Alternatives to Vidyard

Using Screenhero via Slack was getting to be pretty horrible. Video and sound quality was often times pretty bad and worst of all the service just wasn't reliable. We all had high hopes when the acquisition went through but ultimately, the product just didn't live up to expectations. We ended up trying Zoom after I had heard about it from some friends at other companies. We noticed the video/sound quality was better, and more importantly it was super reliable. The Slack integration was awesome (just type /zoom and it starts a call)

You can schedule recurring calls which is helpful. There's a G Suite (Google Calendar) integration which lets you add a Zoom call (w/dial in info + link to web/mobile) with the click of a button.

Meeting recordings (video and audio) are really nice, you get recordings stored in the cloud on the higher tier plans. One of our engineers, Jerome, actually built a cool little Slack integration using the Slack API and Zoom API so that every time a recording is processed, a link gets posted to the "event-recordings" channel. The iOS app is great too!

#WebAndVideoConferencing #videochat

Server side

We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

• Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

• Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

• Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

Client side

• UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

• State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

• Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

Cache

• Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

Database

• Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

Infrastructure

• Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

Other Tools

• Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

• Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

Hotjar GitHub MailChimp Drift

When I started Checkly, I had no clear strategy on collection, managing and acting on customer feedback.

Over the last year, going from private beta to the first couple dozen customers I found my way in the jungle of customer feedback tooling and found something that worked for me and my company.

The linked post is a bit less technical than normally. The post goes into:

• Using Hotjar and how it sorta worked for me.
• Using Drift and why I was totally wrong about chat widget.
• Using GitHub as a public roadmap.

This is my stack in Application & Data

JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

My Utilities Tools

Google Analytics Postman Elasticsearch

My Devops Tools

Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

My Business Tools

Slack

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

Hi,

This is a question for best practice regarding Segment and Google Tag Manager. I would love to use Segment and GTM together when we need to implement a lot of additional tools, such as Amplitude, Appsfyler, or any other engagement tool since we can send event data without additional SDK implementation, etc.

So, my question is, if you use Segment and Google Tag Manager, how did you define what you will push through Segment and what will you push through Google Tag Manager? For example, when implementing a Facebook Pixel or any other 3rd party marketing tag?

From my point of view, implementing marketing pixels should stay in GTM because of the tag/trigger control.

If you are using Segment and GTM together, I would love to learn more about your best practice.

Thanks!