Category Archives: Analytics

Pay-Per-Click Tip – How Mud Pie Generated a 954% Return on Ad Spend


Mud Pie is a B2B and B2C retailer of trendy and seasonal baby clothes, women’s apparel, gifts, home décor and more.


Mud Pie wanted to execute an omni channel marketing strategy for their annual Christmas Closeout sale.


Whereoware promoted the Christmas Closeout sale on Mud Pie’s website and in PPC and email campaigns.

Additionally, Whereoware developed a targeted Facebook advertising campaign, using both Facebook’s standard and retargeting ads. The successful campaign generated additional revenue from previously untapped sources.

The Facebook ads proved to be the second highest source of website traffic for the entire Christmas Closeout campaign, second to email. Even better – 70% of those website visits were new visitors.

See how Whereoware accomplished this task, along with more stats.

Google Analytics Tip – Using Demographics and Interests Data to Develop Personas

Marketers use email and website personalization to deliver a hyper-relevant online experience to every customer, but succeeding with online personalization isn’t easy.

According to Evergage, 88% of marketers say their customers expect a personalized experience, while 55% admit the marketing industry isn’t personalizing to those standards.  What’s more, 46% of marketers would give their own company’s personalization efforts a “C” grade, or less.

Developing user personas is key to executing a successful personalization strategy. Personas, fictional representations of key audience segments, help marketers understand the needs and interests of their audience segments, so they can make their customer journey more personal and relevant.

To get started, we collect and analyze available data (both qualitative and quantitative) to understand what makes different audience segments tick, so we can group them within logical and actionable personas. Today, we’ll walk through how to use Google Analytics Demographics and Interests data (a free tool) to capture additional quantitative data to develop detailed personas.

(Already have your data? Get detailed steps on creating user personas and our handy persona worksheet here.)

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Whereoware Releases Product FastLane, Product Information Management System

Product FastLane

Whereoware adds to its product offering with the release of Product FastLane, a cloud-based Product Information Management (PIM) system. Built specifically for e-commerce companies, Product FastLane is a user-friendly system managing all the moving parts (product data, images, videos, sales sheets, inventory, and more) powering organizations’ e-commerce websites, digital catalogs, and third-party marketplaces.

Product FastLane diagram

Within one centralized location, marketers can upload, optimize, search, and organize product data and related digital assets, ultimately reducing data management costs, while improving data quality and consistency. Marketers can access and update their product data, related images, and other digital assets any time and from anywhere. They can easily share them with third-party marketplaces, like Wayfair, Amazon, Google Shopping, and Zulily, so data stays compelling, consistent, and complete across the web.

Our industry is experiencing a transformation, where technology-driven companies are quickly gaining on competitors. The explosion of third-party marketplaces, and the evolving expectations of B2B buyers are driving marketers to take control of their product data. We witnessed our clients struggling to manage thousands of products while using spreadsheets and USBs. They needed a better way, so we built Product FastLane,” said Eric Dean, CEO Whereoware.

For more information on Product FastLane, visit

Mud Pie: Digital Analytics, UBX, & Real-Time Emails

Mud Pie-ubx-case study


Mud Pie is a B2B + B2C retailer of trendy and seasonal baby clothes, women’s apparel, gifts, home décor, and more.

What We Did

First we installed IBM’s Digital Analytics to the Mud Pie website, then we used IBM Universal Behavior Exchange to establish a connect between Digital Analytics and IBM Marketing Cloud.


Get the case study to see how targeted real-time emails achieve open rates over 35%.


Analytics Tip – Google Optimize

The Skinny

Google is beta testing a new landing page testing and optimization tool that integrates with Google Analytics and Google Tag Manager called: Google Optimize.

Doesn’t This Already Exist?

Sort of. Google has been trying to get into the optimization game for a while now. It started with Google Website Optimizer which was discontinued in 2012 for Content Experiments in Google Analytics. This brought A/B testing into the Google Analytics tool. Google Optimize is their latest iteration. Google Optimize 360 is also in beta, but the main difference between Optimize and Optimize 360 is price. Google Optimize (sans 360) is free and thus a little more limited than the 360 enterprise version.

The Google Analytics 360 suite is the premium, paid-for suite of Google products. There are free versions of these but they have some limits to their capabilities.

Want to see the full comparison breakdown of the two versions? Check it out here.

Continue reading Analytics Tip – Google Optimize

Analytics Tip – Hacked Spam Notifications Now in Google Analytics

Long Story Short

Google announced last month that they will now include hacked spam notifications in Google Analytics, not just Google Search Console.

Wait, what are these two tools?

Google Analytics: A free analytics tool that tracks and reports website traffic. Other functions include goal + benchmark settings and conversion tracking.

Google Search Console: A free search tool that helps you monitor, manage, and gain insight into you’re your site fits into Google’s search result pages.

Short Story Long

In September 2015, Google shared that they’ve seen a 180% increase in the number of websites getting hacked.

On June 21, 2016, Google announced they’re “expanding [their] set of alerts in Google Analytics by adding notifications about sites hacked for spam in violation of our Webmaster Guidelines. In the unlikely event of your site being compromised by a 3rd party, the alert will flag the affected domain right within the Google Analytics UI and will point you to resources to help you resolve the issue.”

To reach a larger audience, Google now notifies webmasters of malicious third party hackers to their site on Google Analytics. Previously, these alerts were only triggered in Google Search Console (formally known as Google Webmaster Tools). You do not need to have your website set up on Google Search Console to get the Google Analytics notifications – you can get notifications for one without the other.
Photo from Google of what the notification may look like:

hacked site notification

Tips to Keep Your Site Safe

1) Verify your website with Google Search Console to get notifications through the Security Issues feature.
2) Continually update your website software, CMS, and any plug-ins/add-ons used on your site with the newest releases.
3) Use a strong password that you don’t use for anything else, if available use two-step verification. Two-step verification usually means you first use your username and password to login, then a code is sent to a secondary email or phone number for you to enter, in order to gain access.

If your site does get hacked, you can find resources like this, for how to resolve any issues.

All in All

Take all necessary precautions to keep your website safe from malware and hackers. Create both Google Search Console and Google Analytics accounts to gain valuable insights from hacked spam notifications to how your site is positioned on Google search results to where your traffic is coming from and how they’re interacting with your website.

Analytics tip: Attribution Models 101

Google Analytics tipAs marketers, we need to understand our audiences’ motivations for buying products, downloading whitepapers, calling for a demo – whatever action meets our business goals. Once we understand customers’ activity leading up to a sale, we can focus resources on the most effective path to conversion.

This would be easy, except each customer interacts across an omni channel journey. Perhaps they’re introduced to our brand on Facebook, sign up for emails, and then click through the email to purchase on our site. Which of these channels motivated them to convert?

Attribution models assign weighted credit to touchpoints across the omni channel journey, so we can best allocate resources and understand marketing effectiveness.

Today, we’ll walk through examples of popular attribution models, and demonstrate how to use Google Analytics’ Attribution Model Comparison Tool to choose the right models for your business.

Attribution Model use case

Let’s pretend you sell barbecue grills. Mr. Grillmaster is performing a Google search for barbecue grills and clicks on your website. He signs up for your emails, but doesn’t buy anything. A few days later, he receives your email, clicks through to your site, and browses models. A week after, he clicks a Facebook post offering free 2-day shipping. Memorial Day is this weekend, and he needs the grill! Mr. Grillmaster clicks from the ad to your website, but decides he needs to talk to his wife first. A day later, Mr. Grillmaster lands back on your website via your URL and purchases the grill.

His customer journey looked like this: Organic Search, Email, Social Network, Direct.

Google Analytics Attribution Models 101

In the Last Interaction attribution model the customers’ last touchpoint receives 100% credit for the conversion. In Mr. Grillmaster’s scenario, the Direct channel receives all the glory, if we’re following a Last Interaction Attribution Model.

The Last Non-Direct Click attribution model ignores direct traffic, and instead gives 100% credit to the last channel clicked before the sale. Social Network would receive 100% credit in a Last Non-Direct Click attribution model for Mr. Grillmaster.

The Last AdWords Click attribution model assigns credit to the last AdWords or paid search click. This attribution model doesn’t make sense for Mr. Grillmaster, unless the Facebook ad was tagged for AdWords, in which case his journey would look like: Organic Search, Email, Paid Search, Direct. Paid Search would then receive 100% credit in a Last AdWords Click attribution model. (Depending how social media ads are tagged, they can fall under Social Media or Paid Ads.)

By now, you’re getting the hang of attribution models. A few more:

Similar to Last Interaction, the First Interaction attribution model attributes 100% credit to the first touchpoint—in Mr. Grillmaster’s case Organic Search.

The Linear attribution model divides credit equally between each channel. Organic Search, Email, Social Network, and Direct would each receive 25% credit for the sale for Mr. Grillmaster.

Next, the Time Decay attribution model gives predominant credit to the channels closest to the sale and less credit to channels further down the line. The majority of credit for Mr. Grillmaster’s purchase would go to the Direct channel and the least credit to Organic Search.

Finally, the Position Based attribution model assigns 40% credit to the first and last channel, and splits the remaining 20% credit evenly between the middle channels. Organic Search and Direct would each receive 40% credit for Mr. Grillmaster’s barbecue grill purchase, and Email and Social Network would each receive 10% credit.

Exploring Attribution Models in Google Analytics

Most of Google Analytic’s standard reports use Last Non-Direct Click attribution, but they now allow you to compare and contrast attribution models to decide which model delivers the most insights into your customers’ lifecycle journey. *Note, you need to set up Goals or E-commerce transactions in Google Analytics for the Model Comparison Tool to work.

In Google Analytics, scroll down to Conversions > Attribution > Model Comparison Tool. Find it:


By default, it is set to the Last Interaction model. You’ll see five channels, and both the Last Interaction Conversion number and percent (the weighted number of conversions under the selected model) and the Last Interaction Conversion Value (the weighted value credited under the selected model).

Compare another model via the Select Model dropdown. We chose to compare Last Interaction with First Interaction. The tool calculates the percent change in Conversions or Conversion value from the initial model.


Additional options: Under the Conversion dropdown, select the data to review. We’re currently looking at both Transactions (E-commerce) and Registrations (Goals). Under Type, toggle between all data or only AdWords data. Adjust The Lookback Window from one to 90 days to set the length of time tracked prior to each conversion.

The Model Comparison Tool enables you to compare up to three models at once. Add secondary dimensions, like Source/Medium, to dig deeper into the path to conversions. For example, adding the Secondary Dimension Source breaks down our Organic Search into Google, Bing, and Yahoo. This information can help us allocate ad spend across search engines.



Attribution models aim to measure the impact each channel has on conversions across our customers’ omni channel journey. They aren’t perfect and don’t simply tell us what to do. Instead, they help us analyze and make sense out of all our behavioral data across touchpoints, so we can make data-driven judgement calls. Play around with these models to tailor your marketing strategy.