A Customer Data Platform Built For Marketers

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Lytics is a customer data hub that lets you create and share segments across all marketing tools.

A Customer Data Platform Built For Marketers

www.getlytics.com 720 NW Davis Suite 400 Portland,Or 97209

Lytics makes it easy to connect all of your marketing data to create behavior-rich segments. Export these custom, predictive segments to your marketing tools to improve your campaign precision and coordinate truly personalized marketing.

A Customer Data Platform Built For Marketers

www.getlytics.com 720 NW Davis Suite 400 Portland,Or 97209

About Lytics

We build predictive audiences by connecting data via apis, and collected events.

Target Audiences

Share segments, across the tools marketers use. Partner with value-added partners to make use of data.

Build and model segments using predictive analytics, content-classification, look-alike modeling.

Create Segments

Lytics: The Customer Data Platform

Customer

Lytics assists marketing teams in unifying cross- channel marketing data to gain new value from this data unification. Our integrations make it easy to connect and unify data so that marketing teams spend less resources on wrangling data and more time on creating powerful campaigns.

Lytics has built cross-channel flexible identity matching, as well as predictive analytics for user- segmentation. This cross channel customer data is valuable for analytics and reporting purposes.

Run on secure servers using Google Cloud Platform, Lytics ensures that user data remains the property and asset of our customers. We just help you empower that data with the latest in data science, predictive analytics, and open API accessibility.

Basic Lytics Export Functions include:

• Fact-based attributes (e.g. name, email, etc), including multivalued attributes (more than one email)

• Behavior-based attributes (e.g. visit, open, click)

• Content-based attributes (discounts, camping, snowsports)

• Flexible schema, varying by data-sources connected and data collected

• Segment Definitions (rules for membership)

• Segment membership history (e.g. users entered active segment on 2/2/2014, left on 1/1/1015)

• Content (urls, email campaigns) with content- classification from third-party enrichments

• Common shared schema across all Lytics customers:  base user attributes, segment definition, content classification, user-event, user-alias, and user-segment-history (additional user, and event level attributes per customer)

• Customer Profiles: Highly normalized, transformed, and enriched data regarding identity resolution, including cross channel matches, multiple keys, and user alias history (e.g. “user a from channel x is user b from channel y”)

Visit Count

Dea Hartz

Portland, OR, USA [email protected] Android

Last Active: April 7, 2015

32

Audiences Most Active Users:

more than 5 engagments Email Leads (web form) Demographics

Gender: Female Age: 30 Income: 79,000 Zipcode: 97212 User

User/Alias

User Event

Segment

Content Aspect

Contentltem User Segment

Transition

Connect Your Data

Audience Membership History & Triggers

Auto Predictive Lifecycle Segments

• Lytics javascript tag collects first-party behavioral data

• Integrate with tag management providers to onboard web and mobile data

• Pre-built connectors integrate with popular marketing tools to collect historical data and campaign/content (for content-classification)

• Custom data onboarding from CRM and other custom data sources (eg. CSV)

• Sync audiences/membership to marketing tools

As customers enter and leave audience segments, the event is tracked and is available as a trigger. This also means all Lytics segment rule definitions are

immediately available as a dimension for reporting.

• We use our predictive scores to segment customers into different aspects of Lifecycle, new, active, inactive, etc.  

• Take many, many channels, signals and boil it down to Trends, not complex rules to

understand where users are.  Are they increasing in level of engagement or decreasing.

Campaign Monitor Customer.io

Google BigQuery Google

Optimizely Facebook

Mixpanel

Tableau

Lyris SalesForce Pardot

Twitter MailChimp

Segment Urban Airship

ExactTarget

Mandrill

Sendgrid Zendesk

Predictive Scores

Lookalike Modeling

Content Classification & Customer Content Affinity

• Predictive analytics creates 6 unique “scores”

on customers that measure different aspects of behavior, all 0-100 scores

• All scoring is measured in real-time

• Momentum measures general trend of interaction with a brand

• Propensity measures likelihood this customer will come back/interact in the future

• Recency is recency of interactions compared to entire audience population

• Intensity measures depth of interaction or duration of interaction

• Quantity measures total amount of interactions

• Frequency measures the relative frequency of interactions

• Lytics automatically enriches customer profiles by analyzing and understanding the types of content preferences for each customer based on their behaviors and interactions

• Lytics uses semantic classification to analyze type of content and customer interactions on

• Customers identify a “target” segment (e.g.

inactive) and compare it to a “goal” segment (e.g active) and Lytics predictive automatically identifies groups of customers with shared attributes from most likely to least likely to move into the desired “goal” segment

• Customer shared attributes to understand what content or behaviors are most likely to move customers from one segment to another

• Execute lookalike audience based campaigns

MOMENTUM QUANTITY FREQUENCY INTENSITY PROPENSITY

48 100 68 62 22

Identity Resolution

• Each data source has 1 to N unique keys that may identify a customer

• Each record must have at least 1 key

• Each record can contain more than 1 key

• A record (say, web data) that contains multiple keys (say, email signup form on the web, as well as cookie id) is remembered so that the email does not have to be sent again

• Keys can be forced to be singular on a customer, or a single customer may contain more than one value (“sets of values”) such as might occur for a customer that has been known by multiple email addresses over time

• Conflicting information could cause a record to not be able to be merged, in which case two profiles may exist.  This happens if, for example, two user_ids are associated with same email, but only one user_id is allowed

• Algorithmic Identity “key” determination, we can find which fields in webdata are user- identifiers, and determine foreign-key relationships to other data sources

• Algorithmic matching functionality is currently in beta and is partially available

Lytics ties together behavior data from multiple channels with flexible matching.

Visit our Website to Learn More

Take a closer look at Lytics (www.getlytics.com), the customer data platform built for marketers, on our How It Works page (www.getlytics.com/how-it-works). If you have any specific questions, you can reach our Customer Success team at [email protected].