AI News, yCombinator 2014 Data Science Start-ups

yCombinator 2014 Data Science Start-ups

There are new and exciting commercial opportunities in the data science space.

Use their new dashboard to receive stats on: acquisition performance, user engagement and user retention.

Fuzziness can of course be a strength: an intelligent search engine will also search for semantically and syntactically related terms.

Where internal search could shine is in customization and personalization: A webshop could rank more profitable products higher, or target the results to the searcher’s purchase history.

As long as Boostable can keep showing uplifts in visitors and sales, it will remain an interesting value proposition to advertisers.

This information is then processed to generate marketing emails with product recommendations tailored to each user.

SendWithUs integrates your email service providers and has built-in support for A/B tests and extensive analytics.

Where SendWithUs distinguishes itself from other mail providers is by providing support for drip campaigns and allowing the marketer to manage email campaigns without the aid of a developer.

From just a name and an e-mail address they try to find data and metrics like: industry, title, number of employees.

If use of StackLead leads to closing more deals and warmer leads, they’ll be in business for a very long time.

Combining custom settings and real-time market data, employees can book hotels and flights, or rent cars, all within the companies budget.

It offers data analysis for retailers, with a focus on: recommendations, performance, customer analytics, growth, and ROI optimization.

With a great team and a lot of dedication to let retailers make better data-informed decisions, we have little doubt that they’ll make a dent.

In a market with rich customers, if Piinpoint manages to onboard new users, they stand to make a lot of money for themselves and their customers.

Terravion lets farmers subscribe to aerial photography: Select just the patches of land you want, and their airplane will start to take pictures.

We find these evidence-based health education programs very interesting: replicating the experience of interacting with a live health coach with a digital app.

Data security and data provenance will only become bigger topics in the future, allowing TrueVault plenty of opportunity for growth (perhaps also in other niches) Kimono Labs.

In short it wants to answer questions posed in natural language, such as “what is the weather like tomorrow in SF?”, turning it into intent, parsing the locations and finding the right forecast.

As devices become smaller and smaller, and our need to use computers to remember facts increases, so does the need for an intelligent platform that understands natural language.

We look at their open job vacancies and see skills like: The intro image is in the public domain and depicts Paul Graham speaking to a new batch of YC companies.

How Airbnb Uses Data Science to Improve Their Product and Marketing

Unable to afford the rent for their San Francisco loft, founders Brian Chesky and Joe Gebbia turned their living room into a mini bed and breakfast, hosting three guests from a local sold-out trade show where lodging was scarce.

The original design for AirBed and Breakfast provided temporary living quarters, breakfast and business networking opportunities for those who were unable to book a place to stay for local events due to demand.

Having grown quickly from a niche site providing accommodations for high profile events, Airbnb turned the hospitality and travel industry on its head and generated a great deal of press and brand recognition in the process.

Since its humble beginnings, Airbnb has made no secret of its heavy use of data science to build new product offerings, improve its service and capitalize on new marketing initiatives.

Much as a company would analyze their customer journey to improve conversions, Airbnb took it upon itself to look at every point in the hiring journey, from candidates displaying poor or “junior” communication in front of a panel of all-male data scientists scrutinizing their approach and making them nervous, to potential bias in grading the take-home challenge because of subjective views of “success”.

Airbnb credits diverse hiring as a key motivator in its product growth The end result was that they were not only able to add more female data scientists to their rosters, but the quality and experience of applicants was also greatly improved.

Originally, Airbnb didn’t know what kind of data to give customers, so it settled on a model which returned the highest quality listings within a certain radius based on the user’s search.

Throw in host’s specific (personal) preferences for last minute versus plenty-of-notice notifications and what started as a small research project turned into a full-blown machine learning algorithm.

As a result of applying these new filters and preferences, they enjoyed a nearly 4% lift in booking conversion as well as a considerably significant increase in the number of successful matches of guests and hosts – a win-win for everyone.

The Net Promoter Score asks, in essence, “How likely are you to recommend Airbnb?” Because Airbnb wants the “likelihood to recommend” to make accurate predictions, they control for other parameters too, including: Airbnb acknowledges that other kinds of loyalty may be in play (like word of mouth referrals) that they cannot account for.

Although reviews do much more than just potentially predict rebooking numbers, and there are other factors from the Net Promoter Score not mentioned here, the data science discovered that predicting rebooking using these categories and the Net Promoter Score was only marginally improved at best.

In this particular case, were it not for data scientists and other team members delving in to do the research on the accuracy of using reviews and the Net Promoter Score to forecast future bookings, Airbnb would never have known if the prediction could have added to their improved guest experience and thus, their revenue – yet another example of data science helping to save hours of time and money, even if things don’t ultimately work out as intended.

Airbnb has its own internal A/B testing framework rather than using an out of the box solution, since there are some aspects of their business model and customer experience that make it more involved than simply changing the color of a button and measuring what happens.

Much of what constitutes a “conversion” in this case is a guest looking for a place to stay in a specific area, and a host setting a price and the two coming together to agree and take care of the necessary formalities.

In another example, Airbnb (which provides professional photo services to hosts) felt that users would have a better experience if listings were made available as beautiful, full-color photos in the search results:

To help stay abreast of these changes and potentially earn hosts (and Airbnb) themselves more money, the company developed Aerosolve, an open source machine-learning system that detects patterns and attempts to use these to see why certain listings command higher prices.

When used correctly, and in partnership with many other departments in your company, data science can be used as a springboard to create new hypotheses, test new ideas and improve on existing ones.

But above all, it is meant to help inspire you and remind you that a successful company is never content to rest on its laurels – it’s always learning, adapting and growing – fueled by data and science.

To Keep Your Customers, Keep It Simple

Marketers see today’s consumers as web-savvy, mobile-enabled data sifters who pounce on whichever brand or store offers the best deal.

The single biggest driver of stickiness, by far, was “decision simplicity”—the ease with which consumers can gather trustworthy information about a product and confidently and efficiently weigh their purchase options.

In stores, shelf labels list key technical attributes, such as megapixel rating and memory, and provide a QR code that takes consumers to a mobile version of the brand’s website, where they can dig more deeply into product specifications.

User reviews and ratings are front and center there, and a navigation tool lets consumers quickly find reviews that are relevant to their intended use of the camera (family and vacation photography, nature photography, sports photography, and so on).

The highly detailed information Brand A provides at every step on the purchase path may instruct the consumer about a given camera’s capabilities, but it does little to facilitate an easy decision.

Brand B simplifies decision making by offering trustworthy information tailored to the consumer’s individual needs, thus helping her traverse the purchase path quickly and confidently.

Our study found that the best tool for measuring consumer-engagement efforts is the “decision simplicity index,” a gauge of how easy it is for consumers to gather and understand (or navigate) information about a brand, how much they can trust the information they find, and how readily they can weigh their options.

Shifting the orientation toward decision simplicity and helping consumers confidently complete the purchase journey is a profound change, one that typically requires marketers to flex new muscles and rethink how they craft their communications.

The processes of aiding navigation, building trust, and making it easier to weigh options often happen simultaneously, or at least aren’t strictly linear, but for clarity we’ll discuss them separately below.

Often what a consumer needs is not a flashy interactive experience on a branded microsite but a detailed exchange with users about the pros and cons of the product and how it would fit into the consumer’s life.

One electronics company has gathered data from four major sources—social media monitoring, ad-effectiveness and campaign-tracking information, clickstream analysis, and individual consumer surveys—to identify common purchase paths.

It studies the resulting maps to determine the volume of traffic on various paths, which paths inspire the most confidence, which touchpoints are best suited to conveying which types of messages, and at what points consumers lose confidence or defect.

Certain auto manufacturers, retailers, and travel brands have been sifting through consumer search data to learn how search terms and the type of search platform (say, mobile versus desktop) indicate consumer intent and position on the path.

They’ve found, for example, that 70% of those using a mobile device to search are within a few hours of making a purchase, whereas 70% of those using a desktop are roughly a week away.

Someone who searches a general term like “luxury sedans” is at an early stage compared with someone who searches a specific phrase like “BMW vs Audi.” Decision-simplicity marketers would guide the former to the latest auto reviews for their sedans and the latter to an enthusiastic owner community.

If the late-phase consumer was using a mobile device (indicating that he was probably out and about), the search engine would serve up a paid link to a dealer locator with a click-to-call feature that enabled him to easily set up a test drive.

This takes the complicated world of teen clothing and accessories—a world fraught with danger from shifting trends and overwhelming choice—and simplifies it, by showcasing fashionable peers who offer trustworthy guidance.

Neither retailer requires that the haulers show only brands purchased at its store, and the haulers are transparent about their links to the companies (Penney, for instance, gives its star haulers gift cards).

Although a consumer can sort them according to a few characteristics—“flavor experience,” “dentist inspired,” “fresh breath,” “classic”—there’s little to help her figure out which features are most important to her and which paste is her best choice.

One-click questions about hair type, length, and texture (straight, short, fine, thick) and other needs (color treatment, volume) allow the visitor to rapidly sort through more than three dozen offerings to find the ideal one. and—clubs for shoe lovers—collect “personality” information on each member, such as favorite fashion icons and general shoe preferences (heel size, color, and so on) and tailor suggestions accordingly.

The Spanish bank BBVA makes personalized recommendations for financial products after assessing individual consumers’ spending behavior—as reflected in credit card histories and questionnaires—and comparing that behavior with the spending of peers.

They can find reviews written by friends or family by connecting to Facebook or Twitter—TurboTax encourages customers to post on either site when they’ve completed their taxes, and the postings constitute what are in effect consumer-generated, highly trusted banner ads.

Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics) 1st Edition

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