archopv
archopv
Archo PV Tech
4 posts
All Tech!
Don't wanna be here? Send us removal request.
archopv · 3 years ago
Photo
Tumblr media
Machine Learning Assignment Help
Machine learning is a data analytics method which is responsible for teaching computers which humans and other animals can do without any help. Moreover, machine learning algorithms make use of computational techniques to master news from data without depending on a prearranged equation as a model. Are you looking for Help with my machine learning assignments? If yes, then take advantage of our Machine Learning Assignment Help services for optimal solutions.
#machinelearning #Machinelearningassignment #informationtechnology #computerscience #computerscienceassignmenthelp #assignmenthelp #allassignmentservices
16 notes · View notes
archopv · 3 years ago
Photo
Tumblr media
When the Algorithm Is Your Boss - At workplaces like Amazon, algorithms have become the worst kind of boss – one who watches you constantly, makes impossible demands and then sacks you without explanation. https://ift.tt/Mtz4YHZoS
197 notes · View notes
archopv · 3 years ago
Text
Building a Two-Sided Marketplace With Regina Gwynn …And uncovering the specificities of your target market.
TresseNoire’s Story
Regina: TresseNoire has been up and running since 2013, but this latest product is a pivot from our original premise, which was to create the first on-demand, on-location beauty booking app that would send a traveling textured hairstylist to your house, hotel, or office to do your hair. Our mission has always stayed the same: it is to give women back the most important resource on Earth, which is time. And we tried to think through multiple ways of doing that by allowing us to find out faster and easier ways to simplify the beautification process. For women of color with textured hair, there are lots of pain points. Everywhere from finding the right products to finding the right stylist, to maintaining the right hairstyles to finding the right long-term maintenance for your hair.
So we kicked off with this on-demand, on-location beauty booking app, which was very successful. We started off in Philadelphia, expanded to New York, and ended up with stylists in Philly, New York, Washington, DC, LA, Dallas, Texas, and Cincinnati. It was through that experience that I became, I guess, a tech founder, which is also a whole separate conversation. You know, I definitely was a non-technical founder, with a background in fashion and beauty prior to my entrepreneurship journey. But it also introduced a very large opportunity with taking the on-demand technologies that we had seen grow like Uber, FreshDirect, and all these other kinds of business models and apply them to the beauty industry.
We kept getting so many questions from our clients. “Okay, so now the stylist has left, I love my hairstyle. Now, what do I do? How do I maintain this hairstyle?” or “What products should I be getting in the beauty supply store?” And so I found myself doing so many additional phone calls, whether it was me advising the client, connecting them to our stylists either before, during, or after the hairstyles. We realized that there was an even bigger opportunity. Through the access to education and personalization, every single client was different, they had a different hair type a different hairstyle, they lived in a different location, they had different hair needs, whether it was fitting edges, or split ends, or dry hair or super curly hair.
So there were all these different variables that to my co-founder: “If we’re in this tech industry, and we have all these tech tools available to us, why aren’t we using all of these unique criteria to develop a more accurate system, a more accurate process?” That was the impetus behind pivoting to our virtual beauty coach app. Now, our clients can take a quiz. We gather 28 points of data to determine the kind of beauty regimen that works for them, and that education is delivered through a text message. So you interact with our beauty coach via phone, and you can actually change the algorithm based on the information you give us. So if it’s raining outside, then we’ll say hey, it’s raining, don’t forget your umbrella. And if you change your hairstyle, then we’ll change the education based on the data that the client provides.
We’re still in private beta, and we definitely have learned a ton this year, in a lot of the iterations that we’ve been working on. But I’m really excited about the long-term opportunities of providing this technology to additional beauty brands. The idea is to make sure that we’re getting education around our specific hair journey and making that type of information available to everyone.
Building a Two-Sided Marketplace
Andy: That’s really exciting because you went from being an entrepreneur that was using technology to build her product and her services to being a tech entrepreneur, and building a tech product. And I imagine that was a very different experience from both sides of the coin. I want to dive a little bit deeper into that, starting with the on-demand service platform that you provided, and the lessons that you learned while you were trying to scale that business into different markets.
Every market is a different market, and I’m sure you had to adapt to each market. What was it like to build an on-demand service?
Regina: Building marketplaces is really hard. Building two-sided marketplaces is even harder. Do you have to start building the offer or the demand?
In addition, understanding the nuances in the type of customer demand was essential to tailor our offer to each market. As we started to move forward, we realized that marketplaces are really hard.
When we look at Glamsquad and other beauty booking app type models, we see that they’ve been around since 2011, have raised over $30 million, and to date, are still in less than 10 markets. It’s become very clear that there was some very inherent kind of long-term challenges that we were going to face while identifying that there were other pain points.
Uncovering the Specificities of Your Target Market
Andy: How did you uncover the specificities of each market? What kind of data did you collect to inform your decisions?
Regina: Social media is really useful. We have a modest amount of followers, probably around 10,000 across Instagram, Facebook, and Twitter. They actually were the main indicator. When we would look at our audience, scroll through the pictures and see what are women wearing in Dallas, New York, Austin, Texas, LA, etc. That way we could see a very different consumer, from market to market.
We took that information along with talking to our stylists. So we always had at least a few boots on the ground in any market that we went into, and between my co-founder and I, we have networks across the country. We always wanted to just pick up the phone and talk to a girlfriend and say: “Hey, what’s going on in that area? What are the hot spots? Who are the arbiters of culture in that area?” Then we would follow them and watch them in order to find out where the beauty trends are going.
youtube
62 notes · View notes
archopv · 3 years ago
Link
“Researchers led by a team at Harvard University have developed a tiny, 175-milligram (about two feathers) device with insect-inspired wings that can both flap and rotate, allowing it to either fly above the ground or swim in shallow waters and easily transition between the two. Researchers think it will one day be used for environmental monitoring studies, according to Science magazine, which dubbed the device the “robo-bee.””
219 notes · View notes