Marketing, by nature, is built on data. How does data assist marketers optimize strategies and deliver personalized content, driving more engagement?
Click-stream analysis, retail data, behavioral models – leveraging data for marketing is driving a golden age of personalization and micro-targeting.
1. You recently joined Crux as the CMO; what does your 30-60-90 day plan look like?
I’m currently in my third week at Crux, so I’m almost through my first 30-day sprint. My initial priorities are to get my feet under me and identify those quick wins for my team. That includes things like setting our Q4 budget, hiring plan, enhancing our tech stack, upcoming launch plans, and engaging new agency partners to kick off new workstreams around brand and GTM. I’m also continuing to establish relationships with various departments and team members–that’s critical at a fully-remote company like Crux, and I’ve put a lot of time into developing those connections.
My 60- and 90-day plans are defined with demand generation at the top of the priority list, and will build on the initiatives that we kicked off in the first 30 days. That means making strategic hires for the team, bringing on new technology that scales with our growth, and beginning implementing projects to enhance the work the current team has done. And since we’re entering the end of 2022, the next few months for me will be focused on setting the team up for success in 2023, and also defining what that success looks like.
2. Why do companies need a cloud-native integration platform?
For scale and agility. Whether it’s public, private, or hybrid, cloud workloads are pervasive given the flexibility clouds provide for rapid scale. Companies that don’t have cloud-native integration platforms struggle to onboard net new external data sets that help to augment internal data and achieve unique insights and analysis. Internal data continues to explode, but the growth of external data sources and consumption to maintain a competitive edge is an under-appreciated challenge for organizations.
3. How can marketers be more data-driven?
Marketing, by nature, is built on data. Even if you look back at Mad Men and what ad agencies were doing back in the day, they were organizing focus groups and studying trends–using data to fuel their decisions. The only difference today is the volume of data.
The benefit of your demand gen expert having an analytical background and a sophisticated MarTech stack is critical for success. Not only does data assist in proving the value of the marketing team with numbers, but it also allows marketers to optimize strategic bets and deliver nuanced and personalized content that drives increased engagement.
Kohl’s is a great example of this. They integrated external data into their targeted advertising and were able to provide specific shoe ads to customers based on their profession.
Click-stream analysis, retail data, behavioral models – leveraging data for marketing is driving a golden age of personalization and micro-targeting.
Enhancing data with third-party sources drives competitive advantage.
4. Could you tell us more about Crux Wrangle? What role does it play in data transformation?
Crux Wrangle is our data transformation product. It’s a feature that’s available within our internal platform and is a service we provide to our customers. It enriches, validates, and transforms datasets so they can quickly be combined, split, or otherwise formatted with other datasets.
A good metaphor for understanding its importance in data transformation is comparing it to eggs in a recipe. They’re the staple for a ton of baked goods, but can be used in so many ways–as just whites or yolks, whipped, mixed, etc. Data transformation is the same concept. Taking a table of data and parsing, combining, segmenting, or blending it can yield different results based on what you need. Data transformation is critical for taking third-party data and creating the useful information you need to serve your business needs.
Without data transformation, there’s no way to allocate different datasets to fit your needs, and you’re stuck with what they have to offer on the surface, and nothing else, which takes away from the value third-party data brings to the table.
5. What are the best strategies for building a data pipeline?
I think the best strategies I can offer are twofold: one, don’t build it, buy it, and two, do everything with scale in mind.
There are companies on the market that have invested millions of dollars into building data pipeline solutions. Sure, you can hire an expert data engineering team and invest loads of your budget into them–but why would you do that? Your business wasn’t founded to become external data experts, and there’s no reason for them to. Instead, buying great solutions to enable your data engineers is a more sensible solution for building your data pipeline, and it leads perfectly into my second piece of advice–everything has to scale.
At some point, your data engineers will hit capacity. Statistically, they spend 80% of their time managing data pipelines, and only 20% doing actual value-add work. With the pace of new data sets being made available to the market and the trend of organizations adding hundreds of unique data sets per year to maintain a competitive advantage, If you build solutions manually that work today, there is no way to keep up without a platform that helps accelerate onboarding and transformation. Keeping up with the volume is quickly becoming a necessity, not a nice-to-have, and maintaining a competitive edge means doubling down on external data to enhance internal data.
6. Do you think the marketing dep must be in perfect sync with sales?
I am a firm believer that marketing is the flip side of the coin to sales. Complete alignment between these two organizations is critical, especially for B2B. It’s all about keeping the lines of communication open between both teams, at all levels, and ensuring that the leadership of both is on the same page. I work very hard to create a supportive relationship with a shared strategic goal in mind.
7. How should brands go about data blending?
By automating as much of it as possible. Data blending is another critical step in the external data integration process, and external data companies know it. Finding the right platform or service that helps your engineers reduce their tedious data management workload by automating pieces of it–like data blending–makes them more productive and happier, because they get to focus on the data science and analytical insights that add value to the business, not spending 80% of their time with operational tasks. Plus, any process related to external data has to scale, and data blending is a piece of that puzzle. The same strategies used for building external data pipelines as a whole should also apply to each process along the way.
8. What would be the benefits of automating external data onboarding?
The biggest benefits are the ability to scale and keep your data teams happy. Augmenting a data engineer’s maintenance work to reduce the time spent on these types of tedious tasks makes them more productive, and keeps them focused on what you hired them to do–which is to add value to your business. Yes, data management is a critical step in the process of getting data ready for that value-add work, but that doesn’t mean it needs to be done manually.
In fact, not automating that process opens your organization up to more risk from human error. Manual labor scales to a point, and as external data continues to grow exponentially, the only way an organization is going to keep pace is to move beyond the current practice of hiring more data engineers by enabling the team to work more efficiently with an automated solution.
9. Centralized vs. Distributed database; which one wins the efficiency game?
Centralized, every time. Distributed databases require more time, resources, and money. A centralized database keeps everyone on the same page, and just because it’s centralized doesn’t mean that access can’t be distributed accordingly.
A good way to think about it is by comparing an external database to an internal one. Imagine if your HR team was expected to manage all your people, but each team used its own system. Some were in Lattice, others in Bamboo, and some just use spreadsheets. That wouldn’t be practical or reasonable, and external data management is no different. A variety of teams use and access the data for a variety of purposes, but maintaining it in one centralized location reduces cost and risk, and increases efficiency and scalability.
10. What is your leadership vision for the company?
Crux is in a unique position in the world of data, with a differentiated position and value proposition to automate and accelerate the onboarding and transformation of external data. Our goal is to establish Crux as a recognized leader in this emerging category, continuing to offer unique benefits to accelerate adoption and consumption of third-party data for our data provider partners, cloud marketplaces, and end-user data consumer customers. The company continues to grow at over 100% year-over-year by offering a unique solution to an increasingly strategic and painful challenge of ever-growing third-party data.
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Gregg Holzrichter, CMO of Crux
Gregg is a results-oriented senior marketing executive with extensive experience and a history of global leadership roles in large enterprise, pre-IPO, and start-up marketing organizations. Crux is Gregg’s seventh startup of his career, and he has previously worked at VMware during its hypergrowth and IPO period, Hazelcast, a real-time data provider, and Aporeto, acquired by Palo Alto networks.
Crux offers a managed data engineering service that helps organizations scale their most critical data delivery, operations, and transformation needs. Our cloud-based technology stack enables you to reliably get the data you need, how you need it and where you need it. We deliver over 14K datasets from hundreds of sources into your preferred destination at a low cost, with custom validations and transformations, and at a consistently high-level of service and security. Crux was founded in 2017 by financial technology veterans and is backed by Citi, Goldman Sachs, Morgan Stanley, and Two Sigma, among others.