5. Google Confidential and Proprietary 5
Agenda
1 Web Analytics & Data Analysis
2 Data Analysis Example – PPHG Setup
3 Digital Marketing Model For Web Analysts
4 Takeaways
6. Google Confidential and Proprietary
3 Areas Of Web Analytics Expertise
6
Data Reporting
Data Capture
Data Analysis
8. Google Confidential and Proprietary
1 Web Analytics & Data Analysis
2 Data Analysis Example – PPHG Setup
3 Digital Marketing Model For Web Analysts
4 Takeaways
8
Agenda
9. Google Confidential and Proprietary
PPHG Web Analytics Overhaul
9
Can Web Analytics help to improve Occupancy rate, especially during off-peak
seasons?
Reports lack actionable insight
10. Google Confidential and Proprietary
Analysing The Problem – Aggregate Data
• Website & Ecommerce tracking was basic
• Profiles were tracking only at an aggregate and country level
• Not tracking loyal customers
10
11. Google Confidential and Proprietary
Actionable Insights – 3 Examples
• Track the usage of promotion codes during off-season periods
• Create profiles to track at a member, property, country and aggregate level
• Track the behaviour of each member type and their actions
11
12. Google Confidential and Proprietary
Implementation – Opportunities For Insights
• Implement tracking code to track promotion code type and usage
• Profile set-up with appropriate logic and filters
• Create Visitor-level Custom Variable to track each member type and their
actions
12
13. Google Confidential and Proprietary
Evaluation - Tracking Can Improve Occupancy
Rate
1. Identify decrease in booking activity for each member type and take action
2. Run promotions, especially during the off-season periods, and measure
each promotion’s effectiveness quickly and optimise accordingly
3. Made the value of each member segment measurable
4. Better understand the performance of each media channel for all objectives
5. Country Managers can now make sharp strategic decisions
13
15. Google Confidential and Proprietary
1 Web Analytics & Data Analysis
2 Data Analysis Example – PPHG Setup
3 Digital Marketing Model For Web Analysts
4 Takeaways
15
Agenda
16. Google Confidential and Proprietary
The Model
16
Business
Objectives
Website
Goals
KPIs
Targets
Data
Analysis
Fact-finding, understanding the business
Translating BOs into digital solutions
Goals (Metrics)
Targeted improvement (x% increase)
Segmentation (Traffic Acquisition,
Behaviour, Outcomes)
Provides clarity on what data to look at
19. Private & Confidential
We expect to bring the number of completed enquiries back to 9%
Objective – Generate Leads
Lack Of Follow Through after “Ask Me” Button Optimisation
• Overall Higher Conversion for “Ask Me”
• However Lower Submitted Enquiries
9%
7%
5. Micro Conversions 5. Micro Conversions
3. Macro Conversion (KPI)
1. Business Objective
2. Website Goal
4. Target
20. Private & Confidential
Objective – Increase Enquiries
Fixing The Leaky Pipe
• 3 Minor tweaks to address leaks
Ask Me Completions
Sales Lead Generation
(Business Enquiry)
1)
Lack
Of Assurance
2) Com
plicated
fields
2) Unrelated
fields
21. Private & Confidential
Design Guideline
3 Minor Tweaks
I. Reordering
II. Adding Reassurance
III. Simplifying Fields
Prior Proposed
22. Private & Confidential
Design Guidelines
I. Reordering II. Adding Reassurance III. Simplifying Fields
1. Reordering to reassure function of form.
Prior Proposed
23. Private & Confidential
Design Guidelines
I. Reordering II. Adding Reassurance III. Simplifying Fields
1. Prominent Titles
2. Text to convey information may be gained.
- eg. “Like to know more about…”
1
2
1
2
Prior Proposed
24. Private & Confidential
Design Guidelines
1
I. Reordering II. Adding Reassurance III. Simplifying Fields
1. Removal of Business Name Field
2. Removal of Subject Field
1
2
2
Prior Proposed
25. Private & Confidential
I. Reordering II. Adding Reassurance III. Simplifying Fields
Type of feedback to change to Type of enquiry.
Default Setting as “General Enquiry”.
Comments to change to Enquiry.
Are you a business owner shifted below.
Design Guidelines
1
2
3
4
1
2
3
4
2
1
3
4
Prior Proposed
26. Private & Confidential
Design Guidelines - Variations
Format Variations
• To test on Generic First
• Follow up on variations - (Require full listing)
Restaurants Hotel Automobile
28. Private & Confidential
The Model
28
Business
Objective
Website
Goal
KPI
Targets
Data
Analysis
Generate leads for businesses
Generate leads for businesses online
Submitted Enquiry
15% increase in macro conversions
Micro Conversions
29. Private & Confidential
1 Web Analytics & Data Analysis
2 Data Analysis Example – PPHG Setup
3 Digital Marketing Model For Web Analysts
4 Takeaways
29
Agenda
30. Google Confidential and Proprietary
Key Takeaways
• Web Analytics works best when business objectives and measurement
expectations are clearly defined in advance.
• Utilise the Data Analysis Process & Web Analytics Model for focus and
clarity.
• Focusing on Data analysis makes you a better web analyst
• Never be afraid to take action on your insights as long as it’s to achieve the
client’s business objectives.
30
I’m here as an interview candidate and this is my 2 nd interview. We’re having this sesssion because Angkea noticed that I’d done a Google Analytics 101 presentation for GA events in both Singapore and KL and hoped that it would be beneficial for the team.
Introduction – Let’s introduce ourselves before we kick this off. I’ll start. I was a web Analytics Lead at clickTRUE I have worked on different verticals and have hands-on regional experience across various Digital Marketing Disciplines including SEM, Display, Social Media, Mobile, EDM and Web Analytics
Analytics 101 is targeted towards offline marketers who are thinking of picking up Google Analytics, and you’re a digital marketing audience, I’ll be skipping a lot of stuff about the tool and talk more about Data Analysis and finding actionable insights. I don’t really know the level of experience here so I’m going to keep things very casual and interactive.
There are 3 areas of expertise in web analytics. Data Capture is the coding and implementation of web analytics tracking. Data Reporting is where we update clients on their performance and sometimes unfortunately this is where we end up data puking. Analysing data is the fundamental stage of creating actionable insights. The process of doing so helps us add value to clients, and also improve our value of work in data capture and data reporting.
There are 3 areas of expertise in web analytics. Data Capture is the coding and implementation of web analytics tracking. Data Reporting is where we update clients on their performance and sometimes unfortunately this is where we end up data puking. Analysing data is the fundamental stage of creating actionable insights. The process of doing so helps us add value to clients, and also improve our value of work in data capture and data reporting.
Pan Pac wanted to overhaul their existing Web Analytics implementation because they felt that existing reports were just not insightful enough. For example, part of their core business is maximising daily room occupation. Empty rooms like these, no matter how warm and homely, it negatively affects their bottomline. So one of the key questions was: Can Web Analytics help to improve Occupancy rate, especially during off-peak seasons?
So we noticed 3 significant problems with their existing setup. Web & Ecommerce tracking was basic. It was just the normal GATC implemented with the basic Ecommerce tracking codes placed at the thank you page of the booking journey. Also, profiles were only tracking at an aggregate and country level, so it was not as easy to measure an individual property’s performance Additionally, we found out from the client that a significant source of revenue were from loyalty program and alliance members, as well as agents. There was no web data available for these loyal customers and their behaviours.
So we came up with 3
So here are 3 of the implementations we decided on
With just 3 implementations, we are now able to: Identify periods when each member types make their booking, and take action Run specific promotions, especially during the off-season periods Measure the value of each member segment. Measure the effectiveness of the various promotions Helps the Country Manager make strategic decisions with comparable property data All this from as low as a property-level perspective and upwards.
Data Analysis is a challenging area of expertise because of all the potential roadblocks, possibilities and data overload. On top of that, we are expected to provide actionable insights that clients appreciate. Especially when there‘s a lack of clarity on objectives, we will tend to feel this way Over the next few minutes, I‘ll share with you an example of analysis work I did for a client and hopefully you‘ll get some insights from it.