Analytics is the key to unlocking the potential hidden within vast amounts of data. It empowers industries and enterprises, enabling informed decision-making and exceptional customer experiences. At SureSwift, as we manage a full portfolio of SaaS businesses, analytics plays a crucial role in shaping our strategies and driving success. Through our trading journal analysis and improvement tool, Tradervue, we empower traders with analytical tools to gain valuable insights, uncover patterns, and optimize their strategies — but we also use those same analytical tools internally to drive growth.
I had the privilege of sitting down with Steven Nash, Marketing Growth Manager for Tradervue to get his take on using analytics to drive growth and the role analytics plays in the platform’s operations. Nash is uniquely qualified to talk about the connections between the products internal and external analytics strategies, as a long-time Tradervue user for years before he joined the team. He’s passionate about day trading, analytics, and helping Tradervue’s users succeed.
How does Tradervue work?
Tradervue is a trading analytics tool for intermediate users who have made it through the first hurdle of “blowing up an account or two,” as Nash puts it. These are people who have made their beginner mistakes and are thinking of making something out of trading for a living. So, they turn to Tradervue.
First, information about a user’s trades is captured in Tradervue’s system. Then, the team takes data from the instances where a user has lost a significant amount of funds. The tool helps to process everything so users can see what useful trades they could have taken and what kind of profits they could make if they took the weaker ones out. The platform’s process is not just about finding what traders are good at, but also skimming away all the bad decision-making and trades until a user gets to a profitable stage.
What analytics tools does Tradervue use?
The Tradervue team uses Hotjar, Customer.io, and Typeform, along with several other third-party tools on the front and back ends. These tools are fed and connected into Segment, which provides a warehouse for all of the data. The team also uses a data visualization tool and hub, called Mixpanel, for all of its information.
Segment is fantastic and truly saves the Tradervue team a lot of time. If it weren’t for Segment, the team would have to build each new integration independently, warehouse the data themselves, and then send it to Mixpanel. Instead, the process is as simple as entering an API key, and, in seconds, a new tool is up and running, backloaded with the data.
Mixpanel – the data hub
Mixpanel is Tradervue’s data hub for everything. Without it, things would be a lot trickier because the team would have to manually export data and build all the reports. The tool is very flexible and the team builds extremely customized reports on its customers.
Any data coming into Segment can be used to create new attributes for each user and segment them into groups based on their actions or user profiles. Instead of just analyzing one set of data from one tool, data for different attributes or events of the same user progressing through the sales funnel is compared and contrasted.
For example, these things can be the number of times charged, what was filled out in a survey, or a user’s sign-up date. The team then uses this information to filter whether and when users complete events or milestones, known as activation. If these go uncompleted, the team can use a Segment integration with Customer.io to encourage users to take the desired action.
Mixpanel also tracks and compares users or affiliates, and the team makes segments and cohorts to track certain users, like new affiliates at signup. The team can monitor how they’re doing before offering them a bigger discount or targeting them with more marketing. So, everyone that came to Tradervue through their link – which comes from that first promoter into Segment and Mixpanel – is tracked as a segment. These segments can be compared with Tradervue’s other affiliates, their performance, and how well their users interact with Tradervue.
What information does Tradervue measure and how?
Tradervue measures just about everything – from the second a user lands on its page, to when they start their trial, to their use of each feature, to how each page loads. Everything the user does is stored in the data warehouse.
The team uses Mixpanel to understand what a good quality customer is or what actions they take and what makes them more likely to activate and, similarly, to reactivate if they churn. It shows them the steps users are taking when these things happen, and what might indicate a user will likely churn. The team can then take initiatives to prevent these things from actually happening.
As well, Tradervue wants to see where people are coming from and what their trading level is, so the team knows who’s using the platform. To help with this, visitors are prompted to complete a Typeform survey when they land on the site that indicates if they’re a beginner, intermediate, or professional trader. Since intermediate and professional traders are more likely to convert and stay longer, Tradervue focuses on these groups.
Case study: Annual plans
There are often biases in data and many people want to think they’re onto something great with a slick, new feature. They know what it does and the potential benefit it can offer users, so they might assume it will have a positive impact. But this isn’t always the case.
Being aware of potential data bias, Tradervue recently worked on launching yearly plans and wanted to see how to best go about it. Nearly every digital product offers yearly plans, usually with a little toggle switch at the top of the pricing page. The team figured it was almost certain that this would impact positively because some people will opt for the yearly plan, but most will stay on the monthly plan originally selected.
The bias was in thinking the change shouldn’t have that much effect. To be sure, the team tested its theory out using A/B testing – 50% of users saw the page as-is, and 50% saw it with the toggle switch at the top for the annual plan option. Turns out, it had a disastrous effect on Tradervue’s signup flow.
Every bit of intuition told the team the change shouldn’t have a massive effect, but the data showed that people were using the switches and then leaving the site. This affected paid and free signups – the yearly sales from the change were next to nothing.
So, the team pivoted back and removed the toggle switch function. Now, instead of targeting users on signup, this is done once they’re activated and on the platform for a while. At this point, it makes much more sense to push a sale or promotion toward a user because they’ve had time to use and understand the product, likely seeing how it could be beneficial.
This exercise confirmed that user behavior is completely unpredictable.
“People like to think that they can see the future, but if that were true, we’d be buying lotto tickets, not running SaaS products,” Nash playfully comments. “That’s how Tradervue thinks – we might have an idea about how to improve the product, but we don’t know or understand how it will affect the user funnel. An idea is not a guarantee.”
That said, information is still helpful to see what happens and help make decisions. In this case, the information and data pulled prevented Tradervue from doing something that would have had a negative effect.
By continuously making tiny changes, there won’t be a cumulative effect but rather a compounding effect. “You’re taking different sections of the funnel and slowly damaging it constantly, as it goes along. And you often don’t realize you’re doing this because there’s no data to prove a certain feature will have a positive effect,” Nash explains.
The team knows that data should be used as a preventative measure and to prove a theory, rather than just to prove an initiative was successful. So, it A/B tests every single thing it wants to do or push out to users. This helps to prove whether the action is actually good for both the company’s bottom line and its platform’s users.
Analytics is useful internally and externally
Analytics is very useful for Tradervue’s team to best help its users. Even better, it also serves another purpose in helping the team succeed internally. The parallel lies in strategy, which is involved in both trading and product management.
In trading, you might have an idea and feel you’re smarter or know more than the markets. But, many traders are often unconstructively trying to prove that they were right instead of that something will be profitable.
No matter what initiative or feature is rolled out, the processes for building a successful trading strategy for Tradervue’s users are very similar to those for its internal product management. For both, you need to:
- Collect the data.
- Store, find, clean, and understand the data.
- Prevent biases by only drawing conclusions for data-supported theories.
This strategy mindset is the basis for the team’s prioritization framework.
Analytics inform the team’s prioritization framework
Tradervue’s team is very data-driven in everything it does in terms of releasing features and functionality. Team members don’t just think, “Here’s a good idea, let’s do it.” Rather, they consider where it fits compared to every other idea they have. It’s a very methodical and strategic process. The team ensures there’s a reason and a point for all that it puts out there.
For example, when deciding what to do for the upcoming quarter, the team aims to understand where and how it’s using its resources to ensure optimization. So, throughout the quarter, team members brainstorm and add everything they can to a long list. Then, in the next quarter, they look at the previous list and roll things forward along with any new initiatives.
The team uses a scoring system, or prioritization framework, to evaluate an idea based on a few things:
- Customer value, or how much it will positively or negatively affect the customer’s business alignment.
- How much it aligns with what the team is looking to accomplish that quarter – whether that’s going after more B2B customers, reducing churn, or something else.
- How it affects customer help or support resources – which is most important.
The ideas are scored based on what’s of highest priority that quarter. So, if the team wants to focus on business alignment or revenue, these types of ideas would get scored higher. First, everything is put into a matrix and ranked. From there, the highest-scoring initiatives become planned activities for the upcoming quarter.
Without analytics and the tools to make sense of data, the team would be planning and executing ideas with no true sense of their chance of success.