EC Column – Page 12 – Product Management Confabulation

May 23, 2023

Cutting your cloud spend? Consider sharing your bill data internally

Naveen Zutshi Contributor

Naveen Zutshi is the chief information officer of Databricks. Previously, he was the chief information officer of Palo Alto Networks.

As budgets tighten, leaders are feeling the pressure to temper spending, and tech teams are no exception. Many companies invested heavily in the cloud during the pandemic and are now looking at ways to optimize resources.

According to a recent report, as many as 81% of IT leaders have been directed by their C-suite to reduce or halt cloud spending, which represents 30% of IT budgets. It’s a critical moment for CIOs and other technical leaders to take stock of their cloud and IT budget and usage.

While it is not advisable to abandon cloud-first strategies in favor of on-prem or hybrid infrastructure, it is possible to reduce cloud spending significantly. Databricks recently cut our total cloud spend by 25%, and we’re tracking to reduce total SaaS IT spending by 30%. We accomplished this by democratizing the cloud spend data: by providing visibility into where and how the team was spending, we were able to bend the cost curve.

Here is the framework we used for successfully cutting cloud spend, what the team learned and how leaders can incorporate data democratization into their cloud spend strategies.

Step 1: Understand the bill

On cloud spend, the first step is to get a clear picture of what you’re paying for through cost allocation tagging. This is easier said than done — tagging one cloud vendor bill is a significant undertaking alone, so multicloud organizations will find this step can be twice or three times more complicated and time consuming. But the output is valuable: This approach — and the data you’re able to garner with it — offers three significant benefits.

Don’t do it flippantly: Reducing cloud spend must be more about optimizing budgets for long-term ROI than short-term cost cuts.

First, it provides visibility into who the owner of each resource, which function it supports and how much the team is using and spending. Second, it enables you to view your spend in different ways, such as organizing the data by service type, cloud, database, network, or department, or viewing month-over-month trends, to understand patterns.

Third, investing time in cost allocation tagging makes it possible to build infrastructure to auto-tag future expenses and easily understand bills on an ongoing basis.

Step 2: Leverage your new field of vision for discounts

Now that you have adequate and reliable visibility into your cloud bills, look into where you can cut and leverage vendors’ native tools to do so.

The goal here is to strike the balance between compute time horizons, high utilization and discount offers. This methodology hinges on economies of scale or ensuring that the team is truly getting the most out of their tool choices.

Next, identify the cheapest option that meets your needs. Whether it’s adjusting timing (scaling up during cheaper timeframes, like weekends) or geography (cloud providers often charge a premium for certain regions), there are several ways to go about this, but there may be hidden challenges based on your roadmap or workflow. For example, you may want to utilize more cost-effective regions for testing, but it’s essential that your product works in the region where you’re going to deploy it — so do this methodically.

Cutting your cloud spend? Consider sharing your bill data internally by Walter Thompson originally published on TechCrunch

May 22, 2023

CRO: Why startups should prioritize conversion rate optimization early

Jonathan Martinez Contributor

Jonathan Martinez is a former YouTuber, UC Berkeley alum and growth marketing nerd who's helped scale Uber, Postmates, Chime and various startups.

Examples of CRO

CRO has historically been confined to running tests on landing pages, but there are many other areas to test, including app store pages, email campaigns, and retargeting campaigns.

Basically, if you are testing methods to push more users through your funnel and subsequently improve their conversion rates, then you are running CRO experiments.

For our purposes, I’ll go through the specific CRO tests you can start running for your startup’s landing page. Below are some of the largest areas to test:

Messaging
Images/videos
Module additions
Module placements

Most startups already test messaging on paid acquisition campaigns, but testing on a landing page is another area to experiment with. When I consulted for a product that appeared on “Shark Tank,” we ran dozens and dozens of weekly CRO tests on the website onboarding questions to find which answers brought in the highest propensity users, a very high rate of testing.

It has been quite surprising at how much I have been able to affect the conversion rate thanks to the repeated testing of different modules, such as testimonials, or FAQ, and where they finally appear on the website. For example, I have found that placing testimonials or press logos above-the-fold, and not requiring the user to scroll down to locate, has always increased the conversion rate.

CRO: Why startups should prioritize conversion rate optimization early by Walter Thompson originally published on TechCrunch

May 19, 2023

To secure early-stage funding, entrepreneurs should build ESG into their business models

Veronique Hördemann Contributor

Veronique Hördemann is an investment partner at Future Energy Ventures who has a strong track record in venture capital, the energy industry and commodity trading.

ESG has been under the microscope for the past 12 months with pressure from some Republican politicians in the U.S. who have called for investment managers to pull their clients’ money from ESG-focused investments.

Simplistically, their argument is that ESG prevents investors being able to access assets like fossil fuels and, by doing so, they will have missed out on soaring fossil fuel company valuations driven by rising energy prices. Those on the anti-ESG side argue that continuing to follow ESG doctrine in today’s market is therefore a failure of fiduciary duty by investment managers.

This of course overlooks one rather fundamental challenge: The Intergovernmental Panel on Climate Change (IPCC) in its recent AR6 report stated that the G7 economies needed to hit net zero by 2040, not 2050, if we are to avoid catastrophic climate change.

At the 2021 United Nations Climate Change Conference, countries pledged to scale down their use of oil and fossil fuels. The latest scientific evaluation from the IPCC sets the scene for a future climate change conference (not too far in the future) making the pledge to scale out fossil fuels and accelerate the already significant investment into an electrified and decarbonized future.

Whether you believe in ESG or subscribe to the “woke capitalism” viewpoint, it simply can’t be ignored.

So the fiduciary duty of investment managers when seen through that lens would suggest a long-term imperative to ensure that the funds they manage are not placed into assets that will become stranded or obsolete. In other words, investing using ESG metrics and favoring renewable and climate tech type investments makes economic and investment sense in the long term.

This approach is one that we follow, and we’re not alone. Despite recent controversy, the ESG investment market is estimated to be worth $53 trillion globally by 2025 and data, reported by Bloomberg, from the European Fund and Asset Management Association (EFAMA) has shown that the EU’s highest environmental, social and governance classification, known as Article 9, drew in €26 billion ($28 billion) in 2022. That coincided with bond funds seeing client outflows that were greater than since the global financial crisis in 2008, while equity funds also suffered, losing €72 billion over the same period.

To secure early-stage funding, entrepreneurs should build ESG into their business models by Walter Thompson originally published on TechCrunch

May 18, 2023

Retail media targeting on the AI maturity curve

Hugh Cameron Contributor

Hugh Cameron is head of data for Zitcha and has worked in the martech space for 15 years.

As the retail sector grows increasingly reliant and focused on data and artificial intelligence (AI), it’s essential that retailers understand exactly how first-party data analysis can be crystalized into insights on customer behavior – and, in turn, a tangible competitive advantage.

To that end, consider the chart below, dubbed the “Data & AI Maturity Curve.”

The data + AI maturity curve. Image Credits: Zitcha/Databricks

This is a simplified view of how a retailer’s data and AI capabilities (charted on the x-axis) directly correlate with the competitive advantage of its retail media network (charted on the y-axis). A general strategic approach following this curve will see retailers making incremental steps towards sophistication, inching ever closer to the vaunted “predictive analysis” that will allow them to anticipate customer needs and deliver finely tuned, personalized experiences.

This is all far easier said than done, however, and some steps are more important than others when it comes to intelligent targeting. Let’s look at the three most important milestones along the road to predictive analysis in the retail media context.

Clean, accepted data

The “on-ramp” to this curve for any retailer looking to harness the power of data and AI begins with a full view of clean and accepted data across all customer interactions and media placements, whether physical or digital, owned or rented. This data is crucial for understanding the opportunity, managing yield, and accurately measuring campaign performance.

As technology formalizes retail media as a category, the chance to lead on metric integrity and data quality is significant. Understanding the unique count of customers along the journey through physical and digital touch points is also crucial, as duplicating customer counts to inflate the value of the media network is a risk to both trust and budget growth in the long term.

Let’s look at the three most important milestones along the road to predictive analysis in the retail media context.

Data is, ideally, streamed to a behavioral data platform (BDP) and stored in a secure, cloud-hosted data lake. Data from SaaS systems updates the BDP via a server-to-server connector. Data is then modeled and enriched by the BDP, where every customer interaction is unified to a single, holistic view of the customer.

This provides a single profile with an event history with thousands of records for each customer. While certainly a critical step, this really is the ground floor when it comes to media targeting – once this foundation is established, maturity can begin to build up.

Predictiveness/complexity. Image Credits: Zitcha/Snowplow

Contextual targeting

The first level of true media targeting capability is delivering a message to a surface – a specific platform or device facing a target audience – based on its context. This is the most fundamental form of targeting and a crucial basis for all other targeting capabilities. The role of data at this stage is to forecast the inventory of placements available by placement type and location, which is key for retailers to manage their media network and optimize yield. Message relevance and brand safety are also dependent on this capability.

Retail media targeting on the AI maturity curve by Walter Thompson originally published on TechCrunch

May 17, 2023

Ask Sophie: Which visas are best for U.S. startup accelerators?

Sophie Alcorn Contributor

Sophie Alcorn is the founder of Alcorn Immigration Law in Silicon Valley and 2019 Global Law Experts Awards’ “Law Firm of the Year in California for Entrepreneur Immigration Services.” She connects people with the businesses and opportunities that expand their lives.

B-1 business visitor visa

You and your co-founder can get a B-1 business visitor visa to participate in the accelerator program, which will enable you to stay initially for six months. To apply for a B-1 visa, you must fill out Form DS-160, the State Department’s online nonimmigrant visa application. Once you complete that form, you will need to print the confirmation page and bring it with you to your interview at the U.S. consulate. If your application is approved during the interview, it can take a couple of weeks for the visa to be processed and delivered.

Even though the B-1 business visitor visa and the B-2 tourist visitor visa are typically issued together as a single visa, make sure to specifically request B-1 business visitor visa status and tell the U.S. Customs and Border Protection (CBP) officer when you arrive in the U.S. that you will be participating in an accelerator program and conducting business during your stay here. That’s really important!

Always be aware that all of your future visits or stays in the U.S. can be affected by your reasons for coming to the U.S., the visa you use, what you say to immigration officials at your visa interview and when you arrive in the U.S., what you do while you’re in the U.S., and when you leave.

Ask Sophie: Which visas are best for U.S. startup accelerators? by Walter Thompson originally published on TechCrunch

May 17, 2023

Here’s what I learned while leading a bootstrapped startup to $40M ARR

Mohannad Ali Contributor

Mohannad Ali is the CEO of Hotjar, which provides product experience insights software to small and medium-sized businesses.

Building a profitable business during a period of economic uncertainty is nothing short of intimidating. But contrary to popular belief, it’s not impossible. Bootstrapping a startup is one of the effective means of building a self-sustaining and successful business, especially as VC investments slow down.

If you’ve been considering bootstrapping, now is the time to commit. To help you set your business up to thrive during economic turbulence and beyond, I’d like to share some of the strategies that proved successful when building Hotjar, the company I lead. It all starts with creating a product, testing it and finding the right market fit.

The journey to $40M ARR

Building a business from the ground up comes with its fair share of learning experiences and missteps. But, one of the greatest challenges we were able to avoid was starting with a finished product. We decided before even coming up with a name, that the most important thing would be to start with a minimum viable product (MVP). In other words, the bare minimum required for someone to use it and get value out of it.

From previous projects, the team knew that waiting to finish the product and then releasing it would invite challenges. Had the team waited to finish the product and then attempt to collect feedback, we would have risked spending a long time moving in the completely wrong direction. Creating our MVP allowed us the flexibility to build and release as we learned, and it proved successful.

Since our founding in 2014, we’ve reached $40 million in ARR through bootstrapping. From there, it was off to the races.

Since our founding in 2014, we’ve reached $40 million in ARR through bootstrapping. From there, it was off to the races. We continued to see exponential product-led growth (PLG): When we officially launched, we had 27,562 users (including customers), and we were growing at a steady 10% per month with a PLG strategy.

The influence of the larger product insights market and our relationship with customers enabled us to move quickly and pivot regularly.

When beta testing, don’t start with a finished product

Product-led growth should play an important role in any business model for bootstrapped startups. If you’re depending on yourself to raise the funds from the ground up, your product should meet a specific demand in the market to essentially sell itself.

To create demand, even in a saturated market, listen to the people who matter the most — your target users. Developing and iterating your product based on feedback from target users is the best way to ensure you’re building something they will commit to.

It’s easy to rush product development, but starting with a finished product won’t do you any favors. Six months is an adequate time frame for beta testing, because it builds trust with a set of users who can provide constructive feedback to troubleshoot and advance the product.

One piece of feedback we received during beta testing that helped change the course of our business was a request to introduce sub/cross-domain tracking. In retrospect, this was an obvious improvement, but until our users asked for it, we hadn’t realized how common this need really was.

Here’s what I learned while leading a bootstrapped startup to $40M ARR by Walter Thompson originally published on TechCrunch

May 16, 2023

5 ways SaaS companies can level up their product-led growth

Christian Owens Contributor

Christian Owens is executive chairman and co-founder of Paddle, a payments infrastructure provider for SaaS businesses.

1. Fix the leaks in your funnel

With your product handling much of your customer acquisition and retention in a PLG setup, you’ll likely experience what is known as ‘delinquent’ churn – customers leaving your service involuntarily due to leaks in your funnel.

This can account for 20-40% of your overall churn rate and is usually to do with failed payments, meaning that leveling up your billing processes should be a top priority. Common ‘leaks’ in the funnel you should be keeping an eye on include:

Insufficient customer funds, which is particularly common for payments made by credit cards with limits. To fix this, try retrying the payments – using smart technology to do so at a time when it’s more likely to be successful – or offer payment methods that can access multiple sources of funds like PayPal.
Cross-border transaction failures, which sometimes happens due to different standards between banks. A strong solution is to bank locally where your customers are based, or to use a payment provider which already has local banking relationships.
Currency conversations, which can often create fraud triggers. Selling to customers in their local currency is essential to prevent this: our data shows that doing so can increase payment acceptance rates by 1 to 11%.

2. Go hybrid or go home

Unsurprisingly, product-led growth motions let the product take center stage, with acquisition, conversion, retention, and expansion all being driven by the product itself. Instead of booking a demo with a sales team, customers are usually offered trials, freemium models and other self-serve calls to action, streamlining the acquisition process.

But that doesn’t mean sales isn’t important, especially as your company scales up. The industry is full of success stories in which small SaaS companies graduate from an exclusively product-led growth strategy to a sales-assisted, or sales-led growth motion (SLG). When they do this, their customer base shifts from individual users and small teams to larger businesses. Just look at the trajectory of some of cloud’s most successful names:

5 ways SaaS companies can level up their product-led growth by Walter Thompson originally published on TechCrunch

May 16, 2023

How our fintech startup became SEC-compliant

Boris Dus Contributor

Boris Dus is co-founder and CEO of Gainy, an investment app.

After the recent failures of financial institutions like FTX and Silicon Valley Bank, regulators have been blamed for poor examination processes and enforcement regarding the regulations financial organizations in the U.S. must follow. However, our experience with the Security and Exchange Commission’s licensing and examination appeared legitimate. From our perspective, they help protect clients.

Initially, obtaining registered investment adviser (RIA) status in the U.S. allows companies to deliver personalized investment advice and comply with relevant laws. As a fintech startup operating in the investment advisory domain, it is impossible to offer services in the U.S. without RIA status, but it also helps to build trust with prospective clients.

In our case, we obtained RIA status approximately 18 months ago. The process involved preparing multiple documents and incurred expenses of approximately $50,000 for legal services and filing fees, which took around three months to complete.

What our experience was like:

We got a call out of nowhere.

Next step: an introductory two-hour meeting.

The list of documents they requested.

Adjustments during the review and outro call.

At some point after obtaining status, you can expect to be examined by the SEC. The agency routinely conducts examinations to ensure that companies or individuals providing financial services or advice comply with securities laws and regulations. Even if there are no claims against your company, these examinations can happen at any time to review your policies, services and records.

As part of the process, the SEC may conduct interviews, scrutinize existing policies and marketing materials, and request a detailed description of the financial services provided to clients. The duration of the examination process can vary depending on factors such as the size and complexity of the firm being examined. A complex examination can take up to six months or more.

We incurred expenses of approximately $50,000 for legal services and filing fees; the examination process took around 3 months to complete.

After conducting the examination, the SEC will provide a letter that outlines its findings. If no major issues are discovered, your firm will have two months to address any concerns raised by the SEC. It is important to take these findings seriously and address any issues promptly to ensure compliance with applicable securities laws and regulations.

We got a call out of nowhere

It was just a regular workday when a call came in to our company phone number and the speaker introduced themselves as a part of the SEC office in San Francisco, double-checked the email information of our company executives and announced that we were under examination as part of the standard practices with SEC. I was also told that soon we would need to arrange an introductory meeting with their team.

I didn’t even know the SEC had an office in San Francisco.

Next step: An introductory two-hour meeting

When we arrived, there were three people representing the SEC end and two representatives from our company: me and Chief Investment Officer Mike Stukalo. As I remember, our discussion was not recorded, which felt like a nice touch. I was impressed by how well prepared they were; they had clearly read our website, blog posts, marketing materials and ADV brochure, the primary disclosure document that we update each year as a company with registered investment adviser status. They had a pretty decent understanding of our product before the conversation.

After an introduction and basic questions, they asked very specific questions about how exactly our product worked to understand every little detail. The majority of these two hours of conversation were related to a product and what it does. Everyone was very polite and nice: It felt more like a demo call to a potential customer.

How our fintech startup became SEC-compliant by Walter Thompson originally published on TechCrunch

May 15, 2023

Europe could be on the cusp of a golden era in robotics. Here’s why

Robin Godenrath Contributor

Robin Godenrath is founding partner and managing director at Picus Capital. His focus is early-stage technology investments, particularly in HR tech, robotics, and healthcare industries.

Alex Rohregger Contributor

Alex Rohregger is an Investor at Picus Capital. His focus is on early-stage robotics, AI, and DevTool investments.

The United States and China have long been ahead of the pack when it comes to robotics funding. However, data from 2022 is showing that these innovation hubs may have some serious competition as the investment landscape in Europe is starting to outstrip robotics’ biggest players.

The quest for technological supremacy has often been seen as a two-horse race between the U.S. and China. Over the years, we’ve only seen this investment tug-of-war intensify as both economies have vied for dominion to become an innovation superpower. Whilst in the past robotics has seen a similar dynamic, based on 2022 data, investors are starting to place their bets on an up-and-coming contender: Europe.

In 2022, nearly $8.5 billion in funding flowed into robotics companies worldwide — a staggering 42% less than the year prior – in line with the overall global downturn in VC investment. Yet despite the change in economic situation, with total USD investment volume into robotics falling by over 50% for both the U.S. and China between 2021 and 2022, Europe has seen a far more modest decline, only dropping 5% in the same period. Although it’s still early, we’re convinced it’s just the beginning of how Europe is finally beginning to find its place within the modern robotics ecosystem.

Europe emerges as a serious contender with a strong rate of growth

Whilst in the past robotics has seen a similar dynamic, based on 2022 data, investors are starting to place their bets on an up-and-coming contender: Europe.

When we compare Europe’s rate of growth in investment volume within robotics to the U.S. and Chinese markets, we observe a few key trends driving the continent’s recent power play in the robotics market.

With a CAGR of 28% in the period from 2018 to 2022, Europe is already surging ahead compared to global growth figures at 2%. This growth is primarily being led by Germany, which has seen a 77% growth spurt of investment volumes into the robotic space.

Close neighbor France has seen a 54% increase in robotic investment amounts. Meanwhile, robotics powerhouses China and the U.S. have experienced a decline in growth, with robotics investment falling 5% and 2% respectively since 2018.

China and the U.S. experience a 60% slow-down in growth/late-stage funding

To better understand these market shifts, we need to take a deep dive into the funding landscape and explore the state of play by funding rounds.

Slicing our data into grants, early-stage (pre-seed to Series A) and growth/late stage (Series B and onwards), we observed a major slow-down across U.S. and China robotics funding across growth and late-stage investment rounds.

Both the U.S. and China saw a decline of growth/late-stage robotic investment volume by 60% compared to 2021. Meanwhile, looking at the European market, the total investment volume for growth and late-stage deals was only slightly less than those of 2021.

Surprisingly, China has seen an 4% uptick in early-stage investments, whilst Europe and U.S. followed a similar downward trend – a potential sign for new ventures brewing. The trends in the growth/late -stage funding environments, accounting for the lion’s share in terms of investment volume, helps understand the relative stability in Europe.

Comparison in investment volume between 2021 and 2022 across geographies. Image: Picus Capital with data from Crunchbase

Under the surface, 2022 saw more European robotic firms consistently raise capital — 20 growth/late-stage rounds — and fewer outliers driving investment volume. By comparison, European robotics investments in 2021 were more prominently driven by outliers across 13 growth/late-stage rounds with an average round size of $108M USD. Meanwhile, the U.S. and China have seen a decline across a number of deals and mean & median investment amounts.

Growth/late-stage funding is complex. Nevertheless, we think that one dynamic influencing the change in investment volume discrepancy between U.S., China, and Europe is the shift in priorities for growth and late-stage funds – from growth to profitability. The continued funding into European robotic companies at these stages indicate that these companies are able to meet growth stage criteria better than U.S. companies. This is what we believe will also continue to be relevant throughout 2023.

Europe could be on the cusp of a golden era in robotics. Here’s why by Walter Thompson originally published on TechCrunch

May 15, 2023

How to avoid AI commoditization: 3 tactics for running successful pilot programs

Chaitanya Vaidya Contributor

Chaitanya Vaidya is the co-founder of Deeprisk.ai, holds a fintech AI patent and has been in the tech innovation space for more than a decade.

With the rise of open-source AI models, the commoditization of this groundbreaking technology is upon us. It’s easy to fall into the trap of aiming a newly-released model at a desirable tech demographic and hoping it catches on.

Creating a moat when so many models are easily accessible creates a dilemma for early-stage AI startups, but leveraging deep relationships with customers in your domain is a simple, yet effective tactic.

The real moat is a combination of AI models trained on proprietary data, as well as a deep understanding of how an expert goes about their daily tasks to solve nuanced workflow problems.

In highly-regulated industries where outcomes have real-world implications, data storage must pass a high bar of compliance checks. Typically, customers prefer companies with prior track records over startups, which promotes an industry of fragmented datasets where no single player has access to all the data. Today, we have a multi-modal reality in which players of all sizes hold datasets behind highly compliant walled-garden servers.

This creates an opportunity for startups with existing relationships to approach potential customers who would typically outsource their technology to launch a test pilot with their software to solve specific customer problems. These relationships could arise through co-founders, investors, advisors, or even prior professional networks.

The real moat is a combination of AI models trained on proprietary data, as well as a deep understanding of how an expert goes about their daily tasks to solve nuanced workflow problems.

Showing customers tangential credentials is an effective way to build trust: positive indicators include team members from a university known for AI experts, a strong demo where the prototype enables prospective customers to visualize outcomes, or a clear business case analysis of how your solution will help them save or make money.

One mistake founders commonly make at this stage is to assume that building models of client data is sufficient for product-market-fit and differentiation. In reality, finding PMF is much more complex: just throwing AI at a problem creates issues regarding accuracy and customer acceptance.

Clearing the high bar of augmenting experienced experts in highly-regulated industries who have an intricate knowledge of day-to-day changes typically turns out to be a tall order. Even AI models that are trained well on data can lack the accuracy and nuance of expert domain knowledge, or even more importantly, any connection to reality.

A risk-detection system trained on a decade of data may have no idea about industry expert conversations or recent news that could render a formerly-considered “risky” widget completely harmless. Another example could be a coding assistant suggesting code completion of a prior version of a front-end framework which has separately benefitted from a succession of high-frequency breaking feature releases.

In these types of situations, it’s better for startups to rely on the pattern of launching and iterating, even with pilots.

There are three key tactics in managing pilots:

How to avoid AI commoditization: 3 tactics for running successful pilot programs by Walter Thompson originally published on TechCrunch