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Department of Customer LoveModerator

published in Blog Archives

The rise of the instrumented workplace

This content, written by Frank Bien, was initially posted in Looker Blog on Sep 27, 2017. The content is subject to limited support.Last year at JOIN, Looker’s annual data conference, I talked about the . The first wave started 30 years ago with monolithic stacks that were reliable but inflexible. You could only get answers to specific questions, but you could trust those answers. It was also the era of the data rich and the data poor, where those without access to data starved as they waited for someone to finally have time to help them. That frustration led to the second wave: a revolution toward self-service tools where users grabbed whatever data they could lay their hands on and threw it into data cleansing, blending, and visualization tools for analysis. But that revolution came at a cost. Scattered tools created a mess of silos that didn’t talk to each other, and you lost the ability to speak in a common language with anyone else at your company. Enter data chaos. Today, we’re

Department of Customer LoveModerator

published in Blog Archives

The reusability paradigm of LookML

This content, written by Joshua Moskovitz, was initially posted in Looker Blog on Apr 10, 2014. The content is subject to limited support.Quick reiteration of metric calculations for powerful data exploration At Looker, we want to make it easier for data analysts to service the needs of the data-hungry users in their organizations. We believe too much of their time is spent responding to ad hoc and not enough time is spent building, experimenting, and embellishing a robust model of the business. Worse yet, business users are often starving for data, but are forced to make important decisions without access to data that could guide them in the right direction. Looker addresses both of these problems with a YAML-based modeling language called LookML. With LookML, you build your business logic, defining your important metrics once and then reusing them throughout a model. That means you can unleash them on your business users to manipulate, iterate, and transform in any way they see fit.

Department of Customer LoveModerator

published in Blog Archives

The Redshift - SORTKEY and DISTKEY

This content, written by Lloyd Tabb, was initially posted in Looker Blog on Dec 4, 2013. The content is subject to limited support.Indexing in MPP analytical databases (Redshift, Vertica, Greenplum...) works very differently than in traditional transactional databases. Suppose I’m looking to count nursing jobs. I’ve got a list of all job postings in Santa Cruz, and they aren’t ordered in any reasonable way. So I start at the first one, read it, and scan down the list looking for the word “nurse.” It takes a couple of seconds—no problem. Suppose I go regional and I’m looking at the San Francisco Bay Area. That’s 20 times the number of Santa Cruz job postings, and it takes me a few minutes to scan the list and make a count. If I wanted to look at all of California, maybe it would take me an hour. The entire United States—even longer. Now, suppose I want to analyze and compare all of these job postings across the entire United States. I need to scan these lists many times quickly. It’s to

Department of Customer LoveModerator

published in Blog Archives

The metrics of success

This content, written by Nicole Beyer, was initially posted in Looker Blog on Feb 28, 2017. The content is subject to limited support.Success is found in what you value At Looker, we believe in putting our customers' success first. It’s a way of life around here. As Margaret pointed out in her , caring for our customers is baked into the way we operate. At Looker, we provide highly technical support over chat, and as you might suspect, we use data - and Looker - to evaluate our efforts. The way we measure success, however, might be a little different than what you would see elsewhere. For example, a normal chat KPI is chat duration, and many organizations are looking for ways to get that duration down. At Looker on the other hand, chat duration is not an immediate indication of success or failure. We see each chat as a fresh opportunity to teach our customer something more about how they can use Looker, and we’re prepared to take all the time needed to help that person become a Looker

Department of Customer LoveModerator

published in Blog Archives

The magic of Looker

This content, written by Dan DeMeyere, was initially posted in Looker Blog on Feb 11, 2014. The content is subject to limited support.Blog post originally published on Dan DeMeyere’s site . Dan is the lead engineer for the web team at Any company that makes data-driven decisions needs to have easy access to comprehensive views of their application's data. Average order value (AOV), customer lifetime value (CLV), cart abandonment rates, etc. are all crucial to understanding where to focus development efforts to maximize the conversion funnel (visits -> sign-ups -> adding to cart -> completing an order). In 's early days, we built a set of internal metric dashboards to help provide insight into this data. There were two flaws with these dashboards. One, all new dashboards or changes to the current dashboards required engineering resources. Two, dashboards are mostly hollow from a data perspective. Even with historical data that could be sliced by a number of dimensions, interes

Department of Customer LoveModerator

published in Blog Archives

The last mile

This content, written by Frank Bien, was initially posted in Looker Blog on Aug 6, 2013. The content is subject to limited support.This week Looker is having fun at the HP Vertica Big Data Conference in Boston. For those of you who know Looker, you know we're a bit contrarian — we run in database; we have a modeling language; we promote wild user curiosity. But we're learning this week that there's nothing contrarian about letting folks capitalize on the giant investments they've made in back-end data infrastructure over the last five years. "New" databases like (or Pivotal, Hana, Netezza, Aster, Redshift, etc.) are really fast. And as compute continues to get faster, denser, and provide larger memory footprints, these analytic beasts are getting even more impressive. When I was at Greenplum back in the day, we always knew workloads would move from storing enormous amounts of data to actually analyzing that data — rapidly, by many users, with giant workloads. But the dirty little secr

Department of Customer LoveModerator

published in Blog Archives

The journey to agile analytics

This content, written by Nouras Haddad, was initially posted in Looker Blog on Mar 21, 2017. The content is subject to limited support.The traditional approach to analytics is far from agile. Before you can even ask a question you need to first extract the necessary data and store it, which involves at least three different additional parties: The ETL Team, DBAs, and the BI team. And should you need to ask a slightly different question, it’s back to the drawing board. This process and its inherent latency causes two related problems: First there’s the obvious bottleneck around how quickly you can iterate and ask new questions. Second, in order to get around this bottleneck, you and your colleagues may be extracting small subsets of the data in order to perform quick analyses, rather than waiting for lengthy processes to conclude. This results in data chaos, since multiple stakeholders are looking at slightly different pieces of the puzzle and applying different definitions. Unfortunate

Department of Customer LoveModerator

published in Blog Archives

The five letters that will change the data world - BYOBI

This content, written by Tomasz Tunguz, was initially posted in Looker Blog on May 23, 2014. The content is subject to limited support.BYOBI is an acronym I first heard on a telephone call with a VP of Technology at a large corporation. The word is almost unknown today, but I think that it will be one of the largest trends to impact data in the next five years. BYOBI means Bring Your Own . This VP of Technology was struggling to enable the sales people, marketers, engineers and others within his business to access the data they needed. The monolithic solution that they had adopted, (I can't remember if it was MicroStrategy or Cognos or BusinessObjects), satisfied the needs of the one department that had catalyzed the purchasing decision many years ago. Several years on, his team had become inundated with custom data requests that the tools failed to answer and petitions to purchase new types of software. Unfortunately, his data architecture just wouldn't allow him to respond to those n

Department of Customer LoveModerator

published in Blog Archives

Elastic analytic databases

This content, written by Lloyd Tabb, was initially posted in Looker Blog on Apr 20, 2017. The content is subject to limited support.I used to buy VHS tapes. It was great. I could buy my favorite movies and watch them whenever I wanted. Except most of the time I owned a movie, I wasn’t actually watching it. And when DVDs came out, I had to replace my whole collection to upgrade. Then video rentals came along and I mostly stopped buying movies. Why pay to keep a movie on the shelf when I could access a vastly larger pool of movies whenever I wanted? Except, of course I was limited to one movie at a time, and sometimes the movie I wanted wasn’t available. That’s why Netflix’s streaming model was such a revelation. Suddenly, I had access to an enormous catalog of movies in an instant and they were never out of stock. And if I wanted to binge watch, I could, without having to drive to the store to trade discs. Movies transformed from a physical thing I bought, to a shared service I leased a

Department of Customer LoveModerator

published in Blog Archives

The end of data bread lines

This content, written by Lloyd Tabb, was initially posted in Looker Blog on Feb 5, 2015. The content is subject to limited support.There is a lot of talk about “Data-Driven” cultures and it’s the right kind of talk. When people have access to the right data they really do make better decisions. When groups of people are presented with decent evidence, in the form of data, they usually start talking about the right things. Information wins wars. In conventional data warfare, companies have data teams: people whose job it is to answer questions. But like the bread lines of the Great Depression, the results aren’t great--there is still way too much demand for analyst time and not enough of it as a resource. When companies want to be more data-driven, they invest to make the data teams bigger so they can answer more questions. Still, there always seems to be too many questions and not enough bread. The result is inequality. Not everyone's questions are going to get answered, so the queue g

Department of Customer LoveModerator

published in Blog Archives

It’s the end of BI as we know it

This content, written by Frank Bien, was initially posted in Looker Blog on Mar 29, 2017. The content is subject to limited support.I’ve often referred to the Looker Platform as the . But as I talk to more and more companies that are winning with data, I realize that Looker is more than just another wave of BI. It actually represents the end of what we’ve come to know as BI altogether. Some history BI started with monolithic stacks, reliable but inflexible. It was the era of the data rich and the data poor, where those without access to data starved as they waited (and waited and waited) for someone to finally have time to help them. That frustration led to the second wave: a revolution toward a hodge-podge of disjointed self-service tools where users grabbed whatever data they could lay their hands on and threw it into data cleansing, blending, and visualization tools for analysis. But that revolution came at a cost. For all the advantages of do-it-yourself analysis, the tools created

Department of Customer LoveModerator

published in Blog Archives

Tenjin Looker Block: run smarter app marketing campaigns

This content, written by Sunny Cha, was initially posted in Looker Blog on Jun 7, 2017. The content is subject to limited support.In today’s mobile ecosystem, app marketers have to be savvy about how they allocate their ad spend. The landscape is simply so competitive that in order to run profitable install campaigns, developers have to use a data-driven approach that lets them target the right users, partner with the right ad sources, and track their data all the way through the customer lifecycle. Built by product and engineering experts with several years of app marketing experience, Tenjin is designed to be the most flexible, comprehensive and intuitive marketing platform in the mobile industry for the biggest apps in the world, such as Yelp and Dots, as well as for indie developers just getting started. And after partnering with Looker earlier this year, Tenjin is now pleased to announce that the is available for Looker customers who want to grow their mobile apps quickly, scalab

Department of Customer LoveModerator

published in Blog Archives

Tale of two SQLs - history and future

This content, written by Lloyd Tabb, was initially posted in Looker Blog on Feb 20, 2014. The content is subject to limited support.There's an ongoing saga in the realm of database languages around SQL. With all of the buzzwords going around with SQL, noSQL, and Hadoop it can be helpful to take a look back in history and see how we got here. Join us as we look back in time to see the long view of database analytics and where we think the industry is heading. Somewhere around 40 years ago, two computer languages were born, and they're still running the world's computers. At Bell Laboratories, Dennis Ritchie was and at IBM, Raymond Boyce and Donald Chamberlin were . The C Language created a common way to talk to machines at the instructional level. SQL created a common way to create, write to, and transform data sets. Just about every computer is still running C code. Just about every business is still using SQL to deal with its data. Why were C and SQL so successful? They created the r

Department of Customer LoveModerator

published in Blog Archives

Taking “the big picture” to your display screen

This content, written by Alex Tchubenko, was initially posted in Looker Blog on Sep 27, 2017. The content is subject to limited support.Data visualization and digital signage are a match made in heaven. When combined, the two concepts bring “Big Picture” data visualization to a targeted audience anywhere, at anytime. This is why a new partnership between UCView and Looker is an exciting new step in innovating signage technology. is a digital signage software company that has been pushing the envelope in this technology field since 2006. We created a versatile signage software solution for a multitude of industries including hospitality, governmental institutions, educational establishments, corporate offices, retail, transportation agencies and even industrial lines. With our system clients can create, schedule and manage entire visual content playlists on many screens, in multiple locations, all at once. Now, with the Looker Plugin API in UCView’s CMS, our mutual customers can direc

Department of Customer LoveModerator

published in Blog Archives

How systems of insight will transform BI & analytics

This content, written by Pedro Arellano, was initially posted in Looker Blog on Jul 29, 2019. The content is subject to limited support.What do you think of when you hear the term “business intelligence”? I suspect most of us immediately see visions of reports and dashboards filled with bar charts, line graphs, maps, and those much maligned pie charts. Spend just a few minutes walking through a BI tradeshow and that’s exactly what you’ll see on the screens of the vendors exhibiting at the event. Much like cat pictures and the internet, the concept of reports has become inseparable from the understanding of BI. The problem with this understanding is it limits the potential impact of data and the insights it can produce. If the purpose of BI is to produce a report or a dashboard, that means additional steps are required before that data delivers meaningful value to an organization. Someone has to take that dashboard, draw some conclusions from it, and take a corresponding action that has

Department of Customer LoveModerator

published in Blog Archives

Customer lifetime value part 1: survival analysis

This content, written by Scott Hoover, was initially posted in Looker Blog on Jan 4, 2016. The content is subject to limited support.Survival analysis - introduction Many businesses view customer lifetime value (LTV) as the Holy Grail of metrics, and with good reason. As an absolute measure, it's an indication of how much money a business can reasonably expect to make from a typical customer. As a relative measure, it's a good gauge of business health—for example, if expected profit from the typical customer is decreasing over time, perhaps there are levers the business can identify and pull in order to adjust its course. There are, however, a multitude of approaches when calculating LTV, each ranging in complexity. Moreover, an LTV formula for one business may not hold for another. What is consistent are the fundamental inputs: revenue, costs, and estimates of customer lifetime. In this article, I'll focus on a popular method for estimating the typical customer's lifetime called Survi

Department of Customer LoveModerator

published in Blog Archives

Surprising and innovative data stories of 2018

This content, written by The Looker Team, was initially posted in Looker Blog on Dec 26, 2018. The content is subject to limited support.As this year comes to a close, we looked back at some of the data stories that we didn’t write, but loved reading in 2018. Here’s what they have to say about how data is changing the way they work... Using data to reduce their carbon footprint from selling burgers at Five Guys International burger chain, Five Guys, goes above and beyond selling burger and fries. In July, Five Guys shared how they’re using data to transform their culture and reduce their carbon footprint. “Looker has enabled us to do just that, giving everyone access to real-time, live data and insights. Based on that knowledge, we’ve improved warehouse efficiency, reduced waste and cut queue-times – all underpinning an improved customer experience.” - Fareldia Jefferies, Integration and Warehouse Manager at Five Guys To learn more, read the . Changing the analytics experien

Department of Customer LoveModerator

published in Blog Archives

Super Bowl LII: a closer look at the matchup

This content, written by Drew Kells, was initially posted in Looker Blog on Jan 31, 2018. The content is subject to limited support.As Super Bowl Sunday 2018 approaches, the matchup between the New England Patriots and Philadelphia Eagles is generating lots of different storylines. Tom Brady and Bill Belichick are going for their astonishing sixth championship together, which would tie the all-time team record held by the Pittsburgh Steelers. The Eagles are trying to overcome a devastating injury to MVP-hopeful Carson Wentz with the incredible redemption story of Nick Foles and three straight wins as an underdog. And of course, Sunday will mark a rematch of the great Super Bowl XXXIX matchup, 13 years later. While there are many different angles to try and figure out who has an edge in the matchup, we wanted to take a closer look at what the numbers tells us by exploring NFL game and player data from . Games against common opponents Zooming in a bit from the statistics above, it’s alw

Department of Customer LoveModerator

published in Blog Archives

Steps for getting started with data-driven marketing

This content, written by Matt Ryan, was initially posted in Looker Blog on May 10, 2019. The content is subject to limited support.No matter your business size or industry, doing more of what’s working and less of what’s not is a no-brainer. With data-driven marketing, you can not only uncover insights on your prospect and customer behaviors, but you can further leverage those insights to inform the strategies that impact the success of your business. Check out these steps for getting started with data-driven marketing at your organization: Step 1 - Define your goals Setting your team up for success starts with defining the goals and outcomes you want to achieve. Whether you’re looking to increase the number of new customers acquired, increase website visits, or grow marketing-generated revenue, setting specific and measurable goals will help your team map out the strategies that will lead to a successful outcome. When developing a plan for the entire team, select marketing channels in

Department of Customer LoveModerator

published in Blog Archives

SQL recipes - beyond the click

This content, written by Josh Siegel, was initially posted in Looker Blog on Jun 11, 2014. The content is subject to limited support.An added bonus to owning your marketing data is agility and speed to insights (avoiding the inherent delay in third party tracking) and an infrastructure that affordably scales with you. Most third party tools will focus on measuring proximate performance -- how many near term conversions came from each click, what was the same day conversion rate, how much revenue can be directly attributed to each advertisement? The full picture goes beyond the events immediately after the click and into a user's first week, month, or year. So why do companies talk about lifetime value but focus on shorter term metrics? Because setting up internal customer tracking can be hard. If you own your data and have the right tools it is easy. Let's look at a basic CPC (cost per click) model -- you pay for traffic from various search, display, and social campaigns and want to k

Department of Customer LoveModerator

published in Blog Archives

SQL processing and data analysis with the EAV model

This content, written by Mike Xu, was initially posted in Looker Blog on Dec 9, 2014. The content is subject to limited support.Much of the world's data is stored in an . The EAV model is a commonly used in scientific research, medicine, healthcare, and popular open source and commercial software platforms such as Magento and Drupal. The key advantage of the EAV Model is that cannot handle and . It was designed with the intent of getting data in, with the tradeoff of being difficult to get data back out. To overcome these tradeoffs, this article will cover how to effectively analyze EAV data. If you are not fully familiar with the structure and pros/cons of the EAV model or need a quick refresher, here's an . In order to fully work with EAV data for traditional analysis and reporting, we will transform the EAV tables to an by creating tables for their associated entities. Directly querying EAV data to analyze cohorts, funnels, and time series is tedious and challenging. These queri

Department of Customer LoveModerator

published in Blog Archives

Speed up your data analysis with augmented analytics by Sisu + Looker

This content, written by Berit Hoffmann, was initially posted in Looker Blog on Oct 9, 2020. The content is subject to limited support.Today, everyone needs the ability to make data-driven decisions quickly. To meet this increased demand for data accessibility, organizations are breaking down barriers to insights by using platforms like Looker to embed dashboards into their teams’ existing workflows. With the level of data granularity and access users can achieve with Looker, it makes sense that the number of questions like “why is my KPI going up (or down) this week?” will increase. Herein lies the challenge. Even with a healthy data model, answering why-questions can require hours of manual work in the data. By the time all the necessary work is done and the data is examined to answer the initial question, new data has already been created — thus resulting in an incomplete answer. Fortunately, teams can bridge this gap between data questions and answers by combining the technologies

Department of Customer LoveModerator

published in Blog Archives

Specializing retention emails using behavioral data

This content, written by Andrea Vidovic, was initially posted in Looker Blog on Aug 3, 2017. The content is subject to limited support.The “user experience” can now be understood to span everything from packaging and delivery, to customer service and troubleshooting, to social content and email communications. People have come to expect impeccable UX across the board, therefore, to remain relevant within a customer’s inbox you must curate highly original experiences at scale. Using Looker, you can identify users ready for various types of nurture, build specific content for them, and showcase value with each and every ‘send’. In this post we'll talk about different types of emails and how to ensure you are sending the right email to the right person, at the right time. Customer nurture like you really mean it Retention emails are an integral part of maintaining a solid relationship with customers who have ‘qualified themselves’ as interested (albeit to varying degrees). Customers may e

Department of Customer LoveModerator

published in Blog Archives

Data analysis with Spark SQL

This content, written by Erin Franz, was initially posted in Looker Blog on Feb 18, 2016. The content is subject to limited support.We’re excited to announce Looker’s support for Spark SQL. Since Looker enables data exploration and transformation by querying data where it lives - analytics directly benefits from Spark’s powerful compute engine and querying power. For all the reasons Spark has become a natural choice for large scale data processing, Looker can provide a complementary exploration and visualization layer because it brings analytics directly to the underlying Spark architecture. Data analysis on Spark with Spark SQL Spark has seen rapid adoption across the enterprise as a solution for data processing. Since it has been designed to perform with data up through the petabyte level and the ability to distribute processing across thousands of nodes, it’s ideal for large-scale data processing and data load. The fact that Spark is also a unified stack containing several closely-

Department of Customer LoveModerator

published in Blog Archives

Driving profitable growth and customer equity with SoundCommerce

This content, written by Tamara John, was initially posted in Looker Blog on Jul 1, 2020. The content is subject to limited support.Eric Best, CEO, recently spoke at Looker’s Retail Solutions Event to share how the SoundCommerce team built a custom data application for retail companies on top of Google Cloud Platform’s BigQuery and Looker. In addition to sharing the retail solution his team has built, Eric invited SoundCommerce customer Tim Morse, Cofounder/President to share how they’re using data to execute on their omnichannel consumer strategy. SoundCommerce: a platform built for retailers Founded in 2018, SoundCommerce provides an out-of-the-box data platform for retail brands. Built from years of retail, eCommerce, and data experience, the platform is designed to collect retail data from any source and build a model around the metrics and relationships that are most crucial to retail organizations. This includes everything from prospects and customers to orders, shipments, and

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