white paper built for cx: inmoment data...

© 2016 InMoment, Inc.

The InMoment Experience Hub is the premier platform used by top brands to listen, understand, and share their customer experiences. At the core of the InMoment hub is a software architecture that is designed from the ground up to create world-class Customer Experience (CX) programs for InMoment clients. This architecture is multi-tenant, metadata driven, secure, stable, and scalable. This paper explains the InMoment data architecture, the platform that powers the best CX programs in the world.

A B S T R A C T

Built for CX: InMoment Data Architecture

W H I T E P A P E R

The InMoment data architecture is unique

and differentiated within the CX space.

While similar to architectures found in

other top SaaS platforms like SalesForce.

com, Concur, Gainsight, Microsoft Azure,

and many others, InMoment’s architecture

is designed to offer clients competitive

business advantages including:

• Built for CX- Designed to solve the

unique problems found in customer

experience programs

• Multi-tenant & metadata based- Easily

handles the wide variety of customer

experience programs found in

every industry

• Built for business users- Designed to

allow business users to modify the data

structure without the involvement of

programmers or database administrators

• Built for real time- Changes in structure

or data are available immediately, not

after a week, a few days or a few hours.

Does not use cubes or aggregation tables

so that data is always current

• Built for flexibility- Can handle constantly

changing CX programs. Adding new data

points is a breeze

• Built for stories- Designed to handle

unstructured data like customer

comments, voice messages, and

video feedback

D I F F E R E N T I A T I N G F E A T U R E S

InMoment uses a distinctive metadata-

based architecture that collects customer

experiences from millions of users around

the world. This proprietary software

was designed from the beginning to

be a new approach to storing CX data

and combines modern best-practices

in SaaS data handling with innovations

that allow better handling of CX data.

The architecture securely separates each

InMoment client’s data from other clients’

data and still allows InMoment to constantly

update and improve the overall system

to the entire community of users. The

InMoment methodology provides superior

flexibility while still allowing great analytical

capability that drives speed to insight.

[email protected] • 1-800-530-4251 • © 2016 InMoment, Inc. 3

Within the CX world there are several different approaches to hosted

database architectures. These architectural styles may be single or multi-

tenant, and have different approaches to databases structure, known as

database schema. Each approach has different characteristics reflective

of the maturity level of the provider. Below are some of the different

approaches and a brief explanation of why InMoment is different:

1. Single-tenant, custom schemas:

This approach offers the greatest

customization, but is the least scalable.

Each customer gets their own custom

database, sometimes hosted by the

vendor. While this sounds desirable, the

cost of maintaining each separate schema

is huge, which means programs will be

very expensive and time-consuming to

alter. The cost of change will result in a

program that goes stale quickly.

2. Multi-tenant with identical schemas:

While more scalable than completely

custom software, this approach limits

configurability options for each customer.

Clients are forced to shoehorn their

program into features that may not fit

well with business processes and culture,

resulting in higher failure rates on rollout.

3. Multi-tenant with custom schemas:

Some competitors try to mitigate the

weaknesses of the first two approaches

by combining multi-tenancy with custom

schemas. While this does offer greater

efficiencies by sharing platform resources,

it limits the vendor’s ability to maintain a

single unified code base and may require

custom code for each project. This

increases cost and time required to make

changes. It also may prevent customers

from receiving the benefits of constant

improvements from other customers.

Different Approaches to Hosted CX Data Architectures

InMoment’s model is highly scalable and secure, can be updated in real-time, allows for analytical performance, and still provides a highly configurable solution.

4. Multi-tenant, identical schemas but

custom analytic schemas: Some vendors

will achieve multi-tenant efficiencies

and limited analytical configurability

by pushing configurability into the

analytical layer. This is typically achieved

using customized Data Cubes, a data

warehouse aggregation technique that

provides performance improvements at

the cost of flexibility. Data cubes pre-

aggregate data, which means they can

only answer questions at the granularity

of data they were designed with. In other

words, if you didn’t think of a question

up-front, you won’t be able to ask it later.

5. The InMoment approach: The InMoment

data architecture is multi-tenant and uses

a single shared foundational schema (like

approach #2) for all customers, but we

have maintained configurability options

for clients by implementing a powerful

metadata layer and data engines to

create virtual structures and views on

the data. This means each customer

benefits from the flexibility of a custom

schema without the typical headaches of

customer-specific complexity. InMoment’s

model is highly scalable and secure,

can be updated in real-time, allows for

analytical performance, and still provides

a highly configurable solution.

The InMoment data architecture is the

foundation of the InMoment Software-

as-a-Service (SaaS) platform. It has been

designed from the beginning to handle the

extreme demands of creating CX solutions

for the the largest and most successful

customer organizations in the world. These

demands include:

• Multiple concurrent enterprise clients

• Complex organizational hierarchies

• Unique and constantly changing data

requirements per client

• Information from every touchpoint

(social, review, post transaction survey,

e-commerce, etc.)

• Real-time analytics shared at every level

of the organization

• Diverse customer journeys that require

unique domain-specific analytics patterns

• Customer data integration where every

organization values different customer

information

• Emphasis on listening to the customer’s

voice in their own words from

unstructured sources including text,

audio, and video

• Data privacy and security

B U I L T F O R C X


The goal of modern SaaS architecture is

to deliver software service more efficiently

and cost effectively than ever before. The

most effective design pattern for improving

the manageability of SaaS platforms is

called “multi-tenancy.” InMoment’s platform

is designed to support multiple client

organizations or “tenants” on the same

single stack of computing resources. This

concept allows InMoment to reliably deliver

an Internet-scale CX application in a cost-

effective manner.

Industry analysts and large-scale software

companies like SalesForce and Microsoft

agree that a multi-tenant architecture is

the most mature and effective strategy for

delivering a SaaS solution. By delivering the

same application to many different clients

or tenants the cost of managing software

development, staff, network infrastructure,

security, and server resources is greatly

reduced. Each tenant (a client of a SaaS

vendor) is given access to a configured

version of the same application that all

other clients use.

The economics of a multi-tenant architecture allow software providers to operate more efficiently and pass along lower total costs to their clients.

M U L T I - T E N A N T A R C H I T E C T U R E

Each tenant is virtually isolated within the

platform and is only able to access the data

and services that have been configured for

them. Their data and configuration runs

on the same platform but is secured and

separated from the other tenants. When

each tenant logs in to the platform the

software effectively morphs into a highly

configured version of the software that is

unique to the tenant.

This architecture provides a massive

economy of scale to the provider. InMoment

is able to apply software patches, secure

networks against threats, backup data,

etc. for all clients simultaneously. The

economics of a multi-tenant architecture

allow software providers to operate more

efficiently and pass along lower total costs

to their clients. But a larger benefit to

clients exists in the form of a community

of customers.

InMoment’s multi-tenant architecture

provides benefits beyond increased

efficiency and cost effective software

delivery. Every client becomes part of a

larger community of users who all share

the same set of CX needs and interests.

Unlike traditional applications, which

are separately maintained and improved

for each customer, multi-tenant SaaS

customers benefit from the collective

improvements made for all other customers.

InMoment’s architecture is designed to

provide a CX platform that is scalable, fault-

tolerant, highly available, secure, responsive,

and tailored to each client’s CX program.

Each InMoment client has completely

unique customer data requirements, unique

touchpoints, unique brand differentiators,

and unique moments of truth in delivering

the customer experience. How can each

client receive a highly tailored version

It is challenging for any company with

a customer-facing experience to stay

abreast of every trend in CX delivery.

By participating in the community of

InMoment client organizations, each tenant

is able to benefit from the requests of all

other customers. This allows InMoment

to help every customer stay out front on

CX technology issues. Every InMoment

customer benefits from a larger team of

engineers who are constantly keeping the

platform fresh with new features.

of the InMoment Platform? How can the

platform be effectively extended for each

InMoment client? How does one client tailor

the interface, business logic, analytics, and

data without affecting the other clients

on the platform? How can upgrades and

repairs be made without breaking

tenant-specific customizations? The

answer is Metadata.

The core of InMoment’s Experience Hub

has been designed to be highly dynamic

in nature. It uses an engine for data

storage that allows each client to have

its own data model and set of business

rules. The activities within the system are

orchestrated via a runtime engine that

generates user interfaces and business rules

based on metadata. Metadata is data about

data. In other words, it is the specification

of the structure of a particular client’s

data. For example, if a client wants to

store a customer’s loyalty number, a rating

for satisfaction, a rating, for employee

friendliness, an overall average score, and

A Multi-Tenant CX Community

Metadata-Driven Architecture

Owned ByData Field

Metadata (Data about data)Experience

Data Type

Platform

Platform

Tenant

Tenant

Tenant

Tenant

Numeric Identifier

Alphanumeric

Numeric Rating

Numeric Rating

Numeric Calculated

Text

ID

Loyalty_Number

Overall_Rating

Employee_Rating

Average_Rating

Comment

Loyalty #

Overall Satisfaction

5 4 3 2 1

5 4 3 2 1

Employee Friendliness

Average Rating

Comment

3.5

When I rented from the Bu�alo airport location it was somewhat confusing There wasn’t anyone at the kiosk.

GC98207X


The InMoment platform has clear

separation between the static underlying

application code and the dynamic

application data that describes the

customer experience. This client/tenant

specific application metadata includes

journey touchpoint and channel data,

organizational hierarchy data, customer

data, touchpoint metadata, and contextual

data. Business users are able to define

this information within the InMoment

platform so that it is aware of the CX

program being created.

InMoment’s platform is completely powered

by the metadata specifications defined

by its clients. However, it does include a

core of standardized CX metadata that is

informed by both best practices and the

industries we serve. This gives program

managers a place to start.

The distinct boundaries provided by the

InMoment metadata system allow the

Experience Hub to be configured for each

client’s unique needs, modify the system

core, or update each client’s specific

components without altering the other

client programs on the system.

Clear Separation

InMoment Dynamic User InterfacesFeedback Forms Mobile Apps Reporting Analytics

Personas

• Structure• Roles• Access Rules

• Response Elements• Social Sources• Story Structure• Program Rules

• CRM Linkages• Transactional Data• Departmental• Loyalty Specs

Journeys Channels Touchpoints

Interface Rendering Rules Processing Data Management

InMoment Static Runtime Engines

Customer Journey Metadata

Organizational Hierarchy Experience Metadata Contextual Metadata

Data

MetadataInMoment Runtime Engines

Tenant Specific Metadata

DynamicUser Interface

a customer comment, the system needs

to know that each record requires five

pieces of data or “fields,” which are an

alphanumeric loyalty number, a numeric

satisfaction rating, a numeric employee

rating, a calculated numeric score, and

finally an open-ended text comment. This

information collectively forms the metadata

for the touchpoint being measured.

InMoment’s metadata-driven multi-

tenant platform is designed to handle the

demands of enterprise-scale CX programs.

The foundation is created by modeling

client organizational structures including

org charts, data classifications, and user

groups. A data model for each channel

and touchpoint is created that includes

information about every field of data.

This information is used to create forms,

surveys, reports, dashboards, and tenant-

specific customizations to business logic.

The platform uses abstract concepts that

represent these artifacts. No actual data

tables are created. They exist as a virtual

data structure that is accessible to user

experiences that are modified at runtime to

reflect the branding, workflow, and business

rules of each tenant on the platform.

InMoment’s platform stores all client data in

just a few large shared tables that act as a

source heap for other operations. The data

engine materializes views on these tables

that are used by the rest of the system

to create reports, dashboards, and data

analytics applications. The metadata also

informs the real-time alteration of those

user interfaces.

A sophisticated data engine powers the

metadata-driven data views. Business

users define the views of data required

for analytic purposes. The engine uses the

metadata and the view definitions to pivot

the CX data at runtime into virtual tables.

Performance of these tables is critical so

the engine optimizes query generation

and caches metadata to provide

reliable performance.

Multi-Tenant Platform Architecture Overview

Cached Metadata

Platform Engines Shared Databases

Virtual Runtime CX App Components

Bulk Data Processing

Data Access

Text/ Audio/ Video Analytics

Business Rules

Recommendations

Surveys,Feedback Forms Reports Dashboard Mobile Apps

Virtual Data Views

Metadata Tables

Data Tables


Platform components are built on top

of, and are informed by, the InMoment

metadata architecture. The components are

combined together to create engines that

solve specific CX program problems. Some

of the major engines provided with the

InMoment platform are:

• Bulk data processing- Import and export

of bulk data including customer data,

hierarchy data, transactional data, and

contextual information.

• Data access- Data access engine

that dynamically creates virtual

views of data that can power other

application components.

Every customer experience is different. As

a result, every CX program must reflect

the highly varied nature of the brands that

own them. Each business has different

customer journeys, resulting in different

customer data points, different features

of text analytics are important, different

business rules alter workflows. Rather than

attempting to manage a vast, constantly

changing set of physical database tables,

InMoment uses a data storage mechanism

based on metadata, data tables, and

pivoted analytics tables.

• Text/audio/video analytics- Unstructured

data is processed as it is ingested and is

translated, transcribed, categorized, and

analyzed for sentiment.

• Business rules- Tenant-specific

business rules that are processed as

data is collected, including workflow,

issue escalation, survey flow, and

data validation.

• Recommendations- Predictive analytics-

powered recommendations are made to

ease the burden of end-users who can

be advised as to the next-best-action

to take.

Platform Engines

D A T A D E F I N I T I O N A N D S T O R A G E

Business Units, Regions, Districts, Categories, User Groups, etc.

HierarchyMetadatafor client specific data

Large Heap Storage of Common Data for all experience types

Data FieldChannels, Personas, Touchpoints

CX Journey

Runtime pivoted virtual table for reporting or dashboards

Virtual ViewPhysically materialized view for analytical performance

Materialized View

Virtual PivotTable Description

Views

ExperienceLarge Heap Storage of structured data for data fields

Field Data

Large Heap Storage for Calculated Values

Calculated Data

Large Heap Storage for Unstructured Text/Audio/Video

CLOB Data

Metadata TablesData TablesPivot Tables

Each enterprise has its own unique

reporting structure. Some businesses

leverage a straightforward top-down

organizational chart hierarchy that follows

leadership reporting structures. For

example, a software services business may

have different departments that roll up to

divisional leadership who roll further up

to executive leadership. Other enterprises

have frighteningly complex overlapping

hierarchies with discrete business units

reporting up through the organization

through multiple pathways. For example,

an agency-based or franchised business

may have corporate owned locations and

franchisee locations that are owned by

separate business entities. In this scenario,

data may need to be organized to provide

overlapping hierarchies to service both sets

of needs.

It is critical for a CX program to model

organizational reporting correctly so that

the right information is broadcast to the

right people, at the right time, in a manner

that fits culturally. CX providers take

several different approaches to solving

this problem, but InMoment’s approach to

handling hierarchical data is unique. Below

are three different methods including

InMoment’s method:

1. Rigid hierarchy model: Some providers

take a simplistic approach and have

created a reporting hierarchy in advance

that clients must conform to. While

simple in nature and easy to provision and

understand, this rigid method is ultimately

limiting. It also doesn’t fit a client’s culture

and will result in cognitive friction.

2. Semi-flexible hierarchy model: Other CX

providers will define a series of nested

levels and will allow clients to define

the names of the levels and possibly

some additional metadata about each

level, including data access and security.

Many organizational structures do not

conform nicely to the concept of levels

which means compromises will invariably

be made to shoehorn the client’s

organizational structure into the level

structure.

3. The InMoment multi-hierarchy model:

InMoment’s hierarchy model is completely

flexible and allows any number of nested

nodes in a tree. It also includes metadata

to describe the role or category of the

different types of nodes. This means

that almost any kind of overlapping

data hierarchy can be represented in

the system. For example, a geographic

organization of insurance agents can

be modeled alongside a mapping of

corporate divisions. Additional metadata

describing the attributes of each kind

of business unit can also be modeled to

provide extra dimensions for reporting.

InMoment’s model is bottom-up and begins

with the individual leaf-node business units.

These units can be described with reporting

attributes that give dimension to the units.

For example, a services business may have

a network of client managers who have

a skills that should factor into reporting.

Or, a product centric organization might

have products with different attributes that

can be used to cross-cut during analysis.

Unit attributes allow these cross-cutting

dimensions to be modeled in the

InMoment system.

Hierarchy Metadata

It is critical for a CX program to model organizational reporting correctly so that the right information is broadcast to the right people, at the right time, in a manner that fits culturally.


Each tenant in the InMoment system

has their own fully configurable set of

hierarchies. The leaf-node business units

can be further organized into hierarchies.

Units can belong to one or more categories

that can further be owned by other

categories. These categories can be

nested in an arbitrarily deep structure.

Metadata about the class, role or type of

each category provides manageability

to the hierarchy. This allows any time of

Individual users can be granted access to

data associated with nodes in the hierarchy.

This association controls the data they are

able to see within the organization and also

controls the type of reports they are sent

and the information visible within online

dashboards and reporting applications.

Data access can be further tailored to

hierarchy to be modeled. For example, let’s

say Acme Insurance has an organization

with the following reporting structure:

Brand, Region, District, Agent. Those are

the classes of category, but the actual

concrete instances of the classes are Acme

Insurance, Northwest Region, Portland

District, Thorton Insurance Agency. The

InMoment model can easily handle his

hierarchy and can add infinitely more layers

next to or on top of this structure.

protect sensitive information such as

personally identifiable information (PII).

Additional reporting hierarchies can be

created with completely separate

access policies.

Arbitrary Nesting & Types

Data Access and Security

Acme InsuranceBrand

Region

District

Agency

Concrete HierarchyHierarchy Type Metadata

Southeast Northeast

Portland

Lewis BrokersThorton Ins. Sand Bridge

Tacoma Seattle

Southeast

Data field Metadata stores information about every unique piece of data stored in the Hub.

Every CX program has the same ultimate

goal ( improve the customer’s experience),

but everything else about the program

is completely unique to each brand

employing a CX change initiative. Therefore,

every tenant on the InMoment platform

wants to store unique data in the InMoment

Experience Hub. Data field Metadata stores

information about every unique piece of

data stored in the Hub.

Each tenant organization on the platform

can define separate data sets and fields of

data to use in InMoment CX applications.

The data field metadata table contains

information about each field, it’s name,

reporting label, internationalization, data

type, and rules about data validity. This

allows information to be stored in the heap

management system of the InMoment Hub.

As data arrives, information from the data

field metadata table allows each field to

be stored as a group, defined in a master

data table. Individual data values are stored

in the heap data table. The tables are

indexed for later retrieval and reporting.

Additional tables follow this same pattern

and allow for storage of computed values

and unstructured data like comment text

and audio.

D A T A F I E L D M E T A D A T A

• OrgId• OrgName...OtherColumns

Tenant Organization• DataFieldId• OrgId• DataFieldName• DataFieldReportingLabel• DataType...OtherColumns

DataField Table• DataMasterId• Date• Time• OrgId...OtherColumns

Data Master• DataMasterId• DataFieldId• Value...OtherColumns

Data


The Customer Journey is an orchestration

abstraction that is superimposed on the

data stored in the InMoment Experience

Hub. Every client organization has a unique

customer journey that spans multiple

channels with key moments of truth

that can be monitored by the InMoment

platform. The platform can be customized

with metadata that models the components

of the customer journey including:

• Channel- Channels model the type of

journey the customer is currently on, and

can include touch points including online/

web, social, in-store experience, product

experience, etc.

C X J O U R N E Y M E T A D A T A

U N S T R U C T U R E D S T O R Y D A T A

• Key moments- Used to model the

moments of truth that are critical to the

customer experience

• Personas- Model the types of customers

having experiences that will be captured

in the InMoment Experience Hub

• Best practices- Recommendations and

standard operating procedures that detail

ways to make improvements to

key moments

The InMoment platform is optimized to take

advantage of the power of unstructured

customer stories in the form of text

comments, audio feedback, and video

recordings. Unstructured data is collected,

analyzed, and stored using a proprietary

workflow that leverages InMoment’s

extensive knowledge of the domains and

industries we serve.

Text, audio and video can be collected

from mobile, web, and API sources. The

InMoment platform analyzes content on

ingestion and is able to extract meaning

including topics, entities, and sentiment.

This data is further used to identify root

causes of dissatisfaction, predict changes in

key metrics, and reveal emerging topics of

interest. The cornerstone of the InMoment

unstructured data analytics system is a

highly tuned proprietary text analytics

infrastructure powered by technology from

the IBM Watson project.

InMoment is the only vendor in the CX industry to offer natively embedded hardware accelerated speech-to-text and voice analysis capabilities.

Unstructured data in the InMoment

platform is processed through a

configurable pipeline for analysis. The

pipeline consists of a contiguous series

of analytic steps that build on one

another to enrich unstructured content

with progressively more contextual

information. For example, a contact center

audio recording is collected and then

sent into the pipeline for analysis. First,

an analyzer transforms the speech into

InMoment’s unstructured analytics

infrastructure is unique within the CX

industry. Some of the most important

differentiating capabilities include:

• Comment-level meaning- Most engines

are optimized for analysis of massive

data feeds alone. While InMoment is

very capable of handling web-scale

unstructured data in real-time, the

key to becoming a customer-centric

organization is listening to each

customer individually.

• Industry- and customer-tuned-

InMoment has embedded its extensive

domain knowledge into the brain of the

The InMoment Difference

Unstructured Data Pipeline

unstructured feedback infrastructure.

That means InMoment doesn’t just have

off-the-shelf generic text analytics, it has

analytics highly tuned and optimized for

each unique industry and vertical that

we serve. It can further be tuned on a

customer-by-customer basis to reveal

insights unique to each customer.

• Real-time hardware-accelerated speech

analytics- InMoment is the only vendor

in the CX industry to offer natively

embedded hardware accelerated speech-

to-text and voice analysis capabilities.

Our platform is able to process recorded

audio from voice messages, call center

text, another analyzer lifts out sentiment,

generic industry specific analyzers extract

information about contact center scenarios,

and finally company specific analyzers.

Each step along the way adds additional

annotations, or contextual markers that

explain the content of the unstructured

audio stream. The InMoment unstructured

analytics pipeline is flexible and can be

augmented to analyze any source of

unstructured data.

TextAudioImageVideo

Speech to Text

Annotation Annotation Annotation Annotation

Sentiment Analytics

Industry Analyzer

Client Analyzer

Fact Fact Fact Fact


C O N C L U S I O N

The data architecture that powers the

InMoment Experience Hub is designed

to listen to, analyze, and share customer

experience stories. It is based on a

scalable and fault-tolerant design that will

support client organizations of any size

or complexity. It’s flexible data model is

purpose-built to embrace the wide variety

of CX programs our clients will have. It

About InMoment InMoment is a cloud-based customer experience (CX) optimisation platform that gives companies the

ability to listen to and engage with their customers to improve business results through better experiences.

Through its Experience Hub™, InMoment provides Voice of Customer (VoC), Social Reviews & Advocacy,

and Employee Engagement technology, as well as strategic guidance and tactical instruction, support,

and services, to 350 brands across 25 industries in 128 countries. The company is the leading VoC vendor

for the food services, retail, and contact centre industries, with deep domain expertise in B2B, healthcare,

hospitality, and numerous others.

recordings, and the audio tracks of video

feeds. Because it is hardware accelerated

it can respond in real-time. A 30-second

recording can be analyzed for topical

data in less than 2 tenths of a second,

faster than the human eye blinks.

• Video Feedback- InMoment offers video

feedback capture and analysis including

deep integration of video sources. The

audio from the video feedback can be

accomplishes this by being multi-tenant

and metadata based. It allows flexible

hierarchy modeling and is specifically

designed to handle and leverage the

power of unstructured customer data. It is

architected using battle-tested strategies

and patterns that can deliver value

today while still being built for the CX

opportunities of the future.

analyzed using the same world-class

text analytics engines as other

feedback sources.

• Industry Tuned Sentiment Analysis- Most

vendors leverage off-the-shelf sentiment

analysis, which often ignores a surprising

amount of data. InMoment’s sentiment

engine is uniquely tuned for each

industry we serve, which provides the

best possible analytics results.

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