣ SUMMARIZATION

Once all inputs have been associated with an individual, many systems will calculate summary information such as lifetime purchases or last

enables real-time response for personalization, and makes the same information available to different systems, saving redundant processing and ensuring

consistency.

␣ EXTERNAL SOURCES

Some data is not stored within the customer database because it changes too quickly (e.g. weather), is too voluminous to extract from source systems

(Web server logs), or is considered too sensitive to copy (certain personal information). In these cases, the customer database may be connected with the

source system so it can access the external data as needed, often in real time. This lets other systems request the data from the customer database, which

can apply preparation, identity resolution, and summarization processes as needed.

␣ DETAIL RETENTION

Although summaries are important for many processes, the customer database usually retains underlying details such as individual purchase

transactions or Web page views. These are often needed by personalization systems that look for particular patterns in the details or want to create

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FORMATTING FOR ACCESS Data formats within the customer database are often not ideal for access by other systems. Those systems may need to have the data in a particular

database management system or file structure, or may need summaries and indexes to provide adequate response times. The customer database needs features to create such reformatted versions of its data. In some cases, these extracts must be updated continuously so they are always current.

In other cases, it’s enough to refresh the data periodically, at any interval from nightly to every fifteen minutes. When discussing speed, it’s important to distinguish between the time it takes newly ingested data to become available, and how quickly the customer database can respond to real-time

requests for data about an individual customer. One or both may be important depending on the use case.

COMPLIANCE The customer database contains a great deal of sensitive information. The European Union’s General Data Protection Regulation (GDPR) and similar laws being adopted elsewhere create substantial new rights for individuals over their data and corresponding responsibilities among businesses for

managing that data. Within the U.S., concerns over privacy and security breaches further increase the importance of careful control over what data is

gathered, how it is protected, and where it is used. The customer database must be designed to meet legal obligations and minimize business risks

associated with managing customer data, while still allowing legitimate uses to run as efficiently as possible.

MARKETER-MANAGED SYSTEM The CDP is managed by marketers in the sense that it’s prebuilt, packaged software, which can be installed, configured, and operated with little or no custom development. This distinguishes the CDP from systems that are custom-built by IT departments, such as most data warehouses or data lakes.

The practical implication is the CDP is built more quickly, at lower cost, and with less risk of failure.

UNIFIED, PERSISTENT CUSTOMER DATABASE The previous section described requirements for a customer database in detail. A CDP is designed to meet those requirements.

Role of Customer Data Platforms

Where does the Customer Data Platform fit in all this? Let’s start with a definition. The CDP Institute defines CDP as “a marketer-

managed system that builds a unified, persistent customer database that is accessible to other systems.” The key components of

this definition are:

ACCESSIBLE TO OTHER SYSTEMS The CDP is designed to let other systems access the database it creates. This distinguishes the CDP from products that build a unified customer database for their own use but don’t expose that database to other systems. External access is a key benefit because it leverages the CDP investment over multiple applications, avoiding duplicate efforts and supporting consistent customer treatments.

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Given this definition, the relationship of CDPs to personalization is easy to see. Personalization needs a strong customer database, and a CDP is an efficient, effective way to build one.

The only additional point worth making is that by reducing the cost of building the customer database, a CDP reduces the cost of setting up for personalization. This makes personalization easier to buy in companies where it has not been defined as a strategic imperative.

So far we’ve seen that CDPs and personalization systems deliver

different though complementary functions: the CDP builds a unified customer database; personalization systems use the data to manage

customer treatments. That said, it’s possible for the same system to

do both. Let’s explore whether combining them is a good idea.

The main arguments in favor of a combined CDP/personalization

system are (1) it avoids the need to integrate two separate products and

(2) it simplifies real-time response to data. It’s true that CDPs are built to make integration easy, but there is only so much CDP vendors can do to

be ready for whatever system might want to use their data. Generally, they can create APIs that allow data access, can make it easy to create extract

files, and can create databases available for direct SQL queries. These capabilities must still be configured to connect with specific external systems, such as separate personalization systems. Such configuration is easier than building custom connectors, but it’s still work.

By contrast, a CDP that’s built into a personalization system can be

designed to automatically present its data exactly as the personalization

system needs it. This could reduce or eliminate the need to copy data

from the CDP into a separate personalization data store, enabling

greater accuracy and efficiency and “in-the-moment” decisioning. The personalization component would probably still use APIs to access

data, but those APIs can include specialized data access methods

that personalization needs to function effectively. For example, a

personalization system might need to assemble transactions in a time

series format: this is not usually built into a generic CDP API but could

be included if the designers knew it was needed. Similarly, the CDP might

include standard indexes or summary tables that the personalization

system will require. Knowing these are needed in advance lets the

developer structure the CDP data store to execute them as efficiently as possible.

A combined CDP/personalization system can also be designed to

combine data ingestion and access functions, which CDPs usually keep

separate. Such combined functions are important to support real-time

interactions where a customer behavior is captured by the personalization

system, immediately appended to the customer history, and then instantly

used in the next step of an on-going interaction. Most CDPs can’t run data

through their normal ingestion process quickly enough to support this, so

a separate personalization system must make its own in-memory copy

of the customer’s data at the start of the interaction, update that copy

directly as the interaction proceeds, and later post the changes back to

the main customer database after the interaction is complete. Designing

the combined CDP/personalization system to update the main customer

database in real time avoids the need to create these complicated data

management features while making the most up-to-date information

available to all systems that access CDP data.

The combined system has other advantages as well. Knowing the

CDP will be used to support personalization, the designers can build

in personalization-specific analytics that make it easier to generate automated predictions and to measure personalization results. In some

cases, this might actually save the marketer from needing to use a

separate business intelligence or reporting system, although chances are

one will still be needed for other purposes. But it certainly saves the effort

of configuring the CDP or a separate reporting system to generate the specialized personalization-related reports.

More broadly, working with one rather than two systems should ease

the administrative burden on technology staff, let users learn a single

interface, and avoids the need to manage two vendor relationships. In

a world where marketing departments are already managing dozens of

different products, reducing the burden by one major system is certainly

worthwhile.

These advantages don’t mean that marketers should never consider

separate CDP and personalization systems. Some situations where

you might want to accept the higher cost and inconvenience include:

you already have a good customer database or personalization system

in place, and thus don’t want to replace it; you need particular CDP or

personalization features that aren’t available in a combined system;

or, you want the option to replace either type of system in the future

as better choices come along.

Integration of CDP and Personalization

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␣ DATA MODEL A CDP must be able to store whatever customer data the company has available. This means the data structures must be tailored to your business situation and should contain raw details as needed. Personalization requires a standardized data structure that often uses summary data to run effectively. Be sure the combined system keeps the raw data and makes it available to systems that need it.

␣ DATA MAPPING Because CDP and personalization functions use different data models, the combined system needs a mapping capability to manage conversion of data

added to the CDP. To the degree possible, the personalization system should read data directly from the CDP data stores rather than making a copy.

␣ UNIFIED INTERFACE Functions that are performed in both the CDP and personalization components should share the same user interface. This includes over-all “look and feel,”

segment, should be immediately available to use in personalization.

␣ EXTERNAL ACCESS All CDP data exposed to the personalization component should also be available to external systems. This is a core CDP capability, but systems that assemble customer data primarily to support their own personalization features don’t always have adequate features to share that data with external

are exposed, access to raw details, access to user-defined elements, and processing available (aggregation, calculations, sequencing, reformatting, etc).

␣ REAL-TIME INTERACTIONS Personalization during real-time interactions generally uses in-memory data processing to achieve the necessary response time. A stand-alone personalization system will do this internally, without reference to an external customer database. A combined CDP and personalization system will still run real-time interactions in memory but should also take advantage of processing rules and methods built into the CDP portion of the system.

What to Look For in a Combined System

A system that combines CDP with personalization needs the same features as stand-alone systems of either type. In addition, there

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Summary

Today’s customers expect personalized experiences and reward companies that deliver them. Customer Data Platforms can make

personalization better because they simplify the critical task of assembling the unified customer data that is required. Systems that

combine a CDP with personalization can further reduce costs and improve performance by avoiding the need to integrate the two

systems and ensuring real-time responsiveness. Marketers should look closely at such combined systems to see if they fit their needs.

About Evergage

Only Evergage’s real-time personalization platform delivers The Power

of 1, enabling digital marketers to transform the dream of 1:1 customer

engagement across channels into reality.

Combining in-depth behavioral analytics, a full customer data platform

and advanced machine learning, Evergage provides the one platform you

need to systematically understand and interact with each person that visits

your site or uses your app – one at a time, “in the moment” and at scale – to deliver a maximally relevant, individualized experience.

Personalization is the future of digital marketing, and we believe it

should be easy for marketers – without the need for developers or

IT – to understand their audiences and respond in real time with the

most engaging experiences and the most relevant recommendations.

Our customers delight their visitors, prospects and customers every

day, building valuable relationships that lead to greater revenues and

customer loyalty.

About the CDP Institute

The Customer Data Platform Institute educates marketers and marketing

technologists about customer data management. The mission of the

Institute is to provide vendor-neutral information about issues, methods,

and technologies for creating unified, persistent customer databases. Activities include publishing of educational materials, news about industry

developments, creation of best practice guides and benchmarks, a

directory of industry vendors, and consulting on related issues.

The Institute is focused on Customer Data Platforms, defined as “a marketer-controlled system that maintains a unified, persistent customer database which is accessible to external systems.”

The Institute is managed by Raab Associates Inc., a consultancy

specializing in marketing technology and analysis. Raab Associates

defined Customer Data Platforms as a category by Raab Associates in 2013. Funding is provided by a consortium of CDP vendors.

For more information, visit www.cdpinstitute.org.

Evergage

212 Elm Street, Suite 402

Somerville, MA 02144

Web: www.evergage.com

Email: [email protected]

Phone: 1.888.310.0589

CONTACT INFO:

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