AIFest will gather a group of 200 people interested in exploring and tackling the tough problems in Artificial Intelligence and overlapping domains.
AIFest is an unconference, in the spirit of BarCamp. It will be a participant-driven meeting. The attendees will suggest topics to define the event’s agenda. Anyone who wants to initiate a discussion on any topic related to AI can claim the time and space.
We can’t wait to participate in the event and hope to see you there!
For more information, visit the AIFest site.
There are a number of fraud detection tools available on the market, but for some companies, it’s important to have greater control over how the fraud scoring algorithms work and to have them keep learning and improving using machine learning. Our team has put together a demo and some sample code to show how you can build this with Fuzzy.ai.
For this example, let’s assume that Fuzzy.ai is being used by a company with an online store, where customers order products online. After running their business for a year, they’ve noticed that all of the following factors often indicate that a transaction is fraudulent:
- Transactions from new users
- Transactions much larger than the online store’s average size
- Mismatches and very large distances between the billing address, shipping address, phone number location and IP address location
They also noticed that in addition to the points above, some lesser indicators of fraud are:
- Using the most expensive shipping option might indicate fraud
- Multiple credit cards used from same IP increases fraud
- Multiple credit cards shipping to the same address increases fraud
With Fuzzy.ai, you could quickly build an agent based on these rules and get a fraud score in real-time for each transaction. In fact, we built one and created a demo interface you could use to try it out:
We’ve also made all of the code behind this demo available. Check it out on GitHub.
Chatbots have become a really popular way for companies to interact with customers. Most chatbot experiences, though, are pretty static. As a developer, you need to set up a messaging flow with content that doesn’t change much based on user interaction.
Some of our users have discovered that you can use Fuzzy.ai along with most chatbot frameworks to create a much more personalized experience for your customers. Some examples include offering dynamic promotions based on user behavior, and recommending the most appropriate products.
To show this in action, we’ve built a sample bot that we call the Fuzzy.ai Promobot. You can chat with Promobot on Facebook Messenger and based on how you answer the questions it asks, Promobot will offer you a personalized discount on your Fuzzy.ai subscription. Promobot is built using Howdy.ai’s Botkit toolkit.
Here’s a sample chat with Promobot:
Behind the scenes, Fuzzy.ai is taking the data from the chat responses to predict the discount most likely to result in a purchase. To get started, the Fuzzy.ai agent powering the discount decision is a pretty simple one. It’s based on these 5 rules:
IF hasAccount IS no THEN discount IS high IF hasAccount IS yes THEN discount IS low IF tutorial IS no THEN discount IS high IF tutorial IS yes THEN discount IS low lastAPICall INCREASES discount
And the discount that Promobot offers in our example ranges from 0% to 20% based on the user’s responses.
Once Promobot offers the user a discount code, it then uses Fuzzy.ai’s machine learning to automatically optimize its rules to offer the lowest effective discount possible.
In our example, Promobot does this by asking the user if they plan on using the discount code and then providing that feedback to Fuzzy.ai. In real implementations, you might send this feedback to Fuzzy.ai once the user actually makes a purchase.
The feedback Promobot provides is the % of full price paid by the customer. So, if Promobot offers a 15% discount and the customer says they plan to use it, Promobot sends “85” (100% minus 15%) to Fuzzy.ai. If the customer says they don’t plan to use it, then we consider it a lost sale and Promobot sends “0” (zero) to Fuzzy.ai.
Fuzzy.ai’s machine learning algorithms are seeking to maximize this value, and will automatically optimize the rules to get the highest possible result.
This is a simple example, but a more complex one could include dozens of questions and external data to create a truly personalized experience.
We’ve released a new version of the PHP SDK for Fuzzy.ai. Version 0.2.0 includes:
- full API support including all Agent CRUD methods
- better error handling
- Agent, Evaluation, and Feedback objects for better object-oriented patterns
Some of our Google Sheets Add-on beta users have been using it to do some really interesting stuff. One of my favorites is the company that is using it to identify their best customers by creating custom user scores using Fuzzy.ai and user data from Intercom.
Here’s how you can build something similar:
First, export user data from Intercom (Instructions here) and upload it to a new Google Sheet. It should look something like this:
Given all of the data available in Intercom, you can really easily do a lot here. To get started, let’s narrow it down to a few columns:
With these columns, we can easily get a few useful facts about each customer:
- How long has it been since they signed up
- How long since their last login
- How many times they’ve logged in
- Whether they’re on a free or paid plan
You can add columns and use an easy formula in Google Sheets to calculate the number of days between today and the Signed up and Last seen dates:
And an if/then statement to show whether or not the user is on a paid plan:
=if(P2 = "free", 0, 1)
The result is a sheet like this:
(To help you get started quickly, here’s a copy of this spreadsheet. Just go to the File > Make a Copy menu to make a copy of it in your own Google account.)
Set Up Your Rules
If you don’t already have a Fuzzy.ai account, you can sign up here to try it for free.
Next, let’s decide on a few rules to start things off. These are a pretty obvious place to start:
- The longer it’s been since a user signed up, the better, so a larger number of days here is good.
- The shorter it’s been since a user last logged in, the better, so a smaller number of days is best.
- The more times a user has logged in, the better, so a larger number here is also good.
- A user on a paid plan is better than one on a free plan.
Let’s also say that rules 2,3 and 4 are much stronger signals than rule 1, so we can give rule 1 a lower weight.
Here’s what those rules might look like in Fuzzy.ai:
Take note of your Fuzzy.ai API key and the Agent ID of the agent you created above. You’ll need them for the next step.
Calculate User Scores
If you haven’t already made a copy of the sample spreadsheet above, you can do that here.
Once the add-on is installed, go back to the Add-ons menu > Fuzzy.ai > Settings and enter your API key and Agent ID.
With the Fuzzy.ai Google Sheets add-on, you can use the function =FUZZYAI() to send data to Fuzzy.ai and display the result. It expects inputs in the following format:
=FUZZYAI(<name of input 1>, <value of input 1>, <name of input 2>, <value of input 2>, etc)
So for this example, paste this formula into cell B2 (in the User Score column):
=FUZZYAI("daysSinceSignup", D2, "daysSinceLastSeen", F2, "webSessions", G2, "paidPlan", I2)
A score should appear in that column. Just copy and paste that formula into B3 -> B10 to get the rest of the scores.
Set This Up For Yourself in 5 Steps
- Create a sample User Score agent in your Fuzzy.ai account by following this link and clicking on Create. (If you don’t already have a Fuzzy.ai account, sign up here to try it for free.)
- Take note of your Fuzzy.ai API key (found on your dashboard) and the Agent ID of the User Score agent you created in step 2.
- Go to this sample Google Sheet and make a copy of it in your own Google account.
- Add the Fuzzy.ai Google Sheets add-on to that sheet.
- Copy the FUZZYAI formula shown above to cell B2.
And with that, you should have a working version of this example! Try changing some of the data in the spreadsheet to see how it affects the score.
The goal here, of course, is to show a pretty simple example of how to do this. We’ve seen users do similar things to create lead scoring, do churn risk analysis, and much more.
Some of our users have built agents to do this type of user score and then send that score back to Intercom daily as custom user attributes.
We’re excited to announce the release of a Fuzzy.ai add-on for Google Sheets. With this add-on, you can test your Fuzzy.ai agents directly in Google Sheets without having to do any integration with code.
To get started, follow this link or go to the Fuzzy.ai listing on the Google Sheets Add-on Marketplace and install the add-on.
Once it’s installed, go to Add-ons > Fuzzy.ai > Settings and put in your API key and the ID of the Fuzzy.ai agent you’d like to use.
Once you’ve entered your API Key and Agent ID, you can make a call to Fuzzy.ai using this formula:
=FUZZYAI("<name of first input>", <location of first input value>, "<name of second input>", <location of second input value>, etc...)
=FUZZYAI("Number of shares", A2, "Number of likes", B2, "Age in minutes", C2)
This add-on is a quick and easy way to start using Fuzzy.ai without any code at all. Try it out and let us know what you think!
(Bonus for developers: If you’re curious to learn more about how the add-on works, check out the project on GitHub and file issues for any comments/feedback.)