Customer Experience Analytics Formula Blog

Enhancing Path Analysis to Optimize Customer Experiences

Posted by Steven Perry on Apr 11, 2017 7:32:46 AM

Introduction

Analyzing the paths that your customers take across your pages can certainly tell you what they are doing and where they are navigating on your site, and this insight can begin to reveal trends in customer behavior and help you determine which paths are most successful. But, today, to truly optimize your online customer experiences requires both quantitative and qualitative digital analytic capabilities, where you also understand why customers choose – or perhaps abandon – particular paths. The integration of these capabilities, as enabled by IBM’s new Watson CXA offering, provides a consolidated view of your customers’ journeys and experiences allowing you to leverage your customer data as a competitive tool.

 

Analysis Overview

By combining the observations from IBM Digital Analytics (DA) Clickstream reports that analyze paths that visitors take through your website with the increased visibility into customer experiences offered through Tealeaf analysis, you are better enabled to optimize your site and maximize customer conversions.

 

Analysis Benefits

  • Analyze paths that visitors take through your website to reveal trends in visitor behavior and help you to determine the paths that are most successful in leading to conversions.
  • Incorporate customer path analysis with additional visibility into your customers’ experiences to assist you with identifying best practices and areas of improvement for optimizing your site.
  • Offers enhanced insight that you can use to improve your customers’ experiences thereby increasing customer satisfaction, building brand loyalty and maximizing sales.

Analysis Formula

This customer experience analytics (CXA) formula will assist you with leveraging insight from IBM DA Clickstream reports and combining this customer navigation data with the analysis of customer experiences available from Tealeaf. At the same time, the formula will illustrate how awareness of customer paths observed in Clickstream reports can be used to assist with building the necessary events in Tealeaf to provide optimal reporting on customer experiences.

 

Understanding customer navigation paths with Clickstream

First, you will want to use Clickstream reports to analyze paths that customers take through your website either before or after they visit a specified page. To ensure these reports offer a valuable view into customer navigation, you should create Clickstream reports that provide the following analysis objectives:

  • Track entry page performance: Analyze how many sessions are departing the website from a specific entry page and/or analyze the path of sessions before or after the customer arrives at the page. This information can help you identify underperforming entry pages and offers opportunities to enhance the effectiveness of your entry page.
  • Track path abandonment from specific pages: This analysis can help evaluate and address causes for path abandonment from specific pages in the path.
  • Improve site search design: Analyze the use of your site search input mechanism and results pages. You can use this information to increase search usage and search usability on your site.

Through the Clickstream reports, you can begin to reveal trends in customer behavior and quickly identify those paths that are the most successful in leading to conversion, as well as determine which paths are frequently abandoned by customers. These reports can help you understand: What were the paths – and pages – associated with successful sessions? Are customers following your designed conversion path? Where do customers typically abandon a path? When customers abandoned a page, where did they typically navigate next? So, the Clickstream reports are very helpful in identifying what is happening on your site and where customers are navigating.

 

Clickstream_image 1.png

 

Insight into Customer Experiences with Tealeaf

Armed with the information on what paths customer are navigating, you can then use the additional analysis offered through Tealeaf to drill down further and understand the customer experience and why customers are navigating – or abandoning – a particular path.   Tealeaf’s capabilities allow you to segment sessions to understand what impacted the customer experiences and influenced the customer behavior.  For example, once you identify particular pages that are involved with successful paths or abandoned paths, you can create the appropriate events in Tealeaf to help segment and better understand this data.  Some of the events and dimensions you could consider building include:

  • Events that capture when customers click on the submit order page and receive the confirmation page (i.e. complete a successful conversion)
  • Events that capture when customers have placed items in their cart but do not click to submit their order or do not receive the confirmation page (i.e. abandoned cart resulting in a lost conversion)
    • The above events can then by segmented by the dimensions of payment type, product ordered, coupon code entered, and browser type used to help you identify what factors may have led to success or failure with conversions on your site.
  • Events that capture search terms on frequently abandoned pages that can then be used to help with improving search and search terms on your site.
  • Events that capture clicks on videos or other call to action (CTA) on popular pages in paths (as identified in Clickstream reports) could also be helpful in understanding if they contributed to shortened customer paths or may have led to longer paths to conversion.

Understanding the customer experience factors that lead to success or failure allows you to take the appropriate actions to replicate best practices or rectify issues that may cause customer struggle.

In addition, the session analysis and session replay capabilities in Tealeaf further enhance your view into the customer experience.  By allowing you to identify and replay particular sessions, you are able to see exactly what your customer encountered on your site for both positive and negative experiences.   Did they try to submit a particular coupon code that was not accepted and then abandoned the check-out page?  Did certain search terms they entered lead to success or failure with converting to a sale?  Was there a particular page that was slow to load on your site, so they navigated to another page?  With Tealeaf analysis offering increased visibility into the customer experiences, you are able to quickly and easily identify where your customers may struggle, as well as recognize what led to positive experiences and sales conversions on you site.

 Session Replay_image.png

Enhancing path analysis to optimize customer experiences

IBM DA Clickstream reporting sheds light on the paths that customers navigate, but combining this data with the capabilities of Tealeaf analysis can further illuminate those paths and help you understand the customer experience and why customers take a particular path – or abandon a particular path.    The integration of this customer data allows you to take the important steps to optimize your site and increase customer satisfaction and brand loyalty, while maximizing your sales.

 

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Topics: Customer Experience Analytics Formula

Support Optimization for Increased Customer Satisfaction

Posted by Steven Perry on Apr 4, 2017 7:00:00 AM

Introduction 

An important component of offering optimal customer experiences on your site is your ability to provide exceptional customer service.  But, how do you know if the support on your site is delivering a positive experience and addressing the needs of your customers?  Our last CXA formula considered how analyzing customer engagements with your service agent can help you understand when and why customers engage with your support.   Taking the analysis a step further can help you interpret what contributed to your customers’ experiences – both negative and positive – and implement your learning to optimize your support agent and increase your level of customer service.


Analysis Overview

By capturing and analyzing customer satisfaction responses, as well as interactions between your customers and support agent, you can identify particular sessions where a customer responded favorably – or unfavorably – to your support.   Then, by applying session replay and session timeline analysis, you can begin to illuminate what contributed to your customers’ experiences.


Analysis Benefits

  • Understand if your level of support is addressing your customers’ needs and contributing to a positive experience on your site
  • Identify key issues with your products or services and how they may be perceived by your customers
  • Offers insight that you can use to improve your customer support experiences and thereby increase customer satisfaction and brand loyalty.


Analysis Formula

This customer experience analytics (CXA) formula will demonstrate how to build reports that offer awareness into customer feedback provided through support agents, and then apply session replay and session timeline analysis to better understand customer satisfaction or dissatisfaction and to optimize your customer support.

Coupling this customer satisfaction data with additional data that examines the levels of customer engagement (see our previous CXA formula “Support Agent Optimization” from March 28th) can further empower you with insight you can use to increase customer satisfaction and build brand loyalty.

Companies can capture customer feedback in various ways, and we have outlined our process for this formula below.  There is flexibility to make adjustments to accommodate your particular process for collecting customer feedback (i.e. survey forms, link to separate survey page, etc.)

For our analysis example, the customer provides feedback on their customer experience through an automated Support Agent where the customer clicks on a feedback prompt provided in the chat box.

The click to provide feedback could follow after the customer has performed the following actions to engage support:

  • Opens the support chat box
  • Enters and submits a typed question
  • Clicks on a response link returned from the chat box

(If you would like details on how to capture the customer engagement actions outlined above, please refer to our previous CXA formula from March 28th, “Support Agent Optimization.)

 Increased Customer Sat_Image 1.png

Clicks on a feedback prompt provided in the chat box

Create events to capture when a customer clicks to provide feedback.  There are various events to consider that are helpful in providing a view into customer satisfaction.  First, there are events that record the number of customers who click to provide feedback.  These events can be built the same way our previous click events were built in our last CXA formula in “Support Agent Optimization. and should include the click event type, the last screenview, and innerText and/or Target ID.

Events to build that record the number of customer responses could include:

  • An event to record the customer response on “How was your experience?”
  • An event to record a “No” response to the question “Was this helpful?”
  • An event to record a “Yes” response to the question “Was this helpful?”

While the data collected by these events is helpful, especially when used in comparison with overall customer engagement data, it is more insightful to take a look at the customer experience that led to the customer response.   Very much like Voice of the Customer tools, this deeper analysis is what can be used to replicate positive experiences, as well as rectify potential issues, with the goal of improving customer experiences and increasing overall customer satisfaction.

For our CXA formula example, a customer can provide a comment in a text box to offer direct feedback on their experience.   Again, you will want to consider what feedback options you offer to your customers and make the necessary adjustments to capture what is available and most relevant to your customer support

Create events to capture the customers’ feedback comments.  These events will identify the customers’ comments respective to both positive and negative experiences and will begin to provide a glimpse into customer perception of your site and support.   We want to create the following events to capture the customers’ comments:

“Support Agent – provideComment – valueChange” that fires when the user enters text in the feedback textbox. 

  • Step -- Last Screenview URL       - Includes <URL from chat window>
    • Checks that the user is in the chat window by checking the last URL of session
  • Step – Target Event Type           - First value Equal textbox
    • Checks that user performs a click to provide feedback in textbox
  • Step – Event Type - First value Equal valueChange
    • Checks that the user enters text into the text box
  • Step – Target ID - First value Equal provide comment
    • Checks that user provided comment/feedback in chat box
  • Step – Target Current Value

“Support Agent – provideComment – submit” that fires when the user submits the feedback message

  • Step -- Last Screenview URL               - Includes <URL from chat window>
    • Checks that the user is in the chat window by checking the last URL of session
  • Event: Support Agent – provideComment – valueChange  - Exists in session
  • Step – Target Event Type   - First value Equal link
  • Step - Event Type -First value Equal click
    • Checks that user clicks to submit feedback
  • Step – Target ID -First value Equal provide comment
    • Checks that user provides comment in chat box

 

“Support Agent – Positive Comment” that fires when the user clicks “Yes” on “Was this helpful?” and submits a comment.

  • Support Agent – provideComment – submit - Exists on step
  • Support Agent – Was this helpful? – Yes    - Exists in session

 

“Support Agent – Negative Comment” that fires when the user clicks “No” on “Was this helpful?” and submits a comment.

  • Support Agent – provideComment – submit - Exists on step
  • Support Agent – Was this helpful? – No    - Exists in session

 

In order to properly dimensionalize our data, we need to create the following dimensions and dimension groups:

  • Dimension “Support Agent – Comment,” that is built from the “Support Agent – provideComment – valueChange” event, so that we can save the message.
  • Dimension Group “Support Agent – provideComment” that includes our above dimension and is attached to the last three events we created above.

 Increased Customer Sat_Image 2A.png

Report Analysis

By analyzing the reports on customer responses, you can gain insight into the level of customer satisfaction – and possible dissatisfaction -- your support is generating.  Through identification of specific sessions where a favorable – and even unfavorable – response and comment is provided, you can use Tealeaf’s session replay and session timeline analysis to focus in on what led to your customers’ responses.  Did they find specific support information especially helpful?   Was there information they were looking for that was not found or was incomplete?

If you combine customer engagement data (see our formula from March 28th on “Support Agent Optimization.) with this data on customer satisfaction, you can then build an even clearer view on customer engagement and how it relates to customer satisfaction.   Did you have a high number of customers engage with your support agent, but then a relative low number who engaged further to provide a response on their experience with your support?  Did you have a high number of engagements and then a high number of dissatisfied responses to your support?  Through this refined lens, you can then begin to recognize what is driving customer satisfaction – or customer dissatisfaction – and work to replicate best practices and rectify damaging issues. 

With the increase in the expectations that customers bring to the support experience – and the fact that customer experiences on your site can be a direct correlation to the level of service provided -- it is important to ensure your support agent is optimized to provide the highest level of customer satisfaction and ensure that you are building customer trust and brand loyalty.  

 

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Topics: Customer Experience Analytics Formula

Support Agent Optimization

Posted by Steven Perry on Mar 28, 2017 7:00:00 AM

Introduction:  

While there are various ways you can provide customer assistance on your site, many companies now deploy automated support agents or bots to administer customer support.  But, without live customer service staff to record the support issues or log the questions being asked, how do you know if your support is addressing your customers’ needs?   How can you understand when and why customers are engaging your support? 


Analysis Overview

By tracking the interactions with the support agent or bot – including what questions customers are asking, what response is given by the support agent and how the customer engaged with the response content – you can begin to understand how well you are providing support to your customers and identify areas for improvement.


Analysis Benefits

  • Gain a better understanding of how and when customers are engaging with your support agent or bot
  • Determine if your support is addressing customers’ needs and questions
  • Identify areas where your support content or processes could be improved or optimized


Analysis Formula

This customer experience analytics (CXA) formula will demonstrate how to build reports that assist with insight into customer engagement with support agents or bots through analysis of clicks and interactions as a means to better understand and optimize customer support.

Customers can engage with support agents in various ways.  We have outlined our particular engagement process for this formula below, but there is flexibility to make adjustments to accommodate your particular support process.   The important point is to be sure to select the customer engagement metrics that will provide you the most insight into how customers are engaging with your support agent.

For our analysis example, the customer engages the Support Agent in the following way:

  • Opens the support chat box
  • Enters and submits a typed question
  • Clicks on a response link returned from the chat box

Opens the support chat box

Create an event to capture when a customer clicks to open the support chat box (i.e. Event name:  “Support Agent click”).  Since this a click event, we need to trigger every step, checking for a click on the Support Agent button.  The event could be set up as follows:

  • Step – Event Type - First value Equal click
    • Step attribute:  Checks that user performs a click
  • Step – innerText - First value Equal Ask <your support agent>
    • Step attribute: Checks that the click matches the test on your Support Agent button

Note:  This condition will only be met if the button is in English (for websites with multiple languages)

  • Step – Target ID - First value includes <contact module>
    • Step attribute: Checks that the button is a contact module button
      1. If your innerText condition is very specific, this condition is not necessary. However, it is good practice to use multiple button identifiers in a click event to ensure accurate tracking.
      2. We use “includes” rather than “Equal” because our Target ID field in this case also includes other information that might not stay consistent but is irrelevant.

 

Support Optimization_Image 1.png

 

Enters and submits a typed question

Create an event to record that a question has been submitted and the content of the question.   There are two pieces of information that we need to capture here:  a message being sent and the content of the message.   One way we can do this is with a valueChange.   A valueChange is an event step attribute that triggers when the user enters text into a text field.

A valueChange event would look something like this:

  • Last Screenview URL - Includes <URL from chat window>
    • Session attribute: Checks that the user is in the chat window by checking the last URL of session 
  • Step – Event Type  - First value Equal valueChange
    • Step attribute: Checks that the user enters text into the text box
  • Step – Target ID -First value Equal btn-input
    • Step attribute: Checks that the valueChange occurs in the chat text box by its Target ID (btn-input)

To use the message content as a dimension, we set the event to track the value of “Step – Target Current Value.”

As this event does not tell us if the message was actually sent, we need to create an additional event that captures the user clicking on the “Send” button (Note: if the user hits Enter instead of clicking, Tealeaf will still register a click).   This event can be set up in the following way:

  • Last Screenview URL -Includes <URL from chat window>
    • Session attribute: Checks that the user is in the chat window by checking the last URL of session
  • Step – Event Type -First value Equal click
    • Step attribute: Checks that the user performs a click
  • Step – Target ID -First value Equal btn-chat
    • Step attribute: Checks that the button is the “Send” button by its Target ID (btn-chat)
  • Step – innerText -Step attribute found Is true
    • Step attribute: Checks that the user actually clicks inside the button.

Note:  If the user clicks in empty space around the button, Tealeaf may pick up the Target ID btn-chat but there will be no innerText.  We could alternatively set the innerText equal to “Send.”

We could then attach our message content dimension (note that the dimension must be contained in a dimension group) to this event to track the content of sent messages.

 

Now we can create a single event that fires when the user sends a message that also tracks the message content (i.e. Event Name:  “Support agent – send message”).  Since we are building the event around a hit attribute, we need to ensure the event triggers every hit:

  • Last Screenview URL -includes <URL from chat window>
    • Checks that the user is in the chat window by checking last URL of session
  • Support Agent – Question    -Hit attribute found is true
    • Hit attribute: Checks that the hit attribute we previously created is found

 Note:  Be sure to track the “Support Agent – Question” hit attribute so you can use this event as a dimension.

 

Clicks on a response link returned from the chat box

Create an event to record when the customer clicks on a response link from the Support Agent   (i.e. Event name: “Support Agent – chat link – click”).

  • Last Screenview URL      -Includes <URL from chat window>
    • Session attribute:  Checks that the user is in the chat window by checking last URL of session
  • Step - Event Type            -First value Equal click
    • Step attribute:  Checks that the user performs a click
  • Step – Target ID              -First value Equal Target ID  
    • Step attribute:  Checks that user clicks inside the message area of the chat box, which is identified by the Target ID
  • Step – innerText              -Step attribute found Is true
  • Step – Target Current State Href   -Step attribute found Is true

Note:  The last two conditions ensure that the user actually clicks on a link, which contains innerText and and href.  If the user clicks in empty space in the chat box, Tealeaf may still pick up the Target ID.

 

Finally, for our reporting, as we want to dimensionalize a chat link click by the innerText (the text displayed on the link), the href (the URL that the link directs to), and the last question asked, we need to create and add a dimension group with these three dimensions to this event.

  • Create a dimension for “Support Agent – Last question that will be populated by our “Support Agent – Send message” event.
  • Create a dimension for “Href” that will be populated by our “Step – Target Current State Href” step attribute.
  • Create a dimension for “innerTextthat will be populated by our “Step – innerText” step attribute.
  • Create a dimension group “Support Agent – Chat Link Clickand add the three dimensions created above.

Support Optimization_image 2.png

 

Report Analysis

By analyzing the various reports generated, you can gain insight into key areas of customer engagement, including when customers engage (i.e. on what page URLs did customers engage your support agent the most?) and why do customers engage your support (i.e. what questions were typically asked?)  You can also begin to get a view of the level of customer satisfaction and whether your support is meeting your customers’ needs by evaluating how many customers engaged your support agent compared with the number of customers who did – or did not – continue down the support path by clicking – or not clicking – on the response link.   

Careful evaluation of this data can help you identify areas where your support agent could be better optimized for customer service.   We will cover how you can further analyze and understand the level of customer satisfaction in more detail in our next CXA formula.

 

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Topics: Customer Experience Analytics Formula

Product Page Optimization through Analytics

Posted by Steven Perry on Mar 20, 2017 7:00:00 AM

Introduction:  

With the proliferation of e-commerce sites available for a customer’s purchasing pleasure, it is crucial that your site – and most importantly, your product pages – are optimized to attract customers and encourage their ongoing engagement with your site.    However, when it comes to e-commerce page optimization, every site’s product pages can differ and what works for one site may not work for all.   So, how do you find out what is working – and perhaps not working – for optimized customer engagement across your product pages? 


Analysis Overview

As product pages can be multi-dimensional, it is important to examine a couple of factors to better understand how – and how well – customers are interacting on your product pages.   Specifically, in this analysis, we will take a look at two components to evaluate levels of customer engagement:   tab engagement rates on product pages that have multiple tabs (i.e. overview tab, purchase tab, etc.) and click through rates (CTRs) by call to action (CTA) on product pages.   We will then compare the data sets to begin to understand the interrelationship between different elements of customer engagement on product pages and to identify some best practices.


Analysis Benefits
:  

  • Achieve a better understanding of customer engagement across your product pages to help determine what content is most effective in engaging customers and how interested they are in your products and learning about your business.
  • Provide insight to identify the correlation between different elements of customer engagement on your product pages that can assist you with optimizing content to increase customer experiences and identifying best practices for engagement.


Analysis Formula

This customer experience analytics (CXA) formula will demonstrate how to build reports for basic page metrics that assist with insight into engagement and then apply advanced analytics to begin to highlight the correlation between the different elements of customer engagement.    

This analysis is flexible and you should select the engagement metrics that are most applicable to your product pages, site and key performance indicators (KPIs.)  So, while we have chosen to analyze engagements by tabs (for product pages comprised of different content tabs) and engagements through CTRS by CTA on product pages, you could easily choose to analyze other engagement metrics like duration of time spent on pages that have videos or downloads, for example, or bounce rates from pages.  The important point is to select the key engagement metrics that will provide you the most insight into how customers are engaging on your specific product pages.

Step 1
As we will be evaluating engagement by reviewing a combination of page visits and CTA click through rates, we will need to create events that we can use for building various reports and for calculating metrics. For this analysis example, we will need to create events to record the page view counts for each of our product page tabs (i.e. overview tab, purchase tab, details tab, resources tab, etc.)   So, we would create an event to record the “Overview tab view (count)” and another event to record the “Purchase tab view (count),” and so on.   In addition, we will need to create events to record the page CTA counts for each of the product page tabs, such as an event to record the “Overview tab CTA (count),” and an event to record the “Purchase tab CTA (count),” etc.   Again, the events to create will depend on those particular metrics you want to evaluate for your specific pages.
 
Step 2
With the applicable events created, we can now build reports to pin to our Workspace as widgets to give us different views of the engagement metrics. To begin to get a perspective of page visits, we will create a report and widget showing overall page views by product, as well as a separate report and widget to show the page views for the product pages broken down by tabs.



Page Optimization_Image 1.jpg

 

  1. To create a report, select “add widget” in the upper right of the Workspace area and click “create report widget.”
  2. Select the metric you want to use to populate the report. (Metrics will appear organized into Tags.)  For our example, to build a report to show the page views for the product pages broken down by tabs, we would select the following metrics:  Overview tab view count, Purchase tab view count, Details tab view count and Resources tab view count.
  3. Then, click “Generate chart,” and Report Builder is displayed. Report Builder is where you create all customized reports
  4. To add dimensions or breakouts to your report, click “add” next to “Breakouts”. Select the preferred breakouts from the list (i.e. day and hour, conversion/abandonment, etc.) and then click “Breakout data by selected.”  For our report on the page views for each tab of the product pages, for example, we can select “Day” for the breakout and our report will provide a day to day view of the data.
  5. Select “Report options” in the upper right of the Workspace, and then select “Save as” and give the report a title and description. Then, select the Workspace (i.e. My Workspace, Shared Workspace, etc.) where you want this report widget to appear and then pin to a Workspace.  Select the desired reporting period and adjust the report position as you would like it to appear on the Workspace.
  6. After saved to a Workspace, you can select “Share” in the upper right of the Workspace to share the widget with others or export the report. To export or schedule a report, select “report options” in upper right and select either “export” or “schedule a report.”

Step 3
To compute the data for some of our reports, we need to create a calculated metric.  For example, we can use the CTA counts for each tab and the view counts for each tab to calculate the CTA rate for each tab.   Performing these calculations can assist with further analysis on our metrics and provide additional insight into our data.
  1. To create a calculated metric, select “Build a new report” from the Workspace
  2. Then, select “create” next to “Metrics” to display the “Calculated metrics” User Interface (UI.) The calculated metrics UI will look a bit like a calculator.
  3. In order to perform calculations on data recorded by IBM Tealeaf, select the appropriate metric(s) from the tagged grouping of available metrics. (Metrics are really just saved events.)   The selected metric(s) will highlight.
  4. Next, select “generate chart” and the metric(s) are then added to the palette. Now, we can perform our calculation and then save and display the calculated metric.  Again, following our example in this analysis, we would select “Overview tab CTA (count)” and “Overview tab view (count)” as our metrics on which to perform a calculation.   Once selected and added to the palette, we would select the “Overview tab CTA (count)” metric to be divided by the “Overview tab view (count)” metric to provide us with the calculated “Overview page CTA rate.”   Once all necessary metrics have been selected and the metrics calculated, you would save this calculated report and place it on your Workspace, just like your other reports.

Step 4
For this formula example, we will build several different reports to assist us in taking a look at CTRs by CTA, including Overall Page CTA Button Usage, CTA Clicks by Product, Overall CTA Click Thru Rate and CTA Click Thru Rate by Product, in addition to reports on page views by tab and page views by product (shown above.) Again, you will want to determine what engagement metrics make the most sense for your pages and associated business goals, and then generate the appropriate reports that will assist you in evaluating engagements across your particular product pages.

Sample CTA chart.pngSample CTA rate chart.png

 

 
Step 5
Then, to better evaluate the engagements across your pages, you will need to export sets of data from your reports and place side by side in tables to start to compare and analyze the various rates by product page in a single view. This consolidated view allows you to more easily identify high and low engagements and strong and weak click through rates -- and ultimately zero in on any best practices that can be identified in the analysis. 

 

Page Optimization_image 3.png

 
Step 6
Moving beyond comparing exported sets of data in tables, we can even apply advanced analytic tools like Watson Analytics to generate additional views of our metrics and further understand the correlation between different elements of customer engagement. We will look to cover this in detail in a future CXA formula, so stay tuned!
 

Page Optimization_Image 4.jpg

 
Customer engagement analysis can be a powerful tool in understanding if your product pages are optimized and if they are performing at their best.   Building the right reports and comparing valuable data sets provides the insight needed to better understand what particular content is especially engaging customers on your product pages.   Is there a video or other piece of content that is drawing a high – or perhaps low – interest on a tab or page?   Is there a frequented page that customers visited without being driven by a CTA?  What appears on a popular page that may have been of strong interest?  Understanding what works – and what doesn’t – for engaging your customers with your product pages can allow you to take important steps to optimize your pages and better achieve your business objectives.
 
 
 
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Topics: Customer Experience Analytics Formula, Page Optimization

Customer Remarketing on Abandoned Carts

Posted by Steven Perry on Mar 10, 2017 1:25:30 PM

Introduction

While it is important to understand why customers abandon carts and to proactively focus on improving customer experience and conversion rates on your site, it can be equally as important to identify customers who abandoned carts and to implement remarketing tactics as a means to increase your online sales and build customer loyalty.

 

Analysis Overview

This customer experience analytics (CXA) formula will help you to capture customers who have abandoned their carts on your site, enabling you to effectively remarket to those customers as a means to recoup lost business and increase customer satisfaction.   

 

Business Benefits

  • Recognize lost sales through identification of customers who abandon their cart and do not purchase on your site
  • Provides foundation to effectively target and remarket to customers who didn’t complete their purchase with targeted marketing messages
  • Recoup lost sales and gain customer loyalty by remarketing to customers who abandon carts

 

Abandoned Cart Report image_1.png

 

Analysis Formula

Identify customers who abandoned their carts without completing a purchase.   To do so, you will want to generate a report that identifies the customers who have placed items in their cart, but then did not move on to complete their purchase or checkout transaction (i.e. did not receive the completed purchase or receipt page.) 

In order to properly extract this information for remarketing efforts, you will need to do the following:

  1. Create Hit Attributes to scrape the customer name, email address, and cart value captured from the checkout page or the particular page on your site where you collect the customer data.
  2. Create Events to record the Hit Attribute values you have defined (i.e. customer name, email address and cart value) Note:  Setting the appropriate condition for when the Events should record takes consideration for the type of remarketing campaign you would like to launch.   Will you plan to remarket to those that abandon carts before submitting payment information for check out (i.e. customers who changed their mind or decided to shop the price on an item, etc.) or will you remarket to those customers who had difficulty completing their order (i.e. encountered issues with their payment method, for example) and then abandoned their carts?
  3. Create dimensions with the three defined parameters – customer name, email address, and cart value – and add them into a single dimension group, allowing you to view the dimensions in the same report.
  4. Build a report populated with the dimensions and the abandonment rate metric, and customize the report with the appropriate dates and time ranges relevant to your business and best aligned to the objectives of your remarketing campaign. Once the report is complete with the key information required to identify customers who abandoned their carts, you can pin the report to your dashboard or export your report to share or send as a file.

 

Added tip:

You can set an alert for this event to help you meet the needs of your business.  For example, if your business relies on a responsive customer call center, you may want to be alerted immediately if you have excessive cart abandonments, so that your customer care team can reach out to customers quickly.

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Topics: Cart Abandonment, Customer Experience Analytics Formula

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