May 27, 2021 by Matt Bahr analysis
What Shopify Marketing Analytics Tools Should I Use?

Which Shopify analytics tool is right for me?

Marketing analytics for Ecommerce and DTC brands can get about as complex as you want to make them -- but fundamentally, you’re really just seeking two answers from your Shopify marketing analytics:

  1. Reporting. The pulse of the business, i.e. “how are we doing?”
  2. Analysis. The growth of the business, i.e. “what can we learn?”

The reporting data Shopify provides is decent enough to give operators a pulse on their business. Unfortunately, the value ends there. Shopify’s native dashboards will not help you grow your DTC brand or generate competitive advantage, because they don’t offer analysis: you can’t integrate third party datasets (like your advertising spend), nor can you query your data (like asking, “What percentage of Product A buyers also bought Product B?”). In many ways, the distinction between reporting and analysis is the difference between lagging indicators and leading indicators.

If you’re asking yourself what Shopify marketing analytics tools to use, you’re already one step ahead of many Shopify DTC brands who focus too much on reporting, and not enough on analysis. Getting a competitive edge in DTC requires deep analytical dives -- you need the right platforms and people involved if you’re going to get actionable answers to the tough questions of growing and optimizing a business. We like to say, “data doesn’t lie until you start asking it questions.”

Our guidance below will cover many independent analytics platforms, but not to recommend one tool over another. Rather, we suggest that the Shopify marketing analytics tools you should use are the ones that match up best with your store’s current maturity stage and resources. In other words, grow with your tools.

Removing Data Silos

The #1 feature of a powerful analytics solution is its ability to traverse or ingest data silos. Some tools have amazing visualizations, some have superior workflows, but none of them will deliver insights on data that has been locked away. What good is a Blended CAC metric if it only includes your Facebook spend, and not the influencer campaign you ran in conjunction?

Data is insight -- but without context, it’s also ignorance. Breaking down data silos is crucial to getting the right perspective in your analysis.

Classifying Marketing Analytics Tools

We can break down marketing analytics tools into three general categories regarding their accessibility and capability:

  • Turn-key analytics: platforms you can implement today, which emphasize ease of implementation over depth. Examples include Tydo and Segments.
  • Database as a Service: managed data platforms that generate actionable insights, not just numbers. An example is Daasity.
  • DIY: a custom analytics stack that best suits your needs, once you know what you want. An example is FiveTran + Snowflake + Looker.

Popular Shopify Analytics and Reporting Tools Used By DTC Shopify Brands

Peel

Peel is a dashboard product with an accessible user experience, and emphasizes insights on customer segments and cohorts. While it’s a reporting dashboard at heart, it can deliver daily reporting and some high-level analysis to users’ inboxes.

Via Peel: Peel’s powerful, automated analysis platform, built for e-commerce teams on Shopify, features 100+ out-of-the-box metrics on their revenue and costs by day and monthly cohorts, with customizable dashboards and daily reports. Its unique technology allows Peel to detect the most relevant segments across dozens of dimensions to identify trends within seconds.

Shopify App Store: 5/5, 10 reviews

Rockerbox

Rockerbox enables DTC ecommerce companies to have a single source of truth and simplify marketing decisions. They track and reconcile all marketing touchpoints and spend, from digital to offline, paid and organic. Rockerbox enables de-duplicated ROAS to see true performance, and optimize budget allocation across channels. They're a bit different than other reporting tools mentioned here, in that their core focus is on marketing analytics and attribution.

Shopify App Store: 5/5, 10 reviews

Tydo

Tydo is another dashboard product, claiming to service a broad range of DTC business units. They allow you to aggregate various data sources, report on them, build segments, then push that data to 3rd parties (e.g. Facebook). Their Report Card product can generate automated daily reports.

Via Tydo: Tydo has simplicity at its core, aiming to democratize analytics for DTC businesses. It's the only platform whose data covers all areas of a DTC business (from acquisition to retention; from finance to operations) and has still mastered simplicity of data visualization. The future of Tydo aims to do the same. As we innovate in how we present true insights to operators, simplicity will remain central to our execution. Sign up for Report Cards: https://app.tydo.com/signup.

Shopify App Store: Their Shopify public app is coming soon

Daasity

Daasity is one level removed from building out a custom data stack in-house. Their value proposition is an outsourced data team. If you want a custom integration or they don’t offer a specific source, you can’t simply add it -- you’ll have to request they build the integration which is added to their roadmap, or they have a paid professional services team that will build the integration.

Via Daasity: Daasity enables DTC brands to make better data-driven decisions, leading to faster and more profitable growth. Daasity centralizes a brand’s data into a working data model and visualizes performance in dashboards that provide insights high-growth DTC brands care about. It saves the time of manually building reports in spreadsheets, and gives immediate access to the powerful insights DTC brands need to grow.

Shopify App Store: 5/5, 16 reviews

Segments

If you’ve never used Segments and are solely relying on Shopify’s reporting, we’d recommend installing it right now (they have a free trial). Segments will very quickly help you build your mental framework for the power of analytics – especially around lifecycle journey mapping (customer who bought Product X, typically buys Product Y next).

Via Segments: Segments is a self-guided data marketing platform that helps brands build long lasting customer relationships. We give brands access to enterprise-grade analytics and prebuilt customer segmentations to increase repeat sales and marketing ROI. It’s just like having your own personal data team, but at a fraction of the cost. Link to schedule a demo.

Shopify App Store: 5/5, 37 reviews

Source Medium

Source Medium provides a reporting dashboard, but one significant differentiator is its report delivery via Slack integration. Pulling data directly from your Shopify analytics, it produces daily report messages in Slack.

Shopify App Store: 5/5, 7 reviews

Statlas

Built upon Common Thread Collective's Ecommerce Growth Formula, (Visitors x Conversion Rates x Lifetime Value) - Variable Costs = Revenue, Statlas is a new analytics tool built to analyze marketing profitability. If Taylor Holiday's track record is any indication, I'd keep an eye on this one.

Shopify App Store: No public app, currently in beta.

SuperMetrics

SuperMetrics borders on being a DIY enabler, given they’re more of an ETL solution than a reporting solution. Said simply, it pulls your advertising data into Google Sheets and builds reports.

Shopify App Store: No Shopify public app

Lifetimely

Similar to other products mentioned above, Lifetimely provides dashboards and automation for key business functions that may have been done manually beforehand. It emphasizes strengths in cohort analysis, LTV modeling, and daily P&L reporting.

Via Lifetimely: Lifetimely is an inexpensive analytics app trusted by 3000+ brands, including names like Four Sigmatic and NOMAD. It’s like cohort analysis on steroids combined with real-time P&L - Lifetimely’s predictive LTV model forecasts what each customer will spend, and how much revenue and profit you’ll earn month-to-month from each customer segment.

Shopify App Store: 5/5, 257 reviews

Excel/Google Sheets

We would be remiss to not add Excel/Google Sheets to this list. We all know the workflow, export Shopify order data, clean, pivot, repeat. Please stop. Once you discover the benefits and efficiency of querying a database there is no going back. Thank us later.

In-House Analytics Stack

Probably the most common analytics stack you’ll find among DTC brands with dedicated in-house data teams is FiveTran + Snowflake (S3) + Looker. That’s data integration, a data warehouse, and real-time business analytics respectively. It’s a thorough combination, no doubt. Looker is the gold standard when it comes to visualizations and exploration -- when set up correctly, Looker allows you to double click into a dataset and explore, which is something many off-the-shelf solutions don’t allow.

It’s also going to run you $5k per month at a minimum, but offers ultimate flexibility and ownership over your data. The bigger consideration is whether or not you have personnel on-hand to make use of such a stack, as DIY solutions place much of the analysis burden on your team. If that sounds more appealing than frightening, the ETL (extract, transform, & load) capabilities of a data integration tool like FiveTran or StitchData will present a ton of opportunities to discover contextual insights.

Another benefit of the DIY route is data ownership and privacy. The minute you connect your Shopify account and other data sources to a 3rd party reporting tool, entire teams get access to your company’s performance. This is again where we’d warn that off-the-shelf reporting products provide limited competitive advantage.

Hot Tip For Getting Started: Create Your Own Database

Off-the-shelf tools masquerading as an end-all reporting solution are, 9 times out of 10, simply databases with SQL + javascript graphs. It’s not rocket science.

The below is recommended to anyone with a desire to obtain a deeper understanding of how databases, querying, and many of these analytics tools are built. Granted, this is meant to be exploratory; unless you have the resources to manage and continue learning advanced SQL, it’s not a long-term solution (but can be a ton of fun if you’ve spent most of your career deep in excel spreadsheets).

It may sound intimidating, but you don’t have to be a developer to set up a database and feel the power that comes with querying your own data. In fact, one of our most popular articles is a walkthrough to set up a SQL database for Shopify data.

Arguably the best way to understand what kind of analytics solutions you want, and why, is to start by analyzing your own data and asking it questions. At less than $150/mo (for an ETL platform), having your own database to conduct research on is key to understanding the capabilities, limitations, and biases around any analytics solution you’ll use in the future. And of course, it’ll also put you in a great position to dive into another best practice:

Build Your Own Analytics Dashboards

A hypothesis among many data analysts and data-driven marketers is that dashboards are only valuable when you build them yourselves. In other words, you spend time on understanding the problems you want to solve, rather than accepting off-the-shelf solutions which, by nature, focus on solving the most generic business problems.

It’s obvious, but usually overlooked: a dashboard that works for everyone is going to provide the least competitive advantage.

Marketers need to think in terms of first principles: “what problems am I trying to solve?” and “what questions do I need to ask?”, rather than “what platform will provide me with useful answers?”

If you want to become a true analytics expert -- or at least, an expert in your own Shopify store data -- you need to start with the problems you have, and the data you own. A dashboard built on those foundations will be significantly more useful to your business, and even if you do end up buying something off the shelf, you’ll now know what you’re looking for and where its limitations lie.

As a starting point for questions, we’ve put together a Google Sheet template.

Build vs. Buy

Ultimately, your build vs. buy assessment rests on your internal talent. Building your own dashboards and data stacks is an incredibly useful exercise for understanding what you need out of your analytics tools, but scaling them is often where you see diminishing returns against the alternative of an off-the-shelf solution. At EnquireLabs, for instance, we often interact with data scientists and data analysts whose sole job is extracting insights and presenting them to the broader team.

If you’ve got those folks on-staff, you’ve got a better case for building your own solutions. If not, our advice is to build enough that you can become a smart shopper of analytics tools.

Segmenting Audiences

Another common output of reporting and analytics solutions is customer segments. These are often built based on similar buying behaviors, grouped, and subsequently pushed to Facebook for look-a-like audience building or Klaviyo for improved email personalization and drip campaigns.

Breaking down data silos isn’t just about the inputs -- it’s also about the outputs, as segmentation proves. Making your data insights portable and applicable to customer touchpoints (especially when it can be automated) is fundamental to data-driven marketing. So to that end, integrations and workflows matter when you’re shopping around.

Can You Query It?

We personally wouldn’t look at any reporting solution that doesn’t let you query your data. The ability to form a hypothesis or question and immediately dive into your entire aggregated data set to see the answer is of utmost importance. Beyond a platform simply possessing the capability, the speed to insight (or, ease of insight) matters when you look at the feedback loop of question → query → answer. When querying feels like a light lift, you ask more questions, and you become a more data-driven organization.

Furthermore, it opens the door to hire a part time analyst or data intern and have them dig deep into your data. On its own, that’s a valuable opportunity -- but as you become more nimble and process-oriented with the insights you glean, the transition to a full-time data analyst or BI analyst will be a lot cleaner. We’d wager most brands doing $10MM in net revenue could create 5x ROI on a $100k analyst position -- but the earlier you start querying data and bringing in big brains to study it, the sooner an FTE hire will bear fruit.

Key Takeaways If You’re Shopping For Shopify Analytics Tools:

  • Reporting = lagging indicators; Analysis = leading indicators. The former tells you how your business is doing, and the latter tells you what your business can do.
  • Get to first principles. Spend most of your initial time on your brand’s data questions & problems, then start searching for a solution.
  • Build your own dashboards to better understand your data before you buy a tool. An off-the-shelf dashboard is going to provide the least competitive advantage.
  • Three things your analytics data should strive to be: queryable, portable, and applicable
  • The earlier you fold people into the loop, the faster your raw data will translate to competitive advantage

Bring A Human Into The Loop: EnquireLabs

This post isn’t about us (EnquireLabs) or zero-party data, but we’d be remiss not to drive home the importance of adding human-in-the-loop data to your existing stack. In other words, ground truth from your customers.

Pivoting your hard transactional data with customer input will open a treasure trove of insight, and fill crucial analysis gaps. For instance, simply building out your attribution model to include a “how did you hear about us?” survey can deliver consumer insights otherwise locked away from analytics platforms (due to the traffic being direct/unknown, or manipulated by a walled garden).

As we get ready to launch our Question Stream product (a perpetual question workflow that develops each customer’s identity through their participation), the opportunity to integrate customer-provided insights greatly expands. We mention all this because we’ve seen from experience -- 1,500 brands’ worth -- how much more useful your data becomes when your customer is in the loop to offer key context or new information.