How I Launched This: A SaaS Story

Welcome back to How I Launched This! This week, Stephanie Wong (@stephr_wong) and Jenny Brown (@jbrojbrojbro) are joined by Justin Behar, the CEO of Trax. Trax is a cloud-based analytics software company that uses the power of machine learning and proprietary technologies to digitize retail for enhanced customer experiences and more effective goods management.

Justin is no stranger to tech, having spent the last 25 years in Silicon Valley founding and expanding various companies. At Trax, Justin has held various leadership roles and recently was named CEO. Trax technology is able to help stores analyze inventory, product placement, some shopping behaviors, and more to make recommendations for how a company could maximize sales and customer satisfaction. Images of store interiors are uploaded and analyzed, providing tons of data from more than a million retail outlets. Justin walks us through an example of this process of shelf monitoring and data analysis, explaining how the system can help measure things like advertising campaign effectiveness, stock issues, and more. From manufacturing to the retail shelf, Trax offers important product feedback via the Trax dashboard to help companies operate more efficiently.

All companies face challenges, and Justin details some of the obstacles Trax has overcome. Situations like new packaging may seem benign but can lead to bad data if not handled properly. Trax's sophisticated machine learning system compensates for this and other stock issues.

Later, Justin offers insights into Trax data management now and into the future. We hear more about his journey to the CEO position and how he hopes to continue the positive trajectory Trax has been on.

Episode Links:
TraxTrax Video

This week on How I Launched This: A SaaS Story, your host Stephanie Wong (@stephr_wong) talks online customer service strategy with Qubit CEO Graham Cooke. A self-described lifelong geek, Graham's fascination with the latest technologies and his interest in online consumer shopping behaviors converged into his most recent project, Qubit. Qubit is a data-driven model that collects information as users shop in an effort to understand how to turn browsers into buyers. Qubit creates profiles that help retailers understand the specific needs of that client, allowing businesses to create a more direct strategy for sales. In the end, the retailer makes the sale and the customer gets exactly what they wanted when they wanted it with less hassle and frustration.

As an ex Googler, Graham tells us the lessons he learned there and how they positively impacted his recent ventures. He details online buying trends he's seeing and how companies should pivot to meet customer needs. From better personalization to nudging the consumer at the right time in the right place, Graham believes retail websites could be doing so much more. Creating a customer-first strategy is the future of retail, and Qubit is helping clients achieve this with the power of machine learning.

Later, Graham tells us about Start, the newest addition to Qubit, and how it uses machine learning to get retailers started with automation and customer personalization. He explains how it compares with the more thorough Qubit Pro, how both systems were built, and the logistics of their personalization platform.

To wrap up the show, Stephanie and Graham talk about Qubit's stack and what Google products they use to get the job done. Graham leads us through onboarding of a client and how each of the Qubit products benefits retailers at different stages of customer-centric platforms. He tells us about lessons they learned along the way and how those lessons changed Qubit for the better.
Episode Links:
QubitCapgeminiRiver IslandQubit StartQubit ProBigQueryLiveTabPub/SubDataflowFigmaGraph QLStripeTwilio

Carter Morgan (@carterthecomic) and Stephanie Wong (@stephr_wong) are back this week with another episode of the SaaS Podcast! This week, we're speaking with Field CTO Steve Mayzak about Elastic and how they're perfecting the thorough search of data in distributed systems.

Steve starts the show explaining the role of a Field CTO and how he helps develop strategies for the future of Elastic. He describes the function of Elastic in detail, sharing that the search capabilities they've developed can do anything from satisfy your curiosity on Wikipedia to protecting submarines from attack. Whether it's online shopping, sifting through years of web logs, or detecting threats to your project, people expect to find exactly what they're looking for fast, and Elastic has the power to do that.

Later, we talk about why open source is so important to Elastic and how it led to the development of programs like Logstash, software that can manipulate and search through logs, Kibana, software that improves UI and simplifies data organization for any system, and Endpoint, security software that searches out threats to the system. Steve talks about real-life use cases of these Elastic products and how companies like car manufacturers can use the system to predict machine maintenance, thus decreasing downtime. We discuss the future of Elastic and learn how these use cases influence future product development.

We wrap up the show with a detailed discussion of the Elastic stack, including the technologies they use to keep the system running smoothly, ensure data is well indexed and organized, and perfect the scalability of Elastic. Steve talks about roadblocks the company has faced and the solutions they found, as well as Covid-specific changes they've made and how they're helping other companies deal with issues caused by the global pandemic. He offers advice for companies now facing this work-from-home scenario and ways to run efficient teams no matter their location, as well as the future of data management.

Episode Links:
Elastic
Elasticsearch
Logstash
Kibana
Elastic Security

Welcome to "How I Launched This: A SaaS Story" from Google Cloud! In our first installment of the podcast, your hosts Stephanie Wong (@stephr_wong) and Carter Morgan (@carterthecomic) talk with the folks at San Francisco-based Aclima about environmental intelligence. Aclima's air quality mapping platform aggregates and analyzes air pollution factors to aid in the diagnosis and remedy of these problems for a safer breathing environment, both locally and globally. The air quality data is collected by sensors on vehicles and input into the SaaS platform for customers. The analyzed data can quickly be interpreted so appropriate measures can be taken to mitigate air pollution.

Davida Herzl, CEO of Aclima, starts the show explaining why she felt the world needed Aclima and how advancements in technology made it possible to collect, store, and analyze data in a new and more useful way. With their hyperlocal data collection system, clients can see city-block by city-block numbers and learn how the air quality affects each individual person, making the system much more personal. The data can of course also be analyzed in larger numbers for city-wide, state-wide, or even global solutions.

Davida and Robert Murphy, VP Strategy and Business Development, later dive into the technical side of Aclima, describing their technology stack, development procedures, and suggestions of SaaS best practices. Davida also elaborates on the development of the hardware and how these sensors are deployed. She addresses the growth of Aclima and how company management and technologies have changed and grown with the business. She wraps up the show expressing her excitement at how technology can continue to improve our world.
Episode Links:
AclimaGoogle Street ViewGoogle Earth Outreach

In this week's episode, Stephanie Wong (@stephr_wong) and Carter Morgan (@carterthecomic) speak with Qubole co-founder & CEO Ashish Thusoo about data lakes, analytics, and how Qubole is solving real problems in the data usage space.

While working together at Facebook, Ashish and his partner Joydeep recognized the power of data to analyze the past, understand current trends, and predict the future, but data analyzation programs at the time fell short of realizing this potential. Thus the data lakes evolution began. Qubole was able to take this new idea mainstream by offering an open platform to simplify data lakes for enterprise companies.

Ashish explains data lakes in detail, telling us that true lakes allow you to store any kind of data and run multiple use cases on that data. Data lakes keep data flexible, allowing users to store whatever and analyze however they need to anytime. Qubole provides customers with this storage while also offering a plethora of features that make it easy for customers to understand and use their data lakes.

Later in the show, we learn how data lakes have evolved and the different ways they can benefit any business. Ashish gives us real-world examples of companies using Qubole today, detailing the Qubole services available and how they're used. We talk tech shop and hear about Qubole's stack, issues they faced building such a big piece of software, and why they value openness and advanced security.

We wrap up with Ashish's ideas on the future of data analysis and SaaS companies.

Episode Links:
Qubole
Qubole Corporate Blog
Qubole Tech BlogQubole Webinars
Kubernetes
Apache Spark
Presto