Driving Marketing ROI with Data Integration

Transcription

1 Driving Marketing ROI with Data Integration

2 Presenters Neil Hoyne Head of Customer Analytics Google Michael Loban CMO InfoTrust, LLC

3 What s on the agenda? Analytics Premium Google Confidential and Proprietary 3

4 Consider: Truly Integrated Marketing is the holy grail for the marketeer. Colin Green, Marketing Director

5 Agenda 1. Overview 2. A perspective from Google 3. Ingredients 4. Case studies 5. Getting started

6 Overview Analytics Premium Google Confidential and Proprietary 6

7 What is Data Integration? CONSUMER

8 What is Data Integration? CONSUMER Technical and business processes used to combine data from disparate sources into actionable insights.

9 Why? Unifying marketing systems to deliver RELEVANT MESSAGE AT THE RIGHT TIME ON EVERY DEVICE

10 Single point of truth Avoid comparisons at all costs! Use a centralized approach (one tool) when you need to compare metrics across the board. - Brian Clifton Successful Analytics

11 Integrating marketing technology Enterprise Priorities in Digital Marketing

12 Things to consider 1. Consumer Touchpoints 2. Info / Data Collected 3.Info / Data Storage 4. How do you plan to analyze? Profile 5. How do you plan to optimize? Purchasing Behavior Demographics

13 This is not easy Integration with current/legacy systems remains the biggest challenge when expanding an ecommerce business, cited by 43% of company respondents and 39% of supply-side respondents as one of the three biggest barriers to success. Technology for Ecommerce Report Econsultancy, in partnership with Neoworks

14 End goal 1. Combine customer data in one system. 2. Ensure that accurate data is available and accessible. 3. Deploy campaigns leveraging 360 degree view of customer data. Photo courtesy of

15 A perspective from Google Analytics Premium Google Confidential and Proprietary 15

16 Current Tools & Reach

17 Case for data integration Solving "missing" data points: 2. Import additional data to complete the story. 1. Capture all site activities using Google Analytics 3. Make smarter targeting decisions based on the combined view.

18 Different Types of Data Integration 1. Campaign Data: Add advertising campaign-related dimensions, such as source and medium 2. Geographical Data: Create custom geographical regions, allowing you to report in ways that are better aligned with your business 3. Content Data: Add additional depth to site content by appending information on subject, audience, author, etc. 4. Product Data: Better insights by importing product metadata, such as size, color, style, or other product-related dimensions.

19 Shifting to Customer-Centricity User-data integration allows you to move away from session-based measurement and begin to understand how customers are behaving over time through a variety of different dimensions.

20 Avvo Case Study Approach Used Data Import to better understand user demand by region and specialty Results Revised ad pricing based on analysis of demand by region and product category Correlation between site traffic and advertising success aids products decisions Greater visibility into advertising success aids to product decisions Source:

21 Ingredients of Successful Integration Google Confidential and Proprietary 21 Analytics Premium

22 Analytics Requirements 55% of big data analytics projects are abandoned for two reasons: 1. Lack of expertise in-house 2. Projects lack context around data -Survey of IT Professionals

23 Top challenges 1 Management support/resources 2 Unifying technology - databases, analytics, POS 3 Lack of strategy & context 4 Unifying and sharing customer data 5 Performing activities in-sync

24 Ingredients

25 1. Map out consumer touchpoints Building Expectations Delivering Experience Social Search Site Kiosk In-Store Demographics Interests Keywords (paid) Visitor behavior PII Data Purchasing history Activity level Past activity Trends ABC Preferences Sales activity Behavior Attributes Behavior Audience Behavior Offline data

26 2. Tying data together Customer ID CRM Data Gender/Age Product ID Product Data COGS Group users/customers/products by attributes that you collect.

27 3. Limitations: technical and business Technical Limitation Business Limitation Why a Limitation Possible Solution Google Analytics 20 Custom Dimensions Can t track all product and user attributes Google Analytics Premium Analytics Inability to tie user data together Can t tie user sessions across devices UserID Implementation Architecture Do not have a dedicated data architect to structure proper integration process Know what can be done, but no clear vision for how to do it. Outsourced solution

28 4. Business value - build your case A study by Nucleus Research states: for every $1 invested into web analytics, a company will receive $10.66 in return that is an ROI of 966%. What is your measure of success and how fast you can can accomplish it?

29 5. Process & technical requirements Search Display User reaches a website GA Cloud platform stores all acquisition, behavior and conversion data. Remarketing based on various data attributes. Customer data sent to backend database. Analyst running models to deliver insights. Personalized based on various data attributes. Personalized website experience.

30 6. Implementation

31 6. Implementation Divide your data integration project into more easily managed modules. REQUIREMENTS DESIGN IMPLEMENTATION TESTING A working version is produced during the first module, so you have a working process early on. Each subsequent release of the module adds function to the previous release. The process continues until the complete system is achieved.

32 Case Studies Analytics Premium Google Confidential and Proprietary 32

33 Consider: Integrated Marketing is obviously common sense, but amazingly still not common practice. -Peter Fisk, Chief Executive Officer

34 1. Increasing auction bidding with GA 1. Track all aspects of online bidding: Item IDs and categories Auction types and IDs Number of bids & bid types 2. Quickly and accurately measure the effectiveness of online marketing campaigns at driving high value bids. 3. In the 30 days immediately following the implementation: 42% increase in online bidding 163% increase in bids 185% increase in bid value

35 1. Increasing auction bidding with GA

36 2. Driving personalization with CRM data Pass users activity captured within GA into the CRM, and then power campaigns with this data. 1. Users actions, such as products viewed, exit pages and ad clicks are passed to internal database. 2. Customized s are sent to users based on what products they viewed or display ads they clicked on. 3. As users open s, all data is sent back to GA via measurement protocol. 4. Remarketing based on activity.

37 3. Combining sales & marketing data 1. Integration between Google Analytics, marketing automation system and CRM. 2. Segmentation and remarketing in Google Analytics by lead status (data from CRM). 3. Depending on the lead status, the prospective student is sent very different messaging as part of Beckfield's lead nurturing efforts.

38 4. In-store and online data Website Visit Remarketing List Targeted Ad Incomplete Purchase In-Store Purchase In-Store Visit

39 Getting Started Analytics Premium Google Confidential and Proprietary 39

40 Consider:

41 Getting started Objectives Processes Commitment

42 Integration objective 1. What data flow do you want to accomplish? 2. How and where do you want to reach your audience? 3. What tools and processes are slowing you down?

43 Processes and workflows Clearly define how systems will be working together: 1. Once I send an to registered users, in GA I will see who opened it, and clicked on it. Both events will be recorded in GA. 2. When a user calls a number listed on the site, call center will list this as an activity in the CRM. Event will be sent to GA. Based on this activity, user will be placed on a remarketing list.

44 Ongoing commitment Even a perfect setup will degrade over time as your website, marketing, customer change. In order to trust your data, you must have ongoing processes and take ownership of data quality and integration.

45 Thank you Questions?