How Data Teams Drive Business Success by Understanding Core Metrics

research@theseattledataguy.com November 13, 2024 Uncategorized 0

A key responsibility for any data team is to understand the core metrics driving their business.

Starting from the top, these metrics often include figures like gross revenue and expenses. However, these high-level metrics can feel too far removed and abstract from the actual business.

Many companies, therefore, break down these top-line metrics into more specific, actionable ones that collectively build up to key business goals. These “metric trees” reveal deeper layers of context, connecting individual data points to overarching metrics. The further you dive, the more insight you gain into the underlying factors that shape each metric.

In this article, we’ll explore a real-world example of a company’s key metric and how data teams can take a strategic approach to understanding and improving it.

Starbucks’ same-store sales decline

If you’ve been following the latest from America’s favorite coffee seller, Starbucks, you’ll know they recently reported a decline in comparable store sales:

“Starbucks said global same-store sales fell 7 percent for the fourth-quarter that ended in September, with a decline of 6 percent in North America and a 14 percent drop in China.” – Starbucks Reports a Slide in Sales and Traffic – NYT

For the purpose of this article, we’ll focus on comparable store sales, also known as same-store sales or comps. This metric compares a store’s revenue over a specific period to its revenue during a similar period in the past. As with many metrics, the calculation can vary by company, with different organizations including or excluding certain data points.

Now that you’ve got a basic understanding, the next question is: what underlying metrics drive this figure? Luckily, Starbucks provides those as well.

“Comparable store sales 6% decline in U.S. comparable store sales, driven by a 10% decline in comparable transactions, partially offset by a 4% increase in average ticket…Additionally, China comparable store sales declined 14%, driven by an 8% decline in average ticket compounded by a 6% decline in comparable transactions, weighed down by intensified competition and a soft macro environment that impacted consumer spending.” – Starbucks 8-K Oct 30th

These two metrics—comparable transactions and average ticket—are the core components of comparable store sales. This makes sense if you think about it, as comparable transactions represent the number of transactions, while the average ticket reflects the average amount spent per transaction.

Thus, as the 8-K report states, the decline in the U.S. comparable stores was “driven by a 10% decline in comparable transactions, partially offset by a 4% increase in average ticket.” This highlights how a drop in one metric—like the number of transactions—can be somewhat balanced by another, such as average ticket size. In other words, even if fewer transactions occur, increasing either the price of the product or the quantity purchased per transaction can help mitigate the loss. This balance illustrates how underlying metrics interplay to shape the overall key metric.

With a baseline understanding of a company’s key metric, data teams are better positioned to make a strategic impact. Instead of being distracted by projects and ad hoc requests, they can focus on initiatives to improve these metrics—assuming their team has the ability to influence them.

A data team doesn’t have to be a passive cost center or task-taker. To elevate its role, it must understand the key metrics, the business context, and the technology that can meaningfully impact the top or bottom line. Without that context, even the best data insights are limited in their potential.

Let’s dig into some of that context.

Digging Deeper Into The Facts

One notable line from the 8-K report mentions that Starbucks’ sales decline in China was “weighed down by intensified competition.”

Having recently spent 10 days in China, specifically in the Shanghai/Hangzhou/Suzhou area, my wife and I both wondered why someone might go to Starbucks given the range of popular, more affordable coffee brands available. During our trip, I found myself at two local chains—Luckin Coffee and Manner Coffee—logging around 20 cups in 10 days.

While price isn’t the only factor driving customer choice, as I talk about in some of my consulting articles, it’s certainly impactful. Here’s a general comparison of prices:

In addition to their competitive pricing, most Luckin and Manner stores I visited emphasized digital orders.

In fact, when my wife and I ordered in person at the first Manner location, the staff seemed almost irritated, and at the second, we were flat out told it would be a “30-minute wait” for an in-person order but only about 5 minutes through their app.

These shops were typically smaller, with baristas focused on rapidly fulfilling digital orders.This works particularly well because of the delivery culture in large cities, even more so in China where deliveries can be made quickly. Incentives like discounts and collectible stickers are also common with app-based purchases.

Luckin’s focus on efficiency is echoed in their 2023 SEC Form 20-F, stating that, “[w]e primarily operate two types of stores, namely pick-up stores and relax stores, for different purposes, and we strategically focus on pick-up stores, which accounted for 98.5 percent of our total self-operated stores as of December 31, 2023.” – Luckin Form 20-F

This approach may have helped Luckin scale rapidly, with a reported 18,360 stores in 2024, giving it a far larger footprint than other brands in the region.

By contrast, the two Starbucks locations we visited in Shanghai offered a more spacious, slower-paced experience, clearly targeting a different market segment. The baristas were attentive and anticipated in-store orders, and the atmosphere was more relaxed. And when my wife browsed for a mug (as she often does when visiting a city, even places she grew up), one of the baristas even came out to assist her.

What To Do As A Data Team

When a key driving metric shows a clear decline, it often overshadows other projects. For data professionals, this presents a valuable career opportunity: you want to be involved in projects or initiatives that directly address these critical metrics. From a career standpoint, making a measurable impact on a business’s core metrics is an invaluable experience.

For example, if Facebook suddenly lost 5% of its monthly active users, everything else would be window dressing as data teams prioritize initiatives to address that core metric.

For Starbucks’ analytics team—or any data team in a similar situation—the first step would be to dig deeper into the problem. To be clear, this isn’t about prescribing solutions to Starbucks, but rather a thought exercise on how data teams might approach a similar scenario.

Here are some critical questions that could guide this investigation:

Are the declining transactions concentrated within specific groups or demographics?
Are promotions or loyalty programs effectively impacting comparable store sales?
Which product categories have seen the largest declines or gains? Understanding what’s still performing well (or not) could help Starbucks decide where to innovate or reduce offerings.
How does digital ordering impact in-store transactions, and are there significant differences in spending behavior between digital and in-person orders? Competitors like Luckin Coffee prioritize digital orders, so understanding whether Starbucks’ digital experience impacts transactions could reveal areas for improvement.
How does Starbucks’ brand perception compare to local competitors in terms of quality, convenience, and value? Knowing how customers view Starbucks relative to local competitors could help refine marketing messages and operational strategies.
Are there specific days or times that drive in-store sales, potentially indicating patterns in customer preferences? It could be valuable to see if Starbucks stores with fewer nearby competitors attract a different customer base, suggesting substitution patterns.

Taking it a step further, here are some projects that different data teams might take on during this initiative. These projects aim to provide actionable insights and address the key metrics at play.

Data Engineering

Integrate Foot Traffic and Geo Data

Collect location-based data on foot traffic, competitor proximity, and nearby amenities (e.g., malls, transport hubs) for each store. This can help Starbucks identify which stores face higher competitive pressures and where there may be untapped traffic potential.

Build Loyalty Program Data Pipelines

If not already in place, develop data flows that capture Starbucks Rewards usage to analyze how loyalty program engagement affects comparable store sales. Integrating this with transaction data allows the team to identify customer behaviors that drive higher sales.

Build an Automated Feedback Pipeline

Create a robust pipeline to gather and process customer feedback from various sources, such as social media, app reviews, and online platforms. Using natural language processing (NLP), the pipeline would clean and standardize data, tagging keywords like drink names, store locations, and common phrases. Processed feedback can then be categorized by sentiment (positive, neutral, negative) and then stored for future analytics.

Data Analytics

Promotion and Upsell Effectiveness

Analyze the impact of specific upsell campaigns (e.g., “Try a Frappuccino with your pastry”) on transaction value and customer loyalty. Insights from this analysis could help Starbucks refine product bundling strategies to maximize the average ticket size per order.

Demographic and Behavioral Segmentation Analysis

Segment customers by demographic factors and visit behaviors (e.g., weekday commuters vs. weekend shoppers) to tailor offers and experiences. This approach enables Starbucks to retain key customer groups and encourage more frequent visits.

In-Store Traffic Pattern Analysis

Examine trends in customer behavior within stores across different regions, focusing on peak hours and weekends. For instance, identifying times when customers tend to make larger purchases or prefer specific products can inform staffing levels and product promotion strategies.

Data Science

Predictive Modeling for Store Sales Decline

Develop a machine learning model to predict which stores are most likely to experience a decline in transactions based on factors like competitor density, economic indicators, and historical foot traffic. This model would help Starbucks prioritize intervention at locations most at risk.

Personalized Promotion Model

Build a model to suggest targeted offers based on a customer’s past purchase behavior, encouraging higher spending or more frequent visits. For instance, the model might suggest a new seasonal drink to loyal coffee-only customers, driving incremental sales.

In-Store Customer Flow Optimization

Use spatial and temporal analysis to understand customer movement within stores. For example, data from store cameras or Wi-Fi heatmaps could help Starbucks optimize store layouts to reduce bottlenecks during peak times, potentially increasing transaction capacity.

Sentiment Analysis on Product Feedback

Apply natural language processing to analyze customer feedback on Starbucks’ products and experiences across regions. Identifying common complaints or positive sentiments around specific drinks or store experiences can guide product development and customer experience improvements.

If data teams take on this level of ownership and successfully leverage these insights to improve key metrics, their contributions should be recognized and rewarded.

What Metrics Should You Know?

For data professionals looking to understand their business more deeply, studying real-world companies provides valuable insights. By examining what drives sales and influences key metrics, you can develop a sharper sense of how metrics interrelate and impact performance.

Start by choosing a company—perhaps one experiencing rapid growth, like Chili’s, or one facing a decline in key metrics, such as Starbucks—and analyze what might be happening beneath the surface.

Consider the following:

What questions would you want to answer if you were in the C-suite, aiming to make better strategic decisions?
What levers could the company pull to influence these metrics? For instance, could it adjust pricing, increase marketing, or refine its location strategies?
What could be the implications of those decisions? For example, if Starbucks adopts a “turn-and-burn” approach in Shanghai, focusing solely on digital sales and reducing costs, will it affect the brand’s perception? Is price the right area to compete in, and does it ultimately improve comparable store sales?

Approaching data with these questions can elevate your data team from a task-oriented group to a strategic partner in the business. Understanding the broader business context enables data teams to make a measurable impact, transforming them from a cost center into an essential driver of strategy and growth.