CUSTOMER ENGAGEMENT ANALYTICS IDENTITY GRAPH

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TABLE OF CONTENTS

INTRODUCTION ............................................................................................. 1

I. Business Benefits of the Identity Graph .......................1

THE CHALLENGE Of CONNECTING MULTI-CHANNEL DATA ................. 1

THE IDENTITY GRAPH ........................................................................ 2

HOw IT wORkS .............................................................................. 3

MULTIPLE DEvICES AND CLEARING COOkIES .................................... 4

MATCHING AT MULTIPLE CONfIDENCE LEvELS ................................. 4

HOw THE IDENTITY GRAPH IS DEPLOYED .......................................... 5

CONCLUSION .............................................................................. 5

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INTRODUCTION

The NICE Customer Engagement Analytics’ identity graph links data from many different customer data sources to assemble a complete record of every interaction an individual or group has made with your organization. The identity graph assembles these customer journeys around individual customers, households or organizations. Analysts and marketers can then create personalized experiences for individuals, households or organizations based on their previous interactions and state of mind.

BUSINESS BENEfITS Of THE IDENTITY GRAPH

The NICE Customer Engagement Analytics’ identity graph empowers marketers and analysts to create value from all of your customer data by connecting it in a flexible way.

1. Maximizes the value of your customer data — the identity graph enables analysts and marketers to use every interaction in a customer journey for real-time decisions. Other systems only act on a small subset of the data typically organized by analysis applications or silos.

2. Reduces time to insight and action — the identity graph acts in real time using all of the available data to assemble the most up-to-date customer journey. Other systems often require nightly processing.

3. Offers flexibility to handle changing business requirements — the identity graph assembles customer journeys on-the-fly on multiple different levels (examples include customer and household) and new levels can be added in an analyst-friendly interface. Other systems require strict business rules that need to be coded by IT.

4. Provides scalability — the identity graph is designed to handle hundreds of terabytes of data. Other systems cannot connect customer journeys at a multi-channel scale.

THE CHALLENGE Of CONNECTING MULTI-CHANNEL DATA

With consumer behavior rapidly changing due to mobile, IVR and web channels available at any time and in any place, analysts and marketers struggle to deliver a consistent, up-to-date experience across different touch-points. Customers think of your brand as a single entity - they neither know nor care that your business operates using complex, older and often siloed analytics and marketing systems across multiple channels.

Your customers expect you to listen to them and become increasingly frustrated if you appear to be disjointed or ask for the same information again and again (e.g., “What’s your customer ID again?”). Worse still, marketers often offer customers products or services they already have or make inappropriate marketing offers to different family members.

Analysts and marketers recognize there is a significant opportunity to connect and analyze customer interactions across all touch-points. The challenge is taking diverse data from customer systems driving mobile, web, call center and physical channels and linking it reliably in a structure that allows analysts and marketers to interact with customers as individuals.

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THE IDENTITY GRAPH

The NICE Customer Engagement Analytics’ identity graph provides a unique view into customers’ interactions with a brand. It spans all customer touch-points and includes both anonymous and known user sessions, stitching them together to produce a precise identity graph for each customer.

NICE Customer Engagement Analytics then constantly updates each customer’s predictive profile, giving analysts and marketers powerful insights into which products and self-services are most applicable for each customer at any given point in time based on their path or customer journey.

Graphic 1: Identity Graph

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HOw IT wORkS

NICE Customer Engagement Analytics combines data from all available channels and automatically arranges it by individual customer. Each customer interaction with a brand has an identifier ranging from the very precise, such as an account number, to more anonymous, such as a cookie identifier. By observing which identifiers occur together, NICE Customer Engagement Analytics builds an identity graph linking together the identifiers that belong to the same individual.

For example, if a customer authenticates online, this allows you to connect their cookie identifier to their account number, linking anonymous browsing to online product holdings. If the same credit card is used both online and in a store, this allows web and offline purchases to be tied together.

Unlike a data warehouse which might store customer data in aggregated form in a highly structured schema, NICE Customer Engagement Analytics’ scalable Big Data platform stores customer

interactions according to time at the most granular event level. NICE Customer Engagement Analytics timestamps each customer interaction the instant it occurs and organizes the data into a chronological timeline for each individual.

As data is collected over time and further links are observed, the identity graph becomes richer and the customer timeline becomes more detailed and predictable. As connections are made, the identity graph and customer predictive profile are dynamically updated in real time, even during in-flight interactions, so that the customer journey is always accurate and contains the most recent data.

The structure is highly flexible and can operate even when data arrives out of sequence, making it easy to augment the customer journey with historical data as well as add or enrich a customer profile with new data sources.

Graphic 2: Identity Graph Scenario

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MULTIPLE DEvICES AND CLEARING COOkIES

By observing co-occurrences of identifiers, NICE Customer Engagement Analytics links customer activity from multiple devices and browsers. For example, a customer might visit your website both from a home and work computer. By connecting their anonymous cookies with authenticated IDs, NICE Customer Engagement Analytics can merge these two sets of anonymous browsing activities into a single customer stream.

Similarly, when a customer switches to another browser or clears their cookies, this again creates new anonymous identities. NICE Customer Engagement Analytics is able to reconnect these interactions to the individual’s customer journey as co-occurrences arise.

NICE Customer Engagement Analytics can also enrich customer profiles with third-party data at the micro-segment level (such as with geo-demographic information) and with slow-moving profile data (such as customer name and address verification) to create rich profiles of customers across channels.

MATCHING AT MULTIPLE CONfIDENCE LEvELS

Not all identifiers belong just to a single person. For example, family members will have the same home address but when they relocate, that address will later belong to another family.

Similarly, family members often share a computer or tablet so each person’s browsing activity may initially be labeled with the same anonymous cookie.

In these cases, NICE Customer Engagement Analytics takes time and frequency of interactions into account, paying attention to patterns of authenticated and anonymous activity to understand how to ascribe household level behavior to specific individuals.

Data can also be filtered to select a subset of the Identity Graph. This allows analysts and marketers to choose whether to query NICE Customer Engagement Analytics data by household or individual.

When combining data involving shared identifiers, there may be some potential ambiguity and it is important to treat this appropriately. For example, when using anonymous browsing data to drive recommendations on your website’s homepage, it makes sense to use all of the connected data in order to exploit the maximum predictive signal.

In some cases identity accuracy is more important than predictive power – for example when sending a triggered email to a customer who has abandoned his shopping cart or referring to a financial transaction. NICE Customer Engagement Analytics allows you to query at specific identity confidence levels, ranging from most optimistic (include all connected data) to most rigorous (only include data collected during fully-authenticated sessions). Therefore, the same customer interaction data can be filtered differently for different business activities.

To drive real-time decisions, NICE Customer Engagement Analytics computes a predictive profile in less than 50 milliseconds. It makes this possible by taking all customer data connected via an identity graph and storing it close together in an HBASE data store so that it can be read and processed

rapidly with the associated confidence level.

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Graphic 3: Customer Journey

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HOw THE IDENTITY GRAPH IS DEPLOYED

NICE Customer Engagement Analytics’ identity graph is both flexible and easy to use. An analyst can easily configure which elements of an interaction (such as an email address or account number) are used as identifiers.

CONCLUSION

When a customer interacts with your brand across multiple channels, NICE Customer Engagement Analytics combines that data into a single customer journey and builds a predictive profile to enable personalized marketing offers, promotions and self- services.

NICE Customer Engagement Analytics’ identity graph enables this connectivity by linking both anonymous and known sessions to create a rich connected view of each individual customer journey. The identity graph becomes richer over time by connecting data in whichever order it arrives and handles the complexities of shared devices to allow analysts and marketers to understand customers on a household or individual level.

NICE Customer Engagement Analytics then automatically connects these identifiers to build an identity graph, organizing the customer data as it arrives and as new connections are learned. Identity strategies are applied when the customer record is read, either for analytics or to drive a real-time decision, such as a targeted offer or personalized self-service.

Marketers and analysts may often choose to use a combination of identity strategies. For example, an online bank might use household-level optimistic matching to target offers of new products or accounts on their homepage. But once an individual has logged in to online banking, a very strict confidence level could then be applied. This would discard any activity potentially attributed to someone else in the household, using only behavior known with 100 percent certainty to match to the authenticated individual.

Similarly, a marketer for a travel company will design different campaigns for business and holiday customers and might use individual customer records to target business-related offers, but the entire household-level behavior to assign vacation offers.

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DATE 7/2014 • WP-35014• CONTENTS OF THIS DOCUMENT ARE COPYRIGHT © 2014.

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