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Techcrunch Interviews: Piqed – Optimizing Beauty Products for Consumers

Techcrunch Interviews: Piqed - Optimizing Beauty Products for Consumers

Can data lead to a more beautiful you?  Helen and Annie from Piqued (pronounced like picked) think so.  They have created a product designed to help you find the best beauty products for your skin and look.  Using a data driven model and information from your past experiences Piqed will help you choose the beauty products that will work best for you.

The global cosmetics market was $460 billion in 2014 and, according to Hahn, their focus is on the premium segment of the business which she estimates at $50 billion.

Available now as an IOS app the service is expected to have a more formal launch in the fall.

I spoke with Helen Hahn and Annie Peng from Piqed and learned why they started this business,  the problems they are solving and about the business opportunity.

Roy: This is Roy Weissman from MediaJobs.com. Today we’re talking with Helen Hahn and Annie Peng from Piqed.

 

Helen: We tried to solve a problem that a lot of women face when choosing skincare products. You walk into a store and maybe from hearsay or a friend’s recommendation, you pick up a product. You try it and then you realize that it doesn’t work for you, no matter how well it might work for the person next to you. This process is very inefficient. It’s costly and also it could be potentially dangerous if you have an allergic reaction to the product that you’re trying out.

 

We want to eliminate that problem completely by helping our users find products that are tailored just to them. How we do this is we actually use a data driven model in the back that looks at their past experiences with different products, whether it’s worked for them before or things that didn’t work for them are equally good data as well. Using that data, we generate tailored, personalized recommendations just for that user, much like how Netflix can discover movies that you might be interested in.

 

Roy: But if I used a product, how do I have data? People just know I used a product. It wasn’t good. I didn’t like it. It seems very subjective or very qualitative, not quantitative.

 

Helen: That’s a great question. From the user’s input, it is the qualitative data that we’re looking for that actually no one records because no retailer will ask you, “How well did it work for you?” to give you a recommendation later. In the back, coupled with that qualitative data, the quantitative data is the ingredients of the products that are being rated or being recommended, just like traits of a movie. I keep using this example because I think it’s easier to understand. The traits of a movie that Netflix might use to categorize it, will be then used to then generate recommendations for that user, if that makes sense.

 

Roy: If I used a product, like a moisturizer for my face and I said, “Oh, it didn’t seem to moisturize my face. My face was dry ten minutes later.” How would that turn into quantitative data? What do I do if I want to use your service?

 

Helen: You just tell me … You will look for that product in the database in our app, and then you’ll tell me the rating. We ask you how well did it work for you or if it caused any kind of bad reactions. We take that data coupled with that product’s ingredients. That’s the “quantitative” part of that product. That’s what driving the product to work or not work for you. Given that, the collaborative filtering technique then runs on all those specific ingredients and matches it with things that are similar, or in the case where if you did not like this product, things that are dissimilar.

 

Roy: How do you know which of the ingredients were good and which of the ingredients were bad?

 

Helen: That is the power of the actual AI algorithm, which is I don’t need to know the specific ones. If there is enough data driving the model, it will take care of itself. Of course it’s weighted by concentration of the ingredients. The list goes by concentration, if you were to ever look at the packaging of the product.

 

Roy: The algorithm looks at the concentration of item A versus item B and my issues, my feedback on each item.

 

Helen: Exactly.

 

Roy: Based on that, it determines a model that says, “This probably is not working because of X,” kind of thing, and then it goes from there. Is there anyone else doing anything like this?

 

Helen: We looked very hard and thoroughly before we started the venture and we do not believe there is anyone who is taking this approach. The very popular approach is using marketing language. A product will say, “We are combating wrinkles,” or, “We are combating acne,” and then that becomes the categorization for that product’s functionality, even though it may or may not be any different from a product that just claims to moisturize.

 

Roy: You said you’re using an AI algorithm. Where is this coming from? Who is writing this secret algorithm?

 

Helen: I think Annie can take that one.

 

Annie: Helen’s doing all the backend stuff. She’s doing all the algorithm calculation stuff.

 

Helen: We build everything in house, design front-end, back-end. It’s Annie, me and another founder.

 

Roy: You guys wrote the software.

 

Helen: Yes, we did.

 

Roy: What is your background?

 

Helen: I studied engineering and math in college and I was a high-frequency algorithmic trader, so an electronic trader in finance.

 

Annie: I have business and biology background, but I went Coding Bootcamp and I’m learning coding right now as well. I’m responsible for front-end stuff.

 

Roy: Is this just an app or do I do it online?

 

Helen: For now it’s just an iOS app, but we’re launching the web platform very soon. We plan to expand to Android of course as well.

 

Roy: What is the size? Do you have any idea of the size of the market in financial dollars that you’re talking about?

 

Helen: This is almost astronomically hard to answer as a question because the market is so huge. The skincare market itself is projected to be about $120 billion worldwide USD and of that, $50 billion are going to be the premium market, so people who are looking for very specific things. That’s just skincare profits as general. For us, we can also look at the marketing budget of each retailer. That would apply to, for us, if we were to become lead generation for them given that we give good recommendations to users who will probably buy their product and then use it and retain it, that particular market, I think each retailer is somewhere from 60 to 80% of their gross profits are completely for marketing. It’s a huge, huge market in billions and billions of dollars, even just a tiny sector of that for if we can help our users would be really beneficial for them and for ourselves.

 

Roy: In essence, your app is selling everyone else’s products.

 

Helen: We are generating the leads for them. These are solid, solid leads, unlike, say, advertising or hearsay.

 

Roy: If I’m using your product and I determine that Shiseido or an Estee Lauder cosmetic, Clinque, whatever, is the right item, I just go to the store and buy it. How does anyone even know that I discovered that in your app?

 

Helen: We have a click through buy links to be of convenience for the user. Most likely, if you are using the app and you discovered it, you want to, especially if you’re into online shopping, you want to purchase it right away. The click through is there to help you do that.

 

Roy: You’re looking to do deals with companies, online merchants, whatever, to have them pay you a commission, like an affiliate commission, when you sell one of their products.

 

Helen: That would be our basic model, yes.

 

Roy: Have you guys raised any money for the business yet?

 

Helen: We’re still in the seed funding stage. We’re actually starting an accelerator tomorrow, partnered with Google. We’re going to start that process soon.

 

Roy: You’re in the Google accelerator?

 

Helen: It’s the GSV Labs, Google Launchpad Accelerator program. It’s in Silicon Valley. It actually started today, but we’re here today.

 

Roy: That’s exciting. When did you found the business?

 

Helen: February is when we launched the app.

 

Roy: Do you have any sense of how many users you have at this point?

 

Helen: We have about 500 solid users that we’ve retained. Those are solid, organic users.

 

Roy: Have they been buying products?

 

Helen: For now, we just track the actual buying link, so at the very end, we don’t know if they end up purchasing it, but we know that they’re going through the link to find the product. Since we’re not currently partnering with a retailer, we just want to grow our user base and we want to grow our user data base. Before we feel like we have a solid handle on that, we don’t want to monetize it very quickly, if that makes sense.

 

Roy: Do you feel you have an edge? What’s the true barrier to entry with all these great developers out there, what do you think the true barrier to entry is where you’re going to have a head start and it’ll be hard for somebody to take away your business.

 

Helen: I hate to say this, but most of the developers that can do this kind of machine-learning algorithm are men and unfortunately, as we’ve also witnessed today, they don’t necessarily think of skincare as their first point of entry. We believe our idea is there first and we can better improve it faster.

 

Roy: Where would you like to see this business in five years?

 

Helen: We’d love it if everyone can use the app to discover their holy grail skincare regimen and never have to try something that they don’t like ever again.

 

Roy: What kind of person would be great for your company?

 

Helen: We love people who have a passion for design and data. I think data is probably more important because we can always layer a different design on the product itself, but it’s the actual data and how you use it, how you analyze it that’s driving everything. Doing things more smartly with data is always a better way to go. That’s what we firmly believe in. Anyone who fits that is welcome to look us up.

 

Roy: Thank you very much.

 

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