Panalysis Web Business Insider – June Edition

Angela Conn - Wednesday, June, 07, 2017

Google Spotlight

This month we are letting you know about Google Attribution, Data Studio, AdWords & Optimise Integration and Google Analytics Enhancements.

Google Attribution 

Google recently announced a new product, Google Attribution, with a view to helping marketers measure the impact of their marketing efforts across both channels and devices.

Historically, attribution tools have been difficult to set up, lose tracking visibility over customer journeys when people switch between devices, nor have they been integrated with ad tools, making it extremely difficult to accurately take informed actions.

Google Attribution can help you:

  • Understand how all of your marketing activities are working together
  • Deliver the necessary insights required to make your activities work better together
  • Integrate Adwords, Google Analytics and DoubleClick Search, making it simple and fast to consolidate your data from every channel and thus providing complete visibility over performance
  • Move away from last-click attribution and towards data-driven attribution, using machine-learning to assign credit to each step of the customer journey

 

 

Google Attribution is now in beta and will roll out to more advertisers over the coming months.

 

Data Studio Now available Globally (and it’s free!)

Data Studio is now available for free and without reporting limitations in over 180 countries. The product allows you to easily connect to data to create information and visually pleasing reports which are easy to read, share and customise.

Key Features in Data Studio include:

  • Filters: you can filter your data in many ways, easily reuse filters on multiple charts, utilise compound filtering  to combine multiple AND and OR conditions together and  filter established metric values that are too large or small.
  • GA Segments: Google Analytics segments can be applied to your charts. They are viewable in Data Studio and if updated in GA, the changes are also applied to your segments in Data Studio.
  • Powerful Data Connectors: Popular data connectors in Data Studio include Youtube, DoubleClick, Campaign Manager and Adwords.
  • Google Cloud Platform Integrations: Integration and improvements to the Google Cloud Platform  enables fast data reporting, at scale, with powerful functionality.
  • File Upload: Data Studio can upload 2GB of CSV data for free, lifting restrictions on data access from SQL or Google databases only.

 

 

Adwords & Optimize Integration

From managing media, to optimizing your site and collecting performance insights, your marketing workflows need to be seamless and integrated. This will allow you to easily apply learnings and take action, fast. With new integrations for Google Optimize and AdWords you will now have the ability to efficiently test landing pages, gaining valuable insights about your ads.

The new integration allows you to create new variations of your landing pages and apply them to any combination of AdWords campaigns, ad groups and keywords. It’s fast and easy and requires no coding or webmaster.

With Optimize determining which variation performs best for your company, it aims to assist you in getting more value from your AdWords campaigns.

 

Google Analytics Enhancements

Google Analytics is now even easier to use to gain the insights you need. After a redesigned mobile app for an improved on the go experience last year, followed by  the introduction of automated insights within the app, Google have recently simplified their website user interface.

The most recent enhancements have been designed to help you make better data-driven decisions based on a more insightful user understanding.

New Home

Google Analytics “Home” features:

  • Viewable snippets from a curated set of GA reports with simple controls.
  • Each Snippet is preceded by helpful questions to frame your data.
  • Digging deeper is easier, simply hover on any data point to access more details or drill into relevant reports
  • Your “home” is automatically configured based on your GA setup.
  • Your former default landing page, Audience Overview is still accessible. Open “Audience” section in your navigation and click on overview to access.

Discover

The new “Discover” page holds all of the latest enhancement information. It offers products and experiences to assist you with manoeuvring through your GA account.

Helpful products including Optimize, tools such as the GA mobile app, features like Custom Alerts and educational material from the Analytics Academy are all included in the Discover page.

 

We recommend spending some time exploring these Google Analytics enhancements to make sure you are getting the most out of your experience.

The problem with prediction, in particular presidential prediction

Rod Jacka - Thursday, November, 10, 2016

I really like the word phantasmagorical but I rarely have a chance to use it.

It’s by no means a common word – so a simple definition might be useful:

‘a confusing or strange scene that is like a dream because it is always changing in an odd way’
Mirram-Webster Online Dictionary

Now with that in context, it’s fair to say that Donald Trump’s election has surprised more than a few, and that the use of the word phantasmagorical is highly justified. From many a perspective, it was inconceivable that someone with his rhetoric or political inexperience would be elected leader of the USA.

From the start, Nate Silver’s FiveThirtyEight website has provided great insight. It’s had a solid history of good predictions and is seen as a reliable source of information, condensing large numbers of variables into a predictive state. During the election I also chose to revisit Nassim Taleb’s book Antifragile as a companion to FiveThirtyEight. Ever since reading his first two books, Fooled By Randomness and The Black Swan several years ago I have been fascinated with statistics, volatility and randomness in general.

A key message I took from Taleb’s books was that prediction of rare events is not possible. The world is a chaotic and complex system with many variables where interactions lead to unexpected and unpredictable outcomes. Whilst we can measure many things using solid historical data, it is the unseen variables and their even greater unknown interaction (both seen and unseen) that can quickly send predictions through the floor…..as we saw.

As a long term practitioner of analysis using statistical tools and approaches I have long come to respect that models are just models – they are not the world itself. In the oft quoted words of George P. Box and Norman R. Draper (1987) “Essentially, all models are wrong, but some are useful”. Equally I have come to understand that to correctly use statistical tools you absolutely must deeply understand how they work and what their limitations are. (Like whether the media is involved?)… side thought.

There are many cases where forecasts and predictions based on data and analytics has worked, but we must always be mindful when using these tools that we are making predictions based on past observations. There have been 58 presidential elections in the entire history of the USA. As such, any model trained on predicting an event which only occurs every 4 years with 58 outcomes across 227 years is likely to get it wildly wrong from time to time. To give an analogy, all swans are white until you see a black one.

What I think we have seen with the US election results, and Brexit too, are black swan events.  They’re emerging from many factors which are leading towards some large changes in the future. It seems very likely to me that the forecasts which were being made during the election campaign were inaccurate simply because nothing like this had been observed before.


http://projects.fivethirtyeight.com/2016-election-forecast/

http://www.dailywire.com/news/10660/just-how-wrong-were-pollsters-final-polls-vs-final-james-barrett#Statistics and predictive analytics are great tools, but I also strongly encourage you to examine and understand the work of Nassim Taleb, Daniel Kahneman, Amos Tversky, Philip Tetlock and many others who work in the areas of of understanding human behaviour, the limitations of our minds and complexity science.They may not help you to predict your outcomes, but adding their approaches to your toolkit of techniques will help you to better understand the world, appreciate the limitations of data and analytics, and make you a much better marketer and analyst.

Session Unification in Google Analytics

Timothy Yuen - Wednesday, March, 09, 2016

Those of us who spend their working lives up to their elbows in Google Analytics – like I do – take a lot of its great functions and quirky complications for granted.

So I’ve decided that now is a good time to start sharing some of my knowledge to help you get the most out of this incredibly powerful tool. There will be many interesting conversations to come!

To start with, I have recently been looking at the User ID setting for session unification and I thought I’d share a recent discovery with you. It will help you understand some of those mysterious gaps in your User ID view.

While doing some testing, I’ve discovered a User ID issue, related to session unification, that many of you may not even notice. Until now, I had always thought that all hits before the User ID is set would be unified as coming from the same User. It turns out that this is incorrect.

Session unification only happens for all hits before the first hit containing the User ID. Any hits without the User ID, after the initial hit, are not counted.

What are the implications of this you ask?

Well, imagine you are tracking the User ID when a user logs onto your site. That user logs out, or times out, browses a few pages and then they log back in – still within a session. Their User ID will not be logged in the few pages they browsed after logging out. And any subsequent pages they view or events without the User ID will no longer be tracked or unified into the session.

I had a sift through the Google Analytics documentation, looking for some clarity, and all I could find was this cryptic sentence:

Now that we know this, what should we do?

As per the Google Analytics statement, ALL hits should contain the User ID once it is known. But if it is not known or tracked, you may face some sticky questions about why certain pages or data are missing in the User ID view.

You’re probably wondering, “how on earth do I go about explaining that missing (in between) data to senior management?” And it’s all about the user choosing to maintain their privacy.

If the user has chosen to ‘log out’, then their subsequent pages/events aren’t tracked. By logging out they have opted out from being tracked.

I believe that this is the basis of the Google Analytics logic for not automatically tying together all of a user’s sessions, once their user ID is identified. The missing data is adhering to, and protecting, the privacy of the user.

Predictive Analytics – Your privacy at stake?

Rod Jacka - Thursday, December, 12, 2013

One of the podcasts that I regularly subscribe to has a great interview with Eric Siegel a leading figure in the area of predictive analytics on the topic of the benefits of this area and the potential risks to privacy.

I attended one of Eric Siegel’s classes in Washington DC a few years back and have followed his conference Predictive Analytics World since.

The interview is a nice balance on the benefits that predictive analytics can provide and the risks to our privacy. As a practitioner in this area I am very sensitive to how data can be used for both good and not so good.

Eric’s recent book (Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die) features the case of Hewlett Packard and their use of predictive analytics to score their employees based on their likelihood to look for another job. Eric’s article in the recent Analytics Magazine is a must read on the downside of this approach. Another better known case of the potential for privacy infringement is the now famous Target pregnancy prediction case which started with a presentation at the Predictive Analytics World conference.

In my view the privacy of the individual must be respected with any analytics initiative and that privacy implications must be carefully thought through.

Your thoughts?

Getting Your Charts Right

Rod Jacka - Friday, November, 08, 2013

Communicating complex and varied data in a useful and meaningful way  is always a challenge. How can you convey multidimensional data in a way that allows viewers to take in the whole picture at a glance?

As a dedicated audiobook listener I regularly scour Audible.com for interesting books to listen to. I’m investing 4 to 10 hours of my life in each one, so I need to know whether a book is going to be worth it. For each book, Audible provides charts, based on audience feedback, that provide very detailed data in a simple and comparable way.

Each chart allows me to see how others responded to the book, providing me with both the average (the stars at the top), and the distribution (the bars below), across three dimensions. It also provides the number of respondents in each group.

Audible Customer Rating Chart

The colour scheme of the Audible charts is also helps viewers interpret the data meaningfully — with the overall average rating in red and the detailed information in grey. This is very much in keeping with what Stephen Few and others recommend for the design of charts like this.

How do other online retailers show this kind of data?

Audible’s parent company Amazon provides a similar chart, but only for a single dimension. This limits the value of the chart as an aid to decision making — it’s not clear what aspects of the book were most successful for these reviewers.

Amazon Customer Rating Chart
Australian online store OO.com.au only provides an average rating which doesn’t allow me to see how many people highly rated the product and how many didn’t. While I like the pros, cons and best uses categories, the layout of this chart causes the viewer to do a lot more work than they should have to.

OO.com.au Customer Rating Chart
US clothing retailer LLBean.com uses a popup, restricting the view of the detailed breakdown to those who roll over the average star rating. 

LL Bean Customer Rating Chart

Getting your charts and reports right is a key impact factor in your success, whether you are selling audiobooks, or reporting to the C-suite. Spending time on making your data clear, compelling and instantly comprehensible will always be a good investment.

Google Analytics Academy – Build Your Analytics IQ

Rod Jacka - Tuesday, October, 01, 2013

Learning and acquiring new skills is a key part of any professional’s career and success. As a long term provider of training in Google Analytics I was extremely pleased to hear about the launch of the Google Analytics Academy and in particular to hear that Justin Cutroni was the person behind it.

Justin Cutroni is one of the luminaries in web analytics and someone for whom I have deep respect. I strongly recommend that if you are serious about developing your career in digital marketing that you take this course.

The world needs more skilled analysts and this course is a great first step. You can even take the Google Analytics Individual Qualification after it.

If you have completed this course and live in Australia, we are always interested to discuss your goals and potential career opportunities.

Analytics – Balancing the Logical and the Creative

Rod Jacka - Tuesday, May, 28, 2013

My good friend Daniel Waisberg has one of my articles at Online Behaviour on how I see creativity is a key component in your analytics skill set.

Learn how to be both an artist and an engineer and how both of these skills are necessary for you to excel as an analyst.

Enjoy.

http://online-behavior.com/analytics/balance

Seasonality Considerations in Planning

Rod Jacka - Monday, March, 05, 2012

In this article we’re discussing the impact of seasonality in your online marketing strategy. Not all industries enjoy an endless stream of online traffic, so making provision for the spikes and troughs lows applies to online traffic, as well as offline sales.

Whilst an organisation is hard pressed to forecast them absolutely, predicting seasonal variations as distinct from a normal trend is part of strategic planning. A seasonal variation may be anticipated because of social custom (e.g. Christmas) but closing the accuracy gap between the expected variation and actual becomes the challenge.

How can we leverage the positive and minimise the negative aspects of online seasonality?

1.  Plan correctly

As always, planning is paramount. Some questions which might feature in your plan include;

  • What is the incubation period for a sale during the season in question?
  • How far ahead do your clients make their purchase?
  • How far in advance of that date are they doing their research to aid the buying decision?
  • Is a branding campaign – for the particular season – required if people don’t actively search on keywords?
  • Marketing creep is the sometimes vexing practice of advertisers bringing forward sales period of the particular season and examples might include:
  • Christmas decorations on sale in August
  • Chocolate Easter eggs displayed in January
  • TV coverage in mid-summer of the start of Winter football training

The challenge is how far can this be advanced without irritating/ offending your prospects?

Has your creative been varied to reflect the season? Does it need a new call-to-action for the season? (Remember it will need changing again immediately after the season!)

Does a competitor’s offer need to be matched or bettered to keep you in the race?

2.  Budget correctly

Which serves your needs better? Higher – or steady – spending if the season brings with it more sales

What seasonal sales spikes which occur regardless?  There are examples of predictable highs

  • Activity for tax agents at EOFY
  • Back-to-school supplies & shoes in January
  • Fitness clubs & tanning salons at end-Winter
  • Auto service centres, at start of school holidays

And predictable lows

  • Chocolate sales immediately after Easter
  • Slower retail sales prior to EOFY (stock taking)
  • Family holiday locations at the start of the new school year
  • Party hire suppliers immediately after Christmas/ New Year

Just like the winter ski resort marketing their ‘off’ season as a cooling Summer escape, could marketing spend be channelled to.

What is the year-round average sale value? (How much is the average sale off-season?)

How does it compare to the seasonal average sales value? (Does the average customer buy more in season?)

Can the target CPA be increased? (If yes, could provision be made for a higher CPA inside the season)

Which categories yield best returns? Apportion more budget to higher yielding products/ categories

3. Track, Test & Compare

Comparing the outcome of this year’s seasonal sales (versus forecasts) is the foundation of future forecasting accuracy. It is through the comparison of actual with predicted that insights to future seasonal marketing are offered. This is where well configured web analytics is invaluable.

And a final word: don’t forget to factor your site’s mobile device compatibility into your marketing plans. There’s no escaping the rapid rise of mobile!

8 Tips to Avoid Costly Mistakes When Managing Your AdWords Account

Rod Jacka - Tuesday, January, 24, 2012

“Simplicity is the ultimate sophistication” – Leonardo da Vinci

In this article we explore a few pitfalls to avoid when planning online marketing campaigns.

1. Be Strategic

Figure out what your online goal is before you plan the AdWords Campaign. The page where your visitors will land needs a defined purpose. That might be a

  • Quote Request,
  • Contact Us form submission,
  • Shopping Cart purchase or
  • Newsletter signup (as examples)

2. Keywords

The keywords you wish to bid on have to be popular with Google and the public. Well known industry jargon does not necessarily make for a useful keyword list. Satisfy yourself (get expert help if necessary) that the public does indeed search on the terms you think they do – before building the page

3. Weigh up Cost versus Return

The price of a click varies widely from one industry to another. Find out what the price of a click in your industry is and then set yourself a realistic budget – for both *daily spend and *monthly spend. If you can’t responsibly afford to buy say ten clicks/ day at the market price, it may be wise to either postpone until you can, or explore other forms of media

4. First impressions

Industry insiders say you have a window of around 5 seconds to capture your visitor’s interest before they move on. Ask a stranger (rather than staff members) to road test your page. If they aren’t clear in less than 8 seconds what they’re being asked to do on that page, you may have a problem.

5. Which business model?

Be flexible and willing to tweak your battle strategy, as the market dictates. Perhaps you chose a shopping cart purchase as your online goal earlier. How will you respond if your AdWords is not generating online sales, but is making your phone ring with enquiries? Could your business be better served by a campaign which aims to generate leads by phone or email, rather than online purchases?

6. Simplicity

Avoid the clutter and try to build a page around just one theme. The page needs adequate, unique content (around 150 words as a guide) and this applies to dynamically created shopping cart pages as well

7. Know your competitors

If time suggests that people are visiting your e-commerce site on the right keywords, but not buying anything, could it be that the opposition is stealing your thunder? Is your pricing and service equal to –or better than – your online competitors? People shop around online just as they do in malls. And price comparison is much easier online!

8. Hometown advantage

Whilst not a certainty, it’s possible that competition in AdWords will determine that your budget won’t stretch as far as you’d hoped. If this eventuates, be prepared to campaign in a smaller radius first, and then later expand to State-wide or Australia-wide when your model is proven

What We Can Learn about Customers’ Online Behaviour

Stephanie Forrest - Tuesday, January, 10, 2012

The fundamental things marketing professionals want to learn about customers online are similar to what we want to learn offline. For example, we would like to understand and refine our target segments. We would like to better understand the customer journey, demographics, psychographics, consumer behaviour and decision making process, how best to differentiate, propensity to buy, seasonality factors etc. Similarly to direct mail, the web can provide you with timely data to demonstrate and quantify the effectiveness of campaigns.

We have a feedback mechanism through analytics that can tell us whether or not we are on track with our marketing objectives – provided the data is treated in the correct manner (e.g. ‘comparing apples with apples’). As the ability to measure online activity with analytics increases we can also learn additional things about our customers relating to buying behaviour. For example:

  • Understanding the language that customers use and where they are in the buying cycle by examining the keywords that they use to find the site and search its contents;
  • Watching what customers do versus what they say;
  • Learning what offers visitors respond to best. Visit www.whichtestwon.com to see how small changes to a web page can make big differences in results;
  • Using visitor behaviour information to create targeted offers, identify the propensity to purchase and estimate the sales pipeline.

Successful companies who are ahead of the pack are now investing in learning about and understanding the online customer. They realise that converting digital data into insights has currency. As customers become more empowered the emphasis will be on understanding the individual in real time and responding accordingly.

Further reading on the shift to data-driven marketing and the implications of the empowered consumer can be found in the IBM CMO Study