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Blend uses generative AI to give you a personalized clothing guide



Shopping for clothing online has liberated us from the need to brave the endless aisles, fluorescent lights and sale-hungry crowds of the brick-and-mortar retail inferno. But anyone who has found themselves two hours deep into a fashion rabbit hole, with nothing to show for it but 15 open tabs, four full shopping carts, an earful of YouTube clothing haul reviews and the gnawing anxiety of the overwhelmed, shopping online can feel like a chore.

Enter Blend, a U.K.-based startup that is using AI to cut through the noise and help shoppers find personalized product recommendations to suit their style, budget and size.

“The vast majority of retailers do absolutely no personalization, and in the instances when they do, they only personalize according to historic purchase data,” Blend co-founder Jemima Bunbury told TechCrunch. “When trends are changing relatively quickly, and people’s style does change over the course of their lives, it doesn’t stay relevant for a user to have such historic recommendations.”

Blend participated in TechCrunch Disrupt 2023 as one of the Startup Battlefield 200 companies. At the event, the startup launched its MVP — an app that will slowly open to the 2,000 users on Blend’s waiting list. After raising angel investment in April, Blend is now on the hunt to secure investors for its seed round. The startup will use those funds to build out additional features on the app and push for a full-scale launch.

Blend has already signed on over 250 retailers, including Net-a-Porter, a luxury retailer. The startup’s go-to-market strategy targets users aged 18 to 34, “very digital, native mobile-first shoppers” who are starting to define their personal style as they accrue disposable income. Blend is launching in the U.K. first and then hopes to move into the U.S. market.

“We hope that by attracting first the very fashion-forward, trendsetting crowd, we can then move more mainstream from there, but it’s much more difficult to go the other way round,” said Bunbury. “Ultimately, the vision is really to be the front door for every online shopping experience, and therefore, to be the largest-scale retailer because of that ability to personalize and only present people with the 1% of the internet that is most relevant to them.”

Generative AI we can get behind

Blend co-founders Bella Levin (left), Jemima Bunbury (middle) and Eva Piskova (right). Image Credits: Blend

The fashion industry has tapped the generative AI frenzy in a range of ways. Some companies are using natural language processing algorithms to improve the customer service experience. Others are using image generation to create new designs. There are also applications in production improvement, trend forecasting, inventory management and virtual try-ons.

Blend’s approach centers around transformer technology and recommendation algorithms, powered in large part by user interaction data. Transformer technology, which makes up the tech stack of popular generative AI models like ChatGPT, is a model for teaching computers how to understand and generate human language. In the world of fashion, this means it can better understand user preferences and make tailored clothing recommendations.

“The main thing that’s always important when it comes to AI is what data you are actually putting into [the model],” said Bunbury, noting that the founding team decided on an app rather than a web page in part because it’s easier to track a user’s data that way.

When the user opens the app, they’ll scroll through a feed that is a mix of product imagery and descriptions that have been pulled from different retail and e-commerce sites. Their feed will also feature short-form videos and product curations from influencers who can earn an affiliate commission on any sales they generate.

As the user scrolls, Blend collects data on how they interact with the app, whether they’re liking products, saving them, sharing with a friend, “or simply how long you’re looking at one product,” according to Bunbury. Blend uses all of that data to form a picture of the user, who has already pre-set preferences to size and budget. The more a user interacts with the app, the more personalized their recommendations will become.

On the back end, Blend is comparing products and users to get a statistical picture of which products will be right for which users. So, for example, let’s say there are two users who were actively using the app three months ago. User A pauses engagement with the app, while User B continues to engage regularly, and sees her feed adjusted according to new trends. Rather than let User A’s recommendations stagnate, Blend will use User B’s data to inform recommendations to User A.

“By tracking those cultural trends and how different people’s styles are similar or different, we can use that data to inform other people’s recommendations,” said Bunbury. “So the personalization gets more powerful the more users we have on the platform to base those off of and create cohorts.”

The AI model behind the app is impressive not only because it can recommend you the right outfit today, but also tomorrow, next week, next year. It’s dynamic, and it tracks how a user’s style changes over time.

Blend also helps users find the right fit for their body type, something retailers who have to go through expensive returns cycles appreciate, as well. Part of getting this right is allowing the user to set their preferences for what their size is for different body parts and determine what their body type is. But that information isn’t always reliable — brand sizing charts can differ wildly and most of us aren’t good at classifying our own body shapes.

That’s where the user-generated content enabled by the app kicks in again. The hope is that users will take photos of themselves in their new clothes and post them on the app, giving Blend’s AI engine and other users a diverse representation of what specific products look like on different frames. Down the line, Blend hopes to incorporate reviews and a voting system to help users better determine the right size for them.

Business model

The more a user engages with their Blend feed, the better the personalized recommendations become. Image Credits: Blend

The three moving parts in Blend’s business model are: 1) Shoppers; 2) Influencers; and 3) Brands.

Blend is predominantly trying to solve a user problem, but to do that, it needs to partner with influencers and brands, both of which stand to gain, as well. By partnering with Blend, both influencers and brands can diversify revenue streams and appear across multiple different channels in a very light-touch way.

For brands specifically, Blend could present as a powerful market marketing platform.

“For most brands, the key difficulty is getting your products in front of the right audience and having a risk-free way of advertising,” said Bunbury. “With social media advertising, yes, you can target fairly well according to demographics and user group, but even then it isn’t necessarily based on what their style is. Whereas we should have this incredibly granular style-specific dataset that will allow us to put the right brands in front of the right users when they are actively looking to be buying.”

Blend wins by taking a commission on sales from partner brands and retailers, which can vary depending on retailer, according to Bunbury.

The first version of the app will link out to a brand’s website to complete the transaction there. Future versions will allow users to get to the point of sale within the app for a more seamless user experience.

“There’s huge growth potential just in that, but we’re also aware that with the dataset we have and with our ability to put brands in front of users, there are also lots of B2B revenue lines in the future,” said Bunbury. “Things like advertising, data and analytics on trends, being able to forecast what sorts of products will be selling and at what quantities.”

On the consumer side, Blend says it might launch a subscription service in the future for additional premium features, like end-of-stock alerts, discount alerts or early access to brand products.

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MoneyHash raises $4.5M for its payment orchestration platform serving merchants in MENA




The payment landscape in the Middle East and Africa (MEA) region is marked by significant fragmentation, with numerous payment providers and methods in each country, evolving regulations and diverse customer preferences. This complexity is further compounded by challenges such as payment fraud, low checkout conversion rates and high transaction failure rates.

Although the COVID-19 pandemic accelerated the adoption of digital payments in the region, infrastructure development remains inadequate. Payment failure rates are three times higher in the MEA region than the global average, and fraud rates and cart abandonment exceed those of other regions by more than 20%. This presents a challenge for merchants, who often perceive payments as a cost and risk center rather than a strategic enabler.

Payment orchestration platforms streamline payment processes for merchants through unified payment APIs. Egyptian fintech MoneyHash, one of such in Africa and the Middle East, has raised $4.5 million in seed investment, money it plans to use to further invest in its technology and growth across the region. This comes two years after the startup secured $3.5 million in pre-seed.

Nader Abdelrazik, co-founder and CEO of MoneyHash, highlights that 10% of all payments processed in the MEA region are digital, placing MoneyHash uniquely for a growth phase that the region will inevitably experience over the next decade. However, navigating this burgeoning payments market will demand patience and a commitment to continuous learning.

As merchants or companies launch their platforms, they often start by collaborating with one or two payment processing providers. As their operations grow and expand into multiple regions, they onboard additional payment providers to meet their evolving needs. However, integrating different payment stacks presents significant challenges. Besides the operational inefficiencies and technical complexities, in-house tech teams may take several weeks to complete these integrations. In Africa and the Middle East, these challenges are amplified by variations in payment methods, currencies and the isolation between countries.

MoneyHash payment integration catalogue. Image Credits: MoneyHash

MoneyHash’s product includes a unified API to integrate pay-in and pay-out rails, a fully customizable checkout experience, transaction routing capabilities with fraud and failure rate optimizers and a centralized transaction reporting hub. This is complemented by tools enabling various use cases such as virtual wallets, subscription management and payment links. Fintechs such as Revio, Stitch, Credrails and Recital are similar players in the payment orchestration space.

In an email interview with TechCrunch, Abdelrazik shared insights into MoneyHash’s collaboration with merchants over the last four years. For one, he claims that payment failure rates across the region vary significantly, and relying solely on averages can be misleading. While the typical figures are around three out of 10 payments failing on average, the reality differs widely among businesses, he said. For some, it may be as low as one out of 10, while for others, it could be as high as five or six out of 10. Additionally, these figures do not include customers who abandon the checkout process voluntarily before making a payment. The CEO also noted that most of its customers don’t know much about the complexity of payments and, many times, are not aware that most leakages they have in payments are fixable.

Furthermore, merchants are expanding much faster than their partner payment service providers (PSPs). These PSPs operate under stringent regulations, making the rollout of new products and customizations slower than the merchants’ growth trajectory. As a result, MoneyHash has intensified its collaboration with PSPs, particularly those catering to enterprises and prioritizing customer requirements.

“Businesses appreciate the large network of integration we have not just for coverage but for expertise. When they know that we executed all these integrations in-house, they appreciate the team’s expertise and depth of knowledge and leverage our team to navigate difficult questions in payments. They know that working with us makes them future-proof,” noted Abdelrazik, who founded MoneyHash with Mustafa Eid.

“That means team expertise is key for us. Most of the time, we hire exclusively with payments and/or tech backgrounds, even in non-technical positions. We saw massive effectiveness in building a team where customers trust their knowledge and expertise in something specialized and critical like payments.”

Following a beta launch in 2022, which garnered the participation of key regional players like Foodics, Rain and Tamatem, MoneyHash introduced its enterprise suite last October, targeting large enterprises. Over the past year, the fintech, which integrates with various payment gateways and processors, including Checkout, Stripe, Ayden, Amazon Pay, Tap and ValU, claimed to have expanded its network of integrations, tripled its revenue and increased its processing volume by 3,000%.

At present, MoneyHash boasts 50 active paying customers. It does not offer free tiers; most customers accessing its sandbox without payment are potential clients in the assessment stage, numbering over 100. The payment orchestration platform levies a combination of SaaS and transaction fees, commencing at $500 + 0.4%. SaaS fees increase while transaction fees decrease significantly for large enterprises due to volume, Abdelrazik explained.

MoneyHash’s seed round was co-led by COTU Ventures and Sukna Ventures, with participation from RZM Investment, Dubai Future District Fund, VentureFriends, Tom Preston-Werner (GitHub’s founder and early Stripe investor) and a group of strategic investors and operators.

Speaking on the investment, Amir Farha, general partner at COTU, said his firm believes that the full potential of digital payments in MEA is yet to be realized and MoneyHash’s platform can catalyze the growth of digital payments across the region, enabling both global and local merchants to tap into new revenue streams. “We are thrilled to renew our support to a team that has consistently demonstrated superior execution, not just in securing top mid-market and enterprise customers, but also in expanding value across the entire chain, even under challenging market conditions,” he added.

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Snowflake CEO Frank Slootman stepping down — and Wall St hates it




Apparently Frank Slootman, the veteran tech executive, was popular with investors, at least judging from their reaction that he will be stepping down as CEO of Snowflake. The company stock price has plunged more than 24% in after-hours trading on the news.

Slootman will retreat into the role of chairman of the board, while Sridhar Ramaswamy, former head of Google Ads, who came to the company when it bought AI search engine Neeva last year, will take over as chief executive.

Lost in today’s executive shuffle was the company’s earnings. It reported revenue of $738 million, up a healthy 33% year over year with guidance for the next quarter of between $745 and $750 million, with growth of 26-27%.

Slootman came on board in 2019, taking over for veteran executive Bob Muglia, and was charged with taking the company public the following year. Over the last year, the stock has done well, up around 50% (the exact amount is hard to tell with this afternoon’s downward spiral), as many tech stocks recovered from 2022 doldrums.

He was famously well-compensated, with a base salary of $375,000 and a rather attractive stock option. In fact, Fortune reported that the chief executive was making an eye-popping $95 million a month at one point.

He raised eyebrows in 2021 when he told a reporter that diversity shouldn’t override merit, and eventually walked back those comments after a negative reaction from industry peers.

Prior to coming to Snowflake, he spent six years as chairman and CEO at ServiceNow. With all that cash, perhaps he’ll retire and enjoy his money.

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Fintech giant Stripe’s valuation spikes to $65B in employee stock-sale deal




Payments infrastructure giant Stripe said today it has inked deals with investors to provide liquidity to current and former employees through a tender offer at a $65 billion valuation.

Notably, the valuation represents a 30% increase compared to what Stripe was valued at last March when it raised $6.5 billion in Series I funding at a $50 billion valuation. But it is also still lower than the $95 billion valuation achieved in March of 2021.

While Stripe declined to comment beyond a written statement, a source familiar with the internal happenings in the company told TechCrunch that Stripe and some of its investors agreed to purchase over $1 billion of current and former Stripe employees’ shares.

The company, which counts the likes of Alaska Airlines, Best Buy, Lotus Cars, Microsoft, Uber and Zara as customers, had noted at the time of its last raise that the proceeds would go to “provide liquidity to current and former employees and address employee withholding tax obligations related to equity awards.” That, it added, would result in the retirement of Stripe shares that would offset the issuance of new shares to Series I investors.

A Stripe IPO has been long anticipated and was widely expected to happen in 2024. But with this deal, it appears that an initial public offering may not take place until next year.

In January, TC’s Rebecca Szkutak reported that — in anticipation of that IPO and according to secondary data tracker Caplight — there had been “an absolute flurry of buyers looking to get shares in the company in recent months.” On January 2, a secondary sale closed that valued Stripe shares at $21.06 apiece and valued the startup at $53.65 billion, according to Caplight data.

While Stripe did not name the investors participating in the latest deal, Sequoia Capital Managing Partner Roelof Botha was quoted in Stripe’s announcement and The Wall Street Journal cited Goldman Sachs’s growth equity fund as another backer.

The WSJ also reported that the transaction “is part of a commitment by the Collison brothers to provide liquidity annually to longtime and former employees.” Sources familiar with internal happenings at the company said that commitment is more to provide liquidity “regularly,” and not necessarily annually.

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