We marketers tend to want to spend all their brain power of generating killer ideas, designing brilliant marketing automation, and writing hooky ads. But if you don’t analyze before you create, you’re wasting your money.
In some ways it’s the oldest problem in marketing. Claude Hopkins, the father of modern advertising, railed against “creative” ads with no analysis behind them a century ago.
And yet the problem persists in the modern, online age. Even though analysis tools are more powerful than anything Uncle Claude ever dreamed of!
So what does data-driven analysis look like for a startup? Well, it varies a bit based on each situation but here’s how we typically attack it:
– Analyze user data, Google Analytics, and advertising data to uncover the service or product’s actual aha moment (instead of its assumed aha moment)
– Uncover opportunities by analyzing competitor data
– Utilize surveys to gain deeper insights since data often creates more questions than it answers
Data-Driven Analysis in the Real World
For example, an ag-tech startup that builds cloud-based drip irrigation software hired us to increase their user base to gain user data and to ultimately increase their retention.
So, instead of leading with a popular growth hack that’s helped similar startups, we first committed to analyzing their data and making relationships between KPI-based data points they never analyzed.
We followed this following sequence:
1. We reviewed their Google Analytics data and current/past user data to establish their baseline KPI metrics, and compared them to the metrics they were using.
2. We lined up the KPIs for each segment of their product suite, then analyzed the lifetime value of each segment against its specific cost per acquisition.
3. We loaded all the data into Microsoft’s PowerBI platform to visualize the trends between these previously separate data sets.
Results That Changed Their Business
From this we noticed a correlation between their highest valued organic traffic visiting their most underperforming product segment. This opened their eyes because this product was primarily viewed as a value-add to the main product offering. The data also suggested they were spending the majority of their time promoting the segment that appeared to make the most revenue but to their surprise had the highest cost per acquisition when factoring in the year-long sales cycle; it also had the highest churn rate! This data confirmed the fears of the founders that something was wrong but they couldn’t put their finger on it.
Spotting this trend enabled them to pivot their business by focusing on the value add product to build initial interest, build loyalty, then focus on up-selling their customers into higher tiers of their service.
The results have been staggering over the past year:
– 5x client base
– 68% of clients upgrade to higher tier
– Churn is down 58% YOY
– Retention rate has also improved by 73% YOY
All of this before one creative ad was ever written.
Growth Hacking For the Win
By the way, it turns out had we started our relationship with this company with that popular marketing tactic that works with many of our clients, they might have gone out of business! That specific hack focuses on acquiring real time user data from Facebook mixed with grabbing competitor data. Then modeling the competition’s ads and beat them to the punch with a more compelling product, pitch, placement, and price. It works almost all of the time. Yet had we began our service with this hack, our client might have gone out of business by now because their burn rate was outpacing their sales cycle and they were unaware of it.
This is where businesses waste a ton of time and money: instead of doing the work themselves, they implement growth hacks that worked for similar companies and get limited results, at best.
Startups need to do the work:
1. Aggregate their data (user data, Analytics, marketing, surveys, create new data utilizing Facebook)
2. Create and prioritize numerous tests that can be run weekly
3. Focus this effort on your entire customer funnel: activation, acquisition, referrals, monetization
What’s your biggest challenge to converting your data into new paying customers? I’d love to hear it below!