5 things you need to win your first customer

A startup is a beautiful thing. It’s the tangible outcome of an idea birthed in a garage or on the back of a napkin. But ask any founder what really proves their startup has taken off, and they will almost instantly say it’s when they win their first customer.

That’s easier said than done, though, because winning that first customer will take a lot more than an Ivy-educated founder and/or a celebrity investor pool.

To begin with, you’ll have to craft a strong ideal customer profile to know your customer’s pain points, while developing a competitive SWOT analysis to scope out alternatives your customers can go to.

Your target customer will pick a solution that will help them achieve their goals. In other words, your goals should align with your customer’s goals.

You’ll also need to create a shortlist of influencers who have your customer’s trust, identify their decision-makers who make the call to buy (or not), and create a mapped list of goals that align your customer’s goals to yours.

Understanding and executing on these things can guarantee you that first customer win, provided you do them well and with sincerity. Your investors will also see the fruits of your labor and be comforted knowing their dollars are at good work.

Let’s see how:

1. Craft the ideal customer profile (ICP)

The ICP is a great framework for figuring out who your target customer is, how big they are, where they operate, and why they exist. As you write up your ICP, you will soon see the pain points you assumed about them start to become more real.

To create an ICP, you will need to have a strong articulation of the problem you are trying to solve and the customers that experience this problem the most. This will be your baseline hypothesis. Then, as you develop your ICP, keep testing your baseline hypothesis to weed out inaccurate assumptions.

Getting crystal clear here will set you up with the proper launchpad. No shortcuts.

Here’s how to get started:

  1. Develop an ICP (Ideal Customer Profile) framework.
  2. Identify three target customers that fit your defined ICP.
  3. Write a problem statement for each identified target customer.
  4. Prioritize the problem statement that resonates with your product the most.
  5. Lock on the target customer of the prioritized problem statement.

Practice use case:

You are the co-founder at an upcoming SaaS startup focused on simplifying the shopping experience in car showrooms so buyers enjoy the process. What would your ICP look like?

2. Develop the SWOT

The SWOT framework cannot be overrated. This is a great structure to articulate who your competitors are and how you show up against them. Note that your competitors can be direct or indirect (as an alternative), and it’s important to categorize these buckets correctly.

Trade promotion management startup Cresicor raises $5.6M to keep tabs on customer spend

Cresicor, a consumer packaged goods trade management platform startup, raised $5.6 million in seed funding to further develop its tools for more accurate data and analytics.

The company, based remotely, focuses on small to midsize CPG companies, providing them with an automated way to manage their trade promotion, a process co-founder and CEO Alexander Whatley said is done primarily manually using spreadsheets.

Here’s what happens in a trade promotion: When a company wants to run a discount on one of their slower-selling items, the company has to spend money to do this — to have displays set up in a store or have that item on a certain shelf. If it works, more people will buy the item at the lower price point. Essentially, a trade promotion is the process of spending money to get more money in the future, Whatley told TechCrunch.

Figuring out all of the trade promotions is a complicated process, Whatley explained. Companies receive data feeds on the promotions from several different places, revenue data from retailers, accounting source data to show how many units were shipped and then maybe data directly from retailers. All of that has to be matched against the promotion.

“No API is bringing this data back to brands, so our software helps to automate and track these manual processes so companies can do analytics to see how the promotions are doing,” he added. “It also helps the finance team understand expenses, including which are valid and those that are not.”

What certain companies spend on trade promotions can represent their second-largest cost behind manufacturing, and companies often end up reinvesting between 20% and 30% of their revenue into trade promotions, Whatley said. This is a big market, representing untapped growth, especially with U.S. CPG sales topping $720 billion in 2020.

“You can see how messy the whole industry is, which is why we have a bright future and huge TAM,” he added. “With this new funding, we can target other parts of the P&L like supply chain and salaries. We also provide analytics for their strategy and where they should be spending it — which store, on which supply. By allocating resources the right way, companies typically see a 10% boost in sales as a result.”

Whatley started the company in 2017 with his brother, Daniel, Stuart Kennedy and Nikki McNeil while a Harvard undergrad. Since raising the funding back in February, the company has grown 2.5x in revenue, while employee headcount grew 4x over the past 12 months to 20.

Costanoa Ventures led the investment and was joined by Torch Capital and a group of angel investors including Fivestars CTO Matt Doka and Hu’s Kitchen CEO Mark Ramadan.

John Cowgill, partner at Costanoa, said though Cresicor raised a seed round, the company was already acquiring brands and capital before releasing a product and grew to almost a Series A company without any outside capital, saying it “blew me away.”

Cresicor is the “perfect example” of a company that Costanoa would get excited about — a vertical software company using data or machine learning to augment a pain point, Cowgill added.

“The CPG industry is in the middle of a rapid change where we see all of these emerging, digital native and mission-driven brands rapidly eating share from incumbents,” he added. “For the next generation of brands to compete, they have to win in trade promotion management. Cresicor’s opportunity to go beyond trade is significant. It is just a starting point to build a company that is the core enabler of great brands.”

The new funding will be used mainly to hire more talent in the areas of engineering and customer success so the company can hit its next benchmarks, Alexander Whatley said. He also intends to use the funding to acquire new brands and on software development. Cresicor boasts a list of customers including Perfect Snacks, Oatly and Hint Water.

The retail industry is valued at $5.5 trillion, and one-fifth of it is CPG, Whatley said. As a result, he has his eye on going after other verticals within CPG, like electronics and pet food, and then expanding into other areas.

“We are also going to work with enterprise companies — we see an opportunity to work with companies like P&G and General Mills, and we also want to build an ecosystem around trade promotion and launch into other profit and loss areas,” Whatley said.

Use cohort analysis to drive smarter startup growth

Cohort analysis is a way of evaluating your business that involves grouping customers into “cohorts” and observing how they behave over time. A commonly used approach is monthly cohort analysis, where customers are grouped by the month they signed up, allowing you to observe how someone who joined in November compares to someone who signed up the month before.

Cohort analysis gives you a multivariable, forward-looking view of your business compared to more simple and static values like averages or totals.

Cohort analysis is flexible and can be used to analyze a variety of performance metrics including revenue, acquisition costs and churn.

Let’s imagine you’re the CMO of the “Bluetooth Coffee Company.” You sell a tech-enabled “coffee composer” that brews coffee, tracks consumption and orders replacement coffee when users are running low. The longer your customers are subscribers, the more money you make. You recently ran a Black Friday feature on a popular deals site and you’re interested to know if you should run it again.

The chart below is a simple analysis you might do to gauge your marketing performance. It shows the total customers added each month, and a clear spike in November following the Black Friday promotion. At first glance, things look good — you brought in more than double the monthly customers in November compared to October.

Marketing campaign results in significant uptick to users added

Image Credits: Sagard & Portage Ventures

But before you rebook the promotion, you should ask if these new Black Friday consumers are as valuable as they seem. Comparing monthly customer percentage is a good way to find out.

Below is a monthly cohort analysis of new customers between September 2020 and February 2021. Like our previous chart, we’ve listed the monthly cohort size, but we’ve also included the customer engagement rate (calculated by dividing daily active users by monthly active users or DAU/MAU for each month (M1 is month 1, M2 is month 2, and so on).

This analysis lets us see how the customer engagement of each monthly cohort compares to the next.

Customer engagement by cohort

Image Credits: Sagard & Portage Ventures

From the figures above, we see that most cohorts have a customer engagement rate in their first month (M1, 42%-46%), meaning 42%-46% of new customers use the coffee composer everyday. The November cohort however has materially lower engagement (M1, 30%), and remains lower in subsequent months (M2, 26%) and (M3, 27%). Interestingly, the customer engagement rate only drops with the November cohort, returning to normal with the December cohort (M1, 45%).

5 ways AI can help mitigate the global shipping crisis

With the fourth quarter now upon us, every industry faces a challenge in managing a holiday production calendar that will deliver the goods. The key for startups looking to defend the quarter from disruptions is to adopt a proactive, data-driven approach to inventory management.

Here are five methods we’ve been counseling clients to adopt:

  • Use data and analytics to identify and map out the inventory being affected by the global shipping crisis. If you don’t have the data about what is on a ship transporting your materials, then use this crisis as an opportunity to justify prioritizing supply chain digital transformation with data, IoT and advanced analytics (e.g., machine learning and simulation). You need to know the location of your goods all times if you are going to successfully gauge what impact a shortage will have on your operation.
    Ultimately, AI will help startups understand how myriad disruptions affect their supply chain so they can better respond with a Plan B when the unthinkable happens.
  • If you don’t have the data readily available, then you need to partner with a vendor and use a secure environment to share second-party data to deliver AI-driven actionable insights on the business impact on all parties involved, from startup to retailer to the consumer.
  • Simulate and forecast the impact of these supply-side issues on the demand side. Conduct scenario planning exercises and inform critical business decisions. If this ability is not in place, an emergency like a pandemic, civil unrest or an uncontrollable rate hike will wreak havoc on your business plan. Use this situation as an opportunity to put a disaster management program in place to prepare for the potential risks.

Greylock’s Mike Duboe explains how to define growth and build your team

With more venture funding flowing into the startup ecosystem than ever before, there’s never been a better time to be a growth expert.

At TechCrunch Early Stage: Marketing and Fundraising earlier this month, Greylock Partners’ Mike Duboe dug into a number of lessons and pieces of wisdom he’s picked up leading growth at a number of high-growth startups, including StitchFix. His advice spanned hiring, structure and analysis, with plenty of recommendations for where growth teams should be focusing their attention and resources.

How to define growth

Before Duboe’s presentation kicked off, he spent some time zeroing in on a definition of growth, which he cautioned can mean many different things at many different companies. Being so context-dependent means that “being good at growth” is more dependent on honing capabilities rather than following a list of best practices.

Growth is something that’s blatantly obvious and poorly defined in the startup world, so I do think it’s important to give a preamble to all of this stuff. First and foremost, growth is very context dependent; some teams treat it as a product function, others marketing, some sales or “other.” Some companies will do growth with a dedicated growth team; others have abandoned the team but still do it equally well. Some companies will goal growth teams purely on acquisition, others will deploy them against retention or other metrics. So, taking a step back from that, I define growth as a function that accelerates a company’s pace of learning.

Growth is everyone’s job; if a bunch of people in the company are working on one problem, and it’s just someone off in the corner working on growth, you probably failed at setting up the org correctly.  (Timestamp: 1:11)

While growth is good, growing something that is unsustainable is an intense waste of time and money. Head of growth is often an early role that founders aim to fill, but Duboe cautioned early-stage entrepreneurs from focusing too heavily on growth before nailing the fundamentals.

I’ve seen many companies make the mistake of working on growth prior to nailing product-market fit. I think this mistake becomes even more common in an environment where there’s rampant VC funding, so while some of the discipline here is useful early on, I’d really encourage founders to be laser-focused on finding that fit before iterating on growth. (Timestamp: 2:29)

Where to focus growth energy

The bulk of Duboe’s presentation focused on laying out 10 of the “most poignant and generalizable” lessons in growth that he’s learned over the years, with lessons on focus, optimization and reflection.

Lesson 1: Distill your growth model (“business equation”)

Growth modeling and metric design — I view as the most fundamental part of growth. This does not require a growth team so any good head of growth should require some basic growth model to prioritize what to work on. (Timestamp: 3:09)

The first point Duboe touched on was one on how to visualize your growth opportunities using models, using an example from his past role leading growth at Tilt, where his team used user state models to determine where to direct resources and look for growth opportunities.

Lesson 2: Retention before acquisition

The second lesson is to prioritize retention before driving acquisition, a very obvious or intuitive lesson, but it’s also easy to forget given it’s typically less straightforward to figure out how to retain users versus acquiring new ones. (Timestamp: 4:19)

Retention is typically cheaper than acquiring wholly new users, Duboe noted, also highlighting how a startup focusing on retention can help them understand more about who their power users are and who exactly they should be building for.

Lesson 3: Embrace ideas from all corners, but triage

Bringing on new ideas is obviously a positive, but often ideas need guidelines to be helpful, and setting the right templates early on can help team members filter down their ideas while ensuring they meet the need of the organization.

Edge Delta raises $15M Series A to take on Splunk

Seattle-based Edge Delta, a startup that is building a modern distributed monitoring stack that is competing directly with industry heavyweights like Splunk, New Relic and Datadog, today announced that it has raised a $15 million Series A funding round led by Menlo Ventures and Tim Tully, the former CTO of Splunk. Previous investors MaC Venture Capital and Amity Ventures also participated in this round, which brings the company’s total funding to date to $18 million.

“Our thesis is that there’s no way that enterprises today can continue to analyze all their data in real time,” said Edge Delta co-founder and CEO Ozan Unlu, who has worked in the observability space for about 15 years already (including at Microsoft and Sumo Logic). “The way that it was traditionally done with these primitive, centralized models — there’s just too much data. It worked 10 years ago, but gigabytes turned into terabytes and now terabytes are turning into petabytes. That whole model is breaking down.”

Image Credits: Edge Delta

He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse. The promise of Edge Delta is that it can offer all of the capabilities of this centralized model by allowing enterprises to start to analyze their logs, metrics, traces and other telemetry right at the source. This, in turn, also allows them to get visibility into all of the data that’s generated there, instead of many of today’s systems, which only provide insights into a small slice of this information.

While competing services tend to have agents that run on a customer’s machine, but typically only compress the data, encrypt it and then send it on to its final destination, Edge Delta’s agent starts analyzing the data right at the local level. With that, if you want to, for example, graph error rates from your Kubernetes cluster, you wouldn’t have to gather all of this data and send it off to your data warehouse where it has to be indexed before it can be analyzed and graphed.

With Edge Delta, you could instead have every single node draw its own graph, which Edge Delta can then combine later on. With this, Edge Delta argues, its agent is able to offer significant performance benefits, often by orders of magnitude. This also allows businesses to run their machine learning models at the edge, as well.

Image Credits: Edge Delta

“What I saw before I was leaving Splunk was that people were sort of being choosy about where they put workloads for a variety of reasons, including cost control,” said Menlo Ventures’ Tim Tully, who joined the firm only a couple of months ago. “So this idea that you can move some of the compute down to the edge and lower latency and do machine learning at the edge in a distributed way was incredibly fascinating to me.”

Edge Delta is able to offer a significantly cheaper service, in large part because it doesn’t have to run a lot of compute and manage huge storage pools itself since a lot of that is handled at the edge. And while the customers obviously still incur some overhead to provision this compute power, it’s still significantly less than what they would be paying for a comparable service. The company argues that it typically sees about a 90 percent improvement in total cost of ownership compared to traditional centralized services.

Image Credits: Edge Delta

Edge Delta charges based on volume and it is not shy to compare its prices with Splunk’s and does so right on its pricing calculator. Indeed, in talking to Tully and Unlu, Splunk was clearly on everybody’s mind.

“There’s kind of this concept of unbundling of Splunk,” Unlu said. “You have Snowflake and the data warehouse solutions coming in from one side, and they’re saying, ‘hey, if you don’t care about real time, go use us.’ And then we’re the other half of the equation, which is: actually there’s a lot of real-time operational use cases and this model is actually better for those massive stream processing datasets that you required to analyze in real time.”

But despite this competition, Edge Delta can still integrate with Splunk and similar services. Users can still take their data, ingest it through Edge Delta and then pass it on to the likes of Sumo Logic, Splunk, AWS’s S3 and other solutions.

Image Credits: Edge Delta

“If you follow the trajectory of Splunk, we had this whole idea of building this business around IoT and Splunk at the Edge — and we never really quite got there,” Tully said. “I think what we’re winding up seeing collectively is the edge actually means something a little bit different. […] The advances in distributed computing and sophistication of hardware at the edge allows these types of problems to be solved at a lower cost and lower latency.”

The Edge Delta team plans to use the new funding to expand its team and support all of the new customers that have shown interest in the product. For that, it is building out its go-to-market and marketing teams, as well as its customer success and support teams.

 

Companies should utilize real-time compensation data to ensure equal pay

Diversity, equity and inclusion (DEI) initiatives are often thought to be an issue that can be solved by intuition by some segment of the HR team. However, in reality, it needs to come from a data-driven approach that encompasses the entire workforce.

The primary aspect that companies usually look to, in terms of treating employees fairly, is remuneration. However, having the conversation and agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.

Particular attention should be paid to addressing inequities in the areas of attracting and hiring candidates, integration, performance assessment, compensation and promotion.

In a recent survey from Mercer that included data from more than 1,000 companies in 54 countries, 81% agreed that it was important to have a plan for advancing gender equality, but just 42% actually had one in place. This points toward a tokenism attitude indicating companies are happy to talk around the issue without addressing it directly.

Despite the fact that women make up roughly half of all college-educated workers in the United States, they are underrepresented in positions of power — just 8% of Fortune 500 companies are led by women, and, incredibly, just 1% by women of color. Furthermore, the last U.S. census revealed that women who are employed full time are paid on average 17% less than men.

While there have been steps to ensure equal pay, such as Canada’s Pay Equity Act, which states that men and women in the public sector should be paid equally, it does not cover the private sector. Given that the Institute for Women’s Policy Research estimates that equal pay will not be reached until 2059, there is still plenty of work to be done.

Particular attention should be paid to addressing inequities in the areas of attracting and hiring candidates, integration, performance assessment, compensation and promotion. Companies need to think about initiatives that are supported by objective tools to drive progress, identify problems and strategize solutions. This is where data can be a great tool to provide insight into DEI: by highlighting shortcomings and areas where there is bias.

Start with data collection

The first step is to create a data set so that tangible metrics can be utilized and turned into actionable decisions. To do this, diversity and inclusion officers need to be given the opportunity to weed out bias.

Obviously, the data would drive decisions on areas such as compensation. But far too often, director-level discussions don’t involve the talent acquisition team. To eradicate the pay gap and ensure compensation is equalized on individual merit, this needs to change. Line managers and talent acquisition teams have the best knowledge of their staff and are well placed to procure the right information to help senior managers make equitable decisions.

Productivity startup Time is Ltd raises $5.6M to be the ‘Google Analytics for company time’

Productivity analytics startup Time is Ltd wants to be the Google Analytics for company time. Or perhaps a sort of “Apple Screen Time” for companies. Whatever the case, the founders reckon that if you can map how time is spent in a company enormous productivity gains can be unlocked and, money better spent.

It’s now raised a $5.6 million late seed funding round led by Mike Chalfen, of London-based Chalfen Ventures, with participation from Illuminate Financial Management and existing investor Accel. Acequia Capital and former Seal Software chairman Paul Sallaberry are also contributing to the new round, as is former Seal board member Clark Golestani. Furthermore, Ulf Zetterberg, founder and former CEO of contract discovery and analytics company Seal Software, is joining as President and co-founder.

The venture is the latest from serial entrepreneur Jan Rezab, better known for founding SocialBakers, which was acquired last year.

We are all familiar with inefficient meetings, pestering notifications chat, video conferencing tools and the deluge of emails. Time is Ltd. says it plans to address this by acquiring insights and data platforms such as Microsoft 365, Google Workspace, Zoom, Webex, MS Teams, Slack, and more. The data and insights gathered would then help managers to understand and take a new approach to measure productivity, engagement, and collaboration, the startup says.

The startup says it has now gathered 400 indicators that companies can choose from. For example, a task set by The Wall Street Journal for Time is Ltd. found the average response time for Slack users vs. email was 16.3 minutes, comparing to emails which was 72 minutes.

Chalfen commented: “Measuring hybrid and distributed work patterns is critical for every business. Time Is Ltd.’s platform makes such measurement easily available and actionable for so many different types of organizations that I believe it could make work better for every business in the world.”

Rezab said: “The opportunity to analyze these kinds of collaboration and communication data in a privacy-compliant way alongside existing business metrics is the future of understanding the heartbeat of every company – I believe in 10 years time we will be looking at how we could have ignored insights from these platforms.”

Tomas Cupr, Founder and Group CEO of Rohlik Group, the European leader of e-grocery, said: “Alongside our traditional BI approaches using performance data, we use Time is Ltd. to help improve the way we collaborate in our teams and improve the way we work both internally and with our vendors – data that Time is Ltd. provides is a must-have for business leaders.”

Climate risk platform Cervest raises $30M Series A led by Draper Esprit

Cervest – a startup with a platform that claims to quantify climate risk across multiple decades and threats down to the asset level – has raised a $30 million Series A round led by Draper Esprit. Previous investors Astanor Ventures, Lowercarbon Capital (Chris Sacca), and Future Positive Capital also participated in the round, and were joined by new investors UNTITLED, the venture fund of Magnus Rausing, and TIME Ventures, the venture fund of Marc Benioff. Cervest’s total funding now stands at $36.2 million. It previously raised $5.2M in 2019.

Cervest’s competitors include Jupiter Intelligence, which has raised $35M to Series B level, but Cervest claims it has a more data + AI approach.

The company will use the new funding to expand in the U.S. and European markets through its freemium model
It’s widely accepted now, with unpredictable weather patterns and clear climate “weirding” that these weather events are of huge risk to trillions of dollars of physical assets.

Cervest says its “Climate Intelligence” platform has been built through peer-reviewed research over the five years and combines public and private data sources (i.e. NOAA, ECMWF, CMIP6), machine learning, and statistical science to come up with a view of climate risks to assets.

‘EarthScan’ will be its first product, giving enterprises and governments a view on how flooding, droughts, and extreme temperatures can impact the assets they own or manage,going back 50 years and looking forward 80 years.

Iggy Bassi, Founder and CEO of Cervest said: “Climate Intelligence is Business Intelligence for managing climate risk. Climate volatility has thrown us into a new era where Climate Intelligence needs to be integrated into all decisions. Organizations that fail to do so risk being blindsided by climate events such as the recent floods and fires in Australia, the droughts in Europe, and the winter freeze in Texas. Much of the spotlight is on decarbonization today. While this is absolutely necessary, it is not sufficient to build asset-level resilience.”

Vinoth Jayakumar, Partner and Fintech Practice Lead at Draper Esprit added: “Climate Tech has grabbed a lot of attention recently, with good reason… Cervest’s pioneering approach to quantifying risk, in a way that was never before possible, means we can better understand the economics of the problem and bring real-world market solutions to bear.”

Analytics as a service: Why more enterprises should consider outsourcing

With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.

According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.

MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.

AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.

Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.

In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.