A Customer Data Platform Built For Marketers

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1 720 NW Davis Suite 400 Portland,Or A Customer Data Platform Built For Marketers Lytics is a customer data hub that lets you create and share segments across all marketing tools Connect Your Data Create Segments Target Audiences

2 720 NW Davis Suite 400 Portland,Or A Customer Data Platform Built For Marketers 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. About Lytics Connect Your Data We build predictive audiences by connecting data via apis, and collected events. Create Segments Build and model segments using predictive analytics, content-classification, look-alike modeling. Target Audiences Share segments, across the tools marketers use. Partner with value-added partners to make use of data. p 1

3 Lytics: The Customer Data Platform Lytics assists marketing teams in unifying crosschannel 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 usersegmentation. 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. 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, campaigns) with contentclassification 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) User Segment Transition Basic Lytics Export Functions include: User Segment 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 ) User/Alias User Event Contentltem Content Aspect Customer Fact-based attributes (e.g. name, , etc), including multivalued attributes (more than one ) Flexible schema, varying by data-sources connected and data collected Behavior-based attributes (e.g. visit, open, click) Content-based attributes (discounts, camping, snowsports) Visit Count 32 Dea Hartz Portland, OR, USA llama@lytics.io Android Last Active: April 7, 2015 Demographics Gender: Female Age: 30 Income: 79,000 Zipcode: Audiences Most Active Users: more than 5 engagments Leads (web form) Audience: Actionable predictive scores & metrics (e.g. propensity, momentum) Aggregate Metric attributes (e.g. visit count) map into fields (count of times they have opened, viewed, etc) p 2

4 Connect Your Data Lytics javascript tag collects first-party behavioral data Integrate with tag management providers to onboard web and mobile data Google BigQuery Campaign Monitor Customer.io ExactTarget Facebook Google Pre-built connectors integrate with popular marketing tools to collect historical data and campaign/content (for content-classification) Lyris MailChimp Mandrill Mixpanel Optimizely SalesForce Pardot Custom data onboarding from CRM and other custom data sources (eg. CSV) Segment Sendgrid Tableau Twitter Urban Airship Zendesk Sync audiences/membership to marketing tools Audience Membership History & Triggers 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. Auto Predictive Lifecycle Segments 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. p 3

5 Predictive Scores Predictive analytics creates 6 unique scores on customers that measure different aspects of behavior, all 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 Quantity measures total amount of interactions Frequency measures the relative frequency of interactions MOMENTUM QUANTITY FREQUENCY INTENSITY PROPENSITY Recency is recency of interactions compared to entire audience population Intensity measures depth of interaction or duration of interaction Lookalike Modeling 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 Content Classification & Customer Content Affinity 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 web pages, , etc. Lytics automatically creates a content graph for each customer and makes this available as attributes in the customer profiles and as metadata for Google Analytics tracking p 4

6 Identity Resolution Lytics ties together behavior data from multiple channels with flexible matching. 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, signup form on the web, as well as cookie id) is remembered so that the does not have to be sent again 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 , but only one user_id is allowed Algorithmic Identity key determination, we can find which fields in webdata are useridentifiers, and determine foreign-key relationships to other data sources Algorithmic matching functionality is currently in beta and is partially available 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 addresses over time Visit our Website to Learn More Take a closer look at Lytics ( the customer data platform built for marketers, on our How It Works page ( If you have any specific questions, you can reach our Customer Success team at support@getlytics.com. p 5