IntraSee
Chatbots and User Experience with Oracle/PeopleSoft
Whenever you are in a conversation about chatbots and digital assistants in higher ed, without fail the topic of Financial Aid comes up. Like a good deep-dish pizza conjures up thoughts of Chicago, Financial Aid and chatbots are often linked. The prevailing wisdom is that Financial Aid questions are the most highly demanded questions amongst students. What if I told you the prevailing wisdom was wrong?
It makes perfect sense why we assume Financial Aid questions are ripe for bots when you consider a) the questions are seasonal with volume peaks that are hard to manage, b) Financial Aid is a complicated topic that invokes questions and c) the answers to these questions are common across all schools and easily automated.
These explanations just didn’t feel right to us, so we were curious to test this thesis. What does the actual data say in regards to the demand for Financial Aid answers? We dove into our data to find the real story. We think you will be surprised by how far perception is from reality.
Financial Aid
Ida can categorize questions into topics. For the purpose of answering our question about the popularity of Financial Aid questions, we analyzed the occurrences of any financial question be it aid, account balances, fees, and so on. We narrowed in on a particular client who serves a wide range of questions across all the common topics a student may need help on. It wouldn’t be accurate to look at a client who only deployed their bot to Financial Aid pages or Admissions pages. We call that selection bias 😉.
We want to understand the totality of the student experience when the bot can help them with all their questions. Additionally, this user base has been exposed to the bot for at least two academic years, so the adoption curve isn’t introducing its own bias.
Immediately we noticed an expected pattern to the demand curve (picture below). The curve’s peaks hit right when you would expect it: when the money comes! Outside of those two times a year, students hardly ask that much about financials and financial aid.
Perhaps the more surprising finding is that even during the peaks, the percent of total volume is quite low—around 2%! But wait, isn’t the whole reason to implement a bot because students had so many Financial Aid questions? Maybe not that many students get financial aid, so we took a look at that. Some 98% of undergrads get some form of aid, so it is relevant to virtually all users of the chat.
Now we know students are asking other questions at least 49 times more often. Let’s dig in and see what they are actually doing during a recent full academic year…
Popular Trends
Using Ida’s AI Categorization and Analytics, we can paint some broad strokes around what these same users are interested in. Over the same twelve-month period, we can see the distribution as pictured below.
Let’s start with the winner and still champion: Academics! Academics are questions about things such as programs, policies, GPAs and getting your degree—things not specific to a particular course. These are questions you would ask at the Registrar’s or Advisor’s Office. Maybe we forgot the whole reason students are in school is to get a degree (at least in this 4-year institution!) No wonder it is by far the most popular topic.
Moving down the list, we have Student Life and Residence Life breaking the 10% barrier. These are matters that affect a student’s day-to-day life, so it’s easy to see why they are popular. This popularity lasts all year and not just seasonally like Financial Aid.
My Information and Health and Wellbeing are next up. Health and Wellbeing is obvious, having just come out of a pandemic and dealing with ever-changing policies. Do I need a booster? How long is quarantine now? Has Monkeypox been detected on campus?
My Information is information about my personal records such as name, address, emails, phones and so on.
The big irony here is that Financial questions are just about as popular as studying abroad (International and Travel) and they both come in at the bottom of the list.
In Conclusion
At IntraSee and Gideon Taylor, we prefer a data-driven approach. So, while Financial Aid sounds like logical focus, the data tells us we need to be broad. We need to be a one-stop shop for all sorts of questions across different topics if we want to maximize our service to the student. This post also highlights the importance of being agile. When you launch a bot, pay close attention to how it is used and get ready to adjust quickly. Instead of predicting where the bot should go next, let the bot tell you where the next need is. If you want to talk more on this topic or see a demo, you can contact us below.
The next release of Ida, our digital assistant, will be available July, 2022. Clients can talk to their account teams about a deployment schedule that works for you.
22.02 in Summary
This release of Ida covers a few notable areas: new adapters, bot building efficiency and various bug fixes and improvements. Let’s start with the adapters. We have added or made major improvements in this area including an adapter for Kase ticketing and for handing over to SnapEngage’s live agent chat. Salesforce’s adapter has undergone improvements to use the more modern REST APIs and the addition of using Salesforce fields as a summary answer. Finally, some additions and fixes were made to the PeopleSoft Campus adapter.
Bot training and building has undergone major efficiency improvements which could decrease bot build time over 90%. Some new reports and analytics have been added as well as a new Analytics Center. The Analytics Center is a one-stop-shop page to get that cockpit view of how your digital assistant is running. Finally, many various bug fixes are also included in this release.
Release Notes
- New 22.02 Training Videos
- Improve security packaging for on-prem packages
- Error code exceptions now included in chat
- New Campus Intent: Tell me about History 101
- New opt-in/out option for incremental bot rebuilds
- Livechat adapter for SnapEngage
- Breakout collision fix when running in DA
- Organization Fixes
- Improved remote call request error logging
- Various Suggestions fixes
- Various On-Prem Security Sync Improvements
- Improvements to FAQ summary answers
- New Kase Adapter
- Updated archiving process
- Additional Long Term Trend KPIs
- Month by month conversation Location Report
- Optimizations to bot training and building
- Ida Suggestions Usage Report
- Passing of sub-org to remote DSPs
- Improved help and low-confidence dialogue text
- PeopleSoft environment refresh guide
- Make Suggestions configuration client accessible
- Student immunization answer source fixes
- Added Salesforce summary provider
- Bugfix to Topic Accuracy KPI Tile
- Streamlined sub org answer overrides
- Dynamic location entity and answer source
Contact us below to learn more and setup your own personal demo
Today’s machine learning-driven AI (Artificial Intelligence) is a huge technological jump from just five years ago. However, when it comes to having a successful digital assistant, you need equal parts art and science. While the science is achieving substantial accuracy scores, clients often ask IntraSee, “What else can we do to increase adoption?” The answer to that question lies in the art of the bot response. This post will cover a few tips we have found to maximize effectiveness, drive adoption, and ultimately deliver ROI.
Personality
While it may not seem like a big deal, a bot’s personality is important. A clever name that is easy to recall with some witty responses will leave a lasting impression in a way a bland bot won’t. You will see this very technique with consumer bots like Siri or Alexa.
The bot should never pretend to be a human while making light of the fact you are talking to a machine. Further, it is important that you have a conversational style that is not overly robotic. This element of fun can bring a smile to a user’s face and have them coming back next time.
How’s the weather?
I wouldn’t know, I live to work all day and answer your questions.
Let’s say someone asks the bot how much time off they have. A poorly designed, robotic response may be:
how much time off do I have?
Here is your leave balance…
Paid Time Off: 143 Hours
Sick Time Off: 13 Hours
Compare that to a more conversational style response:
how much time off do I have?
Let me look up your time off balance for you. Everyone needs a day off!
Paid Time Off: 143 Hours
Sick Time Off: 13 Hours
Personalize
All of us have had some bad experiences with a bot. Often poor AI training is at fault, but those bad experiences also happen when you get a distinct feeling that the bot doesn’t know you. Personalization is a fantastic way to build trust with the user. Consider an example in Higher Education where both Students, Faculty and Staff are all using the bot. If the user asks, “where should I eat?” Would you be comfortable recommending a dorm’s dining room to a faculty member?
Knowing your user is key to adoption. This is a primary reason why it is important to integrate into the authentication and HCM/Student system like Ida does.
Nothing is a Yes/No Question
A common mistake in conversational design is to assume you know the question asked when constructing your answer. Natural Language Processing (NLP) engines can match hundreds or thousands of variations of questions and statements to a single answer. As such, don’t assume you know the form of the question that got the user to your response.
For example, let’s say you want your bot to respond to, “do you have my phone number on file?” You may construct a response such as:
do you have my phone number on file?
Yes, I can look that up for you. Here is what I found…
What if the user’s question was, “let’s update my phone number.” Well, in that case the response would feel disconnected, wouldn’t it? What is the bot saying “yes” to? Consider a response with more global application which also repeats key words such as:
do you have my phone number on file?
Let’s see what phone numbers I have for you. From here I can help you update your numbers as well.
Living in a 140-Character World
Technology everywhere is competing for the user’s attention; not to mention that people have day jobs or degrees they are focused on. The reason they came to chat is because browsing or searching web sites is inefficient and slow. Curate your responses with brevity in mind. Get right to the point and do it without requiring a lot of reading. You can always present a way to “Read more” or “Tell me more.” Start with the simple, succinct answer and allow users to opt in for the more verbose detail.
NLP vs. Menus
With most bots you’ll tend to see one of two user experiences (UX): an NLP-driven UX and a Menu-driven UX. Bots present a menu-like experience by generating lists of links inside the chat. Menu styles (picture below) do not scale like a wide-open NLP style where a user can type anything they want into a message box. You can only show so many choices to a user, so the Menu approach quickly becomes problematic. Further, it diminishes the entire point of asking in your own words. Not to pick on the MLB, but you can quickly get a feel for the drawbacks when looking at the Ballpark Digital Assistant.
Menu-style bots are often employed to make up for poor NLP capability. When the bot is encouraging you to click menu links vs. allowing free-form typing, it is often because of NLP accuracy issues.
At IntraSee, we prefer a wide-open, type-anything-you-want user experience. This approach scales to thousands of use cases and the user benefits from the true power of AI. Menu styles often result in a user being confined to a small set of capabilities and never fully exploring all the bot has to offer.
Click-less Responses
One of the most frustrating user experiences is to ask a question only to be pointed elsewhere. Think about that feeling when you call for help and they say, I need to transfer you to someone else, can you hold please? Wouldn’t you have preferred to just get the answer right then, right there?
A click-less response is a response where the user doesn’t need to click. They get their answer directly, succinctly and personalized to them. Giving someone a link may be convenient for the bot developer, but it is not a great experience for the user. By linking them to the real answer, they now must click and scan an entire page to find what may only be a small snippet of information they are really looking for.
Channel and Accessibility Considerations
Be sure not to overlook the accessibility and portability of your bot’s responses. A bot can be one of the friendliest mediums for assistive devices. The experience is linear, chronological, and hyper-focused on one area of content at a time. This can be quickly ruined with the use of images, video or other rich content. While those mediums can be made accessible, they create a noisier experience on an assistive device.
If you do have links in your bot response, be mindful around which words are linked. The link should surround the most descriptive text for accessibility reasons. For example, never have a response that says “to view your records, click here.”. Instead the response should read, “You can view your records…”
Bots don’t only talk to you on web sites. You can have a conversation over Microsoft Teams, Slack, Voice or even SMS Texting. How will a response with links, images or videos work on all those channels? If your response needs channel-specific variations, that will increase your implementation effort and take you further away from a consistent experience on all channels. Keeping your responses in text/html maximizes reach and ease of use.
Conclusion
If understanding the human’s natural language is half the battle, then the other half is your conversational response design. With our platform, Ida, every response can be configured so you can curate the ultimate bot for your users with the personality you want. Ida is not one-size-fits-all; she can become who you need her to be. If you are interested in chatting more or would like to see a demo, you can contact us below.
The next release of Ida, our digital assistant, will be available April, 2022. Clients can talk to their account teams about a deployment schedule that works for you.
22.01 in Summary
There are two big, new features in this release of Ida. The first is called Ida Suggestions. Users of digital assistants tend to ask questions only when they have a problem. It is a very reactive pattern that is hard to break. This behavioral trend can be a barrier to discovering new ways the digital assistant can help you. We are focused on flipping this dynamic to a more proactive model where Ida routinely adds value to your user’s days. Ida Suggestions is a new, proactive feature which will suggest to users ways in which Ida can help. Whether it is what is popular lately to what is seasonally relevant, Ida will give you that little nudge to solve your issue before you even know you have a question.
The second feature of note is a new Feedback Loop mode called, High Value Mode. When High Value Mode is enabled, Ida will algorithmically target certain interactions where you can focus ratings/annotations for maximum value to the machine learning. We expect this mode to provide 10x more value per hour spent rating which ultimately will save our clients a ton of time while keeping the accuracy very high.
The release also includes routine fixes, new reports, training materials and catalog updates.
Release Notes
- PeopleSoft on-prem environment refresh guide
- New 22.01 Training Videos
- Breakout collision fix when running in DA
- Improve security packaging for on-prem
- Dynamic location entity and answer source
- Improved remote call request error logging
- Added “Who built you?” intent
- Feedback loop High Value Mode
- Improved error handling when no questions available
- Friendlier admin previews for remote answers
- Thumbs Results Report
- Long Term Trend KPIs
- Cloud based, realtime thumbs satisfaction data collection
- Update ChatUI to support embedding in ServiceNow
- Add DA specific metadata fields to automated deployment
- New live NLP data reports
- Updates Convo Dashboard to use Convo Log Summary Table
- Updated Convo Log reporting table
- Added Question Type component for client use
- Resolved an issue where phones/addresses weren’t using self-service display flag
- Better handling of step-up authentication when user doesn’t exist in IUC
- Added “incorrectly presented” to auto test output
- Improved performance of chat locations report
- Mobile MS Teams task module fixes
- Ida Suggestions (what’s new, not tried, popular)
- FBL simplified ignored outcome option
- FBL filter by topic option
- Report: Monthly Active Users (by Org)
- Fixed an issue with an excessive margin on reporting pages
- Support for groups in FAQ import file
- Corrected FBL match calculation in Metrics Report
- Added clarity to some intro text
- Removed dependencies on IntraSee WebUX modules for address in-chat form
- Added consistency to labels and naming
- Ability to add an FAQ directly from FAQ Search page
- Updated non-auth-to-auth handoff response
Contact us below to learn more and setup your own personal demo
Today we have a big announcement. IntraSee has joined the Gideon Taylor family. Both companies have been stalwarts in the Oracle ecosystem for more than 15 years. While IntraSee’s focus has been on the user’s experience in the enterprise, Gideon Taylor has been known for the automation of business processes. It was natural to join the two together. Our customers now benefit from the back end to the front end with a focus on driving real ROI whether you are on premise, in the cloud, or on SaaS.
My co-founder, Paul Isherwood, and I started IntraSee in 2005 and what a ride it has been growing from a consulting company, to a software company and ultimately a SaaS Cloud company. We have successfully navigated through major shifts in the enterprise software market, the financial crisis of 2007, the beginning of the cloud era and most recently the pandemic. No matter what was thrown at us, we adapted to serve our clients. 2021 was no exception with the sudden passing of Paul.
In this next chapter of IntraSee, we become a new division of Gideon Taylor where we will continue to serve our existing clients and with our digital assistant, Ida, carve out an exciting path for both companies. I will lead that division and look forward to a long partnership with Paul Taylor and his leadership team. You can read all about our announcement in the press release issued today.
I would like to take a moment to address all the important people who got IntraSee to this point.
To our customers:
Thank you for believing in IntraSee. It has been an absolute pleasure to help you improve your experiences for your employees, managers, students and faculty. We are only getting stronger from here with a broader cloud portfolio, the benefits of scale, and even greater investment in Ida, our digital assistant. We know many of you are planning major investments in the next ten years. We are excited for your future and to help get you there.
To our employees:
The IntraSee family is the reason we are here today. Each one of you, past and present, has contributed to our mission of bringing great usability to enterprise software. I owe you all a heartfelt thank you for your hard work and dedication. The support from the current team over the last year in particular is more than I could have imagined. I, and our clients, have been lucky to work with you and I look forward to continuing on the InstraSee journey with you as my colleagues.
To Paul Isherwood:
I remember the first presentation I saw you give back at PeopleSoft. The entire presentation was built with dynamic HTML and this was about 1999. When I asked you, “Why not use PowerPoint?” you simply responded with “Why would I use PowerPoint? This is so much cooler.” Throughout the 15+ years we were partners, you always helped us imagine something so much cooler. In your memory, I and the rest of the combined InstraSee/Gideon Taylor team, are going to push this mission to the next level like only we know how.
Sincerely,
Andrew Bediz
