Top Analysis Tips & Tricks for 2015 White Paper

Top Analysis Tips & Tricks for 2015By Michele Kiss, Senior Partner at Web Analytics Demystified Sponsored by Adobe

It’s widely recognized in today’s market that analytics drives business success, so it’s a good thing that analytic technology isn’t reserved for just a few key employees in each company anymore. Of course, simply having access might not be enough. You need to understand how a good analytics strategy works. And you need to be sure you can leverage all the functionality within your analytics solution to unlock its maximum value. After all, you can have the most feature-rich marketing analytics tool on the planet, but if you’re only using 20% of its capabilities, you still might struggle to succeed. It’s important then to explore analytics best practices and to educate yourself about what’s possible to make sure you can execute your analytics strategy most effectively.

With this in mind, Adobe has conducted interviews with our digital marketing experts and leading clients, combined with the industry-leading expertise from Web Analytics Demystified, to give you a perspective on the top analytics tips for 2015. Whether you’re just getting started with analytics or looking to enhance the sophistication of your program, these tips help you get the most from topics such as segmentation, attribution, mobile measurement, attribution, and predictive analytics to maximize your investment.

Obviously, every company has different needs, so some of these topics might be more strategic for your organization in 2015 than others. However, taking the time to develop your analytics muscle in each area contributes to a mature practice that is better able to gauge digital marketing campaign ROI, refine the customer experience, respond to competitive threats, and react to environmental change, both internal and external.

ACTION: Review the list and see which tips and uses cases make the most sense for your key 2015 focus areas. Where you can support the tip in terms of your solution and staffing in 2015, create an action list and timeline for building out that capability. If your tip list is too long to easily prioritize, or if you’re not sure which capabilities you want to start with, the Adobe Analytics Maturity Self-Assessment tool can help you take stock of your organization’s current capabilities and recommend a formal prioritization plan based on your goals and your firm’s analytics profile. This process helps identify critical gaps—for example, a suboptimal analytics implementation, insufficient resources for the work that needs to be done, or a systemic failure to act on data—to address to ensure the long-term success of your organization’s analytics practice. Whether you use the maturity assessment and these tips to drive long-term organizational change or simply to pull some key ideas that address more tactical needs, we wish you continued success with your career and team. To join our ongoing discussion on this topic and other topics related to analytics tips and trends, visit our analytics blog at http://blogs.adobe.com/digitalmarketing/analytics.

2Top Analysis Tips & Tricks for 2015 White Paper

Getting Started with Analysis and Segmentation 3Start with your key performance indicators . . . . 3The most important benchmarks are your own. . 3Let the data help you prioritize . . . . . . . . . . . . 4Focus on automation of reporting to allow resources for analysis . . . . . . . . . . . . . . . . . . 4Start with basic segmentation . . . . . . . . . . . . 4Personalize standard segments. . . . . . . . . . . . 5Start basic segmentation by looking at traffic sources. . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Advanced Segmentation 5For richer insight, segment across visits . . . . . . 5Consider not just the what, but the when . . . . . 6Leverage cohort analysis. . . . . . . . . . . . . . . . 6Tie segmentation and personas together . . . . . 6Integrate data sources to judge true success. . . . 6

Data Governance 7Document, document, document . . . . . . . . . . 7Teach stakeholders to fish . . . . . . . . . . . . . . . 7Keep up on analyst training . . . . . . . . . . . . . . 8

Marketing Attribution 8Be clear on what attribution means to you. . . . . 8Start by understanding your buying cycle . . . . . 9Understand the current attribution taking place in your organization . . . . . . . . . . . . . . 9Get your stakeholders onboard first . . . . . . . . 10Educate about what attribution can and can’t do . 10Use cross-channel KPI definitions as a starting point for teams. . . . . . . . . . . . . . . . . . . . . 10Have your basics covered (campaign tracking) . 10Compare simple click-through models . . . . . . 11Determine your official view and what should be continually reported . . . . . . . . . . 11Take the next step to advanced attribution . . . 11

Mobile Analytics 11Be clear about what mobile means . . . . . . . . 11Don’t assume all mobile is on the go . . . . . . . 12Get hands-on with your mobile experience . . . 12Use your own data to create and optimize the mobile experience . . . . . . . . . . . . . . . . . . 12Establish KPIs for mobile discovery . . . . . . . . 12Focus on action, not just time . . . . . . . . . . . 13Avoid distrust by educating users about mobile app offline data collection . . . . . . . . . 13Use data to inform and optimize mobile app marketing . . . . . . . . . . . . . . . . . . . . . . . . 13

Predictive Analytics 14Use alerts to notify you of shifts in data . . . . . 15Make your alerts meaningful . . . . . . . . . . . . 15Use automation to create monitoring reports . 15Use built-in automation features . . . . . . . . . 15Create a typical seasonality curve to provide context to your data . . . . . . . . . . . . . . . . . 15Use historical data to backfill missing data . . . 16Use correlations to better understand your data . 16Use visit scoring to better understand and predict 17Build models to predict the impact of site changes or marketing plans . . . . . . . . . . . . . 17

Real-Time Web Analytics 18Alerts. . . . . . . . . . . . . . . . . . . . . . . . . . . 18Triggers . . . . . . . . . . . . . . . . . . . . . . . . . 18QA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18But: Use real-time data for the right purpose . . 18

Advanced Data Visualizations 19Understand (and appropriately use) reports, dashboards, and analysis . . . . . . . . . . . . . . 19Present to promote understanding . . . . . . . . 19Present information in a way that is easy for the brain to process . . . . . . . . . . . . . . . . . . 20Don’t be limited by the visualizations available in your analytics tool . . . . . . . . . . . . . . . . . 21Abide by the data-pixel ratio . . . . . . . . . . . . 21No 3D! . . . . . . . . . . . . . . . . . . . . . . . . . . 21Friends don’t let friends use pie charts . . . . . . 22Use the right chart to deliver your message . . . 22Don’t mix multiple items on a chart for no reason . 22Be thoughtful about color . . . . . . . . . . . . . . 23

What now? 23About Web Analytics Demystified . . . . . . . . . 24About Adobe Marketing Cloud . . . . . . . . . . . 24

Table of contents

3Top Analysis Tips & Tricks for 2015 White Paper

Getting Started with Analysis and SegmentationWhile high-level reporting can be useful, it’s the deeper analysis and segmentation of your customers that reveal true insight to optimize your business.

Start with your key performance indicators As you move from basic reports to more in-depth analysis, stay mindful of your key business objectives and performance indicators. An analysis focused on what drives your KPIs is more effective at driving action than analyzing random observations on your site.

If your KPIs are not already defined and measured, start there! Why does your website (or mobile site or app) exist? What do you want your customers to do? If they took one action, what would you want it to be? What is the next best action they could take?

Your KPIs are the most important measures in your analytics tool and the primary focus of any analysis.

Some businesses too quickly brush off this process or believe that only businesses where 100% of the process is online (for example, e-commerce retailers) can truly measure digital KPIs effectively. However, every business can translate key business objectives into measurable digital KPIs, even if sometimes those KPIs are more like leading indicators of future offline actions.

Here are a few examples of possible website KPIs based on the business model and key business objective.

Business Model Key Business Objective Website KPIs

Final Conversion Pre-conversion

E-commerce Purchase Online order Add to cart

B2B New paid customer Online lead submission Download white paper

Consumer packaged goods Offline sale Viewed store locator

Printed a coupon

Viewed product page

Content Ad revenue High-value pages viewed Any page viewed

The most important benchmarks are your ownCompanies typically expend unnecessary energy worrying about comparing themselves to others. Consider the common question: “What’s a typical conversion rate?” Conversion rates can vary so dramatically—by vertical, by product, by purchase cycle, by site design.

Consider the action taken in response to benchmarks. Let’s say your conversion rate is 3% and the industry average is 2.5%. What action would you take? Pat yourself on the back and stop trying to further optimize? Of course not!

Ultimately, your goal is to drive your KPIs based on your own historic performance, your users, and your business goals and not get caught up in measuring performance against a generic and typically unhelpful measure.

To help stakeholders focus on progress against internal measures, conduct a discussion to review the trends for your KPIs as well as any spikes or troughs, including what drove them.) Be sure to keep in mind these things:

• Any major traffic acquisition efforts, especially lower-qualified paid efforts, because they might affect conversion rate

• Site projects aimed at increasing conversion

Armed with this information, you can work with stakeholders to set a tangible goal to track toward, such as to increase the conversion rate by 0.5% each quarter or achieve a 2.6% conversion rate by the end of the year. Your historical review helps keep you honest in your goal setting. After all, doubling your conversion rate is a pretty unrealistic goal if you have barely moved the needle in two years!

Be sure to test and measure attempts to optimize this rate to quantify the impact of changes. (For example, removing the phone number from our form increased the conversion rate by 5%.) You want to be able to explain the drivers of the increase as you progress toward your goal.

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Let the data help you prioritizeAn in-depth analysis of a site area that no one ever visits might provide insightful recommendations to optimize the experience, but will ultimately fail to drive business value. For example, let’s say you conduct an analysis that reveals opportunities to double conversion to purchase, but the site area only receives a few visits per month. Even with a huge lift in conversion, the actual increase in orders (and thus revenue) would be minimal.

Deciding where to focus analysis efforts is a sheer numbers game. Start by focusing on your top trafficked site areas and pages and traffic sources. This way, the insights and opportunities identified are substantial enough to impact the business.

Focus on automation of reporting to allow resources for analysisWhether through dashboards in your analytics or business intelligence tool, scheduled reports, or automated export to a tool like Excel, automate as much recurring reporting as possible. Automation frees up analysts time tremendously, leaving time for valuable analysis.

As an analyst, when you put together a report, stop to ask yourself:

• How much additional work over and above the time needed to fulfill the one-time request would it be for me to create it in a way that would be automated for the future?

• What is the likelihood that someone will want to see it again?

• Do I have the time to undertake the additional work? Even if it’s only 10% more work to automate a task, if you are truly under the gun, that 10% might not be feasible.

The diagram shows a simplified approach that can be useful in thinking through that balance.

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Start with basic segmentation Start your segmentation journey using the default segments built into your analytics tool. Take two or more segments and compare how those users behave similarly and differently. For example, do they view the same content? Engage with the same tools? Convert at the same rate?

Common standard segments include:

• New Visitors vs. Returning Visitors

• Purchasers vs. Non-Purchasers

• By Device (mobile, tablet, or website)

• By Browser or Operating System

• Site Search vs. Navigation or other basic functionality

5Top Analysis Tips & Tricks for 2015 White Paper

Personalize standard segmentsAlthough these default segments can be useful to get started, make sure that you take a step back and decide whether the way they have been defined is appropriate for your business. If not, edit or create your own versions of default segments that take into account the nuances of your business.

For example, perhaps your business model considers a loyal visitor to be someone who returned to your website three times, not twice. Or maybe loyal is bestowed to a visitor who has shared or purchased more than once. Perhaps you consider branded, organic search terms to be navigational behavior, more akin to direct traffic than search. Revise your segments to reflect what makes sense to your business rather than relying on the defaults, and make the revised segments easily accessible to stakeholders.

Start basic segmentation by looking at traffic sourcesOne of the simplest ways to get started is to segment how your visitors come to the website. The way users enter your site can indicate very different motivations that might lead to different behaviors. For example, a visitor who enters the site through a branded search term might be ready to buy and thus have a higher conversion rate. A visitor who enters via email might already be a customer and is just looking for support information, and thus be unlikely to convert to purchase.

To further complement your website data, consider running a survey to better understand user intention by channel and why certain visitors engage in certain ways.

Advanced SegmentationWhile the default segments available in your analytics tool are a good place to start segmenting your data, the real value of segmentation is based on what’s important to your business. Thus, advanced segmentation is a custom effort.

Custom segmentation basically just starts from a hypothesis—a theory that one group of your users will behave different to another group because of [variable x]. By building a segment based on that variable, you can then test out that hypothesis.

Examples of custom segments might include:

• Segmentation based on previous purchase behavior For example, style, price, on sale, or shipping options selected. You might even uncover a valuable segment of demanding shoppers who aren’t tied to sale prices and often pay to rush purchases via higher shipping.

• Segmentation based on demographics For example, age or gender. This could be actual gender or imputed gender, such as gender based on shopping patterns.

• Segmentation based on site behavior For example, segmenting users who have a Wish List, sent an article to a friend, signed in, or engaged with social buttons.

But don’t forget: A valuable segment is only truly valuable if you can leverage it to drive the business. If your segment is so narrow that it consists of only a few people, even if their lifetime value is incredibly high, you’re probably not going to move the needle by focusing on them. Segmentation must be a balance of detail and volume.

As you uncover valuable custom segments, make these available to a wider audience as “canonized segments”—built and blessed by the analytics team and available for all to use.

For richer insight, segment across visitsRelatively few brands have a true one-visit purchase cycle. Rather than treating your customers as tabula rasa every time they come to your site, use visitor, rather than visit, segmentation to look at their behavior across every interaction.

Better yet, segment by customer. This requires that your site has a way for users to identify themselves (for example, login) and an integration between your customer and digital data. But if the information is available to you, jump on it!

Watch The Power of Analytics-Driven Optimization to see how Adobe Analytics and Adobe Target can help you use iterative testing to predict behavior of targeting customer segments, helping to drive conversion.

6Top Analysis Tips & Tricks for 2015 White Paper

Consider not just the what, but the whenSequential segmentation is a powerful feature that many analysts don’t use to its full potential. For example, perhaps analysis reveals that it’s better to convert visitors who viewed detailed product information and pricing at a much higher rate than non-converting visitors. However, which order did they view these in?

Contrary to expectations, sequential segmentation might reveal that users commonly view pricing first or that those who view pricing first show a higher conversion rate. This information can be used to influence content. Perhaps you might try providing basic pricing information at an earlier point in the journey or test targeting ideas, such as revealing pricing on the home page for first-time visitors.

Leverage cohort analysisCohort analysis is another type of when-based analysis. The word cohort might make it sound complicated, but cohort analysis is simply a type of time-based segmentation.

Cohort analysis is used to segment and understand the behavior of a group that has a time-based variable in common. For example:

• Users who downloaded your app on the same date

• Users whose first purchase occurred on Black Friday

• Users who signed up for a paid subscription on the same date

You can then compare two cohorts to see if their behavior trends similarly. For example, do users who first purchased on Black Friday re-purchase at the same rate months later as users who purchased in July? Do users who first downloaded your app during a promotional period still use it as much as users who downloaded it while no promotions were running?

For more resources on conducting cohort analysis in Adobe Analytics, see this article.

Tie segmentation and personas togetherHere is an all too common scenario: In one corner of the building, the user experience team is painstakingly defining its various customer personas. In another corner, analysts are furiously segmenting mounds of data. But never the twain shall meet! Yet collaboration between analytics and the user experience can add tremendous value on both sides, marrying the quantitative with the qualitative.

Consider gathering your analytics and UX teams to share insights from customer segmentation and persona development, and attempt to build segments of behavior based on each persona. For example, let’s say you are a restaurant business and one of your personas is a health-conscious mom. A segment for that persona might look at visitors who have viewed the nutrition information and the kid’s menu. Use this data to not only prove out the personas, but also to understand what else that type of customer might do.

Integrate data sources to judge true successFew businesses start and end online. To judge success, it’s critical to integrate data from a consumer’s subsequent actions, even if they are offline. For example, did the application result in a loan? Did the 30-day trial result in a paid subscription?

You don’t have to launch straight into a pricey enterprise data warehouse project to see value. Start small by correlating top-line data in key reports using available data integrations or pulling select offline information into your web analytics tool.

Examples of useful integrations of online and offline data include:

• Which form submits result in actual business by pulling in qualified leads and revenue from your customer relationship management (CRM) system into Adobe Analytics

• Including returns data for a better picture of a customer’s purchase history

• Integrating in-store purchase data for known customers to view a customer’s online and offline purchases together

• Importing cost of goods sold data to get closer to profit rather than just revenue

• Integrating email data with web analytics to understand behavior all the way from send to in-depth site behavior

7Top Analysis Tips & Tricks for 2015 White Paper

Data GovernanceSuccess with analytics is not as simple as slapping an analytics tool in place and letting everyone in the organization have at it. Data governance requires consideration for the appropriate data. For example:

• Collection—Know and document how all data is collected

• Quality—Require a sound, validated implementation and ongoing QA

• Access—Make sure only appropriate parties have access

• Security—Know who has access and where your data goes

• Privacy—Respect consumer privacy in data collection and use

• Integrity—Ensure data is used for its intended purpose

• Presentation—Present data appropriately in its context; don’t deliver a warped message!

For more information, see Demystified’s Data Governance Principles.

Data governance is a broad topic, but here are a few things that analytics teams should be aware of.

Document, document, documentThe average tenure of digital analysts at a company is commonly a mere 18 months. That’s not 18 months in a role and then a promotion to another role in the same company; it’s moving on to another company.

In the face of such high turnover, there is no excuse—and dire consequences for those left holding the ball—for not documenting your analytics implementation, historical work, and processes.

Keep in mind that your organization likely needs multiple forms of documentation.

• A complete, detailed solution design to explain your implementation, including which variables are set where, when, and how, and the business purpose and value. This document is for your analytics and IT teams to deal with the nuances of the implementation.

• A business-users guide that explains key variables, what they represent, and how to use them. It should also describe your solution and how to access key reports and dashboards.

In addition, document your analytics process. For example, how do you request an analysis, how is information distributed, how are reports delivered. Having this information prevents redoing the same analysis over and over again, leading to a more efficient team.

Teach stakeholders to fishAnalytics tools can be complicated to master. It’s not expected that your stakeholders become true analysts diving into the nuances, but teaching business users to perform basic tasks can actually help grow your analytics program.

Keep in mind that tactical business users, such as your social media manager or paid search lead, should receive more hands-on training and be expected to have more familiarity with your analytics tools. They don’t need to understand the intricacies of the implementation as an analyst would, but they should be able to navigate to and use basic reports related to their role.

More strategic senior business users, like your CMO or even CEO, should receive executive summaries in the format that best suits them. This might mean that they never log in to your analytics tool, and that’s okay.

The benefits of this approach are:

• Tactical stakeholders can run simple queries themselves, freeing up analysts for more complicated analyses.

• Familiarity with the data can help bridge the gap between analysts and marketers. Marketers are able to speak a little of the analyst’s language, and in return, the analyst focuses on speaking the marketers language.

• Readily available information democratizes your data, so the analytics team is not viewed as hiding information.

A note of caution: Having multiple users pulling data could lead to inaccurate data being distributed. Educate users on the need to explain where the data came from. Focus on building positive relationships so that business users feel comfortable confirming their data with analysts prior to raising red flags to others in the organization.

8Top Analysis Tips & Tricks for 2015 White Paper

How to get started:

• Make the analytics tool as easy as possible for the business user This might include creating custom dashboards to provide a launching point), creating business-friendly guides for the tool and your implemen-tation, and simplifying menus and the user experience. Make a list of FAQs and how to find the answers within your reports, such as how much traffic does this page get?, how many visits do we get from this channel?, or what is the conversion rate for this product?” An at-a-glance reference—something users can hang up at their desk—can be helpful.

• Never stop training Schedule regular trainings, such as once a month over lunchtime. Stick to the schedule, even if attendance is low some months. If you stop holding them, people won’t know if they’re being held or not and won’t show up.

• Offer regular “office hours ” Provide a forum where business users can ask questions, get hands-on help, or get your blessing on numbers they have pulled together. That doesn’t mean people can’t email or call with questions, but when questions get complex, encourage business users to come to office hours to dive deeper.

Keep up on analyst trainingDigital analytics is still a new industry, and it continues to evolve quickly. Training is critical to both get new analysts up to speed and hone the skills of existing resources in a rapidly changing environment.

Keep in mind this hypothetical CEO and CFO conversation:

CFO: “What if we invest in these people, and they leave?”

CEO: “What if we don’t, and they stay?”

It’s also worth noting that the best analysts want to continue to grow and better themselves, so investing in them via training and education actually helps retention.

Training can take many forms, such as:

• Specific analytics tool training (for example, Adobe ReportBuilder training)

• On-the-job training and cross-training

• General conferences and events (Adobe Summit, ACCELERATE, Digital Analytics Association Symposium)

• Related courses (for example, Statistics 101)

• Presentation or speaker training (Toastmasters, Resonate)

To stay on top of skills, managers should budget a minimum of one general conference and one tool-specific training per employee (ideally the specific event should be the employee’s choice), and establish a program of continuing on-the-job training and cross-training to keep up employees’ skills and engagement.

Marketing Attribution As marketing channels proliferate and spend increases, marketers are typically under more pressure to deliver insight into the ROI of their programs. Attribution is the process of determining the contribution of channels to business outcomes and is critical for a true understanding of ROI.

For an overview of current marketing attribution trends and technologies, view this webinar featuring Forrester Research.

Be clear on what attribution means to youPeople sometimes mean different things when they talk about attribution. At its simplest, attribution is about dividing credit for a conversion among all click-throughs to the website. A more complicated method is dividing credit across all detectable channel exposure, which could include channels like TV that don’t involve a direct digital click-through as well as exposure to digital ad impressions. These two methods involve a very different level of effort, and it’s important to be clear about which one you are embarking on.

9Top Analysis Tips & Tricks for 2015 White Paper

There are also two general attribution approaches: rule based and data driven.

Rule-based attribution is informed by historical data and the application of business judgment—for example, “we highly value channels that introduce customers to us for the first time”—to impose attribution rules on the data. Some example models include:

• First click—The first click-through receives 100% of credit for the conversion.

• Last click—The last click-through receives 100% of credit for the conversion.

• Even—Every click-through receives equal weighting, for example, five touches before purchase attributes 20% of the conversion to each.

• Weighted—Each type of click-through is assigned a custom weighting. For example, 50% goes to the last click, 25% to the first click, and 25% is distributed across all clicks in the middle.

Data-driven attribution uses algorithms to examine detailed conversion data. Based on the data, it decides the amount of credit each touch receives.

Also keep in mind the difference between attribution and marketing mix modeling. Attribution seeks to understand the impact of various touchpoints on customer conversion. Marketing mix modeling looks to the future to predict the best possible mix of channels to drive conversion.

For more information on attribution profiles and attribution models, click here.

The cross-channel marketing attribution capabilities in Adobe Analytics can help you understand your customers’ complete journey to conversion. To learn more, click here.

Start by understanding your buying cycle Marketing attribution starts from the premise that the last-click view of channel performance is flawed because customers might have interacted with multiple channels prior to converting. Attribution projects can also be a significant investment, so it’s important to be sure that you see the return.

The first thing to examine is your visitor return rate. If 95% of your website visitors are new, analysis of multiple clicks only applies to 5% of your traffic. In this case, it’s possible that your organization might not see the ROI from attribution, given that for most visitors, their last click is their only click.

Next, examine your typical time to purchase and the average number of touches. If you typically see only two visits before a conversion, or if your average time to purchase is a day, attribution might not deliver as much value to your company as with a longer or higher touch buying cycle.

Taking the time to understand your buying cycle can avoid unrealistic expectations of the gains to be made from attribution analysis.

Understand the current attribution taking place in your organization “But we’re not doing attribution!”, you say. Actually, if your organization is doing any type of reporting of results by channel, you are applying attribution rules, but they just might not be ideal.

In a business that does not carefully consider marketing attribution, it’s likely that multiple teams are taking credit for the same conversion. Your web analytics tool might use last click, but your media or search conversion pixels are more like any click (or impression). If a user saw the ad, media data will claim credit for the conversion.

Be sure that you and your stakeholders understand the underlying attribution assumptions in existing reports before adding additional attribution-based views of the data.

To see some practical examples of attribution that are taking place in other organizations, view this webinar, “Build world-class marketing attribution.”

10Top Analysis Tips & Tricks for 2015 White Paper

Get your stakeholders onboard firstIdeally, attribution helps you understand which channels are involved at different stages of the consumer lifecycle and ultimately improve your marketing spend, and by extension, your business results. However, a perceived or actual threat to a stakeholder’s marketing programs can lead to nasty political consequences.

Keep the following possibilities and consequences in mind:

• Marketing managers have been distributing results regarding their programs for some time now and applying attribution might significantly change the results, potentially removing credit from their efforts or calling into question the accuracy of their historical results.

• Budgets, including staffing budgets, have been decided based on these historical results and might be reassessed in response to attribution data. It’s important to be sensitive to the impact of the results on teams and individuals and their role in the organization.

• Marketers are often incentivized for their own programs—for example, their bonus plan might be based on achieving a certain click-through or conversion rate or a minimum number of sales from their channels rather than performance incentives based on the success of the business as a whole. If attribution reveals a different picture of marketing channel success, this might have a bottom-line impact to individual marketers and their income. Prepare for some defensiveness!

Educate about what attribution can and can’t doA successful attribution program requires stakeholders be well educated about what is and isn’t possible.

First, be sure that your stakeholders understand what you are embarking on. Is it a website click-through attribution project or a larger project involving additional offline data sources? This understanding helps avoid promises being made that the project can’t possibly deliver on.

Second, make sure that stakeholders understand what is possible within the scope of your efforts. For example, even if you’re planning on a comprehensive cross-channel, data-driven attribution project, it’s still not possible to truly understand that a customer watched a TV ad, researched online, had a long conversation with a friend on the phone about it, and finally purchased at a kiosk in the mall!

The goal of attribution is to get a better understanding of how marketing channels influence your customer’s behavior, but this will never be a perfect understanding. Having these discussions upfront can ensure that realistic expectations are set and that the project isn’t later judged a failure against impossible standards.

Use cross-channel KPI definitions as a starting point for teamsIt’s amazing how many businesses consider investing in large-scale (and pricey) attribution analysis without first having a solid process for defining cross-channel KPIs.

Imagine that your company is investing in a large product launch. The marketing campaign involves television, display, digital coupons, search, and social. Before you begin, gather cross-functional teams to discuss and decide what success looks like, which includes deciding what the KPIs are. This is not “everything we can measure” or “every data point available in Facebook Insights.” Rather, it’s the critical metrics that will help you understand if the campaign was successful. For example, your two goals might be product awareness and trial, so the KPIs could be as simple as total viewed impressions across all channels and coupon redemptions. That doesn’t mean that, for example, cost per click for paid search isn’t important to the team managing paid search, but it’s not a KPI for the campaign.

Getting stakeholders aligned on the KPIs across channels is a good introduction to the kind of collaboration required for an effective attribution program.

Have your basics covered (campaign tracking) By default, web analytics tools can detect some information about where the visitor came from. However, this information is not nearly as detailed as most marketers want. Ensure that you have consistent and detailed campaign tracking configured for all inbound traffic to give you the best possible insight into the sources driving visitors to your site.

11Top Analysis Tips & Tricks for 2015 White Paper

To ensure that marketers append campaign tracking, create an easy-to-use system to minimize the level of effort on their part. The easier you can make it, the more likely they are to implement the required tracking codes on all marketing links.

Compare simple click-through modelsIf you determine that attribution is a worthwhile investment for your organization, start by doing a simple model comparison by looking at click-throughs to your site. If you compare first click to last click to equally weighted attribution models, how does it change the picture of each channel’s conversion rate? This analysis helps you identify marketing sources that introduce customers to the site, those that are influencers along the way, and those that close the sale.

For example, you might find that display skews more prominently to the first touch and branded organic search to the last. A display ad might get the customer’s attention early on, but when ready to convert, the customer might seek your company out via search. Use this insight to optimize and test marketing initiatives and copy.

Determine your official view and what should be continually reported It’s one thing to do a one-off analysis to compare your channels from a first touch, last touch and equal weighted perspective. The value, however, comes from continually monitoring these as you make efforts to optimize.

Come to a consensus on a model that makes sense to your organization and include this insight in ongoing reports. Be sure to test how the model influences your results, and refine as needed.

Take the next step to advanced attributionIf you have successfully introduced—and are seeing value from—website click-through attribution, your organization is probably ready to grow to the next level. Gather your stakeholders to discuss launching a more comprehensive attribution program covering additional marketing channels and data-driven attribution. These are not small projects, so you need buy-in and cross-department support for this kind of investment, but your attribution work to date should have set the stage.

Mobile AnalyticsMobile is no longer in the early stages of a growing trend. One-third of mobile users report that their phone is their primary Internet device,1 91% of smartphone users report using their phone at home2 rather than as a purely on-the-go device, and for many consumers, it is their only Internet connection. Customers demand a seamless and effective mobile experience from the organizations they interact with.

Whether your business engages customers via a mobile web or app experience, business is going to continue to shift to mobile, and it’s critical to apply the same analytical rigor to your mobile business as your traditional desktop site.

For an overview of mobile analytics best practices, read this paper by Forrester that gleans insights from hundreds of mobile marketing executives.

Be clear about what mobile means People often mean different things when talk about mobile. Mobile can be defined in several possible ways.

By device—Mobile might be referring to specific types of devices. However, even here, definitions may differ. For example, some consider mobile to refer only to smartphones. Others include both smartphones and tablets. Even wearables, like Google Glass and Jawbone UP, can be considered mobile devices. Be clear about which devices you are referring to when you discuss and analyze mobile behavior.

By experience—Does mobile refer to your mobile app, your mobile-dedicated website, your responsive design website when viewed from a smartphone? Or perhaps even your main desktop site when viewed from a mobile device? Be clear on which experiences are included in your data.

1 www.pewinternet.org/2013/09/16/cell-internet-use-20132 www.iab.net/guidelines/508676/mobile_guidance/mobileusage

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Don’t assume all mobile is on the goMobile, both smartphones and tablets, can differ tremendously in its use. On the one hand, we have the on-the-go behavior, such as getting directions, making a last-minute dinner reservation, or booking a taxi. On the other, we have the leisurely, couch-based content consumption, like watching a movie, reading a book, or pinning recipes and crafts that we’ll never actually make. Treating mobile as a purely on-the-go device when deciding features and functionality could lead businesses astray by ignoring what your customers do and expect.

To better understand mobile use in context, segment not only by device type (desktop, tablet, or smartphone), but also by time of day. Add in time-spent metrics to provide an indication of the user’s usage mode, but be sure to look deeper at the actions taken. This information helps you categorize different types of mobile behaviors—task based, on the go, relaxed content consumption, and so on— and reveal different opportunities for optimization, rather than treating all mobile behavior as equal.

Get hands-on with your mobile experienceTo effectively analyze your mobile data, it’s critical to go through the experience as a mobile user. Navigate your site or app on the intended mobile device as you analyze the data. Doing this can identify far more opportunities for analysis than viewing the mobile site on your computer! For example, that button on your mobile site might be an easy click-through with a mouse but a frustrating user experience on a touchscreen.

Use your own data to create and optimize the mobile experience Even if you don’t currently have a mobile-optimized experience, your existing data can shed light on the opportunity for development. Start by looking at which mobile devices visitors use to access your site. Next, look at the content they view and behaviors they perform and then compare this to the desktop site. Pay attention to behaviors that might be a cumbersome user experience on mobile, like long, non-mobile-optimized forms. This is why you need to go through the experience on your actual device because frustrations can be missed when you are using a desktop. If visitors are persevering despite a poor mobile experience, address this motivation as soon as possible to provide this critical content in a more mobile-friendly way.

If you have a limited mobile site—for example, your mobile version includes some but not all of your content—examine users who intentionally switch from the mobile-optimized site to the full website. Choosing to navigate an unwieldy desktop site via mobile indicates high interest in the content selected, so consider adding that content to your mobile experience.

Don’t forget to consider the business requirements for your mobile experience and how users will typically engage with it. Do they need to integrate with device features like the camera or microphone? This can help you decide between investing more heavily in a mobile app versus a mobile site. Or do you receive most of your content engagement from sharing and search? If so, consider a mobile site, because users typically won’t download an app just to view a link posted by a friend.

To learn more about optimization tactics that allow marketers to take advantage of current mobile consumer trends, read the Adobe Guide to Mobile Optimization.

Establish KPIs for mobile discoveryMobile gets a bad rap sometimes, because many businesses find it shows a lower conversion rate than desktop traffic. However, this can be for a myriad of reasons, from a poor mobile experience, consumers’ different modes of engaging with mobile as compared to desktop, and even different marketing approaches for mobile customers.

It’s a mistake to treat mobile as a completely separate, pure direct-response channel without recognizing that it’s a part of a customer’s overall journey and engagement with your brand. Research and browsing are as much a part of the mobile experience as the final conversion. Given this, brands should establish discovery KPIs for mobile to ensure that research behavior is considered in any assessment of mobile’s success. Discovery KPIs include:

• Viewing a product

• Reading user reviews

• Checking local stores

• Emailing details of the product to yourself for further desktop research

Monitoring and analyzing these behaviors, in addition to conversion, can shed far more light on how your customers behave on mobile and highlight opportunities for improvement.

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Focus on action, not just time While time can be directionally useful in the right situation, over-reliance on time spent as a measure of engagement can lead companies astray, especially in mobile. After all, some mobile experiences are aimed at efficient task completion rather than long engagements. No one wants it to take 20 minutes to make a quick dinner reservation or check a sports score!

Time spent is not inherently good or bad. Users might spend a lot of time when they’re enjoying your content, but they might also spend a lot of time when they’re highly motivated to find something but an awful UI is making the task difficult. This is why focusing on action—a user performing a specific behavior—rather than a more passive time spent is critical.

Start from your business goal and KPI: What do you want your customers to do in mobile?

If your focus is quick task completion, such as placing an order or booking a service, your goal is conversion, ideally with short session times.

If your focus is on repeated engagement, such as checking sports scores or the weather, your goal is around high frequency and time between sessions, not the duration of individual sessions.

If your focus is content and ongoing engagement, like articles, videos, or games, you might want longer session times. However, be sure to think about action, not just time. The number of articles read or videos completed is far more powerful than 10 minutes spent. Perhaps your user spent 10 minutes because he or she was searching your video collection and couldn’t find anything worth watching!

Aberdeen’s report, “Mobile Analytics: Precision Marketing Across Mobile Touch-Points” offers insights on mobile customer engagement for developing better mobile strategies.

Avoid distrust by educating users about mobile app offline data collectionWhile website data typically stays static after it’s collected, mobile app data can change due to subsequent processing of offline data, for example, data from users who used your app while in low-signal areas or airplane mode. This process has the potential to confuse or even alarm stakeholders if they are not educated on the difference between apps and websites.

If you have an application that involves heavy offline use, such as games or apps that don’t rely on an Internet connection, the user’s data might not appear until the user reconnects. Therefore, yesterday’s data could change over the course of today because yesterday’s play is uploaded when users are back online. Make sure that you and your stakeholders understand how much the data could change and the window in which it is in flux.

Consider providing a “Reported as of” date to reports and analyses in addition to the data time frame. For example, data is from October 1–31, reported as of November 3. If another user looks at October’s data and finds that the numbers have shifted, knowing when you pulled your data provides context to understand why yours might be slightly different.

Use data to inform and optimize mobile app marketingSuccess isn’t defined by users downloading your mobile app. After all, 20% of mobile apps are used once and never opened again.3 Marketers need to continually reengage mobile users, and a variety of options are available.

Push notifications are a way to message users outside the app. The pros are:

• Encourages latent users back to the app

• Delivers high-priority messages that cannot wait until users reopen the app on their own accord. For example: Your car is arriving now, or your flight is delayed.

The cons to push notifications are:

• Because they interrupt the user, brands must be respectful to not annoy the user, or you risk the user removing your push privileges or deleting the app.

• Because they leverage operating system notifications, they are typically limited to a short words-only message.

3 www.smartinsights.com/mobile-marketing/app-marketing/mobile-app-statistics

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In contrast, in-app messaging targets the user while they are within the app experience. A limitation is that they can be used only when the user is engaged with the app, but that’s also a plus, because the messaging is part of the engagement experience and can provide “right time” messaging in context. You can present the message more richly, with images, fonts, and other functionality.

Typically, push notifications and in-app messaging are not an either/or for marketers. Rather, they should be used in different situations based on the goal of the notification.

Companies effectively use mobile app marketing messages for a wide range of purposes:

• Launching a survey, feedback form, or to request an app rating

• Cross-promotion of other apps (for example, Facebook used in-app messaging in its main app to encourage users to download its Messenger app)

• Abandon cart offers or product in-cart reminders

• Flash sales (12-hour sale!), credit notifications (You have $5 off waiting!), or deals of the day

• Transactional notices (for example, an order confirmation)

• Navigational or user experience (for example, an e-commerce site might use an in-app message to prompt users to save their default size for future shopping)

• News alerts (for example, today’s weather, sports score updates, or breaking news)

• Update app reminders

Which type of message is appropriate depends on the situation. For example, let’s say a brand releases an app update that breaks a major piece of functionality. The brand can quickly release another update out to fix the flaw, but it could take some time before users update on their own. What are the options?

• Do nothing and wait for users to update in their own time. This risks negative reviews as users complain about broken functionality.

• Send a push notification to all users with the app installed and encouraging them to download the newest version. However, this could highlight the problem to users who never would have known something was broken!

• Send an in-app message about the available app update only to users who try to use the broken functionality. This avoids negative reviews or widely advertising the problem, but fixes the issue for users experiencing it.

Ultimately, the decision of which type of mobile marketing to use is based on the goal of the effort and informed by historical data about how users have responded to similar messaging in the past. Consider A/B testing offer types and delivery methods (push versus in-app) to see which resonates and drives the greatest value for your mobile marketing.

Read the Adobe Mobile App Analytics Primer to learn about the most essential mobile app analytics capabilities for understanding consumers’ mobile behavior, while optimizing the app lifecycle for improved mobile ROI.

Predictive AnalyticsThe value in data is not just looking in the rear view mirror, but in using it to predict and optimize your future actions. This doesn’t have to mean immediately hiring a team of PhD statisticians. An existing analytics team can do small things to bring elements of predictive analytics into your current practice.

For an overview on how real-time and predictive analytics can have an impact on marketing campaign effectiveness and overall marketing ROI, watch the webinar, “Data Visualization and Predictive Intelligence: Advanced Customer Insights.”

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Use alerts to notify you of shifts in data No matter how large your analytics team is, there is no way for analysts to spot every data shift that might require attention.

Alerts can be a helpful heads-up that something is not going according to plan. Perhaps there’s a market trend to be aware of, or you have an outage or implementation issue. Alerts are best used in the following circumstances:

• KPIs—Use alerts to notify you of a drop or spike in your KPIs. These are the most important alerts to create, because they monitor the critical measures for your site.

• Critical supporting data—Some data points might not be KPIs, but are important enough to cause a lot of problems in the event of an outage or missing data. Taking the time to create alerts for these saves countless headaches.

• Data that is prone to break—There might be areas of your site in which a code break is likely. For example, a custom implementation on a page that IT always forgets to take into account when making changes, or a microsite managed by an agency that has mistakenly pulled the code off time and time again.

Make your alerts meaningful While it’s important to get alerts up and running, don’t haphazardly set up the level of your alerts. Setting alerts at the wrong threshold can cause unnecessary panic and lead to alerts being ignored because they trigger false alarms too often.

Successful alerts are data-informed. Start by examining the historical data for the subject of your alert. Evaluate the highs and lows, and be on the lookout for outliers. Understand what drove historical extremes, and consider at what point you want to be notified of such future extremes. Based on the historical range of values, create an acceptable range and schedule alerts for when values fall outside of the acceptable range.

Consider involving your stakeholders in this process, especially those who are directly responsible for an area covered by the alert. For example, notifying your mobile marketing manager about a sudden decline in mobile users or conversions.

Use automation to create monitoring reports A troubleshooting dashboard provides a quick glance report to see if anything is out of line. It should cover your key data points and show trends at the right granularity for your business. It could report hourly or daily data, with historical context to understand if values are at expected levels. The goal is to produce a report that you can quickly review to spot peaks or drops.

Leveraging Excel and Adobe ReportBuilder gives you additional flexibility to include a wide mix of data points, custom time frame comparisons, and charts to quickly spot trends, as well as applying conditional formatting to highlight outliers. For more information, see this article.

Use built-in automation features Tools like Anomaly Detection can help analysts dig into trends and outliers to better understand historical shifts in the data. Set aside some time to explore your KPIs each week to ensure that there haven’t been emerging trends that you are unaware of.

Create a typical seasonality curve to provide context to your dataWhile determining an acceptable range of values for your KPIs is a good start, many businesses have such shifts in seasonality that one range is not appropriate across time periods. Brands can show extreme highs and lows based on the month, week, or even day of the week. Ideally, your alerts and acceptable ranges take these into account.

Break down the historical data for your KPIs in the context of daily, weekly, and monthly seasonality. Gather as long a history as you can to make sure that stable trends emerge. Is July typically your highest month of the year? Mondays your busiest day of the week? Be on the lookout for time periods when data might be inflated due to one-time marketing campaigns.

Using this data, build a typical curve that can help fine-tune your alerts. For example, Monday’s alert might be set at a different level to Saturday’s.

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This data can also provide useful context for ongoing reporting. For example, you can include your typical yearly seasonality curve in monthly reporting with the actuals overlaid to point out where this year is diverging from the norm. This charts shows a particularly low July–August compared to the typical level.

Typical Seasonality �is Year

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This information can be useful for stakeholders and analysts alike and help identify opportunities resulting from out-of-the-ordinary trends.

Use historical data to backfill missing data No matter how thorough your alerts and how good your QA, implementation issues do arise, and some level of temporary data loss is inevitable.

Even a stats novice can use simple modeling and regression to estimate what the data would have been without the data loss. Digitaria’s Rachelle Maisner shared an excellent how-to for simple regression analysis in Excel at the 2014 Adobe Digital Marketing Summit. You can catch up in her post.

Although estimates of lost data are imperfect, it’s far better to show simulated values than your actuals dropping off a cliff! Use your estimated data points in place of actuals in your ongoing reporting, making it clear that these are estimates, to provide a better view for stakeholders.

Use correlations to better understand your dataCorrelations are not causation, but that doesn’t mean they can’t be helpful to understand your data and trends. Looking at one metric or variable rarely provides the whole story, but trending multiple metrics and layering in additional variables can help reveal the drivers of trends.

Some correlations are obvious, and thus, usually unhelpful. For example, viewing the add-to-cart page correlates highly with purchase because it’s a required step of the purchase process.

Valuable correlations involve separate behaviors that seem to occur together in highly converting visits. For example, perhaps there is a moderate conversion between viewing product photos and purchase but a much more significant correlation between viewing product videos and purchase. Knowing this helps your team focus on creating more video content rather than adding more photos.

You don’t have to have a fancy stats tool to get started on running correlations. You can use the CORREL formula in Excel or Google Spreadsheets or the Excel Analysis Toolpak add-in to quickly and easily examine the correlation between variables and get a better understanding of your data.

Using the CORREL formula on two sets of data in Excel

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Using the Analysis Toolpak add-on to run correlations of multiple variables

For more information on how predictive marketing workflows in Adobe Analytics can help quickly uncover valuable correlation, watch the webinar, “Keeping up with the quants—predictive marketing for everyone!”

Use visit scoring to better understand and predict A consumer can do a lot of things before they convert. It’s helpful to identify pre-conversion actions that indicate a higher propensity to purchase by developing a scoring model.

If your site is like most, it probably has lot of functionality and a lot of content. But all content is not equally important. The goal of your model is to score behaviors based on their likelihood to result in eventual conversions. You can start with an art and science approach to building your preliminary model by combining stakeholder feedback about what the most important areas or pieces of content are, with participation and correlation analysis of different behaviors with conversion. Remember, your focus is on behaviors that occur before the conversion.

For example, your preliminary model might score reading a blog post as two points, while viewing a video to completion is worth five points. (You can learn more about setting up a scoring model in Adobe Analytics in this post from Ben Gaines.)

After you have your preliminary scoring model, group visitors in to low, medium, and high scoring categories, and correlate these to conversion rates to validate the effectiveness of the model. If the high scoring group doesn’t show higher conversion rates, revisit your model to tweak your methodology.

When you feel confident that high scores are correlated with higher conversion, take the next step and score visitors who have not yet converted. These visitors are prime for additional marketing efforts to push them closer toward conversion. For example, you can leverage your highly scored list for remarketing emails or display ads, nurturing campaigns, or on-site personalization of content to encourage them to take the next step.

Be sure to maintain a control group of similarly high scoring visitors who do not receive additional marketing. By comparing the conversion rate of your control group with your targeted groups, you can measure the impact of the additional remarketing and personalization on conversion and use this to continually fine-tune your scoring model.

Build models to predict the impact of site changes or marketing plansUsing existing data and assumptions about an upcoming initiative, build a simple model to predict the revenue impact of projects. Work with stakeholders to decide the right assumptions, and build in ways for them to adjust the estimates.

Let’s say you’re planning on adding a larger Add to Cart button to the product page. A simple model would take your current product page traffic and current conversion to cart and apply a 5% lift to calculate the incremental revenue due to new orders. Allow stakeholders to quickly adjust that lift to see how the estimate changes.

This model can stay simple or get more complex, but either way, it’s effective in getting stakeholders to think through the possible effects a project and the resulting revenue impact. For example, if a 50% increase in conversion—tough to attain!—drives only $1,000 in monthly revenue, is the project still worth it?

For more information, see this article on sample models.

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Real-Time Web AnalyticsDefinitions of what constitutes real-time data vary. Generally, data is considered to be real time when it’s immediately available (less than one minute) after collection. However, analytics tools can vary in how quickly they are able to collect, process, and return data via a user interface. Businesses might refer to data from one minute ago through to the same day as real time. For the purposes of these discussions, we consider real time to be data collected within 15 minutes.

AlertsAlerts are a common and effective use of real-time data to quickly notify users of unexpected values. For example, if a site launch breaks analytics or a recent blog post or sale causes site outages, alerts can help analysts and IT act quickly to rectify the problem.

As discussed previously, focus alerts on KPIs and critical metrics to avoid over-notification that could result in future, and possibly important, alerts being ignored.

Triggers Real-time data can provide valuable information to trigger further marketing. For example:

• Shopping cart abandonment emails to provide an offer to users before they lose interest or purchase on a competitor’s website

• Remarketing of recently viewed products and services

• Website personalization based on earlier behavior, such as customizing promotional areas based on products viewed so far

• On-the-fly content adjustment, such as shifting traffic to a better-performing version of an article headline or a different image based on recent engagement

• Content recommendations based on previously viewed articles or videos

• Customer retention and customer service prioritization to respond quickly to the best opportunity customers

• Event reminders, for example, to trigger a webinar reminder only to users who have not yet joined

• Transactional emails, such as order confirmations, thank you emails, or post-purchase surveys

QAOne of the most common uses of real-time data by analysts is for QA and testing.

During launches, major campaigns, and other timely projects, you can use real-time data to spot tracking issues before they become major data loss. Marketers can use real-time data to check campaign tracking, that new pages are collecting data or tests are distributing to multiple versions.

To use this feature effectively, sit down with stakeholders prior to the launch to determine which key metrics and relevant dimensions require monitoring in real time, and make this a part of your launch plan.

But: Use real-time data for the right purposeReal-time data adds additional capabilities for marketers to seize upon. However, if not dealt with appropriately with legitimate planned uses, it can also distract organizations that do not yet have the tools or processes to act nimbly on real-time data.

A good rule of thumb is to have data frequency resemble the cadence with which the organization makes changes, or the organization must have automated tools in place to take action. For example, real-time data is likely to be unhelpful if your IT team is on a six-week deployment cycle, or if your email or social marketers take weeks to get a single post approved. On the other hand, real-time data can be critical in organizations that use this data to optimize headlines or marketing copy on the fly.

To avoid shiny object syndrome, discuss the right approach to real-time data within your organization. Just because real-time data is available does not mean that it replaces your rich, detailed historical data or that all analysis and reporting is done from real-time data. Although it’s important for the business to stay up to date on the latest events, getting results from your analytics practice also means allowing adequate time for in-depth analysis from your deeper data set.

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Requests for more real-time reporting should be accompanied with discussions of what actions this data will result in. For example:

Marketer: “We need a real-time dashboard!”

Analyst: “Okay. If we make that available, and we spot [some example] happening, what would you do next?”

Be realistic about how quickly your organization can act on the data and let that guide your use.

Advanced Data VisualizationsOne skill that analysts can improve that will have a guaranteed impact on their career is communication, and the communication of data is a vital part of this. You can be the greatest analyst in the world, but if you can’t effectively communicate and visualize data, your insights will go unheard.

Understand (and appropriately use) reports, dashboards, and analysisJust because your analytics tool can spit out a 47-page PDF file does not mean that is a good thing. Nor can you legitimately call that a dashboard.

Let’s start by defining what a dashboard is.

A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance.4

The key? A single screen. Dashboards are a single screen, not a multiple page report or slide deck. Anything longer than a single page or screen that is produced on a recurring basis is more appropriately called a report, and a one-time investigation of data is an analysis.

All presentations of data, whether considered dashboards, reports, or analyses should follow basic principles of data visualization. The principles covered here are not meant to be exhaustive, but provide food for thought as you put together your data visualizations.

For an overview of advanced visualizations available in Adobe Analytics, click here.

Present to promote understandingThe best presentation of data is simple and intuitive. It’s about making data easily understood, not making it look pretty.

Consider this example from Harvard Business Review (http://hbr.org/2013/10/visualizing-trouble/ar/1). Both these charts show the salary ranges for a variety of roles at Google. The chart on the left takes a cool, colorful infographic approach, but ultimately fails to convey the data any more effectively than a descriptive paragraph. To understand the salary ranges, you actually have to read the data! It might look like a data visualization, but really, it’s just a picture.

The chart on the right has less pizazz, but in one glance, you understand which role at Google has the highest possible salary, which has the lowest, and which has the greatest spread.

4 www.perceptualedge.com/articles/ie/dashboard_confusion.pdf

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It is not about which of these is prettier, but about which is more effective. In the age of infographics and readily accessible tools and data, it’s far too easy for anyone to create a data visualization that does not effectively visualize data.5

Take these two examples. These charts break a simple rule: If you have to give a detailed explanation of how to read your data visualization, it is ineffective.

To improve your data visualization and presentation skills, read material from data visualization gurus Edward Tufte, Stephen Few, and Colin Ware, and learn presentation tips from Nancy Duarte and Garr Reynolds.

Consider your job as that of an information architect. Your objective is to take complicated digital analysis and present it cleanly and clearly.

Present information in a way that is easy for the brain to processHuman processing information involves a journey through several memory systems, including data passing from short- to long-term memory. When you present data, your goal is to make that journey as simple as possible so that the message actually makes it through.

For example, the table on the left and the chart on the right show the exact same information. The table requires your stakeholder to read and absorb the information. The chart, on the other hand, is quickly understood: Visits are going up, and conversion is going down. The chart’s message is far more likely to pass from your stakeholders’ short-term to long-term memory.

A good test of your data visualization: Can you glance at it for a split second and take away the key message?

5 http://webdesignledger.com/inspiration/15-stunning-examples-of-data-visualization

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Don’t be limited by the visualizations available in your analytics toolSometimes effective data communication requires some finessing of the visualization. However, most analytics tools are limited in the amount of control they give you over charts and tables in their default reports and dashboards.

Using tools like Adobe ReportBuilder and Excel or the recent integration of Adobe Analytics and Tableau gives you control over data visualizations and the ability to provide the best possible data visualization for your stakeholders.

Abide by the data-pixel ratioTufte’s data/ink ratio (later to become Few’s data/pixel ratio) boils down data visualization to one simple rule: Pixels used should focus on displaying the data, not filler information.

Consider these two charts.

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The chart on the left wastes pixels on non-data by adding a border, including a legend where one is not needed, repeating the word “region” for each item, including grid lines, and including zeros in the dollar amounts.

A few easy ways to focus your pixels on data display and eliminate the clutter:

• Remove borders around charts

• Remove gridlines (or substantially lighten them)

• Condense numbers (for example, indicate that amount are “in K” or “in MM”)

• Remove unnecessary legends and axis titles, but include them where it’s necessary to understand the data

No 3D!Your job is to display information in an unbiased and easy-to-understand way. 3D charts actually obscure the truth by making it harder for the eye to quickly gauge size differences or read axes in line with the data.

Consider the processing effort involved just to read the axis value for this 3D column chart.

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Friends don’t let friends use pie chartsPie charts are everywhere. Unfortunately, pie charts make data more difficult to understand, because they work against our natural perceptual abilities. The human brain is not well trained in judging the size differences between circles. A bar or column chart is a far more effective visual than a pie chart, because it’s easier for us to judge relative size differences.

Use the right chart to deliver your messageKeep in mind that your job is not to deliver raw data; your job is to deliver the message from the data. Whenever we put together a data visualization, we do (and should!) have something we want the audience to take from it. Perhaps you want them to see that one operating system dominates another. Perhaps you want them to understand how fast a new channel is trending.

Although there are no hard and fast rules, these general guidelines can help you choose the right chart based on the message you want your users to receive.

To emphasize a trend Line chart

To emphasize differences between items Bar or column chart

To show the relationship between items Scatterplot

If you have limited space Sparklines

This flow chart from The Extreme Presentation™ Method provides further guidance on choosing the appropriate chart.

Don’t mix multiple items on a chart for no reasonTwo metrics or dimensions should only be charted on the same chart if, together, they tell a story that they cannot effectively tell separately. Our brains in fact assume a connection where series are charted together.

For example, if the purpose of your chart is to show that as paid traffic increases, conversion rate decreases, charting the two together makes sense and helps to convey that message.

The following chart shows Facebook engagement versus spend, with the two series included together to show that they trend similarly:

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If, however, two series of unrelated data are on the same chart, the chart becomes more difficult for users to process, because they naturally search for a connection that doesn’t exist. Do not include two series on the same chart purely to save space. For example, this chart combines two series for no reason.

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Be thoughtful about colorColor is for emphasis, not distraction. Color is another tool to help our brains draw connections between information, but when misused, it can confuse or lead our audience astray. Treat color cautiously. Be sure that color is not used haphazardly without meaning behind it. Consider these two charts.

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Germany

Portugal

Russia

USA

UK

France

Germany

Portugal

Russia

The colors in the chart on the left do not help us understand the data. The chart on the right, however, uses a consistent palette and deeper color to emphasize the data point of significance.

Consider grayscale and color blindness. Red to indicate “bad” or “down” and green as “good” is a common color scheme. However, keep in mind that 10% of the U.S. population is color blind, and anything printed in black and white is, too. Review your reports in grayscale to ensure that the red and green look different when printed or to those who are red/green color blind.

Use natural colors. Our perceptual systems are actually wired to respond best to natural colors, like those in the world around us. Avoid bright, garish colors and focus on more natural hues.

Use corporate colors. A simple color trick to help make your work look more professional is to create a template (for example, in Excel) that matches your corporate colors. This helps your work stand out from the default and brand it as being “official.”

What now? So little time, so much to do! So what should you do first? Start by examining your current analytics competencies, resources, and gaps. Identify which tips add the most value to your organization, and prioritize the short list by the level of effort.

Sit down with your key stakeholders to discuss the value they are getting from your analytics program and where the opportunities are. The most successful analytics teams work closely with the business, so involve them as you decide what to invest your efforts in.

Not enough time to do it all? Use these tips to show your organization what you could do with the appropriate resources and help to justify recruiting additional support.

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About Web Analytics DemystifiedWeb Analytics Demystified, founded in 2007 by internationally known author and former Jupiter Research analyst, Eric T. Peterson, provides objective strategic guidance to companies striving to realize the full potential of their investment in web analytics and optimization. By bridging the gap between measurement and optimization technology and business strategy, Web Analytics Demystified has provided guidance to hundreds of companies around the world, including many of the best known retailers, financial services institutions, and media properties on the Internet.

Michele Kiss is a recognized digital analytics leader with experience in web, mobile, marketing, and social analytics. Her experience ranges across a variety of verticals, including media and online commerce, telecommunications and technology, automotive, restaurant, travel, and home building as a client-side consultant and agency practitioner. Michele leads Web Analytics Demystified’s analysis and analyst mentoring practice, helping our clients to dramatically reduce the time to insight associated with their investment in analytics.

For more information, visit www.webanalyticsdemystified.com, email michele.kiss@webanalyticsdemystified.com or call (650) 690-2805.

About Adobe Marketing CloudNow there’s a place that puts everything digital marketers need in one spot. It’s called the Adobe Marketing Cloud. It includes a complete set of analytics, social, advertising, targeting, Web and app experience management and cross-channel campaign management solutions as well as core services and mobile capabilities that bring together everything marketers need to know about their campaigns. So marketers can get from data to insights to action, faster and smarter than ever.

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