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      Archive for the ‘Google Analytics’ Category

      Sharing is Caring – Unleash your productivity with asset sharing in Google Analytics

      posted by Google Analytics 7:25 PM
      Wednesday, April 23, 2014

      Innovation happens on every level
      Within your organization there are multiple people working on different sides of the same problem. Making it easy for people to quickly and effectively share innovative solutions is a key enabler for more productivity, and better decisions. 
      We are proud to announce a series of asset sharing tools within Google Analytics. To spread all your innovative solutions and assets even easier. Our permalink solution is a simple to use and privacy friendly way to share Google Analytics configurations across your organization, and beyond.
      Narrow the focus for precise insights
      Our popular segments feature helps you to narrow the focus of your analysis. Are you trying to answer a hypotheses for new, or recurring customers? Is this report more meaningful if you focus on a particular region? By sharing a segment, you share a certain point of view on a problem. Invite others to your view by sharing a segment you built, or a custom report.
      Define success, and spread the love
      Goals in Google Analytics help advertisers to map real business value into a conversion signal. Track users site engagement, media interactions, or sales events through Goal tracking. Now it is easier than ever to share your success definition across other views, or with other people in your organization.
      Capture everything with Custom Channels Groupings
      It all starts with traffic to your website. You spend a tremendous amount of effort and resources on getting people to visit. Custom Channel Grouping within Multi-Channel Funnels enables you to identify everything, especially traffic that is custom to your business model. Sharing this important view is now easier than ever. Create a Custom Channel Grouping, and share this among your organization.
      Assign partial value to your marketing efforts
      Custom Attribution Models allow Google Analytics users to assign partial value to the channel interactions which drive business value. You invest time and effort to build a customized attribution model, which reflects the nuances of your business. Now it is easier than ever to ensure all stakeholders are working off the same consistent definition of attribution.
      “Amazing feature! I tried it … and like it.”
      Sebastian Pospischil Director Digital Analytics, United Digital Group
      How it works
      Permalink is a simple to use, and privacy friendly way to share configuration assets. When you ‘share’ an asset, we are creating a copy of that asset or configuration, and create a unique URL which points to that copy. The asset copy will remain private and can only be accessed by someone with the URL. If you want to share your asset, just share the URL. The recipient clicks on the URL, and will be brought to a simple dialog to import the assets into his or her Google Analytics views. This feature also supports Dashboard, and Custom Reports.
      Check out our Solutions Gallery within your Google Analytics account via the “Import from Gallery” button or directly at the standalone site for inspiration, and consider sharing your own permalinks via the “Share in Solutions Gallery” link. 
      Happy Analyzing.
      Posted by Stefan Schnabl, on behalf of the Google Analytics team

      Understanding multi-device user behavior in a single view

      posted by Google Analytics 5:25 PM
      Thursday, April 17, 2014
      In this constantly connected world, users can interact with your business across many digital touchpoints: websites, mobile apps, web apps, and other digital devices. So to help you understand what users do in the increasingly diverse digital landscape, we’re enabling the ability to see web and app data in the same reporting view.

      Here’s a bit more detail on this change:
      Analyze app and web data in the same reporting view
      Now you can see all data you send to one Google Analytics property in a single reporting view, regardless of the collection method you use of where the data comes from. If you send data from the web and from a mobile app to one property, both data sets appear in your reports. 
      If you want to isolate data from one source, like if you only want to see web data in your reports, you can set up a filter to customize what you see. You can also use other tools to isolate each data set, including customizations in standard reports, dashboards, custom reports, and secondary dimensions
      If you don’t send web and app data to the same property, this change doesn’t affect your data or your account.
      Measure web apps
      We’ve also added some new app-specific fields to the analytics.js JavaScript web collection library, including screen name, app name, app version, and exception tracking. These changes allow the JavaScript tracking code to take advantage of the app tracking framework, so you can more accurately collect data on your web apps.
      Benefit from consistent dimension & metrics names
      Until today, some metrics and dimensions used different names in app views and in web views, even though they presented the exact same data. Now, all metric, dimensions, and segment names are the same, regardless if they’re used for web or app data. This gives you a clear and consistent way to analyze and refer to all of your Google Analytics data.

      Visitors are now users and visits are sessions:
      There are two big changes to the names in Google Analytics: First, the Visitors web metric and Active Users app metric are now unified under the same name, Users. And second, Visits are now referred to as Sessions everywhere in all of Google Analytics. 
      We’ll be making these changes starting today, and rolling them out incrementally over the next week. Visit our developer site for more information on these changes:
      Posted by Nick Mihailovski, Product Manager

      Improving Your Data Quality: Google Analytics Diagnostics

      posted by Google Analytics 2:48 PM
      Monday, April 14, 2014
      Google Analytics is a powerful product with a wealth of features. Analytics data can fuel powerful actions like improving websites, streamlining mobile apps, and optimizing marketing investment. To realize this power, you must configure Analytics well and ensure high quality data. For these reasons, we’re starting a beta test with some of our users on Analytics Diagnostics that are aimed at finding data-quality issues, making you aware of them, and helping you fix them.
      Analytics Diagnostics frequently scans for problems. It inspects your site tagging, account configuration, and reporting data for potential data-quality issues, looking for things like:
      • Missing or malformed Analytics tags 
      • Filters that conflict
      • Looking for the presence of (other) entries in reports
      Here’s what it looks like:

      As we get lots more feedback and improve the diagnostics system, we will release this to all of our users. It will take some time to get there; in the meantime, you are welcome to express interest in trying out the diagnostics system on your own GA accounts.
      Posted by the Google Analytics Team

      New user and sequence based segments in the Core Reporting API

      posted by Google Analytics 1:30 PM
      Friday, April 11, 2014
      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.
      paditrack-horizontal-funnel.jpg
      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

      We’re looking forward to seeing what people build using this powerful new functionality.
      Posted by Nick Mihailovski, Product Manager, Google Analytics team

      Smarter remarketing with Google Analytics

      posted by Google Analytics 5:25 PM
      Wednesday, April 9, 2014

      Sometimes, less is more.
      While many marketers love the hundreds of dimensions they can use to create remarketing lists in Google Analytics, others have told us that the sheer number of possibilities can be overwhelming.


      So to simplify the product while still ensuring great results for our users, we’re proud to announce a new type of remarketing list: one that’s managed automatically.

      Introducing: Smart Lists with Google Analytics.
      Now when creating a new remarketing list, you’ll have the option to have Analytics manage your list for you.

      Smart List option in the Remarketing Interface

      How does it work?
      Smart Lists are built using machine learning across the millions of Google Analytics websites which have opted in to share anonymized conversion data, using dozens of signals like visit duration, page depth, location, device, referrer, and browser to predict which of your users are most likely to convert during a later visit.

      Based on their on-site actions, Analytics is able to calibrate your remarketing campaigns to align with each user’s value.


      If you use use eCommerce transaction tracking and have enough traffic and conversions, your Smart List will be automatically upgraded. Marked as [My Smart List], your list will be customized based on the unique characteristics that cause your visitors to convert. Only you will have access to this list, and no new data will be shared whether you use this feature or not (learn more).


      For practitioners, the promise of big data is also the burden – there are so many analyses to run, so much opportunity.  With Smart Lists, as with Data Driven Attribution, Google Analytics is  operationalizing statistical analysis – making us not just smarter marketers – but faster and more nimble. 

      While we might have been able to achieve similar results with ongoing statistical analysis and a complex cookie structure, Smart Lists are simply plug and play. This speeds us along, so we can focus not on list management, but on growing the business. 
      – Melissa Shusterman, Engagement Director, www.maassmedia.com

      For best results, make sure your Google Analytics goals and transactions are being imported into AdWords, then combine your Smart List with Conversion Optimizer using Target CPA or ROAS in AdWords.


      If you’re new to remarketing, the Smart List is a great way to get started with strong performance results.  As you get comfortable with remarketing you can tailor your creatives and apply a variety of remarketing best practices.


      If you’re a remarketer already employing a sophisticated list strategy, stay tuned while we gear up to extend this signal directly for your current lists as an optimization signal used in AdWords bidding.


      We’ll be continuing to iterate on these models in order to help users better understand and act on their data. We’re also working on surfacing these signals elsewhere in your reports and in the product so you can dive into what factors help predict whether a user will likely convert.


      We welcome your feedback and ideas. Please leave them right in the comments!

      Happy Analyzing,

      Ismail Sebe and Dan Stone
      on behalf of the Google Analytics Team

      Analytics & AdWords Bulk Account Linking

      posted by Google Analytics 8:25 PM
      Monday, April 7, 2014
      To maximize marketing investment and return, advertisers need insights into the effectiveness of their ads. However, gaining such insights is often overly cumbersome. This is why we’re pleased to announce that in the coming weeks, the Google Analytics and AdWords account linking process is becoming even more streamlined, making it easier for advertisers to quickly gain rich insights. The new linking process allows you to link multiple AdWords accounts all at once. This enables more tightly controlled linking access for each Google Analytics property. 
      Enable Bulk AdWords Account Linking
      Many Google Analytics users have multiple AdWords accounts. Until now, each AdWords account had to be individually linked. The new account linking wizard allows you to select any of the AdWords accounts in which you have Administrative access. The following screenshot shows what the wizard looks like for a user who has access to an AdWords MCC containing many AdWords accounts. Note that you can select multiple accounts:
      Discover Unlinked Accounts
      Many users want to quickly find unlinked AdWords accounts and link to them, and the new wizard makes this easy. A quick glance at the AdWords account list in the screenshot above shows which accounts are and aren’t linked. To link additional accounts, just mark the “X” in front of each account, and then continue.

      Gain More Granular Control
      With this launch, linking to AdWords now takes place at the Analytics property level instead of the account level. This is a benefit for those with many properties in a single Analytics account; if you have different teams of people managing each property, you no longer need to give them access to the full Analytics account in order to link to AdWords. Now, you can simply give that team access to only the appropriate property, and they can manage AdWords links. All it takes is property-level Edit permission to create and update AdWords links. This is another Analytics feature enabling large-scale Analytics customers to better control access to their Analytics accounts.
      Visit The New AdWords Linking Section
      Once the new linking process has launched to your account, you’ll be able to see all these features. Log in to your Analytics account, click the Admin button in the header, and you’ll see a new AdWords Linking section in the Property column:

      These great new features are rolling out now and should fully launch to everyone in the coming weeks. Here’s what one of our users had to say:
      “The linking process is now a lot more straightforward as I do not need to toggle between 2 different interfaces. Everything can be done in GA. In addition, all of the accounts that I manage are automatically listed in the interface so I do not need to look for them. This is a vast improvement from the previous experience.” Sam Chew, Digital Manager, Air Asia
      Log into your Analytics account soon to update your AdWords account links and gain rich marketing insights.
      Posted by Dan Fielder and Matt Matyas, Google Analytics Team

      Universal Analytics: Out of beta, into primetime

      posted by Google Analytics 1:00 PM
      Wednesday, April 2, 2014
      Universal Analytics is the re-imagining of Google Analytics for today’s multi-screen, multi-device world and all the measurement challenges that come with it. Since we launched UA in beta, we’ve seen some exciting use cases. Today we’re happy to finally announce: Universal Analytics is out of beta and everyone can use it with the same robust set of features you’re used to with classic Analytics!


      Feature parity with Classic Analytics, new reports, better user-centric analysis
      When we first introduced Universal Analytics and ran the beta trial, the number one request from our testers was for full access to all Google Analytics features and tools. Bringing Universal Analytics out of beta means that all the features, reports, and tools of Classic Analytics are now available in the product, including Remarketing and Audience reporting.
      We’re also gradually rolling out the User ID feature to help you better understand your customers’ full journey. This feature shows anonymous engagement activity across different screens and visits to your site to provide a more user-centric view of your traffic, and help you build a more tailored experience for your customers as well. It will also enable new Cross Device reporting that shows how your users are interacting with your business across multiple devices. 
      Additionally, Universal Analytics is also now covered by our Premium service-level agreement, which means that same level of service and additional product features Premium users have come to expect will stay the same when their accounts upgrade to Universal Analytics.

      New Cross-Device Reports in GA let you see the full customer journey (click image for full-size).

      Time Zone Based Processing: Fresher, more timely data
      Today, all properties are processed in Pacific Standard Time. If you’re in a different time zone, this can create a lag in the data you see in your reports. With time zone based processing, you’ll see fresher data in your reports in a more timely manner.
      Updates to the Measurement Protocol: User Agent / IP Override 
      A top developer request, this feature allows developers to proxy data from devices and intranets, through internal servers, and finally onto Google Analytics. To support this, we added two fields to set the IP address and User Agent directly in the Measurement Protocol. With these features, we are also announcing the deprecation of the legacy mobile snippets. Users should update their code to use the Measurement Protocol
      Our early Universal Analytics adopters have already seen some great results. This case study highlights some of the inspired ways our Certified Partner InfoTrust LLC has helped Beckfield College unlock the full capabilities of Universal Analytics including the use of Remarketing and Audience Reporting:
      “Once we saw more than 25% of visits to Beckfield College’s website were coming from a mobile device, we migrated them to Universal Analytics with plans on leveraging its cross-device tracking capabilities, and better understanding the full visitor journey across devices.” — James Love, InfoTrust LLC
      If you use Google Analytics today, get started with Universal Analytics by upgrading your account. Learn more about the process in the Universal Analytics Upgrade Center, including auto-upgrade process, and timeline. 
      If you are new to Google Analytics, learn more about Universal Analytics in the Help Center. 
      We’ll share more creative implementations, case studies, and Universal Analytics resources in the coming months that we hope will inspire you to continue to grow your business with the insights you gain using Google Analytics. 

      Posted by Nick Mihailovski, Product Manager, Google Analytics

      Mastering the science of random chance: Dataless Decision Making comes to Analytics Academy

      posted by Google Analytics 10:07 AM
      Tuesday, April 1, 2014
      The world of digital analytics changes fast. From Attribution Modeling to Universal Analytics. From App Analytics to Remarketing. From Tag Management to Audience Reporting. We’re constantly trying to help analysts and marketers measure their business and make better decisions. But sometimes we ignore alternative ways to make business decisions.
      That’s why we’re excited to introduce our next Analytics Academy course: Data-less Decision Making.

      In this four-unit course, we’ll present some of the most popular ways to avoid using data when making business decisions.  We’ll cover everything from mystical tools, like crystal balls and divining rods, to traditional data-avoidance techniques, like coin flipping. You’ll find that once you adopt these methods you’ll be able to make hundreds, and maybe thousands, of decisions a day!
      Still wondering if this course is right for you? Check out our FAQ for more information.
      We hope you enjoy the course!

      Posted by the Google Analytics Education Team

      Sending data from Lantronix to Google Analytics

      posted by Google Analytics 6:25 PM
      Friday, March 28, 2014
      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

      Tell a Meaningful Story With Data

      posted by Google Analytics 10:25 AM
      Wednesday, March 26, 2014

      This article was originally posted on Google Think Insights.

      Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience. Marketers are responsible for this story; as such, they’re often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. As marketers, we can tailor the story to the audience and effectively use data visualization to complement our narrative. We know that data is powerful. But with a good story, it’s unforgettable.

      Rudyard Kipling once wrote, “If history were taught in the form of stories, it would never be forgotten.” The same applies to data. Companies must understand that data will be remembered only if presented in the right way. And often a slide, spreadsheet or graph is not the right way; a story is.

      Executives and managers are being bombarded with dashboards brimming with analytics. They struggle with data-driven decision making because they don’t know the story behind the data. In this article, I explain how marketers can make that data more meaningful through the use of storytelling.

      The power of a meaningful story

      In her “Persuasion and the Power of Story” video, Stanford University Professor of Marketing Jennifer L. Aaker explains that stories are meaningful when they are memorable, impactful and personal. Through the use of interesting visuals and examples, she details the way people respond to messaging when it’s delivered either with statistics or through story. Although she says engagement is quite different from messaging, she does not suggest one over the other. Instead, Aaker surmises that the future of storytelling incorporates both, stating, “When data and stories are used together, they resonate with audiences on both an intellectual and emotional level.

       In his book Facts Are Sacred, Simon Rogers discusses the foundations of data journalism and how The Guardian is using data to tell stories. He identifies ten lessons he’s learned from building and managing The Guardian’s Datablog, a pioneering website in the field. I found three of the lessons particularly insightful:

      1. Data journalism (and analytics in a broader sense) is a form of curation. There is so much data and so many data types that only experienced analysts can separate the wheat from the chaff. Finding the right information and the right way to display it is like curating an art collection. 
      2. Analysis doesn’t have to be long and complex. The data collection and analysis process can often be rigorous and time consuming. That said, there are instances when it should be quick, such as when it’s in response to a timely event that requires clarification. 
      3. Data analysis isn’t about graphics and visualizations; it’s about telling a story. Look at data the way a detective examines a crime scene. Try to understand what happened and what evidence needs to be collected. The visualization—it can be a chart, map or single number—will come naturally once the mystery is solved. The focus is the story. 

      Stories, particularly those that are meaningful, are an effective way to convey data. Now let’s look at how we can customize them for our audiences.

      Identify the audience

      Most captivating storytellers grasp the importance of understanding the audience. They might tell the same story to a child and adult, but the intonation and delivery will be different. In the same way, a data-based story should be adjusted based on the listener. For example, when speaking to an executive, statistics are likely key to the conversation, but a business intelligence manager would likely find methods and techniques just as important to the story.

      In a Harvard Business Review article titled “How to Tell a Story with Data,” Dell Executive Strategist Jim Stikeleather segments listeners into five main audiences: novice, generalist, management, expert and executive. The novice is new to a subject but doesn’t want oversimplification. The generalist is aware of a topic but looks for an overview and the story’s major themes. The management seeks in-depth, actionable understanding of a story’s intricacies and interrelationships with access to detail. The expert wants more exploration and discovery and less storytelling. And the executive needs to know the significance and conclusions of weighted probabilities.

      Discerning an audience’s level of understanding and objectives will help the storyteller to create a narrative. But how should we tell the story? The answer to this question is crucial because it will define whether the story will be heard or not.

      Using data visualization to complement the narrative

      Analytics tools are now ubiquitous, and with them come a laundry list of visualizations—bar and pie charts, tables and line graphs, for example—that can be incorporated into reports and articles. With these tools, however, the focus is on data exploration, not on aiding a narrative. While there are examples of visualizations that do help tell stories, they’re rare and not often used in meetings and conferences. Why? Because finding the story is significantly harder than crunching numbers.

      In their “Narrative Visualization: Telling Stories with Data” paper, Stanford researchers discuss author versus reader-driven storytelling. An author-driven narrative doesn’t allow the reader to interact with the charts. The data and visualizations are chosen by the author and presented to the reader as a finished product, similar to a printed magazine article. Conversely, the reader-driven narrative provides ways for the reader to play with data.

      With the advent of data journalism, we’re now seeing these two approaches used together. According to the Stanford researchers, “These two visual narrative genres, together with interaction and messaging, must balance a narrative intended by the author with story discovery on the part of the reader.”

      A good example of a hybrid author-reader approach is the presentation of The Customer Journey to Online Purchase tool. A few short paragraphs explain why the tool was created and how it works, and an interactive chart allows marketers to break down the information by industry and country. Additional interactive data visualizations provide even more context.

      Another extremely efficient and visual way to tell a story is by using maps. In a tutorial on visualization, I show how a large data set can be transformed and incorporated into a story. It’s an example of how to take charts and graphs to the next level in order to add value to the story. In this case, I use Google Fusion Tables and some publicly available data to illustrate analytics data with colorful, interactive maps. The visualization provides more content for those interested in diving deeper into the data.

      A good data visualization does a few things. It stands on its own; if taken out of context, the reader should still be able to understand what a chart is saying because the visualization tells the story. It should also be easy to understand. And while too much interaction can distract, the visualization should incorporate some layered data so the curious can explore.

      Marketers are responsible for messaging; as such, they’re often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. By rethinking the way we use data and understanding our audience, we can create meaningful stories that influence and engage the audience on both an emotional and logical level.

      Posted by Daniel Waisberg, Analytics Advocate

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