Editor’s note: Today we’re hearing from Ryan Mason, Director, Head of DTC Growth & Strategy, at alcoholic beverage firm, Constellation Brands on the company’s shift to Direct-to-Consumer (DTC) sales and how Google Cloud’s powerful technology stack helped with this transformation. 

It’s no secret that consumer businesses have been up-ended in a lasting manner after 18 months of the pandemic. Consumers have been forced to shop differently over the past year – and as a result, they’ve evolved to be more comfortable with online spending and have grown to expect a certain level of convenience. While the e-commerce share of consumer sales has grown steadily over the past decade, the pandemic was the catalyst for the famous “10 years of growth in 3 months” which many argue is here to stay. 

Facing this reality head-on,  we placed a new emphasis on Direct-to-Consumer (DTC) with our acquisition of Empathy Wines, a DTC-native wine brand that sells directly to consumers via e-commerce. To accelerate our innovation in the DTC space, we added headcount and new functions to the existing Empathy team and empowered the newly-minted DTC group to apply their digital commerce operating model across the rest of the wine and spirits portfolio, which includes Robert Mondavi Winery, Meiomi Wines, The Prisoner Wine Company, High West Whiskey, and more. 

One pandemic and one year later, DTC sales have surged in the wine and spirits category with Constellation positioned as a leader armed with a unique and powerful cloud technology stack, best-in-class e-commerce user experiences, modernized fulfillment solutions, and data-driven growth marketing. 

Benefits of Going DTC 

A report from McKinsey estimates that the strategic business shift to DTC has been accelerated by two years because of the pandemic and argues that consumer brands that want to thrive will need to aim for a 20% DTC business or higher, which is already taking shape in the market: Nike’s direct digital channels are on track to make up 21.5% of the total business by the end of 2021, up from 15.5% in the last fiscal year, and Adidas is aiming for 50% DTC by 2025. But outside of the clear revenue upside, the auxiliary benefits of going DTC are robust.

For Constellation Brands, each of these four pillars ring true, and our shift toward DTC is as much about margin accretion and revenue mix management as it is about consumer insights and data. The added complexities of the alcohol space add wrinkles to our DTC approach and manifest in many areas like consumer shopping preference, shipping and logistics hurdles, and more. In order to win share early and continue to lead the category, we recognized the need to harness the immense amount of first-party data to power impactful and actionable insights. 

Our DTC technology architecture has fostered a value chain that is completely digitized: website traffic, marketing expenditures, tasting room transactions, e-commerce transactions, logistics and fulfillment events, cost of goods sold (COGS) and margin profiles, etc. are recorded and stored in a data warehouse in real time.  For the first time, at any given moment, we can easily and deterministically answer complex business questions like “what is the age and gender distribution of my customers from Los Angeles who have purchased SKU X from Brand.com Y in the last 6 months? What is the cohort net promoter score? Did that increase after we introduced same-day shipping in this zip code? By how much?” 

The ability to answer these questions and understand the root causes allows us to stay nimble with product offerings and iterate marketing strategies at the speed of consumer preference. Further, it enables us to optimize our omnichannel presence in the same manner by leaning on DTC consumer insights to develop valuable strategies with key wholesale distribution partners and 3-Tier eCommerce partners like Drizly and Instacart. At its core, Constellation’s DTC practice is designed to be the consumer-centric “tip-of-the-spear” responsible for generating insights from which all sales channels, including wholesale, can benefit. 

Constellation’s DTC technology approach prioritizes consumer-centricity and insights generation

We have taken a modern approach to building a digital commerce technology stack, leveraging a hub-and-spoke model built around Shopify Plus and other key emergent technology providers like email provider Klaviyo, loyalty platform Yotpo, Net Promoter Score measurer Delighted, Customer Service module Gorgias, payments processor Stripe, event reservations platform Tock, and many more. For digital marketing and analytics, we use Google Cloud and Google Marketing Platform, which includes products like Analytics 360, Tag Manager 360, and Search Ads 360.

To help gather, organize, and store all of the inbound data from the ecosystem, we partnered with SoundCommerce, a data processing platform for eCommerce businesses. Together with SoundCommerce, we are able to automate data ingestion from all endpoints into a central data warehouse in Google BigQuery. With BigQuery, our data team is able to break data silos and quickly analyze large volumes of data that help unlock actionable insights about our business.  BigQuery itself allows for out-of-the-box predictive analytics using SQL via BigQuery ML, and a key differentiator for us is that all Google Marketing Platform data is natively accessible for analysis within BigQuery. 

But data possession only addresses half of the opportunity: we needed a powerful and modern business intelligence platform to help make sense of the vast amounts of data flowing into the system. Core to the search was to find a partner that approached BI in a way that fit with our future-looking strategy.

Our DTC team relies on the accurate measurement of variable metrics like Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), Churn, and Net Promoter Score (NPS) as a bellwether of the health of the business and monitoring these figures on a daily basis is paramount to success. To enable us to keep an accurate pulse on strategic KPIs, we considered several incumbent BI platforms. Ultimately we selected Google Cloud’s Looker for a range of benefits that separated it from the rest of the pack.

From a vision perspective, in this particular case we felt Looker was most aligned with our belief that better decisions are made when everyone has access to accurate, up-to-date information. Looker allows us to realize that vision by surfacing data in a simple web-based interface that empowers everyone to take action with real-time data on critical commercial activities. Furthermore, Looker’s ability to automate and distribute formatted modules to a myriad of stakeholders on a regular cadence increases data literacy and business performance transparency.

From a product perspective, we chose Looker for it’s cloud offering, web-based interface, and  centralized, agile modeling layer that creates a trusted environment for all users to confidently interact with data — without any actual data extraction. While other BI tools have centralized semantic layers that require skilled IT resources, we’ve experienced that those can lead to bottlenecks and limited agility. With Looker’s semantic layer, LookML, our BI Team, led by Peter Donald, can easily build upon their SQL knowledge to add both a high degree of control as well as flexibility to our data model.  The fully browser-based development environment allows the data team to rapidly develop, test, and deploy code and is backed by robust and seamless Git source code management. 

In parallel, LookML empowers  business users to collaborate without the need for advanced SQL knowledge. Our data team curates interactive data experiences with Looker to help scale access and adoption. Business users can explore ad hoc analysis, create dashboards, and develop custom data experiences in the web-based environment to get the answers they need without relying on IT resources each time they have a new question, while also maintaining the confidence that the underlying data will always be accurate. This helps us meet our primary goal of providing all businesses users with the data access they need to monitor the pulse of key metrics in near real-time.

Impact and future of DTC BI at Constellation

In short order, taking a modern and integrated approach to the DTC technology stack has delivered economic impact across the portfolio, helping our team understand and combat customer churn, increase conversion rates, and optimize the customer acquisition cost (CAC) and customer lifetime value (CLV) ratios. Perhaps most important is the benefit it can provide to the customer base. Mining customer data and consumer behavior generates data into what our customers are seeking, giving us insights to supply more, or less of it. For example, observing sales velocity and conversion rates by SKU or by region can help us better understand changes in customer taste profiles and fluctuations in demand, providing the foundation for a more powerful innovation pipeline and more effective sales and distribution tactics in wholesale. Our team has also been an early pilot tester for Looker’s new integration with Customer Match, which contributes to the virtuous cycle between data insight and data activation. In the future, our plan is to leverage this cycle to amplify the impact of Google Ads across Search, Shopping, and YouTube placements for the wine and spirits portfolio. 

The operational impact of Looker is also substantial: our team estimates that the number of hours needed to reach critical business decisions has been reduced by nearly 60%, boosting productivity and accelerating the daily operating rhythm. A thoughtfully curated technology stack together with a modern BI solution allows us to stay at the vanguard of the industry. While the DTC sales channel is not designed to surpass the core business of wholesale for Constellation in terms of size, the approach enables unparalleled insights and measurement abilities that will pay dividends for the entire business for years to come.

As lead for analytics and AI solutions at Google Cloud, my team works with startups building on Google Cloud. This puts us in the fortunate position to learn from founders and engineers about how early-stage startups’ investments can either constrain them or position them for success, even at the seed level. In this post, I want to share a few of the best practices to keep in mind as you’re building. 

Understand your value proposition before diving into a technology stack

If you’re launching a startup in the cloud, you’re no doubt thinking about a technology stack, but it’s important to step back a bit and think carefully about the major value proposition that your startup offers to your customers. That value proposition is going to fundamentally drive the kind of technology that you should pick.

For example, does your system need processing in real time, or can it be done in a batch mode? Can you rely on once-a-day insights or do the insights have to come in as events happen?

Additionally, what kind of latency will your customers face? That latency makes your value proposition either usable or unusable. Early on in Google’s development, leaders realized that no one was going to wait more than a few hundred milliseconds for a web page to show them their results, and that realization drove the technology decisions that have allowed Google to scale from being a startup in a garage to being a trillion dollar company. Your startup needs to define its value to customers with this level of specificity before it can build a technology stack suited to its needs. 

Focus on customer interactions

A few companies have gracefully pulled off big IT pivots that reshaped their value proposition. Netflix, for example, moved from mostly sending DVDs through the mail to becoming a streaming service and major content producer. That’s a huge shift in the user experience and the technology stack necessary to support it, even if the underlying value proposition (i.e., get content to customers) was broadly the same. But it’s also an outlier. If you’re planning for potential changes of this magnitude, rather than focused on getting your value proposition to users, you probably need to sharpen what that value proposition is.

Specifically, you need a clear vision of how customers will access and interact with your business. Typically, they’ll do so over a website or a mobile app, but there are still so many variables. 

Are customers going to transmit documents? If so, in what format? Is handwriting supported or is input limited to typing? Can they use images for optical character recognition? Will it mostly be forms? Will the data be structured or unstructured? If all that sounds  a little overwhelming, don’t worry, it’ll seem simpler by the end of this article—but also be aware: we’re just getting warmed up.

Imagine that most of your customers will access your business via voice, so you know you’ll want to prioritize conversational workflows. That’s a start—but dig deeper.  Even if we suppose you’re usingDialogflow, a Google Cloud conversational AI platform that lets you build and deploy virtual agents, we’re still not really seeing the value proposition.  How will all this work, from the beginning of a typical full customer interaction to the resolution? How many interactions will have to be facilitated over low-bandwidth connections, for example? When it comes to user interactions, make sure you can see an end-to-end use case.

Another example: you’re building a retail website, and one of your end-to-end use cases involves the customer asking if a certain amount of a given product is in stock, whether it’s one unit of the product, ten or hundreds. If the product is not sufficiently stocked, you want your app to offer similar items that are. Will your technology stack support this end-to-end use case?

These considerations are not an argument for premature optimization. There’s value in moving fast, getting minimum viable products to users, and then iterating. But in the early stages, you only get one chance to start on the right foot—and how you navigate that chance will influence a lot of dollars and effort down the road. You need to make sure you have business use cases, not just an idea, before you can start designing a technology stack.  

Here’s how to get in the right frame of mind. Pick three use cases: two that are “bread and butter” and one that is technologically complex.  Make sure your proposed technology stack can support all three, end to end. 

Default toward higher levels of abstraction

Now that we’re in the right frame of mind, we’re ready to think about the technology stack more directly. 

As a startup, you’ll need to conserve resources, and to do that, you’ll want to build at the highest level of abstraction possible for your value proposition. For example, you probably don’t want your people setting up clusters. You don’t want them configuring things if they can use a fully managed service. You want them focused on building your prototype, not managing infrastructure.

This focus has definitely informed how we create products at Google Cloud, as our canonical data stack—Pub/Sub, Dataflow, BigQuery, and Vertex AI—consists of auto-scaling and serverless products.

But management of infrastructure is not the only place where you should err toward a less-is-more philosophy. 

When it comes to architecture, choose no-code over low-code and low-code over writing custom code. For example, rather than writing ETL pipelines to transform the data you need before you land it into BigQuery, you could use pre-built connectors to directly land the raw data into BigQuery. That’s no code right there. Then, transform the data into the form you need using SQL views directly in the data warehouse. This is called ELT, and it is low code. You will be a lot more agile if you choose an ELT approach over an ETL approach. 

Another place is when you choose your ML modeling framework. Don’t start with custom TensorFlow models. Start with AutoML. That’s no-code. You can invoke AutoML directly from BigQuery, avoiding the need to build complex data and ML pipelines. If necessary, move on to pre-built models from TensorFlow Hub, HuggingFace, etc. That’s low-code. Build your own custom ML models only as a last resort.

Focus on getting your vision to market, not chasing technology hype  

The goal is to pick the right technology stack for bringing your vision to market, generating value for customers, conserving resources, and maintaining flexibility for growth. Early IT investments should usually gravitate toward things that preserve flexibility, such as managed services built on standard protocols or open APIs, but they needn’t always rush to the flashiest technologies.  The answer isn’t always ML, for example. The answer might be heuristics to start, with a path to ML once you have collected enough data. You want to make sure that your intelligence layer has enough abstraction so you can mark it up with simple rules at first, but then replace it with a more robust system as you go along. 

Launch and iterate fast with these principles 

The preceding discussion is a reminder that your most expensive resource is your people—and that you really want them to be focused on building your prototype, minimum viable product or production app  You want to launch fast and iterate fast, and the only way you can do that is by focusing on the things that differentiate you. 

But regardless of the technologies you use, the bottom line is the same: follow these four principles. 

• Figure out your major value proposition and design your tech stack around it. 
• Be very careful about user interactions. User experience is super important; you need to make sure you deliver the kind of experience that your customers have grown to expect.
• When you’re building, pick the highest possible level of abstraction possible—the most fully managed tools and no-code/low-code frameworks that give you the functionality that you need. 
• Instead of choosing new or flashy technologies, consider if you can build a “good enough” minimum viable product quickly and come back to a better implementation later. 

To learn more about why startups are choosing Google Cloud, click here.