The Next Generation of SaaS Won’t Optimize for User Engagement

A few weeks ago Hiten Shah explained in a new interesting post why the most successful SaaS companies of the future will focus on usage, just like Facebook. In the write-up, he goes very deep into his explanation bringing examples of world-class SaaS companies like Trello, Slack, and Dropbox that are all building their strategies around this consumer-oriented product approach.

He predicts that this is how the next generation of SaaS will look like.

While I was reading Hiten’s post, I immediately recalled a frugal email conversation I had last month with Patrick Campbell, CEO at Price Intelligently.

Patrick briefly introduced me to the definition of what he calls “anti-active usage” products.

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From Beta to Launch: Customer Acquisition Strategy for SaaS

Building a marketing strategy from the ground up isn’t easy, especially when the product you’re supposed to be selling is still being developed. Here at RepIQ, we knew we wanted to launch and scale quickly, so it was important for us to leverage our time pre-launch to develop an effective marketing strategy. We utilized weekly marketing experiments to quickly test various tactics and determine which would be most effective.

This experimentation process helped up attract 1,000 users pre-launch and also helped us develop a scalable marketing strategy. And while we tested dozens of tactics that didn’t work, we also discovered some clear winners. From our marketing-experiment methodology, to the effective tactics we (eventually) figured out, to best practices for implementing these tactics, I’ll be covering all that and more in this post.

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Data Dictionary: How to Build one for your SaaS business

At the beginning of every startup, the most helpful data usually isn’t quantitative; it’s qualitative. You can’t measure very much in the early days for the simple reason you just don’t have enough data.

Analytics products like Google Analytics, Hotjar or Mixpanel won’t help you understand what problems your users really need to solve or what features you should prioritize after launch. At this stage, the only way for you to have a reasonable perception of the needs and the trends is to go out of the office and talk to people.

But when companies start to grow, they rapidly orient from a qualitative to a quantitive data collection approach. Which means they quickly start paying more attention to statistically significant numerical data than descriptive data, usually harder to analyze.

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AI implications on Marketing and Analytics

In his annual letter to shareholders, Jeff Bezos wrote:

Big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.

We’re probably not yet in the middle. We’re just getting started in shaping up the next industrial revolution with AI. This post is a deep dive into the why and the how Analytics and Marketing SaaS products will use Artificial Intelligence (AI).

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The Modern SaaS Stack and the Unexploited Amount of Data

This post appeared on Hacker News, join the discussion here.

SaaS products represent the building blocks of a huge part of today’s B2B technologies. The ability to understand the impact of new consumer-facing technologies it’s more important than ever. This brings a lot of new challenges also for people who are not directly involved in software. This post is a walkthrough in how startups use Modern SaaS Stack (Marketing/Support/Sales) along with their journeys from day-0. How they adjust their product/marketing strategies based on new technologies. How they measure (or don’t measure) the impact of those technologies on their customers’ experiences.

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Emails as Extensions of Product Interfaces

For years emails were seen as a way to deliver messages. Today the line between products and emails is blurring. We are now seeing a silent shift in this approach where emails are not just a medium to deliver valuable information but a way to extend product (core in some cases) functionalities and give users a secondary way to interact with products.

A few notables startups started as Email-First products. Product Hunt started as an email group, Groupon, Craiglist and Angelist started as email lists,  TimeHope was a simple email reminder of where you checked in on Foursquare a year ago, Sunrise – eventually acquired by Microsoft – was a simple email digest of your appointments.

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Introducing The PlainFlow Digested Week

The PlainFlow Digested Week is a crowdsource email newsletter that goes to all the PlainFlow subscribers. You can contribute to this email by submitting useful links in this public Github repo. Every Monday morning 9 am PST we will review and approve your pull requests aiming to send the email to your inbox by 10 am PST.

A few days ago we published this introductory post to explain what we intend to do with our Blog.
Over the next few months – while we’ll be working hard on building the technology behind PlainFlow – we will start creating a few new Open Projects.

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