Be lazy and smart. Find and automate your leading indicators.
Be more knowledgeable in your forecasting and planning when you develop automated leading indicators.
Here are examples of industries that use leading indicators for business success:
In retail, managers use a forecast of rain to schedule fewer staff to be working since rainy days lead to fewer in-store visitors.
In financial markets, investors pick apart the comments of the Federal Reserve Chair word for word to find changes in tone that might signal movement of interest rates being changed higher or lower in the future.
Hedge fund managers are constantly looking for “alternative data” to obtain an advantage. Many forms of alternative data are leading indicators.
Surfing forecasters use data from buoys located in the open ocean to measure heights of swells to schedule surfing competitions located hundreds of miles and days away.
For 4 years, I was the General Manager of a shopping website that peaked at 5.5 million monthly active users. It was a one-stop research destination with 100 million product reviews on 20 million products...think of it as the “Yelp for product reviews”.
My leading indicator was to use the count of website visitors on one day, Monday, to predict the revenue for the entire week.
Leading Indicators: Change in Daily Visitors Week over Week
We compared this Monday to last Monday to understand the percentage change in visitors. Then we could apply that percentage change to last week’s revenue to approximate this coming week’s revenue.
It wasn’t about the absolute number, it was about the percentage trend.
With a few clicks, every morning I could see how yesterday’s website visitors compared to the same day of the week from the previous week.
Comparing this Monday to last Monday told me an incredible amount of information.
Here’s an example from a different company. This is a graph of the visitors to an app from a company I’m working. It shows Labor Day 2020 compared to the previous Monday. I expanded the time range and compared a whole week to the previous week.
Turns out that app visitors started reducing their visits on Friday by 21% even before the weekend started.
Predictably on Labor Day, visitors were down 25% week over week.
If I was the leader in charge of this app, I would have received an automated alert telling me about this abnormal traffic trend. I knew about Labor Day being slow but didn’t anticipate the Friday slow down. I usually set my alerts threshold to a change of 15% or more to trigger an alert.
Leading Indicators: Instant Analysis with Change in Hourly Visitors Week over Week
For over 10 years, Google Analytics has shown hourly traffic. This can give even quicker access to a leading indicator.
If you notice a website problem or a server problem and need to correlate with website traffic then the hourly comparison for today vs the same day last week is a great double check.
Week over week hourly trends tend to hold up as leading indicators.
Leading Indicators: Be Lazy, Get Alerted Automatically
Even better than checking leading indicators yourself is having the leading indicator sent to you automatically. So have your analytics system alert you when it happens. Using automated alerts based on thresholds helps leaders be interrupted only when necessary.
For this app, we set the threshold at a 15% change either negative or positive. This allowed for natural fluctuations of less than 15% to occur without alerting me.
You may choose a different number. I tuned the percentage value until I thought that the alerts were actionable. This took several weeks.
Here’s the screenshot for enabling this in Google Analytics. Adobe Analytics and other analytics systems also support these kinds of alerts.
So what’s your leading indicator?
Jim knows how to build successful teams and products from scratch.
He co-founded PowerReviews, a B2B2C product reviews SaaS platform, that had an exit in 2012 for $168 million at a 13x multiple. In the early days of the web, he product managed and architected one of the original ecommerce sites that had a $66 million IPO in 1999, online sporting goods retailer Fogdog.com.
For his Product teams, he’s created a curriculum and training program that pulls from his 20+ years of experience and the best minds in Product Management. In addition, he relies on his software engineering background and experience to bridge the gap between their Product and Engineering teams. He graduated from Stanford University with a degree in Computer Science.
Jim is based in San Francisco and works with clients from 2 to 20,000 employees in a variety of industries and business models. Previous clients include VSP Global, PagerDuty, Dictionary.com, and Hallmark. He’s also worked with startups in machine learning, API development, computer vision, payments and digital health.
