All posts by xhtmlchop

APPTENTIVE

FAN SIGNALS – NEW INNOVATION



Could Fan Signals disrupt the NPS SaaS industry?
The ROI from this unique data and customer feedback proved to be more valuable to understanding customer lifetime value (LTV), customer retention, and customer churn prediction modeling than Net Promotor Score (NPS).

Fan Signals: Dashboard
The dashboard represents a plot chart show customer segments through the lens of loyalty. Shifting loyalty is broken down into sentiment segments known as Fan Signals. At a high level there are 3 types of customers New Customers (CPA), Existing Customers (frequent users), and Customers who Churned (App abandonment). The business value to connect with customers before they Churn and offer incentives (i.e. discounts, targeted ads) to bring back App loyalty.

Mike Saffitz – CTO
Quote: “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 – Early Data Modeling
From inception to productization, the team shipped Fan Signals to all customers as a feature flipper. The POC from sales interviews received positive feedback as a new up-sell revenue stream. The left diagram shows a redash.io dashboard showing customer data before the font end work started. The insights began to show and classify consumers into segment cohorts known as Fan Signals, in which New Fans broke down, Switched Fans, and Repeat Fans, all of which correlated to customer behavior. Using AWS AI/ML to augment the feedback data was vital to feature success.

Business Results – Proprietary metrics
The tailored reports were designed to reveal more profound insights into how customers feel about the brand/product. When they reached the C-suite, they received tremendous praise. With continuous agile improvements, the 20+ page reports closed $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), and 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 before productization.

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, loyal, and repeat customers to ensure brand/product success; the purple symbolizes 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 a 90% response rate. Based on their answers, consumers are redirected 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 in soliciting and capturing 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
The product goal was to close the feedback loop. At a high level, the system is like an Entity Management, Crud system, and Query builder that allows customers to re-engage with them through automation. In customer interviews, customers told us they wanted the ability to reconnect with their customers through 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, DMAs, Devices, Locations, memberships, 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.

BITTITAN

ENTITY MANAGEMENT / FEED ANALYSIS: AI/ML



Since they are tightly connected, I was on point to spearhead feature design: Entity Management, Feed Analysis, and Device Management Agent. Every feature had a purpose; we started with entity management and collected and analyzed the data. Once the data was analyzed, we needed to build technology to scan and analyze it at scale for security issues, threat analysis, sensitive data, compliance, and viruses. The exciting part was that we were experimenting with and using ML or AI technologies, which were cutting-edge in 2017.

The CEO’s vision was to monitor customers’ systems remotely and alert system admins to take action. The heart and soul of the MSP Complete system was making it easy for IT to deploy remote automated services under management. The press had early access to the platform. View

Activity Feed: The business vision was to deploy hundreds of services remotely. The activity feed would be the first responder for IT professionals who could 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, 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 understood the fundamental requirements before stories were created.

Discovery Phase: I created this high-level user flow to align the UI with the customer journey and 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 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

OVERVIEW
As lead designer, one of the most essential features of the managed service platform is the ability to view customer insights remotely. This enables full utilization of the platform’s capabilities. Once you add an entity (user or computer), new services will be reflected in the platform, analyzing customer data to alert you in the activity feed.

USER INSIGHTS — VIEWS 
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 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 is 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 micro-services for IT professionals to 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 fly-in panels to prevent the customer from drilling down to another page.



Task Launcher – Full Modal Experience
Once customers choose a path to migrate their users, the task launcher opens in a modal experience, showing the simple steps to complete the required tasks. An example of BitTitan’s target market is universities with 40k+ users who need to migrate from Office 365 to Gmail or vice versa. The Overview is the first step, which provides clear customer guidance and a video tutorial showing how easy it is to deploy the DMA agent to all its students.



Device Management Agent (Empty State): Since I designed the deployment agent (DMA) application, creating the user invite workflow for setting up and deploying the agent from the platform is essential. The empty state lets customers choose how the DMA agent is installed on their systems.





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 IT physically touching the computer. DMA helps companies of all sizes deploy new software applications remotely and can migrate mailboxes to the cloud with a few simple user tasks.

Results — The redesign increased the customer success rate by around 80%. The redesign improved customer satisfaction, and the animation reduced the number of users closing the app.

HEARTBEAT – Animation
I thought it would be a nice touch to add a little bit of eye candy (.gif animation is only supported) to let the user know the app is progressing in the configuration process. Deploying services on customers’ systems takes time. I worked with a dev (1:1) and we pushed the design by adding the animation and overhauling the entire app.

The Design Challenge
Once installed, the Agent App automatically installs services, or users can install them remotely. The App was developed as a universal shell app (C++) compatible with any OS. I was tasked with redesigning the client app to improve the customer journey. I tried to push the design, but the app’s technology limitations limited me.

Deployment Pro Agent (DMA):  Before – After
The original Deployment Pro remote client agent looked like malware, with a high customer drop-off rate because the customer thought they had installed malware.