My MeasureCamp presentation covering how I've used the beta version of Universal Analytics and Measurement Protocol to extend the e-commerce analytics functionality of GA.
The tools used by the CRO masters round the world to optimise analytics, UX, VOC,insight and testing - all to optimise your insight or conversion figures.
MeasureCamp - Returns Management System HacksMatt Clarke
A MeasureCamp presentation I didn't get time to cover which explains how we used some Google Analytics hacks to analyse e-commerce Returns Management System trends.
Why Does My Conversion Rate Suck? Craig Sullivan, Senior Optimisation Consult...PRWD
Craig Sullivan, Senior Optimisation Consultant covers the top 10 reasons why your conversion rate might suck. Packed with actionable tips and resources this presentation is for anyone wanting to improve their Conversion Optimisation. Craig covers common problems and topic areas such as issues with Google Analytics setup, inputs, tools, testing, testing cycles, product cycles, Photo UX, how to analyse statistics / Data, segmentation, multiple channel optimisation. The resource pack also include a maturity model, Crowd-sourced UX, collaborative tools, testing tools for CRO & QA, Belron Methodology example, and CRO and testing resources.
The tools used by the CRO masters round the world to optimise analytics, UX, VOC,insight and testing - all to optimise your insight or conversion figures.
MeasureCamp - Returns Management System HacksMatt Clarke
A MeasureCamp presentation I didn't get time to cover which explains how we used some Google Analytics hacks to analyse e-commerce Returns Management System trends.
Why Does My Conversion Rate Suck? Craig Sullivan, Senior Optimisation Consult...PRWD
Craig Sullivan, Senior Optimisation Consultant covers the top 10 reasons why your conversion rate might suck. Packed with actionable tips and resources this presentation is for anyone wanting to improve their Conversion Optimisation. Craig covers common problems and topic areas such as issues with Google Analytics setup, inputs, tools, testing, testing cycles, product cycles, Photo UX, how to analyse statistics / Data, segmentation, multiple channel optimisation. The resource pack also include a maturity model, Crowd-sourced UX, collaborative tools, testing tools for CRO & QA, Belron Methodology example, and CRO and testing resources.
My Measurecamp slides from my presentation.
Will also be writing up a blog post covering this in more details and will post updates here and in the g+ community
User-Centric Analytics (MeasureCamp Talk)Taste Medio
Why and how to focus on users, not cookies. How to identify and track users across multiple devices and data sources. Why GA User-Id sucks and how to handle that. Using tools like Identity Aggregator. Slides from MeasureCamp London 2015 talk.
Basic information on Customer Lifetime Value models.
- Demything frequent doubts with CLV.
- You can not calculate CLV in Google Analytics.
- First steps and outputs that you have to prepare when thinking about CLV.
- Presentation of possible outputs a CLV model can give you.
- Discussion on early estimation of CLV using cohort analysis and simple models to understand what interactions lead to a success.
The presentation was prepared in the pub White Swan for MeasureCamp London, March, 13, 2015.
Voxxed Days Cluj - Powering interactive data analysis with Google BigQueryMárton Kodok
Every company,
no matter how far from the tech they are,
is evolving into a software company,
and by extension a data company.
For a small company it’s important
to have access to modern BigData tools
without running a dedicated team for it.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQueryMárton Kodok
Every scientist who needs big data analytics to save millions of lives should have that power. Powering Interactive Data Analysis require massive architecture, and know-how to build a fast real-time computing system. You will learn how Google BigQuery solves this problem by enabling super-fast, SQL queries against petabytes of data using the processing power of Google’s infrastructure. After this session you will be able to work with BigQuery, do streaming inserts, write User Defined Functions in Javascript, and several use cases for everyday developer: funnel analytics, behavioral analytics, exploring unstructured data. You will be able to run arbitrary queries on open-data such as historical data about Github commits, Stackoverflow Q&A data, or analysing Reddit comments to find out books the community talks about.
Machine Learning is transforming every industry with innovative techniques receiving deserved attention. But turning innovation into value requires integrating into practical technology products, often with the leadership of product managers. We'll talk about how to help your friendly neighborhood Product Owner: identify where ML can make a difference, develop metrics to validate and refine it, identify data to feed it, prioritize work to develop it, and structure teams to deliver it in a satisfying way.
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
How to Build Enterprise AI Products by fmr Google Product LeaderProduct School
Key takeaways:
-Understand the current market for enterprise AI products.
-Learn about the framework for designing enterprise AI products.
-Be able to capture requirements for complicated AI use cases
BrightonSEO October 2022 - Dan Taylor SEO - Indexing Ecommerce WebsitesDan Taylor
The thing is, we’re hitting an indexing ceiling.
The internet advanced at a quick rate as it had all of human history to be written about and featured; now aside from some small niches and edge cases, we’re regurgitating the same content, the same “how to” guides, and the same products and information.
My Measurecamp slides from my presentation.
Will also be writing up a blog post covering this in more details and will post updates here and in the g+ community
User-Centric Analytics (MeasureCamp Talk)Taste Medio
Why and how to focus on users, not cookies. How to identify and track users across multiple devices and data sources. Why GA User-Id sucks and how to handle that. Using tools like Identity Aggregator. Slides from MeasureCamp London 2015 talk.
Basic information on Customer Lifetime Value models.
- Demything frequent doubts with CLV.
- You can not calculate CLV in Google Analytics.
- First steps and outputs that you have to prepare when thinking about CLV.
- Presentation of possible outputs a CLV model can give you.
- Discussion on early estimation of CLV using cohort analysis and simple models to understand what interactions lead to a success.
The presentation was prepared in the pub White Swan for MeasureCamp London, March, 13, 2015.
Voxxed Days Cluj - Powering interactive data analysis with Google BigQueryMárton Kodok
Every company,
no matter how far from the tech they are,
is evolving into a software company,
and by extension a data company.
For a small company it’s important
to have access to modern BigData tools
without running a dedicated team for it.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQueryMárton Kodok
Every scientist who needs big data analytics to save millions of lives should have that power. Powering Interactive Data Analysis require massive architecture, and know-how to build a fast real-time computing system. You will learn how Google BigQuery solves this problem by enabling super-fast, SQL queries against petabytes of data using the processing power of Google’s infrastructure. After this session you will be able to work with BigQuery, do streaming inserts, write User Defined Functions in Javascript, and several use cases for everyday developer: funnel analytics, behavioral analytics, exploring unstructured data. You will be able to run arbitrary queries on open-data such as historical data about Github commits, Stackoverflow Q&A data, or analysing Reddit comments to find out books the community talks about.
Machine Learning is transforming every industry with innovative techniques receiving deserved attention. But turning innovation into value requires integrating into practical technology products, often with the leadership of product managers. We'll talk about how to help your friendly neighborhood Product Owner: identify where ML can make a difference, develop metrics to validate and refine it, identify data to feed it, prioritize work to develop it, and structure teams to deliver it in a satisfying way.
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
How to Build Enterprise AI Products by fmr Google Product LeaderProduct School
Key takeaways:
-Understand the current market for enterprise AI products.
-Learn about the framework for designing enterprise AI products.
-Be able to capture requirements for complicated AI use cases
BrightonSEO October 2022 - Dan Taylor SEO - Indexing Ecommerce WebsitesDan Taylor
The thing is, we’re hitting an indexing ceiling.
The internet advanced at a quick rate as it had all of human history to be written about and featured; now aside from some small niches and edge cases, we’re regurgitating the same content, the same “how to” guides, and the same products and information.
Four Key Considerations for your Big Data Analytics StrategyArcadia Data
Learn 4 of the key things to consider as you create your big data analytics strategy from John Meyers (Enterprise Management Associates) and Steve Wooledge (Arcadia Data).
SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS WorldSimo Ahava
Slides from my talk at the SuperWeek analytics conference. The focus was on organization transformation necessary to improve data quality, especially when using a tag management solution like Google Tag Manager.
Predictive Analytics - Big Data Warehousing MeetupCaserta
Predictive analytics has always been about the future, and the age of big data has made that future an increasingly dynamic place, filled with opportunity and risk.
The evolution of advanced analytics technologies and the continual development of new analytical methodologies can help to optimize financial results, enable systems and services based on machine learning, obviate or mitigate fraud and reduce cybersecurity risks, among many other things.
Caserta Concepts, Zementis, and guest speaker from FICO presented the strategies, technologies and use cases driving predictive analytics in a big data environment.
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
Jewelry has been a really unstructuredsomewhat chaotic with resp.docxvrickens
Jewelry has been a really unstructuredsomewhat chaotic with respect to in innovation. and also had sSome advancements in technology have been leverage throughout these past years, but innovative new technologies are not leverage nearly as regularly as in other industries. In the past, it was thejewelry businesses were used to passed on and run through the generations and family properties or entities. However, with the But now when emergence of technological advancesy emerged and introduce itself with different numerous businesseswithin the parallel industries, the jewelry business is finally incorporating innovations to boost production, sales, and profitability. it also develop some new surprising technologies for the jewelry business too, which really changed this stereotype in this business. This technological stigma has slese restrictions slowly but surely has worn off, and allowing jewelry firms showing the versatility of having an independence not seen in the days of the t nature of business with the unrestricted family run and bound and heritage business culture. The latest inventions in this business industry, such aslike the solution for the identification all items in the supply chain leveraging radio frequency identification (of RRFID) solution for the jewelry makes the better logistics management system and also the nearly effortless in the tracking system of valuable items. Another mature innovation invention for the for jewelry business areis the highly successful upcoming E-business through the ecommerce websites and applications for mobile devices. These applications are the fastest and most efficient way of selling the items to the customer. They increase profits through both accessibility of inventory for purchase online as well as reducing or eliminating the need for brick and mortar stores.It refrains the security threat and also the idea of touching the jewelry makes it dark or something like these dilemma. These are the real tides turning changes in this industry.Comment by Tim Stewart: Nusrat, the beginning of this sentence needs some clarity. If you are talking about the production of fine jewelry, then start your sentence, “The mining and manufacturing of fine jewels and jewelry…”
If you mean the selling, then you can just say, “The Jewelry business…”
There are several data analytic products which helps the user to verify the cut, quality, and clarity of the items they desire. One the data for the jewelry specific area of expertise exploitation which includes IRYS trinity. This tool do performsthe data analytics on the jewelry business in the area of the product viewership, staff performance analysis, inventory stock audit, and many more (Business Analytics for Jewellery - Irys Pte. Ltd.: Jewellery Analytics Software, Jewelry Business Intelligence Tool, n.d.).Comment by Tim Stewart: Best practice is to spell out the acronym and then put it in parentheses as I did above with RFID.
Over the couple of yearsRece ...
The talk will describe the results we got by adopting MongoDB in key areas of our business. Backcountry.com is a midsize company in constant evolution looking for growth in a extremely competitive ecosystem. We try to be agile and target fast prototyping and data-based decisions. Our dev stack used to heavily rely on Postgres and Oracle, but in a short period of time we were able to introduce MongoDB in a key set of applications and we've seen positive results. We're less dependent on monolithic applications and we're progressively moving to Microservices. By choosing MongoDB as one of our main technologies, our dev teams became more productive as well as mature. They see beyond relational approaches and explore more options to tackle different problems.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
4. What’s the conversion
rate for product X?
You can’t easily determine the conversion rate
for products, categories or brands to monitor
trends or optimise them.You might be able to
do it externally with API hacks, but it’s not easy...
@techpad
Sunday, 17 February 13
5. How much stock of
brand Y did we sell?
@techpad
Sunday, 17 February 13
6. How much stock of
brand Y did we sell?
Oddly, brand isn’t a standard dimension. Reporting
on brand performance requires manual custom
reports or dashboards.
@techpad
Sunday, 17 February 13
7. How profitable is that
Adwords campaign?
@techpad
Sunday, 17 February 13
8. How profitable is that
Adwords campaign?
Google Analytics doesn’t consider Cost Of Goods
Sold (COGS), so to calculate profit, you need to do
it all externally. There’s no cost import option for
product costs yet.
@techpad
Sunday, 17 February 13
9. What’s the average
margin for that
category?
@techpad
Sunday, 17 February 13
10. What’s the average
margin for that
category?
Similarly, because there are no cost data for product
costs, you can’t easily monitor margin trends...
@techpad
Sunday, 17 February 13
11. When we increase the
price of product X,
what proportion of
customers buy, defect
or switch brands?
@techpad
Sunday, 17 February 13
12. When we increase the
price of product X,
what proportion of
customers buy, defect
or switch brands?
Price Elasticity of Demand analysis would be nice, as
would metrics on latency to indicate which of the
customers we acquired are still customers.
@techpad
Sunday, 17 February 13
13. Google could fix some
of this for us now...
• If you could import product cost data, you
could see product profit and margin, and
the actual ROI/profitability of ads.
• If you could connect Google Merchant
Center data to GA you could get product
conversion rate, brand performance data
and new variables to use as custom
segments. (You can do it with the API.)
@techpad
Sunday, 17 February 13
14. That’s unlikely. But Universal
Analytics might help...
• You can create custom dimensions for
product, brand, manufacturer and supplier
to improve reports and aid analysis.
• You can create custom metrics for product
cost, profit, margin and shipping costs.
• It’s in beta and not everything works
properly (or at all) yet. It has limitations, but
it’s still a big step forward.
@techpad
Sunday, 17 February 13
16. Measurement Protocol and
Universal Analytics
• I opted to use a combination of the client-
side Universal Analytics (analytics.js) and
the server-side Measurement Protocol.
• Using Measurement Protocol allows me to
bypass the event limits of Universal
Analytics and to send data (such as profit
and product cost) that I don’t want to
appear in the client-side JavaScript code.
@techpad
Sunday, 17 February 13
17. Server-side data layer
• To ease data access, I created a server-side
data layer containing data objects for page,
product, customer and transaction.
• I push data to the client-side code from the
(private) server-side data layer.
• If I later decide to move to Google Tag
Manager, or even switch analytics vendors,
it’s a relatively easy task.
@techpad
Sunday, 17 February 13
18. How it looks...
Web analytics
Database extension
Page object Customer object Order object Product object
Measurement
Data layer
Protocol and UA
@techpad
Sunday, 17 February 13
19. How it works
Web analytics
Data layer Page type?
extension
Order page E-commerce tracking,
Product page Other page
plus product, supplier,
manufacturer custom
dimensions. Plus...
Sensitive stuff I don’t
want to be visible in the
The usual, plus product, Universal Measurement client-side source code,
supplier, manufacturer Analytics Protocol ie. profit and margin.
custom dimensions to
connect at order page...
@techpad
Sunday, 17 February 13
21. The basics of UA
• Uses analytics.js instead of ga.js and
replaces all the old GA cookies with _ga.
• Has a totally new syntax, but all the good
stuff remains - including events.
• Custom dimensions and metrics replace
the old custom variables.
• Will support user ID (but doesn’t yet).
@techpad
Sunday, 17 February 13
22. How I’ve used it
• On the product page, I’m firing custom
dimensions for: product, brand,
manufacturer, supplier, author and product
cohort.
• I’m hoping to connect these with data fired
on the payment complete page (attached to
transaction/item) to allow me to get data
on stuff bog standard GA can’t measure.
@techpad
Sunday, 17 February 13
23. An overview of custom
dimensions and metrics
• First, define your custom dimension/metric
in the Google Analytics UI.
• Send your CD/CM with another data type,
such as a pageview or an event.
• ga(‘send’,‘pageview’,{dimension1:‘Apple iPad
3’});
• You can combine several together.
@techpad
Sunday, 17 February 13
25. The basics of MP
• A server-side way of sending GA data (like
PHP-GA), based on a simple URL system.
You send a payload to an HTTP endpoint.
• Sending data with an HTTP POST returns a
200 OK. (Sadly, even if it’s malformed...)
• There’s currently no client library, so you’ll
need to code your own. However, this is
quite straightforward with PHP and Curl.
@techpad
Sunday, 17 February 13
26. How I’m using it
• Used on payment page to send data I want
to hide from anyone viewing the source.
• I want to supplement the e-commerce
tracking with extra custom dimensions and
metrics, such as product, brand, supplier,
manufacturer, profit and margin. *
• I’m joining these to UA dimensions fired on
the product pages attached to pageviews.
@techpad
Sunday, 17 February 13
27. What do I send?
• Latency - days since last purchase (CM)
• Customer cohort (event)
• Days as customer (event)
• Lifetime spend (event)
• Carrier (CD)
• Newsletter preferences (CD)
@techpad
Sunday, 17 February 13
28. What do I send?
• Product (CD)
• Brand (CD)
• Manufacturer (CD)
• Supplier (CD)
• Product author (CD)
• Product cohort (CD)
@techpad
Sunday, 17 February 13
29. What do I send?
• Product cost (CM)
• Product margin (CM)
• Product profit (CM)
• Shipping cost (CM)
• However, the CMs and CDs attached to
the item and transaction don’t work yet.
@techpad
Sunday, 17 February 13
30. Using Measurement
Protocol alongside
Universal Analytics
@techpad
Sunday, 17 February 13
31. Using Measurement Protocol
with Universal Analytics
• Measurement Protocol and UA use a
visitor ID called the client ID (or cid).
• This isn’t to be confused with the user ID.
(That doesn’t exist yet.)
• If you want to use Measurement Protocol
and UA together, you need to use the same
client ID. Otherwise GA won’t connect the
data to a given visitor.
@techpad
Sunday, 17 February 13
32. 32-bit IDs vs UUID v4
• Universal Analytics (ga.js) cookies use the
old-style 32-bit UUIDs for compatibility
with the current version of GA.
• The docs (and GA team) recommend using
UUID v4 for client ID, but (undocumented)
it can also support 32-bit IDs.
• If you want to use both together, you need
to use the _ga 32-bit ID, not UUID v4.
@techpad
Sunday, 17 February 13
33. Obtaining the 32-bit ID
• Parse the new _ga cookie to obtain a given
visitor’s 32-bit ID to send with MP data.
• Example: 1.2.123456789.9876543219876
• Extract the last two items from the cookie
and use that as your 32-bit ID.
• I’ve written some cookie parsing code. Get
a copy from here: http://goo.gl/HgCew
@techpad
Sunday, 17 February 13
35. Measurement Protocol and
Universal Analytics are amazing
• They open up new possibilities for tracking.
• They should help improve analysis.
• They’re better integrated than CVs.
• You get 20 CDs and 20 CMs.
• The new user ID (when supported) should
make even more cool stuff viable.
@techpad
Sunday, 17 February 13
36. But there are a few issues...
• You can’t send custom dimensions and
metrics with either a transaction or a
transaction item yet. However, the GA team
is planning to add support, which is great.
• At the moment, custom metrics can’t
include a decimal point. Why? (Consider
rounding and multiplying, extracting in API,
then dividing to get the original value.)
@techpad
Sunday, 17 February 13
37. Tips
• Using a data layer means you can use data
in Measurement Protocol and Universal
Analytics easily and keeps things tidier.
• Use Measurement Protocol to send stuff
you don’t want visible in the JS code.
• Parse the 32-bit ID from the _ga cookie
and use it as the Measurement Protocol
client ID, if using both together.
@techpad
Sunday, 17 February 13