Brierley smith 2014 edge

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How Predictive Analytics and Real-Time Marketing are Changing the Face of Loyalty Don Smith SVP, Consumer Insights

Professor’s Institute, January 2014

For the Marketer - Loyalty Programs Add Value Beyond Revenue Loyalty programs provide identity and customer-centric data and insights that align businesses and company decisions based on “one truth” – single view is an essential pre-requisite.

Mobile

Customer-centric experiences: Enable a customized and improved customer experience across all channels in realtime conversations.

email

In-Store

Social

Direct Mail

Web

SMS Offers

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Influencing the Customer Journey‌

Single View of The Customer: Then and Now

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Presentation Focus: Online Engagement and Social Media

Web Analytics/Site Browsing Social Media Engagement

These 2 channels are now prevalent, but marketers have been slow to capture and leverage true customer‐level data.

Outline for Presentation Part I: Web Analytic Data

Case Studies

 Getting Started: Implementation  Capture Mechanics  EDA, Data Audition/Reduction  Aggregation and ETL  Leveraging the Data  Segmentation  Engagement‐Conversion  Modeling Enhancement

Retail Fashion Apparel

Part III: Additional Touch Points

Part II: Social Site Data

 Getting Started: Implementation  Social Login/API Mechanics  EDA, Data Audition/Reduction  Aggregation and ETL  Leveraging the Data  Profiling and Segmentation  Engagement‐Conversion  Modeling Enhancement

Part IV: Real‐Time Case Study

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Part I: Customer-Centric Web Analytics

Phased Approach to Click-Stream Integration

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Getting Started: Capture Mechanics 1

3

2

4 Hosted Click-Stream Data

CRM

CRM

Customers opt-in to customized web experience (via ‘Remember Me’ tracking selection).

Customers visit ecommerce site. Web analytics tool records browsing details.

Web analytics tool stores all clickstream data – linking it to customers’ CRM IDs.

Click stream fields transferred; ETL process aggregates data at customer-level and stores in CRM DB.

EDA, Data Audition/Reduction: Examples from Retail Apparel • Extensive exploratory data analysis of > 165 straight fields from Omniture menu > > 300 different combinations of derived measures > 1-year time-series activity for >1.5 million guests

• List winnowed down to a manageable size > ‘Dirty Dozen’ raw data fields to > derive ~20 key predictor variables which > are updated daily and housed in larger CRM database

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Leveraging the Data: Engagement-Conversion Analysis • Engagement-Conversion Analysis: Process by which online brand engagement is matched to overall customer spending (across purchase channels) to derive focused tactics for value migration.

Leveraging the Data: Predictive Model Enhancement (continued) • Average Performance Lift by Model Type

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Leveraging the Data: Value of Engagement-Conversion Marketing Focus • Digital Brand Engagement Lifts Customer Value for Retail Fashion Clients

Leveraging the Data:

Using Site History to Drive In-Store Traffic Track guests’ site browsing histories to identify key conversion opportunities

Leverage engagement segments to identify optimal delivery channels

Serve personalized instore conversion offers with strong CTA In-Store Try-On Offer for Jeans

3x lift over control

4.5x lift over control

+

or

Suit Offer with Free Accessory with In-Store Purchase

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Part II: Using Social Networking Data

Getting Started: Capture Mechanics – Enabled by CRM & Loyalty 1

2

3

4 SNS

X CRM

Brand integrates ecomm site with SN Service – app creation, social login and linkage icons.

Logged-in CRM guest establishes SNS association with brand via linkages – customer grants permissions on SNS and returns to brand site.

Brand calls to SNS via API to pull a customer’s available data fields – member identified via SNS token.

Customer SNS data stored in CRM and refreshed following recurring ETL schedule.

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Getting Started:

Capture Mechanics – Enabled by Social Tools • New Tools Enable Aggregate Listening as well as Customer-Centric API Integration > Audition process is still necessary to incorporate key fields > Tokenized tracking across SNS sites increases complexity

Brierley LoyaltyWare, powered by MutualMind

EDA, Data Audition/Reduction

• SNS Data Audition > Follow our 6-Step Audition Process > Begin with a Single SNS Service/Site - Twitter is easy to access - Facebook has a rich field of offerings

> Add Additional Sites Iteratively - Maintain handle maps or token translations

> Test ‘Free’ Data Before Paying for Measures

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Leveraging the Data: Segmentation • Begin with Existing Segmentations > Append Segment Profiles with SNS Data

• Develop New SNS Engagement Segmentations > LOE and ROI Are Inversely Related

Leveraging the Data: Predictive Model Enhancement (continued) • Average Performance Lift by Model Type

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Part III: Additional Touch Points

Customer Journey Channel Incorporation Call Center

Mobile App

Key Fields:

Key Fields:

Frequency, Wait Time Duration, Resolution

Action Flags:

Actions:

locator; buy; search; -in

 Recognition Flags – VIP and complainer;  RT CSR Messages – context + history (RTIM)

Actions:

 Feed Next Best Action and propensity models  Personalized Offers geo-fenced + context

Survey/Profile

Key Fields:

Demos, NPS, SOW, Brand Affinity, Product Interest

Actions:

 SVOC Profile Enhancement  Tracker Panel Recruitment  Sourcing: CRM and Loyalty

Kiosk

Key Fields: Demos, CS/NS Planning, SOW, Potential Value

Actions:

 Inform 360 SVOC and Next Best Action models  Sourcing: CRM sign-up and loyalty enrollment

Counter/POS/CSR Key Fields:

Up-sell flags; DM Capture; Loyalty Enrollment

Actions:

 Real-Time Messaging  CRM Capture and Opt-In  Personalized Tactics

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Case Study: Symbiosis of Loyalty & CRM

Incorporating Touch Points for Member Engagement and Coattails for Non-Members Program Engagement Component

Member/loyalty Impact

Reward for Online Product Reviews

Encourage In-Store Check-in at POS

Non-Member/Loyalty Coattail Effect

300% increase in number of reviews posted by members

Incremental transaction for 1 in 4 participants –  Enrollment driver (highest rated program component)

Incent Social Media Linkages and Viral Marketing

Doubled the number of Twitter followers in 1 year –  Tripled the number of retweeted promotions

Promote SMS Opt-In

Tripled the SMS-marketable audience (1 yr)

Increased ecomm sales conversion –  12% lift in key-margin merchandise categories. Enhanced purchase path patterns applicable to larger CRM audience –  Linked to increased mobile app adoption Viral sourcing contributed to better than 250K new CRM customers –  Steady YOY rise in SOC

SMS optimization allowed increase in direct mail/email investment for non-members

Part IV: Real-Time Case Study

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Questions

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