New Domo Feature: Dashboards

Marquin Smith - Tuesday, April, 30, 2019

Domo has recently released new Dashboarding functionality within their platform.

This is a shift away from the Page – Collection – Card way of organising visualisations, towards the more typical organisation people expect from the term dashboard. This move allows Domo to emulate much more closely what people have been building in Tableau, PowerBI and Google Data Studio.

 

Before Dashboards in Domo:

 

With Dashboards in Domo:

With this dashboarding functionality there is now much more flexibility in the organisation and sizing of cards for the content’s end users.

In the Page – Collection – Card paradigm it was almost impossible to control with detail what pages looked like to users. This was because the cards placed on a page would organise themselves in a responsive-like fashion, depending on the users’ screen size. This meant that 2 cards which were side-by-side on your screen might appear one beneath the other on the users’ screens.

The dashboarding functionality puts an end to that. 2 cards placed side-by-side will always be side-by-side. If the screen does not fit them, then one of them will simply hang off the edge of the screen.

To showcase what this new functionality is capable of, I have designed a dashboard within Domo showing some statistics about Australia from the Australian Bureau of Statistics and the Reserve Bank of Australia. This data was collected into Domo using the built-in Quandl connector.

Within a Domo dashboard all the functionality of the cards remains so it is still possible to slice and dice the data as you wish within the dashboard.

Have a look at the quick video to see this in action.

Get in Touch

Panalysis is an official Domo partner.

Reach out to sales@panalysis.com if you would like to know more about how Panalysis can help you with an existing or potential instance of Domo.

Top Tips for future-proofing data within Domo

Marquin Smith - Wednesday, April, 24, 2019

Domo is a powerful enterprise level visualization tool. Domo differs from many other players in the field in that they incorporate data collection, storage and transformation into a single platform.

For a plethora of data sources, you can simply arm Domo with your credentials and Domo does the work of authenticating and pulling the data in, ready for you to visualize.

From this point you can schedule Domo to collect fresh data every day, week, month or year – however often the data is required.

In an ideal universe, that would be that and we would move on with our lives. However, it is rarely that simple. Things break, connections fall over and credentials change; all of which might mean your reporting does not get updated with the latest data as expected. After getting your stakeholders hooked on the steady stream of relevant data, they will be stalking your desk for the next update if things start to go wrong.

The following are some tips that can be employed within Domo to help minimize disruption in the data flow to your stakeholders:

Auto Retires

Each connection to an external API service will have some scheduling options associated with it.

Within these settings you can specify how often and how many times the report should retry to get the data before giving up.

Panalysis recommends setting the retry settings to the following:

Retry every 15 minutes up to 10 times.

This setting helps to mitigate instances where the connection or the service is temporarily down.

Historical Intermediate Dataset

This tip is best explained with a hypothetical scenario.

You have just received a brief that requires data from 1st January 2017 to current day.

One option is to set the API connection to pull the entire date range each time:

However, this may not be very efficient as it collects the same data repeatedly. Depending on how much data exists in your time period, it may take a long time to collect all the data.

An alternative approach would be to only collect the latest day’s worth of data, and add that to the bottom of the data with the following settings:

With this configuration you may end up with duplicate entries. Also, if this dataset is accidently run with the “Replace” update setting all historical data collected will be lost.

A more robust implementation of this strategy would be to update the connection data one day at a time but then to append this data to an ETL dataset. To do this you need to set up what is known as a recursive dataflow. This means that the output of the ETL is used as an input the next time the ETL is performed.

To set up a recursive dataflow:

  1. Initialize output dataset.

2. Run the ETL so the output dataset actually exists.

3. Re-open the ETL and add the output dataset as an input

This will raise a warning that a dataset with that name already exists in the ETL.
4. Connect the input data together, usually with an append.

The append function will stack the data one on top of each other.
The Remove Duplicates will remove duplicated entries based on the values in one or more column. In this case we don’t want duplicate days being pulled for view so we can set the remove duplicates as follows:

5.  Set the ETL to run every time the Google analytics connection collects the data.

Now with the connection dataset only pulling the latest day, this intermediate data set will append only the data it doesn’t already have.

This strategy allows more flexibility with the initial connection dataset, should the need arise. You can re-pull certain date ranges of data if you need to and don’t have to worry about any duplicates flowing through to your visualizations.

Set Column Types

Even with all the API powered technology that Domo provides, it may still be necessary to upload manually updated data into Domo’s systems. This can include .csv files, Excel files or even Google Sheets.

These tools are great because of their flexibility but can cause some hiccups within Domo. The most common issues arise because Domo expects columns to consist of a single data type, whereas Excel and Google Sheets do not share this constraint.

For example, if an Excel file of sales data is uploaded containing a column for revenue, I may want to work out the tax (GST) on this revenue. Within Domo I can create a calculated column which is 10% of the revenue value. If someone enters a date, some text, or a number as a text string into the revenue column of the spreadsheet, the tax calculation within Domo will fail.

Using the set column types transformation to force the revenue column to a decimal is best practice to get this column into the correct format. This also has the added bonus of creating a break point within the ETL, ensuring that no downstream visualizations experience any unwanted effects.

With these 3 tips in place you will have a much more robust business intelligence tool, which is more resilient to a wide range of common issues. This allows stakeholders to have a more reliable stream of relevant data.

Get in Touch

Panalysis is an official Domo partner.

Reach out to sales@panalysis.com if you would like to know more about how Panalysis can help you with an existing or potential instance of Domo.

ITP 2.1 What it Means to Your Digital Marketing and Analytics 

Rod Jacka - Tuesday, April, 02, 2019

Unless you are deeply engaged in digital marketing or analytics it is unlikely that you would have heard about ITP 2.1 The acronym ITP stands for Intelligent Tracking Prevention and it is built into the latest versions Apple’s Safari browser and the latest update has big consequences to your tracking and marketing.

This technology actively prevents tools such as Google Analytics from tracking users for periods longer than 7 days. In essence, your users will be forgotten about if they don’t return to your website within 7 days from their last visit.

So, what does this actually mean?

Well, if your company is trying to send remarketing campaigns or to track how users engage with your brand over time, then you have an upper limit of 7 days for all your Mac and iOS users who use the Safari browser.

How big an impact is this likely to be?

We sampled 40 of our clients, we found that on average, 35% of their users use the Safari browser, with the impact being even higher on mobile and tablet devices.

On mobile devices, there was an average of 56% of users who use the Safari browser.

On tablet devices, these numbers grow larger, with an average of 70% of users using Safari.

The bit impact of ITP is on returning users who have not returned to the site for more than 7 days. Our study reflects that the impact ranges between 1% up to 12% depending on the client.

If you are running remarketing campaigns, then this is likely to be a big impact on the effectiveness of this marketing in reaching these users.

What will be the effect?

You will see a rise in new users coming from Safari users and a decrease in the days since the last session and sessions per user.

Is it just Google Analytics that is impacted?

No, most web analytics and tracking technologies that are used are impacted by this change.

Is there anything that I can do?

Yes there are techniques that can fix this however each of them requires significant technical work.

Simo Ahava has written a very detailed blog post on the topic that covers the options that are available. https://www.simoahava.com/analytics/itp-2-1-and-web-analytics/

Why has Apple done this?

Apple has repeatedly demonstrated its staunch position on privacy with a key example being its refusal to comply with FBI directives to circumvent the encryption in its iPhone during 2015 and 20161. Later Tim Cook the CEO of Apple in his speech in 2018 to the European Union Privacy commission stated Apple’s position strongly and clearly with “Our own information, from the every day to the deeply personal, is being weaponized against us with military efficiency.” and later “At Apple, respect for privacy—and a healthy suspicion of authority—have always been in our bloodstream.”2

In its recent blog post on ITP 2.1 Apple has made the following statement “Apple believes that privacy is a fundamental human right and that users should not be tracked across the web without their knowledge or their consent.”3

Whilst a more cynical view could equally hold that Apple is directly seeking to compete for public perception to be the good guy in the battle for privacy against its competitors such as Google. It must not be forgotten that Google has products that directly compete with Apple’s.

One way or another Apple’s position indicates that it is likely that this will be an increasing problem going forward.

Firefox appears to be following Apple’s lead and now appears set to implement something similar in the near future.4

Panalysis can help.

If you are concerned about this topic and the impact on your business, please contact us.  We can undertake an impact assessment and help you to select and implement alternative approaches to reduce the effect of ITP 2.1.

View Sample Impact Report

 

Request an Impact Assessment Report

https://en.wikipedia.org/wiki/FBI%E2%80%93Apple_encryption_dispute

https://www.computerworld.com/article/3315623/complete-transcript-video-of-apple-ceo-tim-cooks-eu-privacy-speech.html

https://webkit.org/blog/8613/intelligent-tracking-prevention-2-1/

https://groups.google.com/forum/m/#!topic/mozilla.dev.platform/lECBPeiGTy4

Recency, Frequency, and Monetary Analysis using Power BI

Marquin Smith - Friday, March, 29, 2019

A common question that businesses ask themselves is “Which of my customers are the most valuable?”.

While this is a simple question, there are ways of answering this which can prove to be quite complicated. For example, how does the business define “valuable”? This could be customers who spend the most in total, or customers who have a large number of transactions. Alongside this there are other considerations, such as how recent the last purchase was, or the average basket size.

Luckily, we can use the Recency, Frequency and Monetary framework (or, RFM) to help us identify high value customers.

What is RFM?

As the name suggests, RFM takes into account 3 factors for determining the value of different customers:

  1. Recency
    When did the customer last buy an item? Can we identify if a customer is “active” or has “lapsed”?
  2. Frequency
    How many times has a customer made a purchase? Can we identify customers who make many purchases, or few purchases?
  3. Monetary
    How much in total has each customer spent?

Using these three dimensions can help identify different segments of the user base which would respond to different marketing messages and promotions.

Analysis using PowerBI

PowerBI is a business analytics service that delivers insights by transforming data into stunning visuals. We have loaded example customer data into PowerBI to create an interactive visualisation for you to explore the RFM framework.

 

After loading sample customer data into the PowerBI platform, we can look at the demographic profile of those customers who score highly in the Frequency and Monetary dimensions.

The demographic data can help marketers keep their messaging relevant, should they choose to reach out to the different segments.

 

 

A particularly interesting group of customers are those who have a high Monetary score but a low Recency score. In other words, they are customers who have spent a lot of money but have not done so in a while. The business may want to consider strategies to re-engage those customers as doing so would likely prove to be very profitable.

 

 

In another tab of the dashboard we can see the (fabricated) names and email addresses of the customers in a table. This can be filtered and exported to create custom lists for marketing purposes.

The screenshot below shows those customers who:

  • Are married
  • Belong to a 2 adults, no kids household
  • Have previously spent a lot with the business
  • Transacted recently (active customer)

 

 

With the flexibility in filtering that PowerBI offers, it is a great platform for ad hoc customer segmentation for frontline marketers and stakeholders. There is also the ability to export the data once filtered, so users are really empowered to do what they wish with the data.

Get in Touch

Panalysis is an industry leader in data and analytics.

Reach out to sales@panalysis.com if you would like to hear more about how we can help you with customer segmentation and visualisation using PowerBI.

How Big Data Affects SEO Strategy

Angela Conn - Monday, December, 17, 2018

By Amanda Peterson, Enlightened Digital

Big data has a major influence on the way the world operates. For many businesses, big data has become a crucial technological tool, granting those who utilize it, access to patterns, trends and data-driven insights. Such a wide scope of data can be extremely useful across a range of industries, which is why many businesses are finding ways to take advantage of it.

As technology has opened up new ways for businesses to reach, understand, and communicate with their target audience, SEO marketing has changed significantly. Because big data has created a flood of information that is now is available to marketers, companies can assess their strategy in new and exciting ways. Marketing teams working in SEO now work closely with technical professionals to integrate big data into the online marketing mix.

It’s evident that SEO and big data will forever be intertwined, if for no other reason that Google is the original and largest big data organization in the world.  As SEO expert Jiyan Wei explains, “They have become the institution they are today by analyzing enormous sets of data, making automated inferences, and providing intelligence back to consumers. By studying Google’s methodology and applying their findings, search professionals have been intimately involved with big data for quite some time.”

With SEO professionals continuously combating for the top spot in search engine results pages, big data will prove to be a critical component in SEO strategy. If you’ve yet to realize big data’s impact on your business— keep reading.

What is Big Data?

Data in itself isn’t new, but the amount of data created since the beginning of the digital age has transformed the way we utilize it. In fact, studies have predicted that the vast majority of data today was created in just the past two years.

“Big Data” refers to this rapidly expanding and often unstructured sets of information that’s compiled daily from online activity like checking social media, streaming music and television, and shopping online. It’s what makes the growing amount of content on the internet decipherable. Though the internet is nothing more than a confusing collection of code, big data allows it to become neatly organized in a systematic fashion. Today’s advances in data storage and analytics mean that businesses can capture, store and assess many types of data, including photos, videos, audio recordings, written text and sensor data.

The greatest benefit of big data doesn’t rely on how much data you have, but what you can do with it. Decisions supported by massive amounts of data enable businesses to anticipate needs, mitigate risk, deliver more relevant products and personalized services. From beginning to end of the decision-making process, big data ensures that all decisions are well-informed.

How Big Data Can Influence Your SEO Strategy

If you’re looking to improve your SEO strategy, you must first look at big data and how it interacts with search engine algorithms and optimization. Here are some of the ways big data and SEO can work together and produce significant business results.

Improve Competitive Analysis

Thanks to a number of tools like Google AdWords Keyword Planner and SEMRush, SEO professionals can determine which keywords perform best, how competitors are ranking, and where there’s room for improvement. These tools, which supply trends and rankings powered by big data, allow marketing teams to tackle SEO with objective data.

With these tools, SEO professionals can filter by particular themes, estimate search volumes and level of competition for specific phrases. Understanding the intent of a user’s search query helps interpret large volumes of data and deliver meaningful insight which can be applied to marketing strategy.

More Precise Analytics

While many businesses rely on metrics such as page views, likes and sessions to determine the value of their online content, this isn’t always the most reliable and insightful source of data. In addition, these “vanity metrics” don’t accurately measure the needs of an audience or customers, nor gauge the relevance of a specific piece of content.

As companies gather more information about how, when, where, and what their customers are engaging with SEO marketers can determine why content is valuable to specific demographics. The more tailored content is for a particular audience, the more likely it will be to engage and convert customers.

Better User Experience

Big data enables marketers to make better decisions and align their campaigns with the needs of their audience. Marketers can use trend graphs or other data visualizations to help identify pain points, exit traps and buyer preferences in content.

Catering to the needs of users allows SEO professionals to create content that ranks at the top of the search results page because of relevancy and uniqueness, rather than just following a set of formulas. Creating content that best serves user need is a much more sustainable approach to SEO than previous methods.

Big data is proving to be a driving force behind tactics used by marketers to find new and innovative ways to reach their target audience. Not only can it improve efficiency and provide data-fueled insights, but is essential in building an effective SEO strategy.

 

Is the channel ready for Big Data?

Rod Jacka - Monday, August, 27, 2018

Check out MD Rod Jacka’s opinion as he weighs in on the subject in CRN Magazines August 2018 feature.

When the concept of big data first burst onto the scene earlier this decade the focus was largely on the very big problems that very big organisations could tackle using very, very, big data sets.

Over time big data has shrunk a little. The tools that enable analytics have become more commoditised, affordable and, therefore, applicable to more modest projects.

But while the tools themselves might now be better suited to Australia’s 250,000 or so mid-sized enterprises, channel companies aren’t yet developing big data practices.

Why not? Because the opportunity in big data may not be the one that everyone expects, according to the Sydney-based managing vice president at Gartner, Ian Bertram.

“The opportunity is to sell how to increase data literacy and the ability to use the data that [clients] have got,” Bertram says. “Teaching them how to understand what their data is telling them and what needs to be done in their organisation is the opportunity.”

That means starting with the basics around data literacy, before moving them on to more advanced analytics, and eventually on to AI.

“That midmarket is very competitive, and they want to be able to pick up trends and that little bit of insight to get that competitive edge early enough, because competitive windows are closing much faster than in the old days,” Bertram says.

“And unless they are investing in some of these tools that allow them to pick up what is happening with their business early enough and give them time to react to change, then they are going to go out of business quicker.

“So there is the demand for it, they are very scared about where to start.”

That may be the case, but it seems to be an opportunity that much of the channel is yet to pursue. Even those organisations that are servicing midmarket companies generally do so as an adjunct to their main work with larger organisations.

The D-word

Analytics might seem like a new opportunity for many channel companies. Not to Rod Jacka.

His 17-year-old firm, Panalysis, has provided these services for years. Panalysis was the first Google Analytics partner appointed in Australia, and while its focus remains in the digital space working with enterprise clients, a year ago he signed a relationship with Domo to help clients better work with data from different systems.

Jacka says Domo has proven useful for SME clients, helping them demystify data by presenting easy-to-consume dashboards and alerts that deliver owners what they need to be successful, such as whether they are on track or not. This has been of particular benefit to one of Jacka’s SME clients, the gift hamper company Gourmet Baskets.

“He uses a combination of tools, one of which is [marketing and sales management platform] Infusionsoft, and Xero for his invoicing,” Jacka says. “For him to understand his market and break his market into different stratas, he can’t do that in Infusionsoft easily. And so Domo has allowed him to connect this up and get visibility across each of the platforms he’s used in a single, easy-to digest manner.”

Jacka says one of the secrets to working with smaller clients on data projects is to never talk about data.

“The business owners know what they want to do, but they often don’t know how to do it,” Jacka says. “Data isn’t really what they are talking about, but they need data in order to achieve their goal.

“I actually prefer not to use the word ‘data’. I prefer to use the word ‘evidence’.”

Other organisations that now offer services in the data space often started out somewhere else. Melbourne-based automation and DevOps consultancy Vibrato built its analytics practice 18 months ago through the evolution of its devops consulting business to encompass the concept of dataops.

Vibrato’s chief executive and founder, Peter Gatt, says over time his firm has learned to package up analytics services into fixed-price solutions that are suitable for SMEs. He says smaller thinking is required to help smaller clients.

“With a small and medium business we say, ‘Let’s look at one analytical thing you are trying to ask or answer, and that traditionally reporting could help answer’,” he says. “It is not about bringing in petabytes of data and running Hadoop protocols. We are just going to coordinate the data they have into something that’s logical so they can start to answer questions about their clients and commercialise that data.”

Send in the clouds

In some instances Gatt says engagement with smaller businesses is actually easier, as it is possible to speak directly to the boss or function head, such as the CMO, who owns the actual problem needing to be solved.

When there this is a specific question to be answered through an analytics project, Gatt says Vibrato can deliver a return-on-investment through a proof-of-value in as little as four weeks.

“With data, that’s possible,” Gatt says. “But it really needs to be locked to answering questions, not just playing.”

Vibrato has also established relationships with technology providers such as Hortonworks, and makes use of the Snowflake cloud-based data warehouse tool to help accelerate data flows to make the fast turnaround possible.

Indeed, the migration of analytics tools to the cloud has proven beneficial for a number of channel companies, including Empired.

“I can stand up a Microsoft solution on my credit card and do it really quickly,” says Empired’s national business manager for data insights and integration, Ben Johnson.

“The other change is you used to have to knit all kinds of different tools together – Oracle for database and Business Objects for the front end and IBM for predictive analytics. All of that is blown away because the market is converged. Microsoft has an end-to-end data and AI capability that you can stand up quickly and start getting value straight away.”

However, reducing the barriers to entry from a technology perspective has also had other consequences.

“It’s new and it’s sexy and everyone is a data scientist and everyone is talking about AI,” Johnson says. “The realty is most of them are pretenders, because there has been such an influx of people who are interested but don’t understand the space.”

Are you experienced?

It seems experienced skills remains one of the key attributes that set the successful players apart from Johnson’s pretenders, and Johnson has hired a team of 70 specialists over the past two years to build out the necessary experience.

Jacka also rates staff development as one of his top priorities. He has hired everyone from scientists to mathematicians and artists, and then trained them up on the technology.

“The thing I look for in my team is thinking skills,” Jacka says. “If they have a good problem-solving mindset and aptitude for creative thinking, I like them. But it can take three to six months to get them ready, and I’d argue that it takes 10 years to become an expert.”

Justin Parcell, director of Arq Group business InfoReady, says the requirement to get the right skills meant he and founder Tristan Sternson personally oversaw the hiring of the first 60 or so staff.

Originally the firm was focused on consulting services focused on the information management domain, primarily around IBM environments, but more recently InfoReady has built a competency in AWS and Azure, and three years ago rebranded as a data and analytics organisation, with a greater focus on delivering systems that support operational decisions, rather than for strategic decision making.

He agrees that the shift to the cloud has opened opportunities to service smaller clients.

“As the services that are provided by the cloud platforms become more sophisticated, and as the cost to stand-up those solutions and implements them reduces over time, I think they are going to become more accessible to those smaller companies,” Parcell says.

But while the tools are becoming easier to stand and up work with, he believes that skills remain the limiting factor preventing other organisations from easily following in InfoReady’s footsteps.

“To become a fully fledged data analytics organisation there is a significant barrier to entry,” he says.

 

Domo Partnership

Angela Conn - Monday, August, 13, 2018

Panalysis Domo Partnership

Panalysis is committed to helping clients make better business decisions. As a Certified Domo Channel Partner, we are able to deliver additional value with the world’s best business optimisation platform.

Domo’s mission is to be the operating system for business, digitally connecting all your people, your data and your systems, empowering them to collaborate better, make better decisions and be more efficient, right from their phones. Domo works with many of the world’s leading and most progressive brands across multiple industries including retail, media and entertainment, manufacturing, finance and more.

We’re excited about the future with Domo. We haven’t seen such a transformative tool until now. What we are most excited about is the extent to which we can now help our clients bring so many data sets together (web, marketing,social, operations, sales, customers and product), with the ability to curate the data story that matters most to their businesses. Data democratisation is important, but the data story will focus decision making, avoid misinterpretation and increase efficiency and profits. Together with Domo, we can do exactly that for our clients.” Rod Jacka, Managing Director.

Domo offers you:

  • A single solution for all of your business data and decision making.
  • Rapid set up, cleaner data and faster insights
  • Hundreds of prebuilt connector apps to streamline your existing data sources into one centralised platform
  • Visuals to help you sell your data story
  • Business collaboration within the tool and saves your commentary
  • A seamless mobile experience so you can access and interact with your business data on the run
  • Intuitive notifications via email or text if your key metrics are underperforming, in real time

If you are interested in a platform which brings your people and the data they rely on together in one place, contact Panalysis to arrange a demonstration.

Introducing Google Marketing Platform

Rod Jacka - Monday, July, 09, 2018

Since the introduction of DoubleClick in 1996, changes in technology have meant changes for digital marketers. With more channels, formats and data and an increase in consumer awareness around how they are being marketed to and how their data is being used, there has been a need to provide marketers with new tolls to make it easier to get better results with privacy at the forefront.

To meet these new realities, Google announced Google Marketing Platform at the end of last month.

The platform brings together DoubleClick Digital Marketing and the Google Analytics 360 Suite to aid in the planning, buying, measurement and optimisation of digital media and customer experiences. In one place, Google Marketing Platform.

The platform brings together DoubleClick Digital Marketing and the Google Analytics 360 Suite to aid in the planning, buying, measurement and optimisation of digital media and customer experiences. In one place, Google Marketing Platform.

Google Marketing Platform helps marketers to better understand their customers by offering tools to collaborate and share insights, achieving a customer- first approach to marketing.

 

The Highlights

  • Google products work even better together. For example, the new integration centre helps you to discover and set up valuable connections between products.
  • The platform supports over 100 integrations. You can choose what media you buy, how you buy it and how you measure it.
  • DoubleClick Search is now Search Ads 360
  • Display & Video 360 fuses features from display advertising products and allows you to execute ad campaigns in one place, end to end, improving efficiencies.

 

Find out more about Google Marketing Platform from your Panalysis Contact.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Reference: https://www.blog.google/products/marketingplatform/360/introducing-google-marketing-platform/

 

Symantec SSL Certificates

Rod Jacka - Thursday, February, 22, 2018

Executive Summary

Summary of the Situation

The Chrome team no longer trust previously issued Symantec certificates, and DigiCert have been selected as the new primary provider of SSL certificate infrastructure.

Symantec will maintain their role as an issuer of certificates, but DigiCert will manage the ‘Managed Partner Infrastructure’ on their behalf.

Technically Symantec are now selling certificates with this ‘new’ infrastructure, so certificates purchased from them can be renewed.
However, given the situation we recommend going straight to DigiCert.

April 17th 2018:

Chrome 66 scheduled for release, which removes trust in Symantec-issued certificates issued prior to June 1st 2016

October 23rd 2018:

Chrome 77 is scheduled for release, which removes trust in all ‘old’ Symantec-issued certificates issued on or after June 1st 2016

How to check SSL Certificate Status

Step 1) Visit the website using Chrome

Step 2) Open Developer Tools and refresh

(CTRL+SHIFT+I) ← (letter i, not L) or F12

Step 3) Review the Console

If the SSL certificate in use was issued prior to June 1st 2016, the console will display this message: (M66, must be replaced by mid April)

If the SSL certificate was issued on or after June 1st 2016, the console will display this message: (M70, must be replaced by mid October)

Full Background Information

Google Security Blog – Chrome’s Plan to Distrust Symantec Certificates

This article provides a full overview of the situation.

Key details:

CHEAT SHEET: Apple Analytics vs Google Analytics App Measures

Rod Jacka - Monday, February, 19, 2018

Have you assumed that Apple Analytics and Google Analytics provide very similar app measures? Unaware of some key differences between the two analytics platforms?

We’ve compiled a helpful cheat sheet for you, providing a side by side comparison to aid your understanding and maximise your app measures.

METRICAPPLE ANALYTICSGOOGLE ANALYTICS
App DownloadsApp Units are the number of first-time app downloads made on the App Store (from iOS 8 onwards).  Updates and redownloads onto other devices are not included in this numberNew Users is the number of first launches, or the number of sessions in which the app is first opened on a device, it excludes users who have opened the app previously during the selected period
CampaignsThe attribution window for campaigns is 24 hours.  If a customer downloads your app for the first time within 24 hours of using your campaign link, you will see this counted as an App Unit (app download).  Attribution goes to last click, so if the user clicks on more than one campaign link, only the most recently clicked on will be attributed to the App Unit.  These settings are not adjustableThe attribution window for first-open is 30 days.  The attribution window for any subsequent in-app conversions is 180 days.  These settings are not adjustable
ImpressionsThe number of times your app was viewed in the Featured, Categories, Top Charts, and Search sections of the App Store. Includes Product Page ViewsImpression data is not available in Google Analytics.  However, in the Play Store, you will be able to see Play Store (Organic) who are the unique users who visited your app’s store listing by browsing or searching on the Play Store app
RetentionRetention is the usage of your app over time. The number of devices still active with at least one session on a date during the retention periodThe retention cohort report describes a set of users (a cohort) who started using your app at the same time (such as on the same day, or during the same week). This report illustrates how well the app retains users
SessionsSessions are counted when the app has been used for at least two seconds. If the app is in the background and is later used again, that counts as another session. Totals are based on app users who agree to share their data with you.  There is no current documentation on the length of a session as is available in GASessions are counted when the user is actively engaged with the app.  Google Analytics groups hits (page views and events) that are received within 30 minutes of one another into the same session
SourcesCurrently, there are 4 different types of sources that are recorded in Apple Analytics.  These are App Referrer, App Store Browse, App Store Search and Web ReferrerThis is not measured by default, it requires tracking to be added to the url under utm_source.  The source in GA describes where the user arrived from (website name, social network name, email list name etc.)

Search