Dollars & Sense

Data, Filters, and Search Walk Into a Soundbar

Apple announced last month that it's launching a new classical music app. What’s novel about the Apple Music Classical app is how it lets music fans more easily search thousands of variations of the same song. For example, let’s say you want to find a Johann Sebastian Bach song. Over the past 300 years, many musicians have done ‘covers’ of his works, and there are variations by conductor, music, year, etc., thereby producing thousands of variations. Existing software doesn’t make it easy to find that Bach concerto you crave. In contrast, Spotify is adequate if you’re just looking for U2. There is an official version by U2 of their song ‘One’, a handful of variations by them, and a few covers. You don’t need Apple’s new app in this case. (Imagine this: in 300 years, U2 will be considered classical music, have hundreds of iterations, and we’ll need Apple’s classical app to search U2, too.) 

For Apple to solve this problem and help classical music aficionados, it bought a Danish company last year that had collected tens of millions of data points about classical songs. Apple then made this database accessible via their app's search engine and filters. End result: You can search 50 million data points while sitting at Lincoln Center.  

With lots of data, a search engine, and filters, one can create a better–and even new–product. Apple joins a pack of companies helping people sift, search, and filter large amounts of data, including Amazon (for products), Airbnb (for lodging), Kayak (for flights), and Spotify (for, ahem, non-classical music).  

Filters: Filters Rock
Filters aren’t a feature. In an era of big data, they are a defining and differentiating trait. Apple’s new app makes it easier to filter bounties of data. But maybe you’re not a classical music fan, so let’s use a travel example. Have you booked a flight on Kayak? It’s easier for me to find a Delta flight on Kayak than it is on Delta.com—thanks largely to filters. Kayak takes millions of data points and allows you to filter your search by 10+ variables, including departing takeoff time, airline, and number of stops.

Kayak beats Delta not because of different Delta data. It’s the filters. 

We’re going to be adding more filters to Shop Local’s platform. Currently, on the platform, a shopper can’t look at common brand searches or filter results by price or color. Amazon allows this. We’ll be adding these filters.


Data: Getting More Data to Filter
Without lots of data, you have nothing to search for or filter. Apple needed that Danish company’s iPod. Now, Apple’s classical music app has 50 million data points. (Likewise, without a good search engine or filters, a huge trove of Bach data is useless.)

How much data does Shop Local have? We have 2.5m data points (64k Syncing products x 40 variables per product). The goal is to get more data and advertise how much data we have in order to showcase our value. 

What classical music data is to Apple and flight data is to Kayak, product data is to our Shop Local platform. We want our platform to suck up and spit out as many data points as possible. (Notably, we can’t afford to buy a Danish company, so we have to get this data by sheer willpower.) When we have this data, our clients’ Online Stores rank higher in Google, shoppers and registrants shop more easily, and we make more in sales commissions.

We want to get more data into our system. To do this, we can:

• Have coaches manually collect data for top 100 sellers.
• Motivate Syncing brands to share more data. We want to get a snapshot of a Syncing brands database so we can better copy it. 
• Get more brands to share stock.
• Give more of our services away for free. Why? When we increase usage, we get better data quality. 
• Empower our users to contribute data. We want our members, customers, and registrants to edit and submit data. Think of our platform as 'Wikipedia for products.' 

Marketplaces Require Filters & Search
Our plan is to build a consumer-facing site that lets the public search registries and shop them. Filters and a smart search engine will be crucial for this to work well. The reason is that we'll have 1m data points at play. As a reference, retailers, such as the Ivy House, have 800 registries. When we combine all of the registries across the network of 1,200 stores on the marketplace site, we’ll have 70k+ registries. There are about 15 data points per registry. 70k registries x 15 public data points per registry = 1m data points. 

Our goal is to use data, filters, and search to offer a better member experience. That's (classical) music to our members' ears. 

P.S. ~ Please find my post about the power of search in e-commerce here:  https://www.shoplocal.org/news.cfm/22056/Search-Party-

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