“The biggest challenge to performance marketing is the ability to gather data.”
Hi, Dan. Welcome to our Interview Series. Please tell us a little bit about your journey in the data management and analytics industry and what inspired you to start Daasity.
I joined Provide Commerce in 2008 after 8 years in financial services working in credit risk and analytics with an emphasis on consumer behavior. Hired to build out consumer analytics for a $300MM e-commerce company, I was challenged initially because data was all wrong at the customer level. So we had to build an internal data warehouse. The company grew to $750MM and was sold to floral and gifting giant FTD.
Some of my colleagues went to small Shopify brands like MVMT, Rothys, Kopari and others, and asked me to build a data warehouse for them to help with much deeper analytics. I couldn’t get what I needed with tools on the market like StitchData and Fivetran, so I had to build my own connectors. Parachute Home and a few others then asked me to build out what I did for initial customers—and Daasity was born.
The last two years have accelerated digital transformation for businesses of all sizes and stature. What has been the biggest lesson for you that helped you stay on top of your product-led growth strategies? Would you like to share your pandemic experience on how you managed to continue your business development works during these uncertain times?
A key lesson I’d share is to really be focused on the problem you are trying to solve and your initial thesis. Don’t get distracted with other directions or with what’s ultimately noise. But, and it’s a big but, track your thesis to make sure it’s still right. We’ve had a lot of ups and downs over the past couple of years from COVID (which at first killed our pipeline but then accelerated it) to iOS14 and impact to our smaller brands. Ultimately, our overall course has not changed even though there have been some big waves that have made us navigate those waters.
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Please tell us a little bit about your core offerings? Which set of customers / business titles are you targeting to expand the reach of your products?
Our core product is unique: a modular data stack. It includes purpose-built extraction, load, transform (with pre-built data models that can be fully modified), data orchestration, visualization, and reverse ETL. If you’re a consumer brand, you can use some or all of Daasity’s data architecture and analytics that you want to use, and you can modify and configure elements as your needs change.
We have two core customers and the target depends on where a consumer brand is in their data journey:
- For a smaller brand that sells in digital channels only or only cares about analytics on their digital side, we have an all-in-one solution that gives you rich, deep analysis that will help your brand scale.
- For a larger brand that has more complexity, we have our ELT+ offering, where your data team or a data agency can take our base offer and modify or customize as desired to give you the analytics you need.
What is the genesis of your partnership with Snowflake? How does it empower your customers to implement a full-stack or partial stack for data analytics and business intelligence?
When we launched Daasity we started with AWS Redshift, but we found ourselves having to do a lot of management on that part of the data stack. The nice thing about Redshift is you know the cost since it’s based on the size of the cluster. We explored Snowflake and found that it would be much easier to manage and maintain. We also found, if we set up our product correctly, it would be more cost effective. It’s one of the best decisions we’ve made. For our customers, using Snowflake is a no-brainer as we are optimized for them. Our customers can leverage all the ELT+ we have built to set up their entire data stack in a matter of days or weeks.
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Your take on the new buzzwords in AI-driven attribution models and technologies:
I believe I have a bit of a contrarian view. Models and AI are only as good as the data that is fed into them, and I think this is the challenge. They often don’t have enough context. Consider marketing. You can have AI try to optimize your marketing spend; let’s say you focus on ROAS or CPO. It’s going to direct you to the best converting campaigns. But a marketer will understand that different campaigns have different purposes. Some are for awareness, some for conversion, etc. Unless you take the time to feed your model/AI all of that info, is it going to do a good job? In my opinion AI is a great tool, but it needs oversight from a person to make sure it’s doing what you really want it to do.
Which brands are you currently working with? How has their experience been? How do you measure these in your organization to improve your ELT solutions?
We successfully work with many brands. One I’ll mention is MANSCAPED. MANSCAPED’s first data team had been building data extractors from scratch and custom transformations prior to using Daasity. The challenge with those in-house data pipelines is a lot of technical upkeep and troubleshooting. They spent the majority of time on ELT upkeep rather than analytics. Now, their data team has automated ELT, and they customize Daasity’s pre-built transformations on a daily or weekly basis to adjust to business needs, which saves them hours or even days of work on projects.
We talk to brands to understand what features they need or want and more importantly to different people at the brands, as a data team might have different needs than a marketer who has different needs than the CFO.
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What is the future of performance marketing and how would the CIO’s decision help in upgrading attribution models?
The biggest challenge to performance marketing is the ability to gather data. We previously lived in an easy world where we could track everyone online—and that world has changed with iOS14, GA4, a cookie-less world, and other developments. But marketers actually were faced with this problem before. Most people just don’t remember it. Back in the (*gulp*) 90s, performance marketing (by mail) was very analytical, and so we just need to go back to those concepts: doing hold-outs tests, building response models to segment customers, etc. For a CIO, it’s about getting as much data as you can and then determining how to put it all together to let your marketing or analytics team provide direction.
Thank you, Dan! That was fun and we hope to see you back on cioinfluence.com soon.
[To participate in our interview series, please write to us at sghosh@martechseries.com]
Daasity CEO & Co-Founder Dan LeBlanc is an analytics, customer experience, and business technology expert who is passionate about helping consumer brands achieve their goals through more informed insights. Before founding Daasity, he held senior executive roles with companies such as Provide Commerce, Encore Capital Group, and Groundswell Equity. In his spare time, Dan can enjoy the San Diego weather outdoors or visit one of the city’s many great craft breweries.
At Daasity, we believe every company, no matter the size, should have access to the data and tools they need to be successful. But far too often, that’s not the case. Founded in 2017, Daasity is the first and only eCommerce analytics platform built exclusively for consumer product brands by industry leaders in the space. Daasity’s flexible Modular Data Platform helps brands uncover insights from their data that i*************, optimize spending, and cut costs. Over 1,600 of the world’s fastest growing brands trust Daasity.
