Handpick
London,
United Kingdom
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Investment
sought: - £76,000
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Equity
offered: - 4.22%
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Investment
amount: - £111,150
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Valuation
(pre-money): - £1,724,948
Idea
Introduction
Handpick is a shopping app for people who don't necessarily enjoy shopping.
Our mission is simple: to make shopping online and on your phone as intuitive as going to a store, only with the selection the entire Internet can offer. Shoppers should be able to easily find exactly the right item in the size they want.
To achieve this, we developed our focused discovery engine — a machine-learning algorithm that applies findings from choice psychology and behavioural economics.
Shoppers today can, in theory, find anything they want online. Yet online fashion retailers contend with return rates as high as 40%. Conversion rates have halved over the past 5 years. Shoppers spend 34% more time browsing — a further half-hour — before each purchase as compared to 10 years ago.
When shopping with our Focused Discovery engine, nearly 30% more users can find and buy exactly what they want. Those who do are nearly 4 times less likely to exchange it after the fact.
Intended impact
Handpick is based on research into the question of how shoppers choose well: what techniques are used by shoppers who are happiest with the purchases they make? By building these shortcuts into the shopping experience, we can make shopping online (and on a phone) so intuitive that anyone is happy to do so.
By way of analogy, consider cloud storage. Saving your files online makes sense: access them anywhere; never worry about physical damage; share them easily. However, it was not until Dropbox that this intuitive possibility became practical — and popular.
Similarly, the reasons to shop online, and on your phone, are obvious: find anything; shop in your free time; don’t leave your home (or office). However, with cluttered interfaces, overwhelming selection, unreliable sizing, and recommendations that favour retailers’ merchandising needs over users’ preferences, the vast majority of shoppers still find it easier to go to a store.
In engineering our Focused Discovery engine, we found that three features make the difference for users:
1) Natural Language Attributes — articulate your preferences in natural language and in order of importance (e.g. pastel, light-weight, casual spring dress).
2) Focused Discovery — Easily find more items with attributes you like, based on items you see (e.g. more dresses “with sleeves like this item”).
3) Decisive Feedback — Witness options converging to match your preferred attributes in real-time, until you locate the ideal option.
Substantial accomplishments to date
1) Grant Funding — Handpick has been awarded a £250,000 grant from the UK government to facilitate the commercialisation of Handpick over the coming year.
2) Technical Pilot — We piloted the Focused Discovery engine as a desktop browser widget and found that:
• when shopping using Handpick, the rate of post-purchase exchanges dropped from 31% to 8% (p=.03) as compared to shopping the same retail site without the Handpick widget;
• the proportion of shoppers able to stay completely focused (p=.08) and find the exact right item (p=.07) was nearly 30% greater with Handpick; and
• shoppers who exchange items were significantly less satisfied with both the shopping experience (p=.06) and outcome (p=.005).
3) Commercial Traction — Our current pre-launch landing page is converting an average of approximately 15% of daily visitors. Our first PR campaign is commencing shortly.
Monetisation strategy
Handpick is an affiliate. We would collect affiliate fees from retailers when shoppers purchase items found using Handpick. Affiliate fees paid by retailers are typically 5-20% of an item’s purchase price.
Every 1% of merchandise returned to a retailer translates to a 1% decrease in profit. With our pilot demonstrating that shoppers are nearly 4X less likely to exchange items found through Handpick, we plan to become a preferred affiliate. Not only could we provide a new sales channel, but we aim to help reduce the return rates of items purchased through Handpick.
Use of proceeds
The intention of funding at this stage is threefold:
1) With the approach proven, we will use funding to support completion of the mobile release of Handpick.
2) Complete the team with the addition of a mobile developer and social media manager.
3) Paid user acquisition. Clever sharing incentives should reduce our current CAC by 2/3.
Market
Target market
Everyone buys clothes. In the US and UK, shoppers spent nearly a quarter of a trillion pounds (£240bn / $410bn) on clothes, shoes and accessories last year.
However, over 80% of fashion sales still occur offline. Online, sales in the US and UK exceeded £40bn in 2013 ($50bn in the US plus £11bn in the UK).
Further, our research into online shopping patterns revealed:
1) ‘Choosy’ Shoppers — For 50% of shoppers, price is not the most important attribute (I.e. style, fit, etc, is more important).
2) ‘Specific’ Shoppers — While roughly half of purchases today are serendipitous (i.e. shoppers receive marketing emails, notice sales, or notice posts/ads on social media), the other half are made with a specific intention (i.e. an occasion, look, to match another piece they own, etc.)
Handpick is targeted at the roughly £10bn ($17bn) in purchases by shoppers who are ‘specific’ and ‘choosy.
Perhaps most importantly, with the amount spent online growing by 18% YoY the number of shoppers shopping online for the first time is larger than ever before.
Characteristics of target market
1) Search is prior to sizing — More online shoppers complain about sizing (can’t tell if an item will fit) than search (can’t find the right thing). Retailers find that while on-site sizing technologies reduce returns, they do not increase conversion rates. Whether something will fit is relevant only once you find exactly what you want.
2) Know ‘it’ when you see it — Existing approaches are ill suited to fashion shopping: search requires too much specificity; social discovery isn’t sufficiently comprehensive; and most retailers’ on-site filters are too rigid. Handpick combines specificity (e.g. “casual”) with discovery (e.g. more with “these sleeves”).
3) Understanding free shipping — Retailers have found that free shipping and returns is the single easiest way to improve sales, suggesting the primary issue isn't distance per se — but trust. By helping users find the right thing in the right size, Handpick should be able to further reduce the issue of returns for online retail.
Marketing strategy
Did you know that the average man will spend 26 hours this year waiting outside of changing rooms for his partner?
Reaching our primary market of ‘specific, choosy’ shoppers who do not necessarily already shop online is a challenge.
To do so, our marketing will combine relationship humour, a simple message about eliminating shopping frustration, and a practical in-app feature.
Our first PR campaign is scheduled to commence shortly.
Competition strategy
Careful examination of B2C products demonstrates that the fastest-growing, most sustainable ones share three characteristics which we have incorporated into Handpick:
1) Be the best at your core functionality — We've proven that Focused Discovery helps shoppers find and buy exactly what they want. Focused Discovery combines key insights from choice psychology with our own machine learning algorithm. The accuracy of recommendations will only improve as traffic increases.
2) Provide integral features for social collaboration and user lock-in — Organically acquiring users, and retaining them once they start using the service, are both crucial to success. Two secondary features provide direct, practical incentives to ‘collaborate’ using the app and to continue using it repeatedly and frequently.
3) Have an ’unfair’ business model linked to the core functionality — The dramatic reduction in exchanges will be integral in reaching favourable deals with retailers and brands.