We also make it easy for anyone in the business to access and play with data. “Anyone” includes the analyst who builds models to predict shopper behavior, the CEO who wants to know how many users are shopping the mobile site, and the new hire who wonders how many widgets the business has sent to customers in his home state.
All humans are wired to see patterns, but our cognitive ability is limited by how much time we can spend looking for them.
We saw this first hand when a customer service employee at an e-commerce company in San Francisco noticed that she was processing what seemed to be a large volume of returns for a specific vendor. Customer feedback ranged from “I didn't like the feel” to “the color is not what I expected,” but nothing stood out as a red flag.
Struck with curiosity, she opened her company's Looker to dig into the return rate from that vendor. Overall, nothing seemed out of the normal ranges. Layering on dimensions like color, material type, and distribution center, she noticed that most of the returns were for a specific fabric. Returns for that fabric were 4x higher than other fabrics from that vendor. Drilling deeper, she found a list of customers whose first purchase included that fabric — and did not come back.
After following up with the Vendor, the company decided to contact all customers who purchased any item using that fabric and refund their purchase, whether they kept it or not. The idea was that although most customers had not returned their orders, they did not want to risk alienating the “silent sufferers” who would keep a bad product regardless.
Empowering non-technical employees to do basic data lookups like this is revolutionary. I used to believe internal data at most companies was easily accessible to employees who needed it (unless it was sensitive or protected for some reason). It turns out that most of the time, data is in the hands of those with skills like advanced SQL and other scripting languages. The rest of the company reads automated reports and published workbooks.
Looker gets past this by letting someone who knows your data describe it in the platform. That might be a CTO, an analyst, or someone at Looker.
There are a lot of companies that claim to “leverage big data” and “turn data into insights” but fail to deliver on their promises in an optimal and scalable way. This is a challenging problem to solve for, so we focus less on buzz words and more on building the best product.
There are some other data products that let analysts generate pre-defined dashboards and metrics, but they don’t let users drill into, pivot, and manipulate data as you would in a program like Excel. Looker is about enabling curiosity, not telling you what metrics you should care about.
At Looker, it’s great to be part of team that is building something so quietly revolutionary. There are a lot of ways to make an impact on the world, but for a generation that grew up on Moneyball, Freakonomics, Google, and Facebook, this is pretty cool.