Google Data » Aditi Rajaram

Rooms To Go Improves the Shopper Experience by Integrating Google Analytics Premium with BigQuery

Aditi Rajaram — Tue, 15 Jul 2014 17:56:00 +0000

Rooms To Go, a home furnishing retailer, simplifies the shopping experience by offering completely designed room packages. When the company wanted to better understand how its customers purchase its different furniture and decor variations and add-ons to streamline online customization options, it turned to its agency - LunaMetrics - who integrated Google Analytics Premium and BigQuery. This approach helped to identify which items customers commonly buy together, leading to smarter and easier customization for its users.

The Google Analytics Premium integration allowed Rooms To Go to:

Better understand what their site visitors were purchasing
Organize the data and isolate the products that were frequently purchased together in order to discover customer buying patterns
Expand functionalities of the website to accommodate these customer patterns—for example, making it easier for users to add extra dining chairs when purchasing a dining room set

Overall, this strategy helped Rooms To Go create a better user experience for its customers, and the company expects an increase in sales because of it. Read the full case study on Think with Google, and learn about being a Google Analytics Premium customer here.

Join us for a conversation on the future of digital advertising

Aditi Rajaram — Tue, 13 May 2014 21:30:00 +0000

Digital is no longer just another marketing channel. It’s a medium that’s changing the art of storytelling and reinventing the way companies connect with consumers.

How will digital advertising evolve next? You’re invited to tune in live as industry leaders tackle the question at DoubleClick’s annual event on digital trends.

Speakers include:

Jeffrey Katzenberg, CEO of DreamWorks Animation
Nikesh Arora, Chief Business Officer at Google
Neal Mohan, VP of Display Advertising at Google

Wednesday, June 4

9:30 am PDT - 10:45 am PDT / 12:30 pm EDT - 1:45 pm EDT

Time not convenient? When you register you’ll receive a link to watch the recording on demand.

We hope you can join!

Here’s footage from last year’s event to give you a taste of the 2014 live stream:

Posted by Aditi Rajaram, Google Analytics team

New user and sequence based segments in the Core Reporting API

Aditi Rajaram — Fri, 11 Apr 2014 17:30:00 +0000

Segmentation is one of the most powerful analysis techniques in Google Analytics. It’s core to understanding your users, and allows you to make better marketing decisions. Using segmentation, you can uncover new insights such as:

How loyalty impacts content consumption
How search terms vary by region
How conversion rates differ across demographics

Last year, we announced a new version of segments that included a number of new features.

Today, we’ve added this powerful functionality to the Google Analytics Core Reporting API. Here's an overview of the new capabilities we added:

User Segmentation

Previously, advanced segments were solely based on sessions. With the new functionality in the API, you can now define user-based segments to answer questions like “How many users had more than $1,000 in revenue across all transactions in the date range?”

Example: &segment=users::condition::ga:transactionRevenue>1000

Try it in the Query Explorer.

Sequence-based Segments

Sequence-based segments provide an easy way to segment users based on a series of interactions. With the API, you can now define segments to answer questions like “How many users started at page 1, then later, in a different session, made a transaction?”

Example: segment=users::sequence::ga:pagePath==/shop/search;->>perHit::ga:transactionRevenue>10

Try it in the Query Explorer.

New Operators

To simplify building segments, we added a bunch of new operators to simplify filtering on dimensions whose values are numbers, and limiting metric values within ranges. Additionally, we updated segment definitions in the Management API segments collection.

Partner Solutions

Padicode, one of our Google Analytics Technology Partners, used the new sequence-based segments API feature in their funnel analysis product they call PadiTrack.

PadiTrack allows Google Analytics customers to create ad-hoc funnels to identify user flow bottlenecks. By fixing these bottlenecks, customers can improve performance, and increase overall conversion rate.

The tool is easy to use and allows customers to define an ad-hoc sequence of steps. The tool uses the Google Analytics API to report how many users completed, or abandoned, each step.

Funnel Analysis Report in PadiTrack

According to Claudiu Murariu, founder of Padicode, “For us, the new API has opened the gates for advanced reporting outside the Google Analytics interface. The ability to be able to do a quick query and find out how many people added a product to the shopping cart and at a later time purchased the products, allows managers, analysts and marketers to easily understand completion and abandonment rates. Now, analysis is about people and not abstract terms such as visits.”

The PadiTrack conversion funnel analysis tool is free to use. Learn more about PadiTrack on their website.

Resources

Read the segmentation conceptual guide to learn how these new segments work.
Get started with the segments developer guide.
Understand the richness of the segments language in the reference guide.

We’re looking forward to seeing what people build using this powerful new functionality.

Posted by Nick Mihailovski, Product Manager, Google Analytics team

Sending data from Lantronix to Google Analytics

Aditi Rajaram — Fri, 28 Mar 2014 16:33:00 +0000

The following is a guest post from Kurt Busch, CEO, and Mariano Goluboff, Principal Field Applications Engineer at Lantronix.

Background

Google Analytics makes it easy to create custom dashboards to present data in the format that most helps to drive business processes. We’ve put together a solution that will make several of our devices (networking and remote access devices) easily configurable to enable delivery of end device data to Google Analytics. We use the Lantronix PremierWave family of devices to connect to an end device via a serial port like RS-232/485, or Ethernet, intelligently extract useful data, and send it to Google Analytics for use in M2M applications.

What you need

To get started, grab the Pyserial module, and load it on your Lantronix PremierWave XC HSPA+. You’ll also want a device with a serial port that sends data you want to connect to Google Analytics. A digital scale like the 349KLX is a good choice.

Architecture overview

With the Measurement Protocol, part of Universal Analytics, it is now possible to connect data from more than web browsers to Analytics.

Lantronix integrated the Measurement Protocol by using an easy to deploy Python script. By being able to natively execute Python on PremierWave and xSenso devices, Lantronix makes it very easy to deploy intelligent applications leveraging Python’s ease of programming and extensive libraries.

The demonstration consists of a scale with an RS-232 output, connected to a Lantronix PremierWave XC HSPA+. The Python script running on the PremierWave XC HSPA+ parses the data from the scale, and sends the weight received to Google Analytics, where it can then be displayed.

The hardware setup is show in the picture below.

The technical details

The Python program demonstrated by Lantronix uses the Pyserial module to parse this data. The serial port is easily initialized with Pyserial:

class ser349klx:

# setup the serial port. Pass the device as '/dev/ttyS1' or '/dev/ttyS2' for

# serial port 1 and 2 (respectively) in PremierWave EN or XC HSPA+

def __init__(self, device, weight, ga):

while True:

try:

serstat = True

ser = serial.Serial(device,2400, interCharTimeout=0.2, timeout=1)

except Exception:

serstat = False

if serstat:

break

self.ser = ser

self.weight = weight

self.ga = ga

The scale used constantly sends the current weight via the RS-232 port, with each value separated by a carriage return:

def receive_line(self):

buffer = ''

while True:

buffer = buffer + self.ser.read(self.ser.inWaiting())

if '\r' in buffer:

lines = buffer.split('\r')

return lines[-2]

The code that finds a new weight is called from a loop, which then waits for 10 equal non-zero values to wait for the weight to settle before sending it to Google Analytics, as shown below:

# This runs a continuous loop listening for lines coming from the

# serial port and processing them.

def getData(self):

count = 0

prev = 0.0

#print self.ser.interCharTimeout

while True:

time.sleep(0.1)

try:

val = self.receive_line()

weight.value=float(val[-5:])*0.166

if (prev == weight.value):

count += 1

if (count == 10) and (str(prev) != '0.0'):

self.ga.send("{:.2f}".format(prev))

else:

count = 0

prev = weight.value

except Exception:

pass

Since the Google Analytics Measurement Protocol uses standard HTTP requests to send data from devices other than web browsers, the ga.send method is easily implemented using the Python urllib and urllib2 modules, as seen below:

class gaConnect:

def __init__(self, tracking, mac):

self.tracking = tracking

self.mac = mac

def send(self, data):

values = { 'v' : '1',

'tid' : self.tracking,

'cid' : self.mac,

't' : 'event',

'ec' : 'scale',

'ea' : 'weight',

'el' : data }

res = urllib2.urlopen(urllib2.Request("http://www.google-analytics.com/collect", urllib.urlencode(values)))

The last piece is to initialize get a Google Analytics connect object to connect to the user’s Analytics account:

ga = gaConnect("UA-XXXX-Y", dev.mac)

The MAC address of the PremierWave device is used to send unique information from each device.

Results

With these pieces put together, it’s quick and easy to get data from the device to Google Analytics, and then use the extensive custom reporting and modeling that is available to view the data. For example, see the screenshot below of real-time events:

Using Lantronix hardware, you can connect your serial devices or analog sensors to the network via Ethernet, Wi-Fi, or Cellular. Using Python and the Google Analytics Measurement Protocol, the data can be quickly and easily added to your custom Google Analytics reports and dashboards for use in business intelligence and reporting.

Posted by Aditi Rajaram, the Google Analytics team

Storytelling with data using Measureful and Google Analytics

Aditi Rajaram — Fri, 07 Mar 2014 18:08:00 +0000

The following is a guest post from John Koenig, CEO at Measureful.

The democratization of data within organizations over the last few years has put data even more under the purview of marketers. This shift has created a necessary discipline in digital intelligence: data storytelling. Data storytelling strives to create a clear, more meaningful picture of complex metrics through effective storytelling techniques.

Combining Measurable and Google Analytics brings together a powerful measurement and presentation tool to help quantify efforts and present a compelling case. Google Analytics is the vehicle for discovering stories, while Measureful brings these stories to life.

A Beginning, Middle and End

A top down, linear approach following these 3 steps helps keep your marketing reports focused and your audience tuned in.

1. What happened?

If you’ve built even a basic Google Analytics strategy, you’ll have already identified your objectives or KPIs (key performance indicators). Start each report by covering these first. Be short and concise with KPIs and focus on basic performance to set the tone for the rest of the report.

These are most often a conversion event such as revenue or a user-defined goal such as a new lead. This is one portion of your report that should be fairly static. Objectives generally don’t change frequently and thus other portions of your report should roll up to these. The narrative of your report will largely be focused around explaining changes to this key group of metrics.

2. Why and what caused it?

This is where most reports fall into trouble. Even if you have access to large amounts of data and reports, it doesn’t mean you need to use all of it. The reality is you only have the attention of your audience for a small amount of time so be selective, focus on bringing together cohesive points, and leave everything else out.

This means your reports should be dynamic and change each month. That’s right, your reports should change. If they aren’t changing you’re not telling a story, you’re regurgitating data.

Focus on identify 2 to 3 subtle narratives to focus on but do not bypass exploratory and quantitative analysis. You still need to begin each period analyzing changes and interpreting data to determine the most effective points. This is analysis work, but if you’ve set up a strategy, this doesn’t have to be time-consuming or overly complex.

I suggest looking at 3 areas to help build your storylines -

1. Attribution

2. Campaigns

3. Outliers

If Revenue (your KPI) increased last period, drill into theAll Traffic reporint in Google Analytics and begin to attribute why this change occurred. It is not importatnt to report on every segment and dimension but instead focus on why this change occurred.

This is also the portion where you can outline any specific campaigns that were run during the period and include metrics specific to these and their performance.

Lastly, look for outliers. While these may not be immediately apparent, both Measureful and Google Analytics provide tools for helping with these. In Google Analytics, set up rules in Intelligence Events. With Measureful, use the Smart Reporting feature. This works similar to Intelligence Events, but runs automatically and covers trends for many different segments and time-periods. Turn it on and let it help you identify unique stories in your Google Analytics data.

3. What’s next?

Give your story an ending by reiterating your points, making recommendations and covering next steps. This is where you can push your agenda, ask for more budget or suggest some new strategy or tactics.

Storytelling in Practice

Gerber relies on a sophisticated measurement strategy using advanced Google Analytics features to quantify marketing efforts and drive campaign decisions. John Robbins is the Digital Marketing Manager at GerberGear.com and is responsible for a myriad of digital channels and campaigns and is expected to report on performance.

John leverages both Google Analytics and Measureful to help keep the whole team easily informed and knowledgeable of key findings and changes.

Tying it all together with Measureful

With analytics data in place, the linear approach is easily applied and the Gerber Monthly Marketing Report built using Measureful’s WYSIWYG editor.

For example, Gerber’s top-line of metrics were setup to provide a quick view of performance for the month while two over-time visualizations were add for context. Measureful’s reporting platform includes automated narratives with analysis on performance versus the previous month, year and compared to the 12-month average.

After a bit of analysis, it’s clear that a few channels performed very well and thus the focus of the reports begin to take shape around these narratives. While Gerber’s digital strategy goes well beyond the contents of this particular report, it's most effective to report on the metrics that are important to business objectives. Measureful helps Gerber focus a report on the key take-aways and points and thus steer an audience’s attention to what’s most critical.

And finally, it’s helpful to end a report with clear points and next steps.

Gerber went from long and time-consuming marketing reports that were often overlooked to a 4-page, focused report that drives home the main points in their marketing and analytics strategies.

Data storytelling is an essential skill to effectively cross the chasm of understanding and ultimately action. Charts and tables do not necessarily mean you’ve done a good job of communicating important findings. Meausureful can help weave Google Analytics data into a coherent narrative, and turn your data into a powerful communication tool.

Posted by Aditi Rajaram, The Google Analytics team

Using Universal Analytics to Measure Movement

Aditi Rajaram — Fri, 13 Dec 2013 18:08:00 +0000

The following is a guest post by Benjamin Mangold, Director of Digital & Analytics at Loves Data, a Google Analytics Certified Partner.

Universal Analytics includes new JavaScript tracking code for websites and new mobile SDKs. But Universal Analytics is a lot more than that - it also gives us the Measurement Protocol, which allows us to send data to Google Analytics without the need to use the tracking code or SDKs.

Earlier this year, the team at Loves Data used Universal Analytics and the Measurement Protocol to measure their caffeine consumption and tie it to the team’s productivity. Our next challenge: measuring our team’s movement into Google Analytics. With the help of an Xbox Kinect, movement recognition software, and of course the Measurement Protocol, we started getting creative!

Business Applications and Analysis Opportunities

So measuring movement is fun and although we can measure total and unique dance moves you might be wondering about the business applications. This is where the power of measuring offline interactions can really start to be seen. The Measurement Protocol enables business applications such as:

Measuring in-store purchases and tying purchases to your online data
Understanding behaviour across any connected device, including gaming consoles
Comparing offline billboard impressions to online display ad impressions
Getting insights into your audience’s online to offline journey

Once you have tied your online and offline data together you can begin to analyze the full impact of your different touch points. For example, if you are collecting contact details online, you can use Google Analytics to then understand who actually converts offline, whether this conversion is attending an information session or making a purchase at a cash register. The analysis possibilities made available by the Measurement Protocol are truly amazing.

New Secondary Dimensions Provides Deeper Insights Into Your Users

Aditi Rajaram — Thu, 21 Nov 2013 18:38:00 +0000

Today we’ve added many new secondary dimensions to standard reports, including the much-asked for Custom Dimensions.

Custom Dimensions is a new Universal Analytics feature that allows you to bring custom business data into Google Analytics. For example, a custom dimension can be used to collect friendly page names, whether the user is logged in, or a user tier (like Gold, Platinum, or Diamond).

By using Custom Dimensions in secondary dimensions, you can now refine standard reports to obtain deeper insights.

In the report above, Direct Traffic delivers the most traffic, but these are Gold users (lower value). At the same time, Google Search delivers the third and fourth most site traffic and these are Diamond users (high value). Therefore, data shows this site should continue to invest in Google Search to attract more high value users.

The new data in secondary dimensions gives analysts a powerful new tool. We’d love to hear about any new insights in the comments.

Posted by Nick Mihailovski, Product Manager, Google Analytics API team

The New Dimensions and Metrics Explorer

Aditi Rajaram — Fri, 01 Nov 2013 18:29:00 +0000

To make it easy to navigate all the 270+ data points the Core Reporting API exposes, today we launched version 2 of the Dimensions and Metrics Explorer.

Click to see the list view.

This tool makes it easy to browse all of the dimensions and metrics, identify valid combinations, and get comprehensive definitions and descriptions.

Click to see the description view.

No wonder it’s the third most visited page on the Analytics Developer site.

Today’s update to the tool added:

Modes - easily see how API names map to Web View Names, and App View Names.
‘Allowed in Segments’ - Quickly see which data can be used in segments.
Updated descriptions- See more details like data type, index ranges, UI names, deprecation status, calculations. For example, see the Custom Variables and Columns group.
It’s fast! - No more page loads as you browse and switch between modes.
Deep links - Share details of a specific dimension or metric by copying the URL of any view.
Automatic Updates - The Metadata API is now used to power the Dimensions and Metrics Explorer. This is a big change and means the tool will automatically update with the latest dimensions and metrics as soon as they’re released.

Visit the Dimensions and Metrics Explorer and tell us what you think.

This tool is built completely using the Metadata API. If you’re thinking about developing your own tools with this data, get started here!

Posted by Pete Frisella, Developer Advocate, Developer Relations Team

Measuring Twitter with Universal Analytics

Aditi Rajaram — Wed, 30 Oct 2013 19:24:00 +0000

In between rolling out new features for Google Analytics, we also like to feature how users and companies are actually using our products. Matt Stannard of 4PS Marketing details how to easily measure Twitter using Universal Analytics. We’ve excerpted parts of his post below, read on to see the results, and don’t forget to click through to see the technical details!

How?

Step 1 - Create a new account

First, we need to create a new account, which is accomplished easily through the

Step 2 - Install PHP / MySQL

I downloaded a WAMP stack called XAMPP as I wanted to use PHP as my Twitter monitoring library. XAMPP includes Apache, PHP and MySQL. You can use any tool of your choose provided you are able to edit the code and add the necessary Measurement Protocol requests. The library I used is was from 140Dev.

Step 3 - Create Twitter Application

In order to use the PHP monitoring library you need to have a Twitter Application. You can create this by signing in at https://dev.twitter.com/. Click My Applications:

Create your application and after you've done this you will need to note the Consumer Key, Consumer Secret, Access Token, Access Token Secret.

Step 4 - Start Monitoring

So, now we've got our Twitter application we can begin monitoring, in the 140dev package you need to modify a few files, firstly the db_config.php. You can find the code here, on the original blog post.

Results

The reporting interface of Google Analytics is actually very effective at monitoring Twitter as you are able to look in Real Time, use Dashboards, or custom reports.

The Real Time Analytics is fantastic at showing how active the things your are monitoring on Twitter is. If you just look at the Real Time overview as this screenshot shows:

(click image for full-sized version)

You can use Dashboards to report on key areas of interest and apply whatever filtering you need, the dashboard below just shows the key hashtags, users, users mentioned and urls shared:

(click image for full-sized version)

Custom Reporting also allows us to produce charts such as what times of the day users were active:

(click image for full-sized version)

The full post can be found here.

Posted by Aditi Rajaram, Google Analytics Team

New AdSense Data in the Core Reporting API

Aditi Rajaram — Mon, 28 Oct 2013 18:02:00 +0000

Google AdSense is a free, simple way for website publishers to earn money by displaying targeted Google ads on their websites. Today, we’ve added the ability to access AdSense data from the Google Analytics Core Reporting API. The AdSense and Analytics integration allows publishers to gain richer data and insights, leading to better optimized ad space and a higher return on investment.

In the past, accessing AdSense data using the Analytics Core Reporting API has been a top feature request. We’ve now added 8 new AdSense metrics to the Analytics Core Reporting API, enabling publishers to streamline their analysis.

Answering Business Questions
You can now answer the following business questions using these API queries:

Which pages on your site contribute most to your AdSense revenue?

dimensions=ga:pagePath

&metrics=ga:adsenseCTR,ga:adsenseRevenue,ga:adsenseECPM &sort=-ga:adsenseRevenue

Test in query explorer

Which pages generate a high number of pageviews but aren't monetizing as well as other pages?

dimensions=ga:pagePath

&metrics=ga:pageviews,ga:adsenseCTR

&sort=-ga:pageviews

Test in query explorer

Which traffic sources contribute to your revenue?

dimensions=ga:sourceMedium

&metrics=ga:adsenseCTR,ga:adsenseRevenue,ga:adsenseECPM

&sort=-ga:adsenseRevenue

Test in query explorer

Reporting Automation

By accessing this data through the API, you can now automate reporting and spend more time doing analysis. You can also use the API to integrate data from multiple sites into a single dashboard, build corporate dashboards to share across the team, and use the API to integrate data into CRM tools that display AdSense Ads.

Getting Started

To learn more about the new AdSense data, take a look at our Google Analytics Dimensions and Metrics Explorer. You can also test the API with your data by building queries in the Google Analytics Query Explorer.

Busy? In that case, now’s a great time to try these Analytics API productivity tools:

Magic Script: A Google Spreadsheets script to automate importing Analytics data into Spreadsheets, allowing for easy data manipulation. No coding required!
Google Analytics superProxy: An App Engine application that reduces all the complexity of authorization.

We hope this new data will be useful, and we're looking forward to seeing what new reports developers build.

Posted by Nick Mihailovksi, Product Manager, Google Analytics API Team