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.

Whether they’re reading the newspaper on the way to work, or catching up on the latest headlines on their smartphones, readers expect up-to-the-minute news wherever and whenever makes the most sense for them. As a result, media companies are increasingly looking for ways to improve, expand, and simplify their offerings, and they’re increasingly looking to the cloud to do it.

For more than 160 years The Telegraph has been counted on by readers across the United Kingdom and globally for award-winning news and journalism. An early adopter of cloud technology, it’s been a G Suite customer since 2008 and has already been using Google Cloud Platform to analyze digital behaviors to improve engagement and advertising performance since 2016.

Recently, The Telegraph announced it’s migrating fully to Google Cloud. By migrating all their production and pre-production services, they aim to deliver content faster, provide compelling experiences to readers, and reduce environmental impact.

“We are delighted to announce our newest collaboration with Google Cloud,” said Chris Taylor, Chief Information Officer, The Telegraph. “We have always worked closely with Google as they help us to provide our readers with great experiences on our digital products, collaboration software and internet scale through search. Their continued leadership in projects such as Kubernetes are enabling us to build flexible development environments that truly support DevOps.”

Powering the Digital Publishing Ecosystem

The Telegraph produces large volumes of digital content every day. It was imperative for them to find a cloud provider they could trust to support this ecosystem. By working with Google Cloud they have changed the way they see and engage with data: they can collect new information about their products every second and use that to continually hone their strategy. The Telegraph are placing more confidence and trust in the data captured about their content and now have one of the best available pieces of technology for capturing and analyzing the stories they publish in real-time.

Leveraging AI to support journalists

Time is critical when journalists are on a story, and The Telegraph wants to put important data in the hands of its journalists right when they need it. To do this, it will be using AutoML to classify content for journalists and make it more discoverable. For example, a reporter will be able to bring up relevant assets that link to their stories. It will also apply AutoML to classify Telegraph stock photos to help journalists attach compelling visual content to their stories faster.

Building compelling reader experiences with the help of APIs

Readers have an ever-increasing expectation of personalization. To meet this need, The Telegraph launched My Telegraph, currently live in beta, to offer registered readers personalized news experiences based on their interests or the particular journalists they want to follow. My Telegraph was developed on an API management platform provided by Google Cloud’s Apigee. You can learn more about how it’s applying API management to My Telegraph, in this blog post.

Working for environmental good

The Telegraph is the biggest selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print production is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste. This makes great business sense for The Telegraph but also has great environmental benefit.

Budget concerns and inflation shock are causing noticeable shifts in consumer behavior, a head-spinning turnabout since the pandemic days of 2020. We went from not having access to enough goods to now not having the right goods at the right price points. Heading into the holiday season, retailers will need to look sharp and be able to deliver value to the increasingly cost-conscious consumer — with frictionless product discovery and digital shopping experiences, relevant inventory, and personalization.

Those are a few of the insights gleaned from a recent eMarketer webcast on how retailers can better combat the effects of inflation, with pragmatic recommendations from experts including Amy Eschliman, managing director of retail solutions strategy and industry engagement at Google Cloud; Alexis Hoopes, vice president and head of e-commerce and direct-to-consumer (DTC) at Mattel Inc; and Elissa Quinby, head of retail insights at Quantum Metric. We’d like to share five key takeaways that retailers can adopt.

1. Be prepared for earlier holiday planning. Budget-conscious buyers are planning their purchases earlier than ever. According to our partner Quantum Metric’s latest Retail Benchmarks research, 40% of U.S. consumers and 30% in the UK have already started their holiday shopping1. At Mattel, Alexis noted, “We’ve learned to be nimble and early for our consumers, and to have products available for them when they’re ready to shop.” With higher prices, she added, “Consumers are preplanning and researching more, viewing product detail pages multiple times to check a higher-price item before adding it to their basket.”

Quantum Metric’s same research also shows that while average cart values grew between January to July 2022 — larger than they were at the same time last year1 — consumers were shopping less frequently. Because of higher prices, 37% of shoppers are now pre-planning and purchasing items all at once to help keep to their budget1. Amy observed. “That means retailers need to make it super easy for customers to find what they want to avoid shopping-cart abandonment.”

Retailers who make it easier for shoppers to find the right products, by providing Google-quality search and recommendations, can help reduce cart abandonment and increase conversions.

2. Get a handle on out-of-stock issues. While many of the supply-chain issues that surfaced during the pandemic have cleared up, inventory continues to be a challenge in retail. During the 2021 holiday season, Google/Ipsos research shows that consumers saw a 253% increase in “out of stock” messages versus pre-pandemic2. Elissa further pointed out that a great majority of both U.S. and UK consumers experience out-of-stock issues several times a month. It’s a fluid situation, however, with retailers facing bloated inventory as consumer demands quickly pivot from, for example, branded goods to generic or white-label items. “In the next six months or so, you really need to make sure you have the right inventory to meet consumer demands,” she advised.

The need for end-to-end visibility and the ability to act in real-time across the supply chain has never been more pressing. Google Cloud and our partners are tackling the top supply chain issues head on with solutions that enable end-to-end visibility, analytics and alert-driven event management as well as AI solutions and automation to streamline processes like procurement, fulfillment, and delivery.

3. Introduce value: communicate the quality of the product and the experience. While rising prices are paramount for many shoppers, it’s a common misconception that consumers are making purchase decisions based solely on price. Alexis remarked, “In e-commerce, we talk about price, product, service, and experience. Price is only one of the pillars for consumers. At Mattel, we are fully focused on product and experience. What makes this special? How do we connect directly in new ways to the consumer? Create ‘wow’ moments and deeper connections. When we think about value for our consumers, it’s the strength of the products that will drive the purchase.”

Elissa also noted that while consumers are doing a lot of comparison shopping, it’s really about value, whether a high-quality product or a high-quality experience. Quantum Metric’s Continuous Product Design solution, which is built with BigQuery, ingests data across multiple digital touchpoints, including from mobile and web applications, and connects customer signals to every stage of the product lifecycle to help deliver the products and experiences that customers actually want.

4. Ensure consistent experiences across channels. Today, consumers crave the cross-channel shopping experience. “The channel experience has gone from online to in-store to now everywhere,” Amy commented. “We call it ubiquitous digital shopping.” Elissa added that 75% of consumers do most of their shopping digitally. However, mobile drives 67% of digital traffic, but just 49% of sales.1 “Recent trends in traffic and conversion rates by device show that we can expect the most traffic for the big sale days on mobile,” she said. “People are looking for discovery and awareness, maybe even adding items to the cart as a placeholder or reminder. But they prefer to complete a sale on a desktop. It will be critical for retailers to offer a consistent experience, especially on major sale days like Black Friday and Cyber Monday.” Elissa also advised wrapping up any experiments with product and site design early, making sure to understand the customer experience holistically across the organization and prioritizing efforts to eliminate friction.

Consumers now expect to be able to shop wherever and whenever best fits their needs, whether in a store, on your website, through your app, or from within a social media ad, and have it be a consistently good experience no matter how they first entered or exited your commerce site. Google’s 2022 Retail Marketing Guide provides useful insights and tips on how to grow your online and in-app sales.

5. Personalize touchpoints to build loyalty. Connecting with your customer base and making sure they understand the value of your offering is essential. Alexis noted that the key is to keep your messages fresh as the holiday shopping season expands, providing new messages as they keep coming back to your store. “We need to engage them by helping them find what they are looking for at the right time,” she said. “Recognize that they are doing more planning and wish-list building early, then buying last-minute gifts at lower prices towards the end. Make sure those are front and center.”

Alexis also pointed out that today, consumers are providing more data points with the products they view or the items sitting in their carts. “What’s so great about e-commerce is that we can use all of this to create more personalized, direct messages targeted to those consumers,” she commented.

Amy recommended continuing to focus on conversion with product discovery and personalization, harnessing customer data to drive insights and action. “Any company’s biggest asset is their data. Using it in as many ways possible and activating it across the company is incredibly important,” she remarked. “Take advantage of your first-party data to activate everything from marketing campaigns to a more efficient supply chain. For every customer who comes to one of your digital properties, make sure your product discovery is as easy as possible. Pay attention to your recommendations, driving personalization to make that experience as unique and fruitful for the customer as possible.”

Achieving these objectives requires a modern cloud data warehouse and activation of a customer data platform. Retailers can explore how Google Cloud’s advanced data capabilities and our ecosystem of partners can power a customer data platform that supports more personalized marketing, shopping experience, and customer service.

While these are our key takeaways, we invite you to register to watch the on-demand webinar, “5 Ways Retailers Can Combat the Effects of Inflation,” for even more insights.

1. Quantum Metric Retail Benchmarks, “Adjusting for Inflation.” The report is based on aggregated browsing behavior from January to July 2022, paired with a survey of 3,400 consumers in the U.S. and UK.
2. Google/Ipsos, Holiday Shopping Study, Oct 2021 – Jan 2022, Online survey, US, n=7,253, Americans 18+ who conducted holiday shopping activities in past two days

At Google Cloud, our customers are at the forefront of digital transformation—launching entirely new businesses and products built in the cloud, redefining entire industries with data and artificial intelligence, delivering innovative new consumer experiences, or committing to sustainable new ways of doing business.

We’re committed to our customers’ success, and over the past two years we’ve invested significantly in providing ongoing and integrated support through our customer care portfolio, including launching new Premium and Mission Critical Support offerings that allow us to monitor, prevent, and mitigate impacts quickly, while delivering the fastest response times in the industry.

Today, I’m proud to unveil several new resources and offerings for our customers, including a new Custom AI Solutions practice, new Global Delivery Centers, expanded Executive Briefing Centers, and new leaders to help continue to drive our organization forward.

Helping businesses innovate with a new Custom AI Solutions practice

We are investing in services and offerings to help our customers innovate and drive innovative change to business processes, culture, and customer experiences with Google Cloud products and services. Artificial intelligence (AI) and machine learning (ML) technologies are foundational for many such digital transformations, and to help customers create real business value with these technologies, we’re excited to launch a Custom AI offering to address our customers’ most critical innovation needs. 

AI and ML technologies are foundational to digital transformations, yet they are not “one size fits all.” Each customers’ problems and opportunities are unique; a rideshare company will use AI differently than a brick and mortar retailer, or a healthcare company, or an insurance firm. To help organizations deploy AI and ML more effectively, we’re launching a new Custom AI Solutions practice, offering customers custom-built AI and ML solutions built into Vertex AI; access to Google’s engineering expertise; and predictable, subscription-based pricing. 

Our teams are already partnering closely with early customers to build and deploy custom AI solutions. For instance, USAA, the large North American insurer, is using Google Cloud ML to process near-real-time damage estimates based on digital images to create a streamlined operations experience.

Learn more about our Custom AI Solutions practice offering here, and how we work together with our customers here. To get in touch, please contact our sales team.

Launching new Global Delivery Centers

Our drive to digitally transform our customers’ businesses is often manifested through our Global Delivery Centers, which expand our professional consulting and emerging practices capabilities available to customers and partners around the world.

The teams at our Global Delivery Centers help customers get up-and-running on Google Cloud quickly and cost effectively, and consult with customers to rapidly build capacity in areas like data analytics, hybrid and multi-cloud, artificial intelligence, and machine learning. More importantly, they help customers successfully execute projects in support of their most mission-critical business objectives. Critically, these centers also help our global partner ecosystem quickly ramp their Google Cloud practices, and get fast, expert consultation for their customers too.

In 2022, we aim to triple the size of our Global Delivery Center teams in Argentina, Poland, and India. In addition, we’ll invest in building deep Google Cloud talent in Mexico and Portugal, furthering our commitment to the industry’s best expert consultation for customers and partners around the world.

Expanding our Executive Briefing Centers footprint

In addition to our Global Delivery Centers, we’re also pleased to expand our resources and facilities that enable digital and face-to-face meetings and working sessions with our customers. This year, we will launch four new Executive Briefing Centers, located on Google Cloud campuses in London, Paris, Singapore, and Munich. 

These centers provide an opportunity to listen to our customers, share the best of Google Cloud’s solutions, and inspire digital transformation, in conversations facilitated by Google Cloud leadership, engineers, and industry experts. By bringing this experience to our customers in-region we can foster deeper partnerships and develop cloud solutions that meet their requirements for security, privacy, and digital sovereignty without compromising on functionality or innovation. 

Adding new leadership to enable customer success

Finally, I’m excited to welcome two new leaders to Google Cloud on the Customer Experience team, who will help scale our Delivery Centers and deliver exceptional experiences for our customers.

Heading our Global Delivery Center experience is Sunil Rao. Sunil comes to Google Cloud from Accenture, where he spent 18-plus years working with large technology customers across many industry verticals and managed large global teams in Accenture Advanced Technology Center. Sunil will also lead our Technical Onboarding Center, which helps businesses around the world get up to speed with technologies that are critical to understanding customer and business process contexts, and delivering great experiences.

Additionally, Lee Moore is joining Google Cloud to lead Customer Experience in North America. Lee spent nearly 30 years at Accenture in various leadership positions, including services integration for complex problem-solving, product development across a number of industry verticals, and building long-term customer relationships.

You’ll be hearing much more from us in the coming months, as we build out even more powerful and effective cloud-based services and offerings, work with customers to deliver new analytics- and AI-based tools and services, and work with our growing list of partners to help ensure customer success, across the globe.