In marketing, there was a time when more = better. By casting a wide net, marketers hoped to pull in as many leads as possible. More leads, more traffic to the website, more content — what could be better?
The underlying idea was this: Increase the number of possible “attempts” to increase your chances of success.
With this “More of Everything” philosophy, marketing playbooks suddenly become over-abused, blogs became content farms, and companies started to over-measure and over-optimize commoditized best practices.
Marketing acts like a giant pendulum.
What worked a year ago may not work today. What’s working today may no longer work a year from now. In the era of SaaS and digital products, opportunities get saturated, best practices become overused, everything suddenly becomes highly measurable, more predictable, and over optimized. Things that we relied on become less effective over time and its efficiency fades out.
Everything in marketing is doomed by this rule. Continue reading
All metrics are shortcuts. When we’re faced with uncertainty, we use metrics to break our problem down into simpler, tangible pieces that we can understand.
Metrics are simple proxies that allow us to transform difficult questions into empirical, demonstrable ones.
When we’re faced with a difficult question, we often answer an easier one instead, usually without even noticing this subtle substitution. This is what economists call an availability heuristic — a mental shortcut where we use what we already know, rather than complete information, when making a decision.
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.
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.
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.
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).
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.
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.