Startups
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Reflect, a member of the Y Combinator Summer 2020 class, is building a tool to automate website and web application testing, making it faster to get your site up and running without waiting for engineers to write testing code, or for human testers to run the site through its paces.
Company CEO and co-founder Fitz Nowlan says his startup’s goal is to allow companies to have the ease of use and convenience of manual testing, but the speed of execution of automated or code-based testing.
“Reflect is a no-code tool for creating automated tests. Typically when you change your website, or your web application, you have to test it, and you have the choice of either having your engineers build coded tests to run through and ensure the correctness of your application, or you can hire human testers to do it manually,” he said.
With Reflect, you simply teach the tool how to test your site or application by running through it once, and based on those actions, Reflect can create a test suite for you. “You enter your URL, and we load it in a browser in a virtual machine in the cloud. From there, you just use your application just like a normal user would, and by using your application, you’re telling us what is important to test,” Nowlan explained.
He adds, “Reflect will observe all of your actions throughout that whole interaction with that whole browser session. And then from those actions, it will distill that down into a repeatable machine executable test.”
Nowlan and co-founder Todd McNeal started the company in September 2019 after spending five years together at a digital marketing startup near Philadelphia, where they experienced problems with web testing first-hand.
They launched a free version of this product in April, just as we were beginning to feel the full force of the pandemic in the U.S, a point that was not lost on him. “We didn’t want to delay any longer and we just felt like, you know you got to get up there and swing the bat,” he said.
Today, the company has 20 paying customers, and he has found that the pandemic has helped speed up sales in some instances, while slowing it down in others.
He says the remote YC experience has been a positive one, and in fact he couldn’t have participated had they had to show up in California as they have families and homes in Pennsylvania. He says that the remote nature of the current program forces you to be fully engaged mentally to get the most out of the program.
“It’s just a little more mental work to prepare yourself and to have the mental energy to stay locked in for a remote batch. But I think if you can get over that initial hump, the information flow and the knowledge sharing is all the same,” he said.
He says as technical founders, the program has helped them focus on the sales and marketing side of the equation, and taught them that it’s more than building a good product. You still have to go out there and sell it to build a company.
He says his short-term goal is to get as many people as he can using the platform, which will help them refine their ability to automate the test building. For starters, that involves recording activities on-screen, but over time they plan to layer on machine learning and that requires more data.
“We’re going to focus primarily over the next six to 12 months on growing our customer base — both paid and unpaid — and I really mean that we want people to come in and create tests. Even if they [use the free product], we’re benefiting from that creation of that test,” he said.
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As the pandemic has shut down in-person meetings, and pushed us online, products like Zoom, Cisco WebEx, Google Meet and Microsoft Teams have become part of our daily lives. Into the fray jumps huddl.ai, a 3.5-year-old startup from a serial entrepreneur who wants to bring a dose of artificial intelligence to meeting technology.
Company co-founder and CEO Krishna Yarlagadda says while these companies have introduced the video meeting concept, his startup has a vision of taking it further. “As we move forward. I think the next [era] is going to be about intelligence,” Yarlagadda told TechCrunch.
That involves using AI tools to transcribe the meeting, pull out the salient points and help users understand what happened without poring over notes to find the key information in a long session. “Primarily there’s a purpose for every meeting, or essentially we’re meeting for outcomes, and that’s where Huddl comes in,” he said.
Yarlagadda said that current solutions simply give you a link to a cloud room and everyone involved clicks and enters. Huddl wants to bring some more structure to that whole process. “We’ve developed a very user-centric architecture and also added a layer called meeting memory, which essentially captures the core aspects of the meeting — the agenda, action items and moments and then added search,” he explained.
They call these meeting elements moments, and they involve capturing three key aspects of the meeting: the agenda and collaborative notes participants take during the meeting, screen captures the user takes using a built-in tool and, finally, audio, which captures a recording of the meeting. Users can search across these elements to find the parts of the meeting that are most relevant to them.
Image Credits: huddl.ai
Further, it integrates with other enterprise applications like Slack or Salesforce to move to applicable tools items discussed during these meetings when it makes sense. “Essentially what we’re trying to do is create a five-minute version of your 60-minute meeting that is stored in your memory and that becomes part of your search. Post-meeting this content has a life, and through APIs and integrations, we can [share it with the right programs],” he said.
For instance, if it’s an action item in a sales meeting, it would go to Salesforce, and if it is a software bug in an engineering meeting, it could be shared with Jira.
The company was started in 2017, and has raised $8.7 million in seed money to date. It has 50 employees, with 10 in the U.S. and the others in India, and has plans to hire 15-20 additional people this year between the U.S. and India offices.
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For the first few months it was operating, Shelf Engine, the Seattle-based company that optimizes the process of stocking store shelves for supermarkets and groceries, didn’t have a name.
Co-founders Stefan Kalb and Bede Jordan were on a ski trip outside of Salt Lake City about four years ago when they began discussing what, exactly, could be done about the problem of food waste in the U.S.
Kalb is a serial entrepreneur whose first business was a food distribution company called Molly’s, which was sold to a company called HomeGrown back in 2019.
A graduate of Western Washington University with a degree in actuarial science, Kalb says he started his food company to make a difference in the world. While Molly’s did, indeed, promote healthy eating, the problem that Kalb and Bede, a former Microsoft engineer, are tackling at Shelf Engine may have even more of an impact.
Food waste isn’t just bad for its inefficiency in the face of a massive problem in the U.S. with food insecurity for citizens, it’s also bad for the environment.
Shelf Engine proposes to tackle the problem by providing demand forecasting for perishable food items. The idea is to wring inefficiencies out of the ordering system. Typically about a third of food gets thrown out of the bakery section and other highly perishable goods stocked on store shelves. Shelf Engine guarantees sales for the store, and any items that remain unsold the company will pay for.
Image: OstapenkoOlena/iStock
Shelf Engine gets information about how much sales a store typically sees for particular items and can then predict how much demand for a particular product there will be. The company makes money off of the arbitrage between how much it pays for goods from vendors and how much it sells to grocers.
It allows groceries to lower the food waste and have a broader variety of products on shelves for customers.
Shelf Engine initially went to market with a product that it was hoping to sell to groceries, but found more traction by becoming a marketplace and perfecting its models on how much of a particular item needs to go on store shelves.
The next item on the agenda for Bede and Kalb is to get insights into secondary sources like imperfect produce resellers or other grocery stores that work as an outlet.
The business model is already showing results at around 400 stores in the Northwest, according to Kalb, and it now has another $12 million in financing to go to market.
The funds came from Garry Tan’s Initialized and GGV (and GGV managing director Hans Tung has a seat on the company’s board). Other investors in the company include Foundation Capital, Bain Capital, 1984 and Correlation Ventures .
Kalb said the money from the round will be used to scale up the engineering team and its sales and acquisition process.
The investment in Shelf Engine is part of a wave of new technology applications coming to the grocery store, as Sunny Dhillon, a partner at Signia Ventures, wrote in a piece for TechCrunch’s Extra Crunch (membership required).
“Grocery margins will always be razor thin, and the difference between a profitable and unprofitable grocer is often just cents on the dollar,” Dhillon wrote. “Thus, as the adoption of e-grocery becomes more commonplace, retailers must not only optimize their fulfillment operations (e.g. MFCs), but also the logistics of delivery to a customer’s doorstep to ensure speed and quality (e.g. darkstores).”
Beyond Dhillon’s version of a delivery-only grocery network with mobile fulfillment centers and dark stores, there’s a lot of room for chains with existing real estate and bespoke shopping options to increase their margins on perishable goods, as well.
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As AI and machine-learning tools become more pervasive and accessible, product and engineering teams across all types of organizations are developing innovative, AI-powered products and features. AI is particularly well-suited for pattern recognition, prediction and forecasting, and the personalization of user experience, all of which are common in organizations that deal with data.
A precursor to applying AI is data — lots and lots of it! Large data sets are generally required to train an AI model, and any organization that has large data sets will no doubt face challenges that AI can help solve. Alternatively, data collection may be “phase one” of AI product development if data sets don’t yet exist.
Whatever data sets you’re planning to use, it’s highly likely that people were involved in either the capture of that data or will be engaging with your AI feature in some way. Principles for UX design and data visualization should be an early consideration at data capture, and/or in the presentation of data to users.
Understanding how users will engage with your AI product at the start of model development can help to put useful guardrails on your AI project and ensure the team is focused on a shared end goal.
If we take the ‘”Recommended for You” section of a movie streaming service, for example, outlining what the user will see in this feature before kicking off data analysis will allow the team to focus only on model outputs that will add value. So if your user research determined the movie title, image, actors and length will be valuable information for the user to see in the recommendation, the engineering team would have important context when deciding which data sets should train the model. Actor and movie length data seem key to ensuring recommendations are accurate.
The user experience can be broken down into three parts:
Knowing what a user should see before, during and after interacting with your model will ensure the engineering team is training the AI model on accurate data from the start, as well as providing an output that is most useful to users.
Will your users know what is happening to the data you’re collecting from them, and why you need it? Would your users need to read pages of your T&Cs to get a hint? Think about adding the rationale into the product itself. A simple “this data will allow us to recommend better content” could remove friction points from the user experience, and add a layer of transparency to the experience.
When users reach out for support from a counselor at The Trevor Project, we make it clear that the information we ask for before connecting them with a counselor will be used to give them better support.
Image Credits: Trevor Project (opens in a new window)
If your model presents outputs to users, go a step further and explain how your model came to its conclusion. Google’s “Why this ad?” option gives you insight into what drives the search results you see. It also lets you disable ad personalization completely, allowing the user to control how their personal information is used. Explaining how your model works or its level of accuracy can increase trust in your user base, and empower users to decide on their own terms whether to engage with the result. Low accuracy levels could also be used as a prompt to collect additional insights from users to improve your model.
Prompting users to give feedback on their experience allows the Product team to make ongoing improvements to the user experience over time. When thinking about feedback collection, consider how the AI engineering team could benefit from ongoing user feedback, too. Sometimes humans can spot obvious errors that AI wouldn’t, and your user base is made up exclusively of humans!
One example of user feedback collection in action is when Google identifies an email as dangerous, but allows the user to use their own logic to flag the email as “Safe.” This ongoing, manual user correction allows the model to continuously learn what dangerous messaging looks like over time.
Image Credits: Google
If your user base also has the contextual knowledge to explain why the AI is incorrect, this context could be crucial to improving the model. If a user notices an anomaly in the results returned by the AI, think of how you could include a way for the user to easily report the anomaly. What question(s) could you ask a user to garner key insights for the engineering team, and to provide useful signals to improve the model? Engineering teams and UX designers can work together during model development to plan for feedback collection early on and set the model up for ongoing iterative improvement.
Accessibility issues result in skewed data collection, and AI that is trained on exclusionary data sets can create AI bias. For instance, facial recognition algorithms that were trained on a data set consisting mostly of white male faces will perform poorly for anyone who is not white or male. For organizations like The Trevor Project that directly support LGBTQ youth, including considerations for sexual orientation and gender identity are extremely important. Looking for inclusive data sets externally is just as important as ensuring the data you bring to the table, or intend to collect, is inclusive.
When collecting user data, consider the platform your users will leverage to interact with your AI, and how you could make it more accessible. If your platform requires payment, does not meet accessibility guidelines or has a particularly cumbersome user experience, you will receive fewer signals from those who cannot afford the subscription, have accessibility needs or are less tech-savvy.
Every product leader and AI engineer has the ability to ensure marginalized and underrepresented groups in society can access the products they’re building. Understanding who you are unconsciously excluding from your data set is the first step in building more inclusive AI products.
Fairness goes hand-in-hand with ensuring your training data is inclusive. Measuring fairness in a model requires you to understand how your model may be less fair in certain use cases. For models using people data, looking at how the model performs across different demographics can be a good start. However, if your data set does not include demographic information, this type of fairness analysis could be impossible.
When designing your model, think about how the output could be skewed by your data, or how it could underserve certain people. Ensure the data sets you use to train, and the data you’re collecting from users, are rich enough to measure fairness. Consider how you will monitor fairness as part of regular model maintenance. Set a fairness threshold, and create a plan for how you would adjust or retrain the model if it becomes less fair over time.
As a new or seasoned technology worker developing AI-powered tools, it’s never too early or too late to consider how your tools are perceived by and impact your users. AI technology has the potential to reach millions of users at scale and can be applied in high-stakes use cases. Considering the user experience holistically — including how the AI output will impact people — is not only best-practice but can be an ethical necessity.
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When Quizlet became a unicorn earlier this year, CEO Matthew Glotzbach said he’d prefer to distance the company from the common nomenclature for a startup valued at or above $1 billion.
“The way Quizlet has gotten to this point is by building and growing a very responsible business,” he said. “It’s the result of the hard work of the team for a decade. We’re much more like a camel.”
It’s clear, though, that the tides might be changing. In edtech, the rich are getting richer. Last week, Mountain View-based Coursera announced it had raised a $130 million Series F round a day after The Information broke a story about Udemy reportedly raising new financing at a $3 billion valuation.
For anyone who has been following my edtech coverage in recent few months, this momentum is hardly surprising. Earlier in the pandemic, MasterClass raised $100 million, Quizlet became a unicorn and Byju’s became India’s second-most-valuable startup.
While edtech’s boom is predictable, the industry is known — to the chagrin of founders and to the benefit of long-time investors — for being conservative. Today we’ll look to understand how a boost in late-stage funding may impact the market on a broader scale.
Ian Chiu, an investor at Owl Ventures, tells TechCrunch that the rise of big rounds brings a “watershed moment” to the $6 trillion education market. Owl Ventures was founded in 2014 and is one of the biggest edtech-focused firms out there, but Chiu says the recent strong capital flow shows that the sector is finally emerging as a sector other investors are noticing.
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We’ve aggregated many of the world’s best growth marketers into one community. Twice a month, we ask them to share their most effective growth tactics, and we compile them into this Growth Report.
This is how you stay up-to-date on growth marketing tactics — with advice that’s hard to find elsewhere.
Our community consists of startup founders and heads of growth. You can participate by joining Demand Curve’s marketing training program or its Slack group.
Without further ado, on to our community’s advice.
Insights from Matthew Morley of Savvy
Generally, it’s far more efficient to keep a current client than it is to close a new one. You’ll want a system to help you identify which users are at risk of churning. This way, you can proactively reach out to them before they leave.
Start by identifying your high-value customers at risk of churning:
Who is:
But also:
And so on.
You can stitch this information together from multiple sources like Stripe, Mixpanel, Crunchbase and Intercom. Then, set up an alert to notify your team once someone falls into these buckets.
Then reach out with something personal to win back their enthusiasm. It can be high leverage to get them on the phone to uncover what’s keeping them around.
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Now that I’ve offered an overview to help you think through where concentrated stock sits in your overall plan, let’s take a closer look at why selling can be challenging for some.
In the following section, I reveal the facts of the concentrated stock “get rich” myths that reside in the minds of many first-time concentrated stock owners, and I show why it is prudent to consider greater diversification.
Keep reading to learn more about the benefits of diversification, discover how much company stock is likely too much to hold, and the options you have when it comes to diversifying strategically.
There are several hard facts to keep in mind in contemplating maintaining a concentrated position:
The odds of any new IPO being among the top 4% is just slightly better than hitting your lucky number on the roulette wheel. But is your investment portfolio success and the odds of achieving your long-term financial goals something you want to spin the wheel on?
Excess volatility can harm returns. Note the example below that shows the comparison between a low-volatility diversified portfolio versus a high-volatility concentrated portfolio. Despite the same simple average return, the low-volatility portfolio below materially outperforms the high-volatility portfolio.
Image Credits: Peyton Carr
Beyond the math, unexpected spikes in volatility can cause significant price declines. Volatility increases the chances that an investor reacts emotionally and makes a poor investment decision. I’ll cover the behavioral finance aspect of this later. Lowering your portfolio volatility can be as simple as increasing your portfolio diversification.
The Russell 3000, an index representing the 3,000 largest U.S.-based publicly traded companies, has lower volatility when compared against 95%+ of all single stocks. So, how much return do you give up for having lower volatility?
According to Northern Trust Research, the 5.96% annualized average return of the Russell 3000 is 0.73% more than the 5.23% return of the median stock. Additionally, owning the Russell 3000, rather than a single stock, eliminates the likelihood of catastrophic loss scenarios — more than 20% of shares averaged a loss of more than 10% per year over a 20-year time frame.
If this establishes that the avoidance of overly concentrated portfolios is important, how much stock is too much? And at what price should you sell?
We consider any stock position or exposure greater than 10% of a portfolio to be a concentrated position. There is no hard number, but the appropriate level of concentration is dependent on several factors, such as your liquidity needs, overall portfolio value, the appetite for risk and the longer-term financial plan. However, above 10% and the returns and volatility of that single position can begin to dominate the portfolio, exposing you to high degrees of portfolio volatility.
The company “stock” in your portfolio often is only a fraction of your overall financial exposure to your company. Think about your other sources of possible exposure such as restricted stock, RSUs, options, employee stock purchase programs, 401k, other equity compensation plans, as well as your current and future salary stream tied to the company’s success. In most cases, the prudent path to achieving your financial goals involves a well-diversified portfolio.
Facts aside, maintaining a concentrated position in your company stock is far more tempting than taking a more measured approach. Token examples like Zuckerberg and Bezos tend to outshine the dull rationale of reality, and it’s hard to argue against the possibility of becoming fabulously wealthy by betting on yourself. In other words, your emotions can get the best of you.
But your goals — not your emotions — should be driving your investment strategy and decisions regarding your stock. Your investment portfolio and the company stock(s) within it should be used as tools to achieve those goals.
So first, we’ll take a deep dive into the behavioral psychology that influences our decision-making.
Despite all the evidence, sometimes that little voice remains.
“I want to hold the stock.”
Why is it so hard to shake? This is a natural human tendency. I get it. We have a strong impetus to rationalize our biases and not believe we are vulnerable to being influenced by them.
Becoming attached to your company is common, since after all, that stock has made you, or has the potential of making you wealthy. More often than not, selling and diversifying is the tough, but more rational decision.
Numerous studies have furnished insights into the correlation between investing and psychology. Many unrecognized psychological barriers and behavioral biases can influence you to hold concentrated stock even when the data shows that you should not.
Understanding these biases can be helpful when deciding what to do with your stock. These behavioral biases are hard to spot and even harder to overcome. However, awareness is the first step. Here are a few more common behavioral biases, see if any apply to you:
Familiarity bias: Familiarity is likely why so many founders are willing to hold concentrated positions in their own company’s stock. It is easy to confuse the familiarity with your own company with the safety in the stock. In the stock market, familiarity and safety are not always related. A great (safe) company sometimes can have a dangerously overvalued stock price, and terrible companies sometimes have terrifically undervalued stock prices. It’s not just about the quality of the company but the relationship between the quality of a company and its stock price that dictates whether a stock is likely to perform well in the future.
Another way this manifests is when a founder has less experience with stock market investing and has only owned their company stock. They may think the market has more risk than their company when in actuality, it is usually safer than holding just their individual position.
Overconfidence: Every investor is exhibiting overconfidence when they hold an overly concentrated position in an individual stock. Founders are likely to believe in their company; after all, it already achieved enough success to IPO. This confidence can be misplaced in the stock. Founders often are reluctant to sell their stock if it has been going up since they believe it will continue to go up. If the stock has sold off, the opposite is true, and they are convinced it will recover. Often, it is challenging for founders to be objective when they are so close to the company. They commonly believe that they have unique information and know the “true” value of the stock.
Anchoring: Some investors will anchor their beliefs to something they experienced in the past. If the price of the concentrated stock is down, investors may anchor their belief that the stock is worth its recent previous higher value and be unwilling to sell. This previous value of the stock is not an indicator of its real value. The real value is the current price where buyers and sellers exchange the stock while incorporating all presently available information.
Endowment effect: Many investors tend to place a higher value on an asset they currently own than if they did not own it at all. It makes it harder to sell. An excellent way to check for the endowment effect is to ask yourself: “If I did not own these shares, would I purchase them today at this price?” If you are not willing to purchase the shares at this price today, it likely means you are only holding onto the shares because of the endowment effect.
A fun spin on this is to look into the IKEA effect study, which demonstrates that people assign more value to something that they made than it is potentially worth.
When framed this way, investors can make more intentional decisions on whether to continue holding concentrated stock or selling. At times, these biases are hard to spot, which is why having a second person, a co-pilot, or an advisor, is helpful.
Congratulations to those of you with a concentrated stock position in your company; it is hard-earned and likely represents a material wealth. Understand, there is no “right” answer when it comes to managing concentrated stock. Each situation is unique, so it is essential to speak with a professional about options specific to your situation.
It starts with having a financial plan, complete with specific investment goals that you want to achieve. Once you have a clear picture of what you want to accomplish, you can look at the facts in a new light and gain a deeper appreciation for the dangers of holding a concentrated position in company stock versus the benefits of diversification, considering all of the implications and opportunities involved in rational decision-making and investment behavior.
Most individuals understand they can simply and directly sell their equity, but there are a variety of other strategies. Some of these opportunities may be far better at minimizing taxes or better at achieving the desired risk or return profile. Some might wonder what the best timing is to sell. I will cover these topics in the final article of the series.
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In the wake of the COVID-19 pandemic, as well as the ongoing protests for racial justice, people have been looking for different ways to contribute, which in turn has led tech companies to launch new features and campaigns.
Now there’s a new fundraising platform called Project Bento, created by chef and restauranteur Marcus Samuelsson (best known as the chef behind Red Rooster Harlem), Derek Evans (CEO of the Marcus Samuelsson Group) and the team at Sage Digital (a startup creating tools for reviewers, chefs and other experts to publish content and build a following).
Samuelsson told me that he’d already been working with Sage Digital to create a presence on the platform. Then he mentioned Harlem Serves Up, this year’s version of the annual Harlem EatUp festival — Samuelsson and his team reinvented the event during the pandemic as as a fundraising telethon for nonprofits fighting food insecurity.
But, Samuelsson said that when he surveyed the options available to manage the online fundraising, he wasn’t quite satisfied with any of the available options.
Image Credits: Project Bento
“We were thinking very much about our needs — what were we building, how do we want consumers to utilize it,” he said.
So the Sage Digital team ended up building Project Bento in seven or eight weeks, on top of the startup’s existing platform. Sage CEO Samir Arora said that along with allowing nonprofits to collect funds (without having to pay a platform fee), publish content, promote on social media and track their campaigns, the platform also includes tools for managing sponsorships and matching donors.
The rapid development, Samuelsson said, was a perfect demonstration of “what entrepreneurship is.” Thus far, Project Bento has been used to raise more than $350,000 for Harlem Serves Up, as well as $4.8 million for the Project Bento Fund (which Arora described as a “completely new nonprofit whose purpose is to create matching funds” for campaigns on the platform).
There are several other campaigns live already, as well as links to employee relief fundraisers on other platforms, but Project Bento is also accepting applications from other nonprofits that want to fundraise on the platform. Samuelsson said he wants the website to be a place that can bring many of these campaigns together.
“There are communities not just in Harlem, but across the country, that need a campaign, they need to connect,” Samuelsson said. “[Project Bento] will continue because that need, raising money and connecting a community, will continue.”
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After Wisam Dakka and André Madeira left Snap in 2018, the two longtime product developers and coders cast about for a new app to build.
Looking around they realized there was no financial product that spoke to the generation of consumers they’d spent the last bit of their professional lives working to build for, so they decided it would be their next project.
“Our insight is that an individual’s relationship with money is a delicate and an emotional one. Most financial apps are not adopted by the masses because they are strict, lack empathy, and are unconsciously perceived as judgmental, which is why they are often downloaded and then ignored,” said Madeira, in a statement.
Their solution, launching today, is Meemo .
It’s a combination of a personal financial monitoring, rewards and gifting, and social shopping app all rolled into one.
“One of the things we learned at Snap, if you want to reach the masses you need to change how you create an app. It has to be effortlessly,” said Madeira. “It has to be automatic and social as well so we want to build an app that is all of that combined.”
Once a user downloads Meemo and connects their main bank account or credit card to the app, Meemo will give that person insights into their spending history and potential rewards.
For most users, the initial experience will be through a gift card. Gifting, it turns out is what Dakka and Madeira think will be the secret sauce for the company’s growth (although getting people to use something if they’re being given money or free stuff is hardly rocket science).
There’s also the social element, which the two men think will be a draw as well. Meemo provides recommendations and social validation from friends by harvesting their buying history and sharing it with you.
Once a user downloads Meemo and has the history of their transactions, the app will surface the places where users spend the most money. They can then send gift cards to their friends for their favorite restaurants. The goal, eventually, is to get restaurants to subsidize the gifting portion and have their shoppers act as a direct marketing channel.
Image Credits: Meemo (opens in a new window)
Shops won’t be able to see who’s getting the gifts until they come into the store. What Meemo hopes to do is gather a profile of a user’s shopping behavior based on their purchases and offer them discounts to places that they may not frequent as often, but match their consumer profile.
Backing the company are investors including Saama Capital, Greycroft, monashees and Sierra Ventures, along with individual investors Amit Singhal, Hans Tung and serial entrepreneurs and the co-founders’ colleagues from Google and Snap.
Madeira and Dakka first met working on Google Search and went on to found Snap’s San Francisco office. The team is rounded out by long-time friends like Robson Araújo and Ranveer Kunal.
“We are very excited to back Dakka and Madeira in their creation of a new age finance app at Meemo that will combine improved financial management with deeper social engagement for today’s generation,” said Ash Lilani, managing partner at Saama Capital, in a statement. “With Dakka and Madeira’s past experience of assembling talented teams and building viral products, we believe Meemo has an opportunity to become a leader in this space.”
The company’s name is taken from a Portuguese word “mimo,” which means an affectionate treat, according to a statement. It’s available to download on iOS and Android.
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Think back to the last time you onboarded at a new job. Was it a mishmash of documents and calendar invites and calls and, generally speaking, a mess?
Probably. That’s likely because onboarding is a process that often depends on disparate, unconnected HR tools. Sora, a startup that today announced $5.3 million in collected fundraising, wants to shake up the HR software world with a low-code service that helps companies connect their tooling and automate their HR processes. The startup might be able to make things like onboarding better for employees and companies alike.
Startups looking to bring low, and no-code tooling to non-engineering teams have become a trend in recent quarters. TechCrunch recently covered a $2.2 million round for no-code text analysis and machine-learning shop MonkeyLearn, for example. There have been hundreds of millions of dollars raised by low, and no-code tools in 2020 alone.
By building tools to assist non-engineers do more, faster without developer help — be it analysis, or visual programming — some technology upstarts are helping non-technical teams do what only technical teams were able to in previous years.
Sora fits into the trend because its service allows non-developers to create workflows, to use a term that the startup’s co-founder and CEO Laura Del Beccaro employed when she walked TechCrunch through her company’s product.
The Sora workflows can be built from templates, and employ triggers to fire off various processes (sending emails, pulling in data from other apps and services, that sort of thing), allowing non-engineers to create visual logic flows. The Sora system is “like a no-code workflow builder,” Del Beccaro said in an interview, allowing users to “add tasks where you have to tell someone to do something, and automate the follow-up. That’s actually one of our biggest pain point relievers. A lot of HR teams right now are manually tracking people down: Did you set up this laptop yet? Did you set up this new hire launch for these three people?”
Sora CEO Laura Del Beccaro, via the company.
The Sora workflow system is slick in practice, allowing, for example, customization around a single employee. Del Beccaro explained that her startup’s software can do things like ask a manager who a new hire’s work-buddy might be, and then send that person an email later saying that the hire has arrived.
According to Del Beccaro, Sora, wants to help “democratize your [HR] processes.” Today’s HR denizens are too dependent on data analysts for “people analytics reporting” she said, adding that once a company has all its HR “data in one place, which again, is our core offering, you can set up all these automations that you want by yourself, you don’t have to go to IT or engineering.”
And because Sora can handle swapping out different providers as needed, Sora should help HR teams at growing companies lower the “risk of changing systems,” helping them “stay flexible no matter what [their] processes look like.”
It’s a neat tool.
Sora has raised $5.3 million in capital to date, a funding total that includes a pre-seed round from September, 2018. First Round and Elad Gil led its most recent round, which makes up a majority of its capital raised thus far.
With 11 employees today, Sora has around “25 people on [its] cap table,” the CEO said, telling TechCrunch that it was “pretty important to [her] to have a relatively diverse set of investors.” Del Beccaro provided this publication with a full list, which we’ve included below.
Sticking to the subject of money, after Mixpanel served as an early customer, Sora opened to more customers earlier this year. The CEO said that its customers are on one or two-year contracts, and charges per-employee, per-month, which seems reasonable. With its new cash, Sora has around 2.5 years of runway she said.
First Round’s Bill Trenchard liked Sora’s approach to building its service, saying in an email that the company was “never interested in scaling for the sake of scaling,” highlighting its work in concert with “a development partner to make sure what they were working on was actually solving real HR pain points before they took it to the market” as evidence of its “thoughtful and intentional” product approach.
Today, thanks to that method, in his view “what’s compelling about Sora is their sales momentum this year after launching,” the investor said. The next question for Sora, then, is how fast it can grow now that it has more capital in the bank than it has likely ever had before.
For fun, here’s the full investor list that Del Beccaro provided, which I’m including as it’s rare to get a full cap table:
- Sarah Adams (Plaid)
- Shan Aggarwal (Coinbase, Greycroft)
- Scott Belsky (Adobe)
- Mathilde Collin (Front)
- Cooley Investment Fund
- David Del Beccaro & Arleen Armstrong (Music Choice/Legal)
- Viviana Faga (Emergence Capital)
- Avichal Garg (Electric Capital)
- Elad Gil
- Kent Goldman (Upside VC)
- Jonah Greenberger (Bright)
- Daniel Gross (Pioneer, YC)
- Charles Hudson (Precursor Ventures)
- Todd Jackson (First Round Capital)
- Oliver Jay (Asana)
- Nimi Katragadda (BoxGroup)
- Nicky Khurana (Facebook)
- Brianne Kimmel (Work Life Ventures)
- David King (Curious Endeavors)
- Fritz Lanman (ClassPass)
- Lisa & Mat Lori (Perfect Provenance/New Mountain Capital)
- Shrav Mehta (SecureFrame)
- Sean Mendy (Concrete Rose)
- Jana Messerschmidt (#ANGELS, Lightspeed)
- Katie Stanton (Katie Stanton, #ANGELS, Moxxie Ventures)
- Erik Torenberg (Village Global)
- Bill Trenchard (First Round Capital)
- Jeannette zu Fürstenberg (La Famiglia VC)
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