Join us January 15th for an AMA with Champion Achaia Walton, who will be talking about her article on Event-Based Reporting and Measuring Content Groups!
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Scatter Plots in Analysis Workspace
Last week, I wrote about how to use the new Venn Diagram visualization in Analysis Workspace. Now I will discuss another new Analysis Workspace visualization – the Scatter Plot. This visualization should be familiar to those in the field and has been available in Microsoft Excel for years. The purpose of the scatter plot is to show two (or three) data points on an x/y axis so that you can visualize the differences between them. In this post, I will continue using my blog as an example of how the scatter plot can be leveraged.
SCATTER PLOT VISUALIZATION – STEP BY STEP
The first step in creating a scatter plot visualization is to create a freeform data table. This normally means adding a dimension and a few metrics. I would recommend starting with two metrics that you want to see plotted against each other. Here you can see that I am looking at my blog posts sorted by popularity and also added Visit Time Spent:
Read the rest of this post on analyticsdemystified.com
Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics
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Visual Customer Insights with Adobe Analytics Activity Map
Growing up, I often enjoyed playing flight simulators on my family’s computer. My personal favorite was one that put the player in the cockpit of a powerful fighter jet called the F/A-18 Hornet. The Hornet was sleek and fast, and I loved to kick on the afterburners until my airplane disintegrated under the stress. But, my favorite part of the game was the simulated Head-Up Display (HUD). The HUD allowed you to find information about altitude, pitch, other airplanes, points of interest, and more — right on the windshield of the digital airplane. It was so much easier to process all of this information and have a productive flight with the HUD enabled.
Data Access and Interpretation on an Adult Level
That same, almost-effortless data interpretation — gained from not having to look away from what I’m doing to access it — that I enjoyed as a child applies to customer intelligence on the web as well. In April, Adobe Analyticsgained a brand-new approach to in-context analytics that allows marketers, designers, content owners, and others to view user-click and interaction data directly on the pages of their websites: introducing Activity Map. Activity Map is a browser plug-in/add-on/extension with support for all major desktop browsers. It runs right over the top of your website and makes your data come alive in vibrant color with visual overlays that draw the eye to areas of interest for your customers. For anyone interested in how users are interacting with content, links, A/B tests, and more, Activity Map provides you with insight right on the page and makes it much easier to come to an informed decision about how to optimize. It’s like having a grownup version of the HUD for your Adobe Analytics data.
Using a very simple example, let’s say that I use Activity Map on the Adobe.com homepage. (NOTE: the screenshot above is using fake data.) By putting this data right on the page, I can see exactly where people are clicking — as well as where they are not clicking. I might discover that links to the Community Forum are really popular, while links to another area of the site are not. Having this information enables me to give better placements to the links that people are actually clicking, moving them to the main body of the page. Better yet, I may decide to run a test to see whether that placement yields better results than the incumbent experience. An insight that pops off the page in Activity Map becomes an A/B test, which becomes a better experience for my customer.
Four More Reasons to Love Activity Map
Here are just a few of the reasons we’re so excited about the new Activity Map tool:
Different Customer Segments Respond to Different Content and Links.
Activity Map allows you to apply any segments from Adobe Analytics to your data in context. Maybe your loyal customers gravitate toward one type of content, while prospects prefer something completely different. Segmentation in Activity Map makes this distinction clear and helps you to provide the right experience to the right segment at the right time.
Better Metrics in Activity Map Produce Better Insights.
Of course, Activity Map still offers clicks as a metric, but you can do so much more by using any Adobe Analytics metric to understand the downstream impact of a particular user interaction on conversion. You could have links on your homepage that are clicked a lot but don’t actually produce any leads, revenue, subscriptions, or reader loyalty.
Live Mode Presents Users With Real-Time User Interactions.
Many of you told us that you wanted to be able to see user interactions in real time, so we added a “Live Mode” to Activity Map, which streams data collected from your site straight into Activity Map. If you’re a content publisher, you can not only see, but also immediately understand the most popular articles, videos, and photos over the past 15, 30, 60, or 120 minutes — with granularity as high as minute-by-minute — and then adjust headline stacks accordingly.
Integrated Pathing Helps Users Visualize Broader Customer Journeys.
We’ve integrated pathing data directly into Activity Map so that you can see not only what engaged people when they were on a given page, but also how they arrived at that page, and where they went immediately afterward. This adds to the on-page insights by painting a picture of the broader customer journey.
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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/visual-customer-insights-adobe-analytics-activity...
The Blog post below is from Jeff Allen, Sr. Director of Product Marketing for Adobe Analytics
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New Adobe Analytics Capabilities Make Powerful Insights More Accessible
Behind every great digital experience is robust data. Each time a consumer launches an app or puts an item in a shopping cart, a piece of data is created. Brands that are able to gather all of this and make sense of it can engage their audiences in ways that are intuitive and highly personalized. As consumers become increasingly selective about how they spend their time across digital channels, data is the secret sauce to draw in new customers and retain existing ones.
The tools we provide in Adobe Analytics bring together Adobe’s creative heritage with a robust data platform, to not only help brands analyze data and uncover insights at the click of button, but to also become better storytellers in driving action across their organizations. With Analysis Workspace, users have a visual editing platform to deliver, discover, and visualize insights and curate them to share with the organization.
Today we are announcing new capabilities in Analysis Workspace that furthers our mission to democratize analytics.
Flow Exploration and Fallout Analysis
The first step to delivering great consumer experiences is having an understanding of how people are navigating your digital channels and where they are hitting stumbling blocks. A new Flow Exploration capability within Analysis Workspace let’s brands visualize customer movement through digital experiences, showing the steps taken from entry point through to conversion or churn. Segments of users can be created based on usage patterns. A cohort of customers who are bundling related items, for instance, can be engaged with content on other suggested pairings.
The new Fallout Analysis in Analysis Workspace allows brands to easily drag, drop, and rearrange steps along the user experience to better understand at what point users are disengaging, in addition to insight around where they go after the fallout. With this data available, brands can implement the necessary adjustments to not only retain users but also improve the experience and drive more loyalty. Analysts can also create audience segments from users who stay in the funnel, or those who fall out, at any stage for remarketing and personalization.
A home improvement retailer, for example, might see that, on their mobile app, users who have to leave the shopping cart to view more items abandon purchases at higher rates and don’t return. This insight can be leveraged to re-design the cart experience as a floating interface that remains on the app screen.
Getting A Faster Start
Organizations are increasingly pushing every team member to become more data-driven in their decision-making. Given that, tools must appeal to the most novice user while still serving the needs of the most advanced analysts. We all know how difficult it is to stare at a blank page, which is why we are introducing new starter projects in Analysis Workspace. These will provide a more natural ramp-up for those trying Analysis Workspace for the first time. With out-of-the-box answers to common business questions across the web and mobile apps, as well as specific templates for vertical industries including Retail and Media & Entertainment, no one should have to be a data scientist to leverage insights to improve their work.
Seeing The Forest for the Trees
In this release, we are rolling out a series of new visualizations, as well as enhancements within Analysis Workspace that will provide users more tools to curate and present insights in ways that resonate most with stakeholders.
Time-series analysis: Within line graphs, users can now forecast expected future outcomes or automatically detect data anomalies and run contribution analysis in a single click to determine the cause of unexpected performance. A financial services company could tap into this to determine the cause of a spike in credit card applications during a typically slower month, for example.
Histograms: In a classic bar chart, each bar shows an individual value. A histogram is different in that it presents distribution. This is useful for brands as they’ll be able to pinpoint the most valuable and least valuable segments of customers, as well as the distribution of a certain behavior. A clothing retailer, for instance, can identify that most shoppers spend between $25 and $50 and turn that into a key segment for holiday promotions.
Date comparisons: At the push of a button, users can now compare data year over year, month over month, or day over day. All of this is presented in a single graphic to visualize the change, while serving up data points around percent changes as well as raw values. This can better inform everything from inventory to campaign planning.
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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/new-adobe-analytics-capabilities-make-powerful-in...
Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics
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Empowering Organizations With Smarter Analysis
As we talk with people — from thousands of companies all over the world — about their efforts to drive growth through smarter, data-informed decisions, a few clear trends emerge. First, analysis tools must make it easier for everyone in the organization — from top to bottom — to obtain insights. Second, and relatedly, analysis tools must assist all data consumers with achieving more relevant insights by bridging the gap between Big Data and human intuition with machine learning and other “smart” capabilities.
I’m excited to be part of the team that is realizing the vision of these two themes. The Adobe Analytics Fall 2016 release, available now, includes a handful of new features and capabilities that will directly help you drive better decisions through “smarter analysis,” aided by powerful machine learning within Adobe Analytics. In the rest of this post, I’d like to walk you through what our team has done to make your analysis tools smarter.
There’s a lot of great stuff in here, so grab a coffee, and let’s get going. By the way, everything I share in this post is available today in Adobe Analytics.
Intelligent Alerts
You can now receive an early-stage “heads-up” that something truly significant is happening in your business. We have updated our built-in email- and SMS-alerting capability to use Anomaly Detection as the default mechanism for determining whether to notify you of a spike or dip in a key metric. Anomaly Detection looks at your historical data to determine an expected range of values for a metric, and now it also takes seasonality and major holidays (including Black Friday) into account. This means that you can expect no more false-positives in your alerts — if your traffic always decreases on the weekend, you won’t be alerted every single Saturday morning. Alerts that you set up based on Anomaly Detection will be triggered only when there is a statistically significant spike or dip.
Another huge improvement here is that you can “stack” alerts. Let’s say, you have six different metrics that a certain team is interested in. You can set up a single alert that includes all six metrics and set it to be sent to everyone in the given user group. This carries several advantages. First, you will only need to manage one alert instead of six. Second, if all six alerts happen to spike simultaneously, the recipients will only receive one email instead of six, meaning it cuts down on what we might call “alert fatigue.” Third, by sending to a user group, new employees who are added to that group will automatically begin receiving these alerts; similarly, employees who move out of that group won’t need to be manually removed from the alert.
You can set up alerts a few ways. You can always go to Components > Alerts to create a new alert or manage existing alerts. But, you can also right-click just about anywhere in Analysis Workspace to set up an alert based on the data point you have highlighted. That’s my preferred way, because it preconfigures the alert for you and allows you to stay in your workflow without leaving Analysis Workspace.
Take special note of the “Alert Preview” window in the upper-right corner of the Alert Builder. This view lets you know how many times the alert you’ve configured would have been triggered based on your recent data. By observing how often the alert would have triggered, you can adjust your anomaly thresholds or other criteria so you are not alerting your users too often.
Distribution of alerts has also been improved. The email you’ll receive looks a lot nicer, and you can send alerts by both email and true SMS. International telephone numbers are supported, and you’ll want to enter the country code as well (as shown in the screenshot above).
Automated Anomalies in Analysis Workspace
If you’re like most of us, you value anything that saves you time. This effect is probably multiplied in the world of data, where separating signal from noise can be difficult for even the most polished analyst. That’s why I am so excited about improvements to Anomaly Detection, as we bring this powerful machine-learning technology into Analysis Workspace.
Adobe Analytics has featured Anomaly Detection since late 2013. But to this point, it has only been available for daily data, and it has been confined to a report in Reports & Analytics. With this release, any hourly, daily, weekly, or monthly time-series data in Analysis Workspace — whether in a table or a line graph — will automaticallyshow anomalies based on your historical data and predicted trends.
Create a freeform table, using date as my dimension, and I immediately start receiving insights into statistically significant spikes or dips in my metrics. Add a line graph, and anomalies are called out as hollow data points on the graph. Hover over any of these points in the table or on the graph to gain more details about the anomaly.
As a bonus, if you are an Adobe Analytics Premium customer, you can right-click on these data points to run a Contribution Analysis, which scans hundreds of thousands of values of dimensions to find the likely causes of an anomaly — directly in Analysis Workspace, so you can embed the why along with the what in your projects.
In keeping with the idea of automated insights, there is nothing you need to do to turn this on or to activate this feature — just start building time-series data tables and visualizations, and away you go!
Histograms
Averages (means) are good for quick snapshots, but as any analyst will tell you, they often hide tremendous insights behind their single-number façade. Histograms, which we’ve added as a visualization in Analysis Workspace, tease out those insights by showing the distribution of your audience across buckets (“bins”) representing escalating tiers within any metric. This makes it much easier to identify high- and low-value segments and market to them accordingly.
To use a histogram, just drag it from the collection of visualizations onto one of your panels. It will ask you for a metric, which you can also supply from the left. The histogram will bucket your visits or visitors according to how much/many of the selected metric they had. For example, if I use revenue as my metric, by default, it will bin my visitors by how much revenue they had. Using the advanced settings, I can adjust the number of bins, the size of each bin, and the starting bin (primarily, so I can choose whether to include visitors who had zero of the selected metric).
The distribution of revenue per visitor in my dataset is mostly normal; however, there is a bit of an uptick on the right side, so the group of customers who are spending $600–700 is larger than I might have expected. If I want to analyze that segment further, I can click the dot in the upper-left corner of the visualization and choose to show the data source. In the data table that is revealed, I can find this segment and begin to explore it directly in that data table. I can also drag it to the top of the panel to apply it as an ad-hoc segment, so I can add other data tables and visualizations that will help me better understand this group of customers who are behaving interestingly.
Conclusion
I’m sure you can see how we are advancing our goals to empower your organization with more recommendations powered by machine-learning intelligence and advanced analytics, but these features, and the benefits they provide, are just the start for us. We’re excited to continue our journey in helping you turn your company into a truly brilliant enterprise.
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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/adobe-analytics-fall-2016-release-empowering-orga...
Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics
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Making Insights Easier for the Enterprise
In my last post, I covered the aspects of the Adobe Analytics Fall 2016 release that make Adobe Analytics“smarter,” aiding your analysis with machine learning and intelligence. This is one of the most pressing needs we encounter, as we speak with people from large and small organizations all over the world.
In this post, I break down the features in our most recent release that make it easier for users of all experience levels to generate insights from within Analysis Workspace. And, as with my previous post, everything discussed here is available today in Adobe Analytics.
Customer Journey Analysis
One of the first pieces of feedback we received after we released Analysis Workspace was that you wanted more insight into the customer journey. You explained that you need to understand how your app users and site visitors are progressing through their digital experiences, so that you can remove barriers from the conversion process to optimize revenue-generating opportunities. I’m happy to report that we have added two customer-journey visualizations — Fallout (which includes Funnels) and Flow — to Analysis Workspace (the alliteration was entirely unplanned):
- Fallout — Fallout is designed to help you analyze each user’s progress through clearly defined paths (or funnels). When you know the exact set of steps that you want to analyze, Fallout is for you.
- Flow — Flow, on the other hand, is more exploratory, allowing you to expand and contract your view to examine different ‘branches’ and repeatedly ask, “But, what happened next?”
You can add both Fallout and Flow to any Analysis Workspace project from the left rail where you will find them in the visualizations area. After placing them on the canvas, you can begin adding touchpoints in the forms of dimensions, values, segments, and/or metrics.
One of the great improvements to pathing with this release is the ability to use Custom Conversion (eVar) values as touchpoints in addition to more traditional dimensions like Page, Site Section, Video Name, and more. The Fallout visualization also allows you to use metrics as touchpoints. Here’s an example using a combination of eVar values, page names, and metrics to create a funnel (with the right-click menu expanded as well).
You can also mix and match dimensions in the Flow visualization. Following is an example that shows entry pages along with the internal search keywords that followed from those entry pages. Not shown is the ability to hover over any of these branches to see them highlighted, calling attention to the path you’re investigating.
Included in these customer-journey visualizations are rich segmentation functionality. As you probably expect, you can apply segments to any of these visualizations. However, you can also right-click anywhere to build a new segment based on the path you have defined, so you can use that segment elsewhere to better understand the users who follow (or don’t follow) specific paths. One of my favorite features of the Fallout visualization is the ability to add multiple segments directly to the visualization — by dragging and dropping them into the header — to compare how different segments fell out of these funnels.
Entire blog posts could (and probably should!) be written on just these two new customer-journey visualizations, so I recommend playing around with them and trying out everything they can do. Remember that right-clicking reveals access to a bunch of additional options you can use to extend the power of these new tools even further.
Starter Projects
As mentioned above, roles in the organization that may have ignored data previously are finding that they can no longer do so. But, analysis can be difficult, overwhelming even! There are so many ways to define success — and so many options for visualizing and digesting customer insights — that it is sometimes difficult to know exactly where to start. Curation in Analysis Workspace helps solve this problem by limiting thousands of dimension, metric, and segment choices down to the few that matter to a given team, but it still relies on the analyst to create these projects.
Enter stage left, “Starter Projects” in Analysis Workspace. Starter Projects are easy-to-use templates that Adobe has designed and built around specific areas businesses question such as “Content Consumption,” “Mobile App Performance,” “Acquisition,” and more.
Any user of Analysis Workspace can launch a starter project and be presented with a standardized set of visualizations that address the topic selected. And, since they are in Analysis Workspace, they come with the ability to do additional breakdowns, comparisons, curation, sharing, and more.
If I were a product manager for a mobile app, I’d be very interested in understanding app retention — in fact, this might be one of the key areas on which I am focused. But, it would be difficult for me to understand app retention by building my own project from scratch. Instead, I can now come to Analysis Workspace and launch this handy Starter Project. If I need to do some customization — add a new visualization or apply segments, for instance — I can do that. I can even save my changes as a new project and share it with the rest of my team so that they can check out the progress we’re making toward our retention goals as well.
These Starter Projects are also pre-curated to the dimensions and metrics that are most relevant to the business question at hand to prevent users from being presented with too many customization options when they first launch one of these templates.
There are 15 Starter Projects to choose from today — with many more coming soon in these and other role-based categories. This is just the beginning of our efforts to help you expand the wise and simplified use of data in your organization.
Permissions Improvements
As you may have seen in a previous announcement, this release also includes major improvements to permissions in Adobe Analytics as well as across Adobe Marketing Cloud. Within Adobe Analytics, admin-level users will notice a handful of changes.
Perhaps most excitingly, you can now set permissions on Custom Conversion (eVar) dimensions. In the past, you could grant or restrict access to any conversion metric but not to these dimensions. This change gives you greater control over data access, ensuring that only the right people can see the data in your conversion dimensions.
This change also moves access to various Adobe Analytics tools — such as Ad Hoc Analysis and Report Builder — into custom groups so that you can manage 100 percent of permissions in one place rather than be required to jump back and forth between pages in the Admin Console to control access to these tools.
Ultimately, this update sets the stage for better user management in the future — all across Adobe Marketing Cloud (as well as Creative Cloud and Document Cloud) — through the Adobe Enterprise Dashboard.
Analysis Workspace User Interface (UI) Enhancements
Last, but certainly not least, we made a number of improvements to the core of Analysis Workspace itself. You’ll notice significantly better in-browser performance. Right-click menus, the Segment Builder, and other components of Analysis Workspace load much faster, as do breakdowns you perform.
We’ve refactored the Segment Builder and the Metric Builder so that they use the same left-hand rail as Analysis Workspace — with dimensions, metrics, and segments aligned vertically; and the ability to obtain search results that include all types of components that can be added to your segments and metrics. This not only makes it faster and easier to find the components you are looking for, but also makes segment and metric creation more efficient.
We redesigned date selection in Analysis Workspace to make it more like the calendar in Reports & Analytics. You can now select both the start and end dates from the same calendar rather than have separate “start date” and “end date” calendars.
And — perhaps my personal-favorite, sneaky-cool change to Analysis Workspace in this release — when selecting dimensions, metrics, segments, and time ranges from the left-hand rail, you can now click the “Actions” menu to tag, favorite, approve, share, and/or delete what you have selected. This makes it so much easier to manage your segments and metrics (among other things) while you’re working within Analysis Workspace. You no longer need to go over to the Segment Manager or Calculated Metric Manager.
Conclusion
We’re excited to continue delivering on the promise of Analysis Workspace by making it even easier for organizations to drive better decisions through data-informed insights. The Adobe team will continue working to empower you and your colleagues with simpler experiences throughout Adobe Analytics.
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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/adobe-analytics-fall-2016-release-making-insights...
Below blog post is from Jon Viray, Product Marketing for the Adobe Marketing Cloud People and Activation core services
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Top Four People-Based Marketing Use Cases
A few weeks ago, I flew to Houston, Texas. It was great–a short, comfortable flight. And, there were enough vacant seats to give our youngest her own. After an hour, I picked up the shopping magazine in the pouch in front of me, despite having never bought anything throughout decades of flying. I couldn’t help it.
After flipping through page after page of seemingly useful — yet, unconventional — products, I came to the same conclusion that I’ve come to for years: Not one product had a valuable use case for me. All of them were solving problems I didn’t have.
As marketers, we sometimes slip into this trap of spamming our consumers with products that solve problems they don’t have. Oftentimes, it’s not that we don’t have the right product, but rather, we don’t have the right context. A single-device understanding of consumers doesn’t provide enough context to deliver truly personalized offers, especially considering that the average consumer owns more than three devices.
People-based marketing solves this problem. People-based marketing means that you don’t treat a device as a person. Instead, you can link devices that are used by the same person and then treat that person as an individual across all their devices.
Top Four People-Based Marketing Use Cases
With the correct components in place, people-based marketing can have a major impact on everything marketing — from reporting and analysis to attribution and experience. In this post, we’ll walk through the top four use cases for people-based marketing.
1. People-Centric Reporting and Analysis
Historically, digital-marketing measurement was built on a foundation that was intended to understand people but designed to understand devices. When a device graph is integrated with an advanced analytics solution, the device graph can transform the context of reports from being device-centric to being people-centric. This means that — for the first time — marketers can understand how many people (rather than phones and tablets) visited their site, or how many people (rather than laptops and Apple watches) interacted with their brand across multiple domains, apps, or even a brand’s offsite advertising.
To illustrate this point, let’s imagine a marketer launches two campaigns. The first campaign reports $10 of revenue per visitor. The second campaign reports $20 of revenue per visitor.
Using a device-centric metric (like revenue per visitor) creates the impression that the second campaign, costs being equal, is more effective. However, using a people-centric metric to analyze these campaigns, the same marketer might see that the first campaign touched one person across three devices, making the revenue per person $30; compared to the second campaign that touched one person on one device, giving the campaign a $10-per-person revenue. From this insight, an analyst could build a compelling case for promoting the first campaign to hit their quarterly revenue target.
People-based marketing gives marketers insights on people — not devices.
2. Seamless Cross-Device Experiences
Personalization tools strive to deliver meaningful experiences, but these tools alone can only deliver the right experiences to familiar devices. What happens with unfamiliar devices your customers use to interact with your brand? What do they see then?
For example, if an avid reader made it halfway through a riveting article on her tablet, and then visited the same website from her phone to finish reading the article, what kind of experience would she have? She’d likely fumble over the keys to enter a search, scroll down the page, and meticulously comb through each line of copy until she found exactly where she left off — far from ideal.
A people-enabled personalization tool offers a drastically different story. The reader would get halfway through the article on one device, pick up another device that she has never visited the publisher’s website from, and still be brought to the exact article she was reading on the first device.
People-based marketing makes seamless, cross-device experiences possible — experiences that are continuous, consistent, and compelling.
3. Cross-Device Efficiency for Advertising
Traditional advertising platforms, just like analytics and personalization platforms, are susceptible to the same device-centric limitations.
For the display advertiser, keeping tabs on his ROI is a relentless top priority. A common way to assure the return on ad spend (RoAS) is maximized is to apply a frequency cap on the number of times someone sees the same ad. Unfortunately, frequency caps apply to devices — not people. So, a frequency cap of five impressions per person can quickly become 20 impressions per person if each person uses an average of four devices. This leads to wasted ad dollars and perturbed customers.
People-enabled advertising platforms apply frequency caps that span the various devices used by a person. A cap of five impressions means a cap of five impressions for a person. Using the previous example, a people-enabled advertising platform could have delivered a 75-percent higher ROI than the non-people-enabled advertising platform.
4. Holistic Attribution
Attribution is constantly evolving to include more touchpoints, more-accurate weighting, and machine learning. So, it’s ironic that one of the most important factors in understanding the impact of marketing on the buying behaviors of people has always been missing.
Traditional attribution algorithms only analyze pre-login information from a single device. And, considering that the average person owns three devices, it’s likely that a big part of the attribution story is missing.
For instance, if a consumer visited company A’s website from her laptop, was retargeted with a display ad, and then finally converted on the website; a linear-attribution model would yield something like this:
But, in reality, this same consumer also conducted a search on her phone, read an article from company A’s site, and then went back to her laptop to make the purchase. Using the same linear-attribution model that’s being fed information from a device graph would yield this:
After a month, the marketer sees that this same article is among the top-ten, most-valuable traffic sources according to the linear-attribution model. As a result, the marketing team surfaces the article’s dialogue on all product pages (which truncates the customer journey to three touches), decreases their search spend on the keywords that point to the article, and all-in-all delivers a better experience to consumers as well as a stronger bottom line for the business.
With holistic attribution, marketers can now prove the value of all their marketing within the context of people — not devices.
A Very Real Payoff
Marketing has always been about understanding consumers to the extent that our messages and offers deliver solutions for which they are willing to pay. But, it’s only recently that nailing these key ingredients alone won’t necessarily convince consumers to make purchases. Consumers want consistent, continuous, and compelling brand experiences. Device-centric marketing can’t deliver this — but people-based marketing can!
While I haven’t personally purchased anything from in-flight magazines (like SkyMall), they’re successful business endeavors, and their magazines have been my go-to source for in-flight entertainment for many years. With a captive audience of people looking for a distraction while their devices are stowed, SkyMall may not need people-based marketing. For the rest of us, people-based marketing is like setting your tray table and seat in the upright position — you simply have to do it.
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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/top-four-people-based-marketing-use-cases/
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