Founded in 1993, women’s clothing retailer INTERMIX curates designer and luxury fashion from around the globe. In addition to 43 boutiques across the United States, INTERMIX offers an online shopping experience and complimentary style consultations for its customers.
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Industry: Retail & E-Commerce
Annual Revenue: $140M
# of Employees: ~500
Teams in Opal: Editorial, Design, Brand Marketing, Site Merchandising, Digital Marketing, Web Operations
When Angie Crippen joined INTERMIX in 2018, marketing project management consisted of 15-person email threads and workflows taped to the office wall. “The work management tool we were using only provided a place to house copy and images,” Angie recalls. “It really didn’t support the whole process.” Manual systems led to miscommunication, resulting in unapproved or incomplete content being published. “All the feedback was coming back-and-forth via email for every piece of content, and deadlines were easily missed. It was painful, and we made a lot of mistakes,” Angie admits. “There was no visibility, because everything just lived in my email.”
“Opal is our only point of reference that’s consistent. The visibility enables us to move faster and be more nimble than ever before.”
— Angelic Crippen | Project Manager, Creative & Brand Marketing Operations
Angie began searching for a planning platform that could solve INTERMIX’s biggest challenges: tracking feedback and changes, simplifying approvals, and digitizing workflows.“I demo’ed a couple solutions that weren’t customizable and just wouldn’t work for us,” Angie says. Then an external referral sent Angie to Opal, which was already in use at other GAP brands like Old Navy and Athleta. “During Opal’s demo, I remember thinking, ‘Wow! This is what we need.’” Soon, all INTERMIX content creators and approvers for email marketing, brand marketing, paid and organic social media, site merchandising, and web ops were collaborating in Opal.
Angie and her dedicated Opal Customer Success Manager partner to make the most of the platform’s flexibility, creating custom channels and content types. Today, approvals that used to take two days now take only an hour—centralized, trackable, and conducted directly in Opal. “As soon as a project is kicked off, we put everything into Opal: the brief, timeline, due dates, content, etc.” Angie explains. “Instead of checking three systems, all the teams that need that information now have a single source of truth. And Chat & Activity, my favorite feature, has created a sense of trust and accountability between marketing and leadership.” In fact, all pieces of the process have been streamlined, creating unprecedented efficiency for the INTERMIX marketing team. Since implementing Opal three years ago, Angie estimates that her team saves 15-20 hours every week.
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4 minute read
Lars Kamp is the Co-Founder & Chief Data Engineer at intermix.io where he helps customers optimize Amazon Redshift and their analytics queries for peak performance. We’ve invited intermix.io to share a guest piece detailing the new data stack.
In the past few years, we’ve seen a rapid shift to a “new” or “modern” analytics stack, built around cloud warehouses like Amazon Redshift, BigQuery and Snowflake. This stack has three layers:
On AWS, the stack looks like this:
It’s a shift away from the “old” world of a monolithic on-premise warehouse stack, which is in accelerating decline. Cloud warehouses offer simplicity, performance and scalability. They handle terabytes of data and large-scale transformations across billions of rows.
Companies using this new stack deal with three growth challenges:
Data volume is growing, by a factor ~10x over 3-5 years, with more and new types of data sources emerging along the way.
The number of models is growing. As you ingest more data into your warehouse, you can combine that data in infinite new ways.
The number of users who write queries is growing. Data is becoming useful for more and more people and departments within the organization - we’ve seen teams grow the number of query authors from 20 to over 200 within 18 months.
At the same time, the ratio between data engineers and data analysts/scientists is increasing. A single data engineer must support several query authors.
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This leads to an increase in the number of “data support” tickets related to performance, data availability, and reporting. And it usually falls to the data engineering teams to resolve these tickets. We’ve seen teams that handle hundreds of data support tickets per -week- with a team of 3 data engineers.
Data engineers are under pressure to deliver an SLA for query performance and data availability, all while supporting a growing number of analysts and query authors. And while everyone writes queries - not everyone writes good SQL!
On Amazon Redshift, expensive and long-running queries result in:
At intermix.io, we’ve seen rogue queries that fill up a 40-node Amazon Redshift cluster with a cross-join of two 10-billion row tables, knocking out workflows and reports for the other 150 dashboard users on a Monday morning.
The net result is a lot of finger-pointing between data teams and analysts. Data teams get inundated by requests on Slack or support tickets - “what’s up with our dashboards?” It’s a process that wastes everybody’s time, causes frustration and decreases confidence in working with data.
If that sounds familiar - we’ve got a solution.
intermix.io is a performance management for data teams to optimize their Amazon Redshift cluster for cost and performance. It helps teams spot problems before they turn into fire drills. It gives teams a single view of every tool, user, and query that touches the data in your cloud warehouse.
The intermix.io Mode integration used SQL query introspection to detect Mode queries. Mode embeds a comment block in each query with rich metadata about each individual query, and intermix.io introspects the Mode queries to parse the annotations and index them as first-class labels in our product.
The result is rich visibility into Mode reports, runs, queries, and users.
The intermx.io dashboard makes it easy to see the P95 SLA you are providing to Mode users, and quickly drill into the users who are running expensive queries.
Improving SLA metrics requires a two-pronged approach:
Managing a large-scale Amazon Redshift cluster requires ongoing management of storage, memory and concurrency utilization. The intermix.io dashboards surface timely & actionable recommendations on what to do.
Once you find a query that is taking a long time, drill into the query to get actionable advice on how to re-write it to make it run faster.
With our Query Insights, you can determine the individual queries that are cause your Mode app along all other users running queries in your warehouse. From there, you can remove users, and filter down by each attribute that Mode provides.
Once you see your problematic queries, easily drill into them to get details recommendations on how to optimize the queries to run faster.
In the intermix.io dashboard, you can search, filter and drill down into that history in a much more human-friendly way than your warehouse console.
You will be able to respond to and fix problems fast, which helps you minimize expensive support requests. It also means that when you turn the lights off at night or go on that camping trip on a Friday – you can put your mind at ease, enjoy the outdoors and catch up on those critical Zs. When you return to your desk on Monday morning, you’ll have the confidence to know what happened over the weekend, and show your stakeholders what you’ve done to improve performance.
The Mode integration is available now, data teams at companies like InVision, WeWork and Faire are using it every day. Join them via signing up for a free trial.
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