All posts by xhtmlchop


Could Fan Signals be an NPS-Killer and disrupt the SaaS NPS industry?
The ROI from this unique data and customer feedback validation proved more desirable and valuable to understanding customer lifetime value (LTV), customer retention, and churn prediction modeling than Net Promotor Score (NPS). Customer Driven Innovation (CDI) process starts by listening to the customers, thus ensuring the team is focused on solving the right problem at the right time for their customers.

Mike Saffitz – CTO
“Geoff reported to me (both directly and then indirectly) as part of our product team at Apptentive. He was a very talented UX designer, passionate about addressing users needs and eager to iterate amongst a variety of approaches to identify the best way to solve a particular problem. I was really impressed with Geoff’s energy and creativity– he had an infectious love of the creative build process.”

Fan Signals: Vision Dashboard
The dashboard represents a future where the summary cards and the histogram plot chart show customer segments through the lens of loyalty. Shifting loyalty is broken down into sentiment segments known as Fan Signals.

Business Results – Proprietary metrics
The tailored reports were designed to reveal more profound insights into how customers feel about the brand/product; the reports received tremendous praise as they made it to the C-Suite. With continuous agile improvements, the 20+ page reports ended up closing $100K+ deals.

Solution — I partnered with a data scientist to create a flexible template tailored for any customer. This unique solution was created in PowerPoint (instead of Excel or Word) then the document can be linked to an Excel data connector while keeping the visualization.

Executive Reports – At scale, the tailored reports were designed to reveal more profound insights into customers’ feelings about the brand/product.

Fan Signals – New Innovation
The insights started to show and classify consumers into segment cohorts known as Fan Signals, which were broken down by (New Fans, Switched Fans, and Repeat Fans) which all correlated to customer behavior.  Churn prediction modeling, Retention modeling, and AWS AI/ML to augment the feedback data were vital to feature success. The team shipped Fan Signals to all customers from inception to productization as a feature flipper. The POC from sales interviews received positive feedback as a new up-sell revenue stream. The left diagram shows a dashboard showing the APIs displaying accurate customer data before the font end work started.

Fan Signals – MVP
To get customer feedback, I shipped the first public view for Fan Signals as proof of concept. The green represents missed opportunities, or from a customer’s perspective, the business could be losing customers or money. The purple represents New, Loyalty, and Repeat customers to ensure brand/product success; the purple represents royalty. The customers wanted to know deeper insights into when a customer shifted loyalty or sentiment towards the business.

Customer Interview
I spearheaded the first customer interview to validate the value and gauge the customer’s interest by aggregating all the feedback types. I conceptualized and iterated on various histogram chart designs and landed on a Pos. / Neg. The bar chart best represented the binary question “Do you love our brand?” (Yes/No), which customers intuitively understood. The design helped to visually solve the volume of responses and how the feedback was trending. The prototype and the number of positive customer feedback validated the POC.

The Challenge
This is where my design journey begins – working with Apptentive’s Love Dialog flagship product as the Lead UX Designer. Customers wanted to see the underlying data contributing to the responses received, represented in a single bar chart. It was the start of an innovation hypothesis that the business was willing to invest in understanding the deeper metrics.

The Beginning –
The Love Dialog started with a simple question, “Do you love our brand?” – a query that generated an 80% response rate. Based on how a consumer answers, they will redirect to another type of feedback interaction (survey, message center, rating R&R, etc.), which captures even more VOC feedback at scale.

Segment Builder (pt2.): Customer Targeting

The Love Dialog did well to solicit and capture various feedback interactions (surveys, messages, ratings, NPS) – but only in one-way communication. Most feedback SaaS platforms capture feedback and never return feedback to the customers. At scale, customers would like to know that their VOC was heard.  

Loyalty Builder
Closing the feedback loop was the product goal. At a high level, the system is like an Entity Management, Crud system, and Query builder to allow customers to re-engage with their customers through automation.  In their feedback, customers told us they wanted the ability to reconnect with their customers in the form of some interaction promotion or advertisement. The Game changer…

Audience Segments
The platform’s real power was identifying a Customer ID and advertiser ID and augmenting the data supplied by the customer UUID. Customers can send custom key-value pairs, Zip Codes, DMA, Devices, Locations, membership, loyalty, and other metadata. Then Fan Signals augments the data and builds segment cohorts for the most accurate re-targeting.

UX/UI functionality Spec
Part of my process is documenting high-level UI functionality from a designer’s POV, covering scenarios, use cases, and high-level functionality requirements. The working doc covers a variety of product scenarios to help uncover or determine any unforeseen edge cases.

Domain Diagram
I worked closely with a systems architect to understand the system’s design; then, I created the UX/UI/IA visualization for an intuitive customer journey. The art of the discovery process is creating a wiring diagram to help visualize the data before the UX process begins—the making of Fan Signals.


Collaborated with the CEO 1:1 to understand the big picture, then I was able to translate those visions visually to the team. At a high level, we needed to build a proactive/reactive notification system for enterprise customers to analyze their end-user entities’ using AI/ML technologies.

The recommendation engine would scan/analyze the data at scale for security issues, threat analysis, sensitive data, compliance, viruses, etc. The heart and soul of the MSP Complete system made it easy for IT to deploy remote automated services that are under management.
The press had early access to the platform. View

Activity Feed: The business vision was to have hundreds of services deployed remotely. The activity feed would be the first responder for IT professionals who can deploy a remote solution.

Customer Journey – Mapping
My process was designed to maximize communications with developers. I created UX/UI journey maps to walk developers through the interaction process. This approach helps to speed up the collaboration process.

Customer Feedback – MVE Learnings
My original feed design was rough by necessity but flexible, allowing the team to focus on more important features. We had to ship with an MVP product, which was an amalgam of all individual feed types and events. This is agile thinking — recognizing you can’t expect to build everything, or we would never ship. One apparent feature was that similar feed types needed to be grouped to help with scale issues.

Concept Iterations – Low-Fidelity Mocks
Through multiple whiteboard sessions, the dev team was able to grasp a holistic view which helped synthesize the product features. Through collaboration, the team was able to understand the fundamental requirements before stories were created.

Discovery Phase:
I created this high-level user flow to align the UI with the customer journey to visually represent the initial product feature goals.

Activity Feed – Diagram Flow
Once an IT partner customer adds users and devices from Entity Management the platform will notify the IT professional with proactive/reactive types of notifications. The Activity feed would be the backbone for IT specialists on how to take action. I created high-level user flows to help the team understand the data flow.

Entity Management – Users/Devices

As lead designer one of the most important features of the managed service platform is the ability to view customer insights remotely to take advantage of the platform’s full capabilities. New services will be reflected in the platform once you add an entity (user or computer), the platform will analyze the customer data to be used for the suggestion engine.

From the user list management, an IT customer can quickly view user metadata details to evaluate what actions to perform on a device and other relevant data. The customer feedback was great since they don’t have to drill down to another level to view all relevant data. As a shortcut (Inventive), I added another chart panel to quickly see how many consumers have the heartbeat app (device agent) running, or other issues instead of drilling down to another page.

Customer Profile Management
Once the DMA was successfully deployed to a customer’s device the IT can perform simple tasks on the customer’s computer. The business vision was to support 100’s micro-services for IT professionals can perform system analysis. The DMA could be considered running in the background monitoring the heartbeat of a customer’s device. Using AI/ML an IT professional can receive Proactive or Reactive alerts letting them know how to remediate. In the UX/UI, I added different types of fly-in panels to prevent the customer from drilling down to another page.

Task Launcher – Full Modal Experience
Once a customer chooses a path to migrate the customer’s users the task launcher opens in a full modal experience showing the simple steps to complete the required tasks. BitTitan’s target market is customers such as universities who have 40k users that need to migrate from Office 365 to Gmail or vice versa. The Overview is the first step which gives clear customer guidance and a video tutorial showing how easy it is to deploy the DMA agent.

Device Management Agent – Empty State Since I designed the deployment agent (DMA) application it made sense for me to design the user invite workflow on how to set up and deploy the agent from the platform. The platform can remotely install the agent using GPO, Connectors, DeviceAgent.exe, or installed from an inbox email.

Results — The redesign increased the customer success rate by around 80%. I thought it would be a nice touch to add a little bit of eye candy (.gif animation) to let the user know the app is running during the configuration process since the process takes time to mirror and migrate the customer’s profile to the cloud.

The Design Challenge
DMA was developed as a universal shell app (C++) to be compatible with any type of OS, I was tasked to redesign the client app to improve the customer journey to be user-friendly. Trying to push the design… but being limited due to the app’s language limitations. I worked with a Dev 1:1 to recreate the status tracker progression and some visual animation to ease the customer the DMA was configuring the services on a customer’s machine.

The Beginning – Overview
A Device Management Agent (DMA) is the quickest and easiest way to remotely deploy and configure software applications on end users’ desktops or mobile devices without an IT physically touching the computer. DMA helps companies of all sizes deploy new software applications remotely or it can migrate mailboxes with a few simple user tasks.

Deployment Pro (DMA):  Before – After
The original Deployment Pro, remote client agent looked like some type of malware, with a high customer drop-off rate due to the customer thinking they have some type of malware.