Customer Orchestration and Data Integration
Customer Engagement
Category
SaaS Enterprise
Marketing Software
POC
My role
Senior Product Designer
Team
Office of the CTO
Sales Team
The Rewards Director
The Customer Success Team
One Project Manager
Engineering Director
One Visual Designer
Backend Engineers
My responsibilities
Workshop Facilitation
Conceptual Design
Customer Journey Map
User Interviews
Scope Definition
Wireframes
Testing
Real-time data-driven marketing tool
Tibco wanted to showcase its enterprise cloud capabilities to retail marketers by building a data reporting application that could leverage AI to automate real-time marketing decisions.
I was the lead designer from the initial concept to MVP. My role was to find the most impactful way to apply this technology and package the complexity in a way that is simple but powerful to use.
Through user interviews and workshops, I found that digital marketers need to be able to quickly understand top-performing campaigns, discover insights, and react in real-time to engage with
the right customer, at the right time, in the right place.
The goal of this application is to help marketers drill into their data, surface insights, and interact with the data so they can take the next best action.
Objective
Create a Marketing Decision Tool
The task of this project was to understand the viability of updating a legacy rewards platform and turning it into an autonomous marketing decision tool that could make real-time marketing decisions based on data from multiple sources.
By identifying user and stakeholder priorities, I iteratively defined and completed designs for the MVP.
Solution
A delightful enterprise experience.
This solution offers marketers a concise, unified view of their vast datasets. It empowers them to autonomously orchestrate customer journeys at scale, providing personalized customer experiences that increase customer satisfaction, loyalty, and value.
I included out-of-the-box templates, training models, and visualizations so users can get up and running quickly.
Examples:
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"Most recent purchasers"
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“How likely are they to churn?”
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“People who have churned in the past”
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“Their characteristics and behavior prior to churning”
No-code visual interface
With a card view of audiences, marketers can autonomously navigate and manipulate their data. They can then schedule and publish content directly to social media platforms without waiting for the data team.
The card view gives marketers a quick overview of audience pool sizes and campaigns.
Real-time interactive analytics
Segment and assign customers to audience pools with interactive visualizations.
Out-of-the-box templates
To get up and running faster, templates are provided to segment audiences using common attribute filters.
Re-usable playlists for remixing audiences
A marketer can remix audience groups by combining different groups into new segments.
My role
Ownership of an entire service journey
User-centered participatory design
Plan workshops and interviews
Scoping requirement
Business requirements
Wireframes
Testing
The team
Sales Executives
PM's
Data Architects
Customer Excellence
Tech Support
Dev Team
Deliverables
Use Cases
Personas
Journey Maps
Business Propositions
Prototypes
Wireframes
Testing Analysis
Consensus
Process
Who? Why? What? How?
Everything revolves around these questions. I use various investigation and research techniques to derive a holistic perspective about the end-users so I can craft data-informed design decisions based on user feedback and testing.
Business research objectives
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What's the MVP?
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What is the business proposition?
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What are the key market dynamics?
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Market fit?
Studying the business
To reach my objectives I collected
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Historical data 2014 to 2018
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Forcast reports to 2025
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Market reports
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Forester reports
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Sales reports
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Executive reports
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SWOT analysis
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Online articles
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Trends
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Forums
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Training videos
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System diagrams
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Demos
User Research Objectives
Who is the Marketing Analyst
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What job is the user trying to get done?
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What are the other jobs to be done?
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What are her pain points?
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How can we relieve the user's pain?
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What does she want?
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What are her goals?
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What does success look like for them?
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How do we create value for our customers?
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What do they wish they could do better?
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What is the user journey?
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What are some user stories?
Interviews with
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Customer's Users
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Internal Marketers
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Stakeholders
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Sales team
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Engineering
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Customer Success from the Loyalty Lab.
Her goal is - to generate $50 million, additional profit for her company.
How will she do it? increase retention by 2% or minimize churn
To increase retention or minimize churn
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Her strategy is to do something special for people who are most likely to churn.
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She takes action by sending people likely to churn a discount on a yearly renewal
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She wants to target specific people to give a discount, not the whole customer base.
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Quickly identify top customers, brand evangelists, emerging trends
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React dynamically target audiences in real-time
Persona workshop objectives
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Focus the team on the Primary User
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Unify and build consensus as a team
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Get an early win and show value right away.
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Learn about the "Jobs to be done"
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I dentify "Pains and Pain Relievers"
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Set them up for the Journey map exercise
I later I will validate their assumptions
Customer Excellence Team
User Interview Results
Goals
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Identify the most loyal customers
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Identify the top spenders
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Find brand evangelists
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Create brand endorsers
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Retarget loyal customers
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Harvest social media data
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Integrate top spenders data
What does success look like?
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Increased brand awareness
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More purchases
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Increased customer retention
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Increased customer satisfaction
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Higher Revenue
Monika the Marketer
Synthesizing research to refine requirements
Modern marketers need dynamic, intelligent controls in order to quickly and easily:
Engage
Integrate Unstructured Data
End-to-End Architecture
Play nice with other CRMs
Engage
Easy to Start
Easy to Use
Easy to Scale
Engage
Visualize Data Insights
Contextual Campaigns
Systems Integrations
Backstage Personas: Data-Ops
Many data analytics teams fail because they are focused only on people and tools and ignore processes, similar to a sports team that have the players and equipment but no game plan.
Larger organizations will have many people in each role, while smaller companies might have one person performing multiple roles.
Tertiary Persons
3. Data Engineer
Builds the infrastructure to perform analytics
4. DataOps Engineer
Orchestrates and automates the data analytics pipeline
1. Data Scientist
Creates algorithms to predict buying patterns, address questions or solve problems
2. Analyst Operations
Summarizes and synthesizes massive data,
communicating insights
Secondary Personas
From data lakes to a in store purchase
A journey map to capture the user's journey from the old system to the new
Monika wants to create a cross-channel experience, including the context what the customer is doing, where and when.
What will she be feeling throughout her engagement with this venture?
Lets give her a challenge.
How can we help Monika
Identify who has the highest propensity to
Buy a tent in Boston
in the dead of winter?
Workshop Prompt
This is Monika's 1st day with our platform.
She has a new campaign she needs to promote.
North Face has a new winterized tent
they want her to promote for Christmas in Boston.
Sales on camping gear are traditionally
very low in Boston this time of year.
How do we help her be successful with this Campaign?
Journey mapping is also an iterative process
This is an in-depth journey map that was shared, tested, modified and validated by engineers, sales, execs, marketers, and customer success teams.
Each phase (top row of post-it) is pulled apart into tasks.
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Tasks are grouped using affinity mapping techniques.
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Stakeholders then rank the tasks with a method called value score voting.
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Scores are used to identify the MVP and plan later stages.
Two Scenarios
On the plane
When the doors shut, you are sent points to spend in-flight.
In the store
When the receipt is printed, a contextual discount is sent to bring you back to the store.
The flow of data:
Event - Data - Insights
Insight
Often Marketers don't actually use data available to them because the data is not consumable and it's too difficult to get meaningful insight out of the data, in time, to take the right action.
The System Flow diagrams
Understanding how the Data Flows is one of the most important parts of the design process.
Insight and Pivot
This is where we learned the missing part was integration and so later Tibco Buys Scribe.
We also need the events processing application to be transformed and made accessible on mobile.
This helps identify the MVP and prioritize the design sprints.
Mapping the data
Identifying the MVP 1.0
Key capabilities
What can we build and when?
I identified the MVP by getting them to vote on what was the most important part and doable.
Configuration is mandatory.
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Attributes
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Conditions
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Events processing
Design Solution
THE RIGHT CUSTOMER
AT THE RIGHT TIME
IN THE RIGHT CONTEXT
Agile Design iterations with the stakeholders.
Participating early in the design development phase makes Iterations faster, shortens the approval process, saves bandwidth, is more Agile.
Whiteboard benefits
Deeper knowledge transfer
Less design review meetings
Faster Iteration on more concepts and layouts
Get down to the MVP and plan stages while designing
Rapidly test and iterate concepts viability
Faster approval times
Get to market faster
An example of how I drill down with participants.
What are the characteristics and preferences of your best customers?
Why?
Data Insights
RFM - Recency is the most important factor.
RFM Customers who have purchased from you recently often spend more are more likely to buy from you again. This segment will get a higher rating
but the customers whom you haven’t seen for a while will get a lower rating.
How this relates to UI
The order of the attributes in RFM corresponds to the order of their importance in ranking customers. Therefore I start her off with some of the Standard attributes
like Name ID, DOB, Zip...
Samples come with default orders so she has something to start with.
Creating Attributes concept
Partners
The sales team tests with customers.
Test
I Run internal testing with our Internal marketing experts saves a lot of time.
Roadblocks
are potential opportunities I include engineers at every step to point out friction that can be smoothed.
Product Design Crit
Review with UX team in weekly design standups.
Synthesis
Monika the Marketer doesn't want to spend valuable time consuming the code configurations to get started. She wants to start driving better decisions quickly.
This leads to the idea of selecting audiences by combining charts of customer events, geo-locations, demographics, and many other attributes and conditions.
Validate
I validated that this technology already exists in-house and is possible to implement.
Testing and market fit
Then I created clickable prototypes to test if there is a market fit for these interactive selectable analytics? Customers were very excited.
I iterated on the concept by exploring different entry points to see where they would show up and could we access the data at the point of the audience creation. I test the boundaries of our technology and our different engeering teams to see where the issues are.
Refining the concept
Analytics summary with Progressive disclosure details underneath
This concept would enable the marketer to self-build the criteria for audiences and visualizations. Test and make adjustments to get the answers she needs from the data all on her own. The Marketers Eng team to focus on building the core product by.
Designed with a Mobile 1st approach
A competitive advantage for enterprises giving their customers more flexibility to stay informed and react from anywhere and ahead of the enterprise competition ...
Challenges
TECHNICAL CHALLENGES
Simplifying all of the systems
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Many siloed data systems
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It's difficult to deliver personalized engagement offers
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Predictive is hard (both technically and operationally)
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Testing and learning is inconsistent and not scaled
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Making decisions fast enough is challenging for new digital channels
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Lack of data driven understanding
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Getting timely input and approval from stakeholders fast enough was challenging
How I overcame challenges
Listen carefully
Be nice and diplomatic
Increase morale
Early success stories
Constant communication
Clarify expectations
Involve Eng & PMs
Empathize to gain their trust
Understand their motivation
Accept others' authority
Be assertive when necessary
Work to fix the real problems
Use data to validate
What we learned
As the design iterations developed, we found a critical missing piece in Tibco's tech stack: There needs to be a Connection Hub.
The marketing industry needed easier integration between data systems.
Pivoting to better integration:
At the time there was no easy way to connect different data systems quickly.
If Tibco could solve the connector issue, they could become the connection hub.
This contributed to Tibco's acquisition of Scribe Software, a data integration software that helps CRM-Customer relation management, ERP, and marketing automation.
Examples of ERP system modules include product lifecycle management, supply chain management (for example purchasing, manufacturing and distribution), warehouse management, customer relationship management (CRM), sales order processing, online sales, financials, human resources, and decision support system.
"empowering a wide variety of users to quickly connect any digital asset using any integration style, including modern API-led and event-driven approaches, and to streamline business processes with no-code process automation capabilities."
Other outcomes
I went on to work with the newly acquired Scribe team to helped migrate
the Scribe Connector store to our cloud platform.
Integration Connector Marketplace
Final Outcomes
The result of this project helped showcase the need to update these other on-prem capabilities and transform them into cloud services.
I designed new interfaces for many of the other cloud applications also,
always thinking about Monka.
TIBCO Cloud Services
Cloud Events Web Studio
I also designed the Cloud-Events Web-Studio for the Tibco Cloud Platform.
Artifact and change managment
Artifact deployment
The End
Helful group facilitation techniques
Here are some quick and effective techniques that are useful for aiding help make group decisions, idea generation, knowledge transfer, and raising morale.
1-2-4-All.
A technique that facilitates rich conversation in small groups and integrates small groups ideas around an important issue or question.
25-to-10 (Crowdsourcing).
A technique for quickly generating and rating ideas.
Voting with Your Feet (or post it if that awkward).
A technique for engaging participants to express their views for or against a position by moving from one side of the room or the other.
Card Sorting. A technique of gathering and organizing ideas that draw on the knowledge of the whole group.
Field Trip around the Room.
A technique used to organize how members of the group discuss several topics and integrate ideas on how to address them.
Gallery Walk.
A technique that gets the whole room on its feet to take a walking tour of posters for flip chart pages that reflect each group’s answers to questions.
Knowledge Café.
A method that fosters discussion about topics important to participants.
Popcorn Report.
A technique for eliciting comments from those who feel moved to share.
Speed Consulting.
A technique that draws on experiences of participants to advise another participant on how to address specific problems.
Speed Networking.
A technique that gets all participants to reflect on a question and share their insights.
Storytelling.
A technique of sharing the knowledge that incorporates the context, emotion, and tacit knowledge.
TRIZ.
A technique that helps groups think creatively how to solve a problem or improve a complex process.
Action planning
Identify all possible actions and then group by affinity, vote, priortize, assign.
There are many more