Fireside Chat: Getting an accurate view of your data with Littledata

Data can be incredibly powerful, but intimidating - there’s so much to understand and optimize. From targeting the right customers with effective marketing, to creating a great experience on your website, to defining the roadmap for your products - everything can be improved with the right data. It can sometimes feel like making decisions without data is like flying blind. 

We chatted with Ari Messer, Co-founder and CMO at Littledata to dive into how brands can use their data more effectively in their day-to-day decision making, especially when it comes to subscriptions. 

In the interview below, he shares the surprising truth that most brands don’t even have consistent data they can trust and how to fix that problem. Then we discuss how to use that solid foundation to better understand and target your customers and strengthen your subscription business. 

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Can you tell me a little bit about what inspired you to start Littledata? 

We had a data agency and were helping people set up, track and analyze their data and kept seeing that most people didn’t even have the basics working - like how to track orders, track subscriptions and tie them back to marketing campaigns, track LTV. These things are the most important, but hard to get right. We built an audit tool to help identify what things weren’t tracked correctly and it naturally followed that people asked us to fix the issues that were found, which is why we built the Littledata app. 

Lots of merchants we work with have multiple tools that they use for things like upsells, affiliates, email, subscriptions etc. and they were having to look at all of those tool dashboards to piece the story together because it wasn’t consistent with what they saw in Google Analytics. We make it easy for them to make all of that data correct and consistent so that they can use the more powerful reporting features and tools to really understand their customers and marketing strategies. 

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What are some of the biggest problems you see merchants have when they try to use Google Analytics? 

Most people have installed GA, but it takes time to set up and learn. A lot of people look at it and immediately see that it doesn’t even match up with what they see in Shopify and can’t get past that. You can’t create goals if you don’t trust the basic data. 

A lot of folks can’t differentiate between first time orders and subscriptions in their data, and sometimes aren’t using server side tracking so they’re missing orders completely. For example, recurring subscription orders that are triggered from the server without going through a checkout flow are often not counted. 

Sometimes there are ghost scripts or tracking that agencies added that no one at the company understands anymore - lots of leftover stuff that makes things hard to understand. 

Double tracking is actually a huge problem too. We see it as often as missing data which causes a lot of confusion over conversion rate. 

And I should mention that all of these problems are even bigger with GA4, the newest version of Google Analytics. We already support GA4 completely for Shopify stores, but a lot of merchants have been procrastinating setting up GA4 or haven’t taken the time to explore it yet.

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How does their experience change once they set up Littledata?

The first thing people notice is that all of their data sources are reporting the same numbers -  Shopify matches GA, Awtomic matches GA, and Klaviyo matches GA. 

As you start to dive into the data, there’s less traffic that looks like it’s direct and more attribution for first time payments and returning customers. 

Then people can really start doing more LTV and cohort analysis. It’s a relief to start with data that you trust and lets people make their decisions more confidently and with more conviction. 

A typical scenario we see is that a consultant or agency tried to set up custom tagging. It worked at one point but then things changed - the brand added subscriptions, or started using some new tools or strategies, like ads run to landing pages with Add-to-Cart buttons - and now everything is broken. It’s not just about simplifying the metrics but also the maintenance process. You need to think about what you’re planning to build and if your data strategy will still work and be accurate as you adapt. 

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What are the most impactful ways that you see merchants use their data to inform or help their businesses?

It’s changed over time. We pitched marketing attribution much more in the early days. There weren’t as many tools solving for ecommerce marketing attribution. Now we help a lot with first party tracking and getting data into a data warehouse such as BigQuery or Snowflake. 

We’re finding that now marketing attribution is an even bigger use case because brands can build out comparative attribution models. The marketing mix for every brand is a little different. To reach high value customers you have to adjust marketing and not just aim for a big purchase at the beginning but for the right kinds of engaged customers. You have to use your data to build community. 

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How do people recognize “community” type customers 

We see a lot of people looking at LTV by channel, using custom dimensions to see who is making repeat purchases. Look for types of products or product groups where referral codes or gifting or other activities happen more often. Those are signs that those are the users you want to engage with. More and more brands are doing this without identifying the customer, but using anonymized data. 

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What tools are you seeing customers use to look at their data most effectively? 

It varies for the brand and tech stack. Most people use Google Analytics. Bigger brands use Segment and connect that to Mixpanel or Amplitude. We’re seeing more folks use Google Analytics connected to tools like Daasity or Glew now too. 

Beyond marketing attribution a lot of folks use these tools for site optimization and product optimization. They’ll experiment with product mix, adding more SKUs or focusing on less SKUs. More people use the data now to figure out what to sell next. We increasingly work with agencies like Prismfly that are totally focused on CRO and looking at data and GA. 

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Is there anything that you think a lot of people miss that is important? 

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I’m always surprised by two things. 

One is how many people haven’t trusted their data for a long time and haven’t made a company wide initiative to fix it. The thing that a lot of people don’t realize is that once you start tracking it, you can’t go back and fix it historically. If it’s not being accurately recorded it’s lost - so even if it doesn’t seem like a big priority, you really should start now so you don’t lose the insights and history. 

The other is looking at too many metrics. Some smaller brands in the DTC space are able to do more with the data they have and make better data driven decisions because they’re more focused and have less data to look at. They are hyper focused on specific metrics and OKRs - like “LTV from organic channels” - they know which ones are the most important and that makes a difference. 

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Are there any patterns that you see from the most successful merchants who use Littledata that you think others could learn from? 

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I find that the bigger brands we work with like Rothys and Dry Farm Wines have a really collaborative work environment. Different teams can ask other teams for information- they don’t work in a silo. They’re always asking each other questions. 

We work with some brands that are big but just getting ready to start selling subscriptions. They start out by planning and really trying to understand who they should focus on, what products are more likely to sell as subscriptions before they actually set it up and launch. 

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Given the changing market and shifts in ecommerce lately, does that change how you think brands can or should use data in their operations or planning?

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It’s definitely time to think about what is your paid spend most useful for - is it for acquiring customers? It may not be. It’s different for different brands. For brands like Grind, a lot of their paid spend has gone into converting offline customers from their brick and mortar into online sales. 

It’s really important to know your customers and where the paid money goes. It’s complicated to get accurate subscription data and paid attribution for it, but if you can, it makes your marketing spend much more effective. 

Another tip is to build segments and audiences but don’t get too specific. It can actually hurt to be overly targeted because you don’t really know if it’ll be right. It’s better to set up segments that are based on user activity rather than predict one very narrow type of buyer. 

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How do you think littledata can specifically help folks who are trying to grow their subscription businesses? 

It’s really important to understand where your high value subscribers come from. It sounds easy, but it’s not without the right tools. Whether you’ve just launched or are getting ready to launch or have been selling subscriptions for a long time, there are interesting insights you can find in the data. 

We can help by creating separate views for recurring purchases vs. one-off. You can segment by marketing channels and browsing behaviors. 

My recommendation is to look at data and spend time like you’re reading a book. You may not get the insights from just glancing at the numbers. When you really read the data in depth you never know what you’ll find. 

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How do you think people should shift their thinking about data when they think about subscriptions vs focusing on one-time purchases? 

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It all comes back to LTV for subscribers. Who is more likely to upgrade or try out different bundles over time and tie that back to marketing. Who is the real community? Who is leaving reviews? Look at how subscribers interact with email campaigns and flows vs what someone does in their first onboarding flow. 

Think of your subscribers as a different type of relationship - a long term relationship. I’ve seen brands share information with subscribers that doesn’t seem to matter at all to the brand or sales, but it’s engaging for them. For example I saw an email that worked really well that was about National Women’s Day, or one talking about Black Friday and Cyber Monday trends well before it actually happened that ended up making people more likely to purchase when the time came. Engage people on an ongoing basis and have fun with it. If you make it a fun experience it will lead to more engagement and fewer refunds over time. 

A lot of our subscription brands are health and nutrition brands. They tend to look at content marketing for how to live a healthy lifestyle, drinking natural wine, using nutritional supplements and other topics that are interesting to their subscribers but not just highlighting their products. 

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This all sounds super valuable. For folks new to Littledata, how should they get started? What does the setup look like? 

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Everything gets set up really fast, especially with our seamless Awtomic integration. It only takes about 5 minutes. You replace Shopify’s default tracker with ours and the data starts flowing. Nothing is deleted or removed, we just start improving your data right away. You can install our app on a test store to check it out and use our 30 day free trial too. 

One thing we automatically set up in GA is a subscriptions view. It gives you a quick sense of how subscribers are behaving. You can see bounce rates that actually make sense and see who’s engaging even if not to make a repeat purchase. If they engage and stay subscribed, you can see what they’re doing. 

We have a detailed help center, a bunch of new how-to videos, especially on GA4, and a really active blog and group of partners. We also have high touch onboarding with people in every timezone. 

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We’re really excited to have closed a new funding round. We’re still growing and building out the product in really interesting ways. We’re happy to have a chat no matter what stage you’re in and get feedback on what you’d like to see in an analytics tool. We’re always trying to keep up with new trends and adapt. 

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