Customer Analytics for Startups

This post was written by guest author Trevor Fox

Every startup of a certain size, at a certain degree of success, arrives at a question: “Who are our customers?” Behind this question are a couple of motivations: if I can understand them, I can serve them better (and retain them), and if I can find common characteristics among them, I can acquire more (and better) customers.


This question opens up the door to the whole field of customer analytics. As long as your startup has customers, this door won’t close; it will just lead to different places. 


Before we start exploring, I’ll note that customer analytics means many different things to different people. It’s helpful to establish what customer analytics is for an early-stage startup and what it isn’t

What is customer analytics for startups?

Customer analytics for early-stage startups is the process of progressively honing an understanding of the startup’s customers to make strategic decisions about the startup’s product, marketing, and customer service. 


Customer analytics relates to two success factors among early-stage startups: traction and leverage.

Gaining traction

In the earliest stages, startups are working to establish product-market fit (PMF).  This can be defined in a number of ways, and it varies depending on the type of business, but broadly speaking, reaching PMF comes down to retention, growth rate, and/or cost efficiency. In this stage, you’ll use analytics to identify customer segments that are efficient to acquire, highly engaged, and substantially profitable. Identifying segments you have traction with will help you align your product and marketing efforts toward the right market segment.


Note that early on, you might find it useful to look for ways to reduce the amount you know about your market (current and prospective customers). Therefore, bad customer segments can be just as informative as good customer segments.

Identifying leverage

Once a startup gets a taste of PMF, it’s off to the races! You’ll take what you’ve learned about your ideal customer profile (ICP) and seek rapid and efficient means to grow your ICP. 


If seeking traction is about narrowing your customer focus, identifying leverage points is about expanding your customer base. In this stage, you’ll use analytics to find common characteristics among your ICP to acquire customers disproportionate to your investment effort and resources. These characteristics would be things like the acquisition channel, resonant messaging, and appealing offers.

What isn’t customer analytics for startups?

It’s not uncommon for ambitious individuals to consider all the data and possibilities and dream up expansive data applications with customer data. It may not need to be said, but I’m going to say it anyway: there is very little “data science” when it comes to customer analytics for startups. 


For most early-stage startups, fancy classification of prediction offers little incremental benefit over simple segmentation. In addition, data volumes are likely too low and too varied to provide any meaningful signal. Keep it simple and focus on good questions rather than fancy analysis.


On the flip side, it’s possible to set the bar too low. Web/app analytics is not customer analytics per se. While it’s true that customers generate behavioral data, it often lacks the textural information you need to really understand who your customers are and why they behave the way they do. It’s also more likely to tell you more about your product or service than it is about your customers. 


That said, behavioral data can and should be combined with customer attributes to provide insights about customer segments. This can provide a great means of “scoring” your customers for product-led growth efforts and associating feature usage with customer segments.


This article aims to help you focus on the aspects of customer analytics that provide disproportionate value compared to effort. As I said before, the focus should be on understanding your customers so that you can attract more of them and service them better for longer.

Why is customer analytics hard?

Every founder remembers their first few customers. But success is scale and scaling makes it impossible to know and understand your customers on an individual, conversational level. 


There are many solutions for scaling (and not failing at it): CRMs, marketing automation, customer service platforms, and web/app analytics platforms. They all help you engage and understand your customers, but since they are purpose-built to serve a specific team to do a specific job, each provides only a narrow slice of visibility into your customers. 


Eventually, a founder asks a bright, analytically-minded individual to provide analytics about the startup’s customers. Excited by the task and driven by curiosity, the “analyst” takes on the task and, days later, returns with a slide presentation. 


Leadership gathers in anticipation of the newfound insights. But, as you probably can guess, the presentation fails to reach the expectations of clarity and purpose that the executives hope for. By the end of the presentation, even the most polite executive has responded to their third email if they haven’t dropped it entirely. 


Why is it unsatisfying?


These reports typically feature aggregate demographic information from Google Analytics (which is too obvious to be insightful), incomplete customer information from Salesforce (which is questionable), and anecdotes from customer success managers (which are useful but not entirely representative).


Each executive returns to their fiefdom to oversee their narrow slice of the customer lifecycle. The startup explicitly or tacitly returns to describing its customers as the blind men and the elephant. This causes teams to think and act differently based on their slice of the truth, and chaos ensues.


GA says the user came from here.

Salesforce’s first record is from here.

Are these the same people? God only knows. 


The problem is that slide decks and spreadsheets are too vague, cumbersome, and stale to provide a real solution. The analytics provided by the operational apps just provide an up-to-date but limited view of the truth. 


Now that we’re here, we should talk about the better way…

Customer analytics tools (A.K.A. your customer data stack)

If you’ve spent any time researching customer analytics tools, you’ve probably seen everything from Google Analytics and web session recording software to Zendesk and survey tools. The problem with all these one-dimensional tools is that they lead to the problem I described above. That’s why you should adopt the idea of a “data stack.”


A data stack is a collection of tools or components that allow you to collect and combine data from multiple sources into a single place so that you can analyze it. There are two foundational components and several additional components that can be added on top of those two. Here are the main components of a data stack.

Data warehouse

Put simply, a data warehouse is a database that stores all a business’s customer-related data so that it can be combined together for analytics and reporting. You may have heard of the most powerful data warehouse called Snowflake or others like BigQuery and Redshift. All of these modern data warehouses run in the cloud. Hence, they are capable of swiftly processing terabytes of data quite rapidly and of being accessed from multiple different places at the same time. 


While most startups don’t need to process terabytes of data, data warehouse pricing scales based on usage patterns so that a startup can get all the benefits of cloud based analytics at a rate that is affordable. 

Data integration (A.K.A. ETL)

You might be asking, “What good is a database if it doesn’t have all my data?” That is where the second component comes in: data integration. 


Data is brought into the data warehouse through a simple process with an overly complicated name: ETL. ETL stands for extract, transform, load. In simple terms, this just means that data is extracted from a source system (an application database, Salesforce, Stripe, etc) and structured so that it can be loaded neatly into database tables. It’s complicated if you were to try and do it yourself, but the idea is simple. 


For most startups interested in analyzing typical startup data, such as application databases, CRMs, marketing automation, customer service software, and payments data, it’s much better to use pre-built ETL software such as Fivetran to save the cost of building and maintaining these data syncs.

Data applications

About five years ago, this heading would have simply said, “Data Visualization Tools.” But while visualizing data is a great way to spread the impact of data, in the past few years, many new ways to apply data have come up. These include things like internal alerts (email and Slack), automation (for RevOps and marketing), or even machine learning (but probably not right away for a startup!).


That said, data visualization is the data application that most business users are familiar with. Salespeople want to track their pipeline, Marketing wants to see how advertising and email campaigns are performing, Product and Customer Support teams want to ensure customers are successfully using the product, and Finance teams want to know a bit about all of that. 


All these different uses of data require different types of data. Let’s get into that. 

The customer data startups should care about

Customer data encompasses a broad scope. It includes not only data about your customers but also about their activities. I like to use a simple framing to think about customer data in the big picture. You’ve probably heard it before: who, what, when, and why.

Who are my customers? (Demographic/firmographic data)

The first question startups generally ask about their customers is simply, “Who are my customers?” To answer this question, you’ll need data that describes key characteristics that pertain to their buying and usage behavior. For e-commerce, marketplace, or SaaS startups, this would include demographic data like their geography, job title, or interests. For B2B SaaS businesses, firmographic data is often more insightful. These attributes would include things like the industry, company size, growth stage, or revenue.


This kind of data is most often collected through lead generation forms or product signups, but it can also be purchased from demographic or firmographic data providers. A good rule of thumb, though, is to collect the data that you will find most useful for segmenting your customers instead of relying on third-party data.

Take a Deeper Dive: How We Used Mozart Data and Braze to Boost Customer Engagement at Tempo

What are my customers doing? (Behavioral/transactional data)

Demographic data is interesting but not useful without pairing it up with data about what your customers are doing. Consider the common question, “How much revenue are we generating by geography?” The question is only possible with the demographic dimension and the transactional metric. 


Behavioral data, and an important subset of behavioral data: transaction data, is data generated by your customers’ actions or interactions. This data is usually collected as an activity, and without going into the specifics, these records contain attributes that describe and give context to the activity as well as a timestamp that the activity took place. In many cases, this data will also include metrics such as sale price or amount paid. 

If you want to explore this further, you should check out the longstanding data warehouse theory called dimensional modeling, where behavioral data would be modeled into “fact” tables, and the customers (or leads) table is modeled as a “dimension” table. The modern ActivitySchema is also an updated way to think about behavioral data.

Why are they doing that? (Feedback/surveys)

If you want to stay current on trending data terminology, you’d call this zero-party data (whereas first-party data would be data your business collects and third-party data would be data that your business obtains from an external source).


Zero-party data requires your customers to actively provide it. As such, it has the potential to offer more context and insight into why customers behave the way they do. This means it can be really useful for early product and go-to-market direction. However, because it asks the most of your customers, it’s often hard to acquire at scale, and because it’s textual, it’s also hard to analyze at scale. 


Customer perspectives from surveys, product reviews and recommendations, and demo request notes should be seriously considered early on, but as a startup scales, it will have to navigate its way to the more scalable types of data analysis and segmentation listed above for more comprehensive insight.

Benefits of Customer Analytics for Startups

Now that we’ve completed the world’s shortest survey of customer data analytics let’s discuss why and how you might leverage it for your startup.

Customer Understanding at Scale

In early-stage startups, it’s normal to know customers by their first name and be intimately familiar with their business plans and product needs. But as the CRM system becomes crowded with leads (good and bad), business opportunities span from perfect match to “hail Mary,” and customers that aren’t paying anything to A+++ customers, keeping that same understanding of your customers is hard, if not impossible. 


At this point, you have to start thinking about customers in terms of segments based on behavioral trends and patterns in customer characteristics. You can define segments based on their value, needs, and potential opportunities. This high-level understanding of customers allows you to tailor your product, pricing, and promotion strategies. 


Let’s get into those. 

PMF and Product Development

Keeping a tight feedback loop is imperative if your goal is PMF or you’ve discovered PMF and it’s time to scale. There is an interplay between customer segments, value propositions and willingness to pay. Observing changes in your customer segments and how they engage with your product or services is a great proxy for understanding the demand, value, and stickiness of certain kinds of features. 


You can extend this relationship between proposition and customer segment to strategize how to grow by expanding valuable segments. This is where marketing segmentation comes in.

Targeted Growth Strategies

Once you understand your customer segments—which are valuable, which are expensive, which are growing, or can grow, it’s much easier to identify strong market segments and shape acquisition and promotion strategies to fit. Using your existing customers as a basis for acquisition experimentation will reduce the complexity of your marketing programs and increase their efficiency. It will also allow you to spend more time thinking about product and promotional strategies rather than targeting. By knowing where to focus, startups can achieve higher engagement rates, better conversion, and, ultimately, a stronger brand connection with their audience.

Strategic Customer Engagement

While all customers are important, some are more profitable than others. Customer analytics allows you to measure the lifetime value and cost of serving each customer. This insight can then guide strategic customer service and pricing decisions. It might even open up new opportunities for premium service and personalization. In the competitive startup ecosystem, where acquisition costs are high, nurturing and retaining your best customers becomes a strategic and cost-effective approach to growth.

Better Financial Forecasting

The strategic value of customer analytics is where it all comes together.  By understanding the tree of customer metrics: acquisition cost, cost to serve,  lifetime value, etc, and behavior patterns, startups can make more accurate predictions about revenue, growth, and profitability. This financial foresight is crucial for making informed decisions about resource allocation, investment, and scaling. Moreover, a data-driven approach to forecasting helps startups navigate uncertainties with greater confidence, securing a competitive edge in the dynamic market landscape.

It’s time to ask

As I said at the beginning of this article, understanding your customers is imperative for scaling a startup. The simplest questions (“Who are my customers?”) will yield immediate traction and sustained leverage over time. We’ll dive into greater tactical and technical detail in the next post, but until then, if you’re ready to start thinking about your customer analytics strategy, we’re here to help. Drop Mozart a line.

Become a data maestro

Data analysis

Is Steph Curry a Good Shooter?

This post was written by Mozart Data Co-Founder and CEO, Peter Fishman.  In 2015, I became a season ticket holder


Everyone Uses Data

This post was written by Shai Weener on Mozart’s data analyst team.  I was on a hike through the Marin