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“Your algorithm is cute but it’s just not powerful enough to account for real-world problems with complex, non-linear decision criteria.”

I had just delivered a conference talk for a paper that had won best paper award at the prestigious International Conference on Machine Learning. It was my big breakthrough, and it ultimately paved the way for me to finish my PhD. Over the next several years, the paper received many thousands of citations and was regarded as a seminal piece of work.

Yet despite all of this, detractors were out there with competing methods and alternate approaches. These words came from the NYU professor Yann Lecun – a quirky yet dogmatic researcher who focused exclusively on neural networks. This was in 2007, an era when these methods were still mostly science fiction, and I passed Yann’s criticisms off as just that.

Fast forward 5 years later – his science fiction turned into reality with breakthroughs around graphics processing units (GPUs). Google Brain demonstrated that Yann’s “quirky” neural networks could be trained to solve some incredibly hard problems (albeit identifying cat videos on YouTube). So-called “deep learning” was born, and Yann went on to be Meta’s Chief AI Scientist, and won the prestigious Turing Award in 2018.

Disruption is all about timing. Yann was technically correct with his words in 2007, but the underlying technology requirements (advancements in parallel computing with GPUS) as well as many of the key applications (large scale UGC video) weren’t there. When these two pieces came together, his core thesis and research did as well – opening up a new class of technologies that today do everything from monitor the internet to automatically drive your car.

Fast forward to today, and Simon Data’s Series D again represents a unique moment where we’re seeing a convergence of trends.

• Cloud Data Infrastructure has reached an inflection point where many brands have invested deeply in data teams and centralized data infrastructure with platforms including Snowflake, Bigquery, and Databricks. Yet access to these platforms outside of basic reporting is still incredibly difficult – and unlocking end business value with so-called “data applications” is in its infancy.
• Gen AI & Large Language Models (LLMs) have taken the world by storm. These methods represent a generational shift in the potential that AI can have to understand customer data and to translate this into both improved automation and optimization of customer outcomes. In 2007, state of the art neural networks had 1 million parameters. In 2012, Google Brain was developed with over 100 million parameters. And today, the latest version of GPT-4 has 1.76 trillion parameters.
• Today’s Macro & D2C Disruption has caused brands of all scales to rethink their strategies. COVID brought everyone digital, and today’s “back to normal” trends have omni-channel online + offline strategies as table stakes. The broader macro has put a new lens around growth priorities – shifting priorities in 2021 from acquiring new customers to focusing on LTV and customer value over the past 2 years.

In a similar fashion that Yann told me over a decade ago that my research was fundamentally limited, Simon Data has been preaching our vision and forward looking approach for the past 8 years. Today, we’re entering a perfect storm where the differentiated value of our platform aligns across a massive set of shifts in data, AI, and marketing.

So what does the future look like for us?

Simon will fully understand your data in a way that allows marketing to work independently and fast.

Data access and data workflows is the foundational problem affecting all marketing teams today. Access is still very much gated through IT and data teams – and technology limitations are at odds with much of the process changes that enterprises are pushing toward as they seek to get more out of their first party cloud data investments.

This starts with the right data architectures that permit secure and real-time data access and ends with AI-enabled data access that works by clicking a few buttons or telling Simon in a few words what you want to do. “Include a row of recommended products that you’d wear in rainy weather.”

Simon will both identify & take action on opportunities leveraging deep, AI powered insights

along with robust capabilities that work across the entire lifecycle. We’ve spent the past 8 years building a robust application to target customers in acquisition, first purchase, and core retention contexts across owned, paid, and offline channels.

Our next-generation fully connected architecture builds on this – and the infrastructure we’ve developed across access to high quality data, semantic data understanding, and prescriptive use cases is the perfect set of conditions on which to build our next-generation of AI capabilities. This will include capabilities such as auto segment creation, customer lifecycle predictions, and 1-to-1 content personalization.

Our ability to execute these goals is unlocked by the foundational work we’ve put into our platform to date. Today, our infrastructure is uniquely built to fully leverage state of the art in-cloud data. And our application provides robust end-to-end capabilities to drive a full set of segmented, personalized, 1-1 data-driven actions. It’s somewhat of a “just add water” moment in time to bring everything together.

The last few days since we announced our financing have been a whirlwind of press interviews along with customers and partners reaching out in congratulations. As a values-driven organization, we focus as much on “the how” as we do “the what” – and I was particularly impressed by TechCrunch reporter Ron Miller’s eagerness to understand our values and how they relate to our DEI initiatives.

One aspect that I wasn’t able to cover with Ron was how our values relate not just to how we build our business internally, but also to how we build our product and how we work with our customers. When I talk with the team about ownership, it’s about aligning goals and making sure that everyone individually is in the right spot to affect outcomes directly.

Yet for so many marketers today that isn’t the case. Taking ownership of their enterprise data is just too hard – yet doing so is more critical than ever to affect the data-driven requirements for success in today’s hyper competitive environment.

In short, the problems we are solving are bigger than ever, and the solution we’ve been building over the past eight years is now perfectly timed. We’re thrilled to get back to work – and enable our customers to take full ownership around a next-generation of customer experiences.

Today, we’re excited to announce that we’ve raised $54M in funding, led by Macquarie Capital with participation from all major existing investors, including Polaris, .406, and F-Prime.

This funding round comes on the heels of our launch of Connected Segmentation, a groundbreaking tool that empowers marketers – regardless of technical acumen – to build customer segments directly within Snowflake. But, Connected Segmentation is just the start. This investment will supercharge our product development with other cloud data warehouse providers and enable us to offer a fully-connected CDP deployment to all major enterprises – regardless of cloud data infrastructure.

“Simon Data enables us to deeply connect with our users in more profound ways. We’ve experienced a 300 percent increase in free-trial conversions over the last several years, which is a testament to Simon Data’s ability to individualize and elevate our customer communications. Solutions like Connected Segmentation empower the marketing team to quickly build segments and deliver personalized messages that consistently resonate.”

Scott Grove, Vice President of Marketing Operations, Vimeo

The future of Customer Data is Connected

The problem with traditional CDP architectures is that customer data is replicated multiple times as it moves across data centers and marketing end channels. This movement is outside the centralization and security standards set by the cloud data warehouse, and also creates tremendous workflow inefficiencies for both tech and marketing teams.

With Simon Data’s Connected CDP, marketers access customer data directly within the cloud data warehouse (like Snowflake), build complex customer segments and easily activate them in end channels, all while the resulting data is transmitted back on a continuous loop between marketing channels and the data warehouse. We call this the “Connected CDP” because enterprises enjoy the benefits of using a CDW for their data infrastructure, while deploying a workflow and UI custom-built for marketing teams.

This new, Connected CDP architecture captures the centralization and security benefits of the cloud data warehouse while building workflows specific for marketers.

Onward and upwards from our CEO

“We’re seeing a fundamental shift in the CDP space – pitting packaged solutions that exist independently of enterprise cloud data warehouses against composable solutions that are purpose-built to unlock its potential. Simon’s unique architecture enables our customers with a marketer friendly-UI typical of packaged CDPs, while at the same time offering the composability benefits of reverse ETL tools. With this investment, we are furthering our commitment to solving the data and marketing divide, and cementing our position as leaders in modern, warehouse-native customer data activation and personalization.”

Jason Davis, CEO and Co-Founder of Simon Data

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As I approach my 5-year anniversary at Simon Data, I have been reflecting on the significant evolution of the marketing technology space during this time.

Back in 2018, customer data platforms (CDPs) were not yet a prominent concept. In the early stages, we actually referred to Simon as a retention marketing platform, which was a fitting description at that time. Our core focus as a business was to empower brands to leverage their data, with a primary emphasis on enhancing personalization at scale to drive customer retention.

Given that our initial customers were high-growth, venture-backed eCommerce and subscription brands like Bark, Peloton, Allbirds, and Casper, it made sense for us to prioritize these objectives. Essentially, we aimed to enable non-technical individuals lacking SQL skills, like myself, to create impactful marketing experiences fueled by data. This was our guiding principle.

We concentrated on solving real business challenges while simultaneously educating the market about this new category within the already crowded marketing technology landscape. Needless to say, we faced some challenges, but the wins felt good. Just take a look at Scott Brinker’s Martech 5000 from 2018; it was a chaotic sight, and CDPs didn’t even have their own distinct category!

The proliferation of sub-categories within the CDP landscape

As the years passed, our core objective remained steadfast while the category itself evolved. The competitive landscape underwent significant shifts, resulting in a proliferation of sub-categories and vendors with tangential capabilities within what was once referred to as “CDP.” Much of this transformation can be attributed to M&A activities: Optimizely acquiring Zaius, Amperity acquiring Custora, Twilio acquiring Segment, Bloomreach acquiring Exponea, and Acquia acquiring Agilone, among others.

Additionally, the growth of the cloud data warehousing sector spawned a new breed of platforms focusing on data movement, or rETL. Companies like Hightouch, Census, and GrowthLoop (f.k.a Flywheel) have entered the arena, many of which are powered by cloud data warehouse partners like Snowflake or Google’s BigQuery.

All of this implies that the marketer’s job has become increasingly challenging, compounded by the existing hurdles presented by iOS updates and the eventual phasing out of third-party cookies. The category’s growth has introduced more nuances, rendering previously effective CDP RFPs obsolete. How could it remain relevant?

There are now customer engagement platforms, composable CDPs, reverse ETL solutions, customer data infrastructure, and connected CDPs: each with distinct core competencies and, notably, different primary end users. Yet, somehow, all these platforms still claim to be the same type of technology – a CDP. Quite perplexing, isn’t it?

Navigating the complexities: five essential questions for marketers evaluating CDPs

Before you distribute a massive RFP comprising over 200 questions to acquire an enterprise-level CDP, marketing leaders should ask the following questions to their team. The answers to these questions should help you zero in on the right category of CDP.

1. What are the underlying business problems we aim to solve with a CDP?
2. What is the business impact of addressing those problems?
3. Which use cases are necessary to tackle those problems?
4. Based on the answer to question #3, which team should be the primary end users of the CDP?
5. Depending on the answer to question #4, which buying group has a budget for a CDP?

If the primary use cases revolve around democratizing data and enhancing the customer experience across marketing channels like email, SMS, mobile push, website, and paid media retargeting, it would be logical for marketers to be the primary end users.

Conversely, if the use cases focus on data movement, such as a schema-less architecture that transfers customer segments to various marketing tools and platforms, data teams should take the lead as primary end users.

Often, I come across brands evaluating CDPs and zeroing-in on platforms like Simon Data, Amperity, and Segment. However, apart from being grouped into the somewhat fuzzy and broad CDP category and having some level of segmentation capabilities – there isn’t significant overlap among them.

Needless to say, brands that find themselves in this predicament are rarely satisfied with their decision a year later. Why? It’s because the chosen platform doesn’t align with the answers to the five questions above. They may have selected a platform designed for data teams and instead needed one where marketers are the primary end users. The result is a ton of internal frustration and misaligned resources, blocked workflows, tech debt and more.

As someone who has been operating in this complex space for quite some time, I take great pride in my ability to demystify the CDP category. For marketers venturing into this category for the first time, I recommend taking a look at Scott Brinker’s latest MartechMap below. Based on that you can rest assured that the journey is not getting any easier but I’m always happy to help.

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At CommerceNext 2023, Simon AI CEO and Co-Founder Jason Davis hosted a fireside chat with Steven Mastrocola, Director of CRM at SeatGeek, where they discussed the transformative power of collaboration between data and marketing teams. Read on for takeaways from the conversation, or watch the interview below.

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SeatGeek, the premier live event ticketing company, has made personalization a core part of its CRM strategy. “When you think about live events,” said Mastrocola, “it’s somewhat complicated to determine who likes Taylor Swift, who likes the New York Yankees, who goes to a whole slew of football games..and so on”

The key to personalizing the SeatGeek experience lay in untangling this complexity. To do so, SeatGeek’s CRM team joined forces with their data team to develop a point system that assigned scores based on user engagement with various sports teams and events.

“Higher level engagement gets you more points, lower-level engagement gets you fewer points…” said Mastrocola, expanding further:

“If you go on to SeatGeek and you track the New York Mets, we might give you 1000 points for the Mets. If you purchased the New York Knicks, we might give you 500 points for the New York Knicks. If you click on the New York Yankees, we might give you 100 points to the New York Yankees. And at the end of the day, we can add up all those engagements and give each user a score for each team which then allows us to say, ‘Hey, this person’s favorite sports team is the New York Mets.'”

These scores enabled SeatGeek’s CRM team to use platforms like Simon AI and their downstream marketing channels with precision, allowing for targeted campaigns with dynamic content specific to users’ team preferences. The seamless data integration enabled the team to personalize messages and drive more ticket sales.

It’s a two-way street: The secret to forging effective data and marketing collaboration

Steven emphasized the essence of open communication and a shared purpose to bridge the divide between marketing and data teams. Regular meetings and cross-team training are pivotal in nurturing a collaborative culture and a shared understanding that neither team should operate as simple order-takers.

“I think a relationship with the data team is just like any relationship with a coworker… It’s a two-way street. It’s not just the data team serving up info for my team to use…There’s stuff my team should be doing to help them as well.”

How data and marketing collaboration solved incrementality and attribution challenges – at the same time

A great example of this symbiotic relationship came in the form of an initiative to measure the incrementality of SeatGeek’s CRM marketing using a holdout test – a type of experiment that withholds a segment of users from specific CRM programs to assess the impact of those campaigns on user behavior.

“We worked collaboratively with the data team to design the test together, with my team owning the operational execution of it and the data team owning the actual analysis and reporting,” said Mastrocola.

The holdout test allowed SeatGeek to accurately measure the effectiveness of its CRM marketing efforts. It provided valuable insights into the incremental value of their marketing campaigns, something that was impossible to achieve through traditional campaign reporting alone.

But the test results also had a broader impact on the data team, which, for some time, had been trying to pitch a new attribution model. The numbers that came back from the incrementality test matched what would have been in the new attribution model – and demonstrated the flaws in the old one. The results ultimately helped the data team make the case to the rest of the company to use the newer attribution model.

How data and marketing collaboration revived a price drop campaign

Before the COVID-19 pandemic, SeatGeek had a high performing price drop campaign that operated similarly to an abandoned browse campaign: if a user viewed a specific event page and the price dropped after viewing, the brand could send a message alerting the user of the price change.

When COVID-19 hit, there weren’t any live events to market, so the team turned the experience off. As the world began to re-open, the team went to turn this campaign back on and couldn’t. “This email just did not work – no matter what our product team did…it would not send.”

Around the same time, Mastrocola worked with the data team to implement Simon AI as their new Customer Data Platform. Thanks in part due to the tight relationship between the two groups, the data team knew how effective the old campaign was – and knew the problem needed to be solved quickly.

With Simon AI in place, the data team could pass the raw price drop events for every single event, as well as user engagement data. With that information available in Simon, Steven’s team could go in and construct the segment however they saw fit and begin deploying messages almost immediately.

Steven had been going back and forth with the product team for two months to repair the old price drop campaign. And in two days, the data team had it live in Simon AI – better than ever.

“Instead of having a product trigger the email without any input from us – we can now choose the events, we can test the events, and we can test the thresholds: a 10% price drop versus 25% and so on.”

Ultimately, the quick fix was possible because both teams understood how their respective infrastructures worked.

“I think it’s very much about having those open lines of communication, sharing priorities, and making sure you know how each others systems work.”

Speed and efficiency: critical drivers of success in data-driven marketing

Understanding the drivers of success at SeatGeek requires a quick tour of their underlying data and marketing architecture.

Redshift is SeatGeek’s data warehouse, which sits at the center of everything. Both Simon and Iterable – SeatGeek’s cross-channel marketing platform – integrate bi-directionally with the data warehouse, meaning they can receive data from RedShift and send data back into it. For reporting, Looker sits on top of the entire stack, enabling Steven and his team to view campaign performance holistically and by segment.

For example, Steven can evaluate a personalized message’s performance, like an email with dynamic content based on the users’ favorite MLB team. Using data from Simon, he can see the click rate for someone who got an email featuring The Mets versus the click rate for someone receiving an email featuring the Yankees. He can also see what they purchased and any other downstream events or metrics.

SeatGeek’s data-driven marketing success is a testament to the importance of speed and efficiency in campaign execution. Steven emphasized the need to remove friction from workflows and streamline processes, ensuring that his marketing team could quickly solve problems, optimize campaigns, and keep iterating to drive growth.

“If something is hard, if it requires going to IT… then I think you have a problem,” said Jason, underscoring the significance of swift and seamless workflows.

Adding to that, Steven remarked on his team’s change from relying on batch data uploads to a more streamlined process that came with their CDP investment.

“It’s one of those situations where it works until it doesn’t,” he said. “We were very much in a world where we used to upload CSVs of users to our ESP, so we’d be uploading massive files…I can literally tell you when you can no longer open an Excel file, it’s at 800,000 lines – that’s when the Excel file breaks.”

For SeatGeek, the long wait times required for data to upload became a non-starter. “When the team’s sitting there waiting for users to upload or download from SQL…it’s just an inefficient use of time.”

Moving to Simon AI was a no-brainer for both the data and marketing teams at SeatGeek. “It’s about being able to quickly solve and optimize and keep growing, versus just settling for what’s already going out the door,” said Steven.

With Simon AI implemented, the CRM team could also centralize all of their SEM, paid social, and our brand channels within the CDP through the platform’s direct integration with LiveRamp. This means they can seamlessly send audiences to those channels from within a single platform and optimize the delivery of those ads to specific audience segments.

“Simon has allowed us to look at users holistically,” said Steven. “We’ve been creating…value segments where we look at users in specific cohorts-like ‘frequent purchasers’ versus ‘lapsed users’ versus someone that has never purchased. With Simon, data and marketing teams are all talking the same language and using the same data to identify these segments. So when we run a test or campaign, we can say, ‘Hey, this person should be part of our SEM audience, but not paid social’ or vice versa.”

SeatGeek’s story is a remarkable case study of the transformative power of collaboration between data and marketing teams. Through shared goals, effective communication, streamlined processes, and good technology investments, SeatGeek has harnessed the power of personalization to revolutionize the ticketing industry. The teams’ success is a powerful inspiration for other businesses seeking to unleash the true potential of their data and marketing. Of course, this is just a small sample of the insightful examples and case studies shared over the course of the conversation.

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Identity resolution is the process of attributing customer behavior and interactions across all touchpoints, platforms, or channels to a single, unified customer profile. It is a foundational element for modern digital marketing.

Identity resolution is powered by a customer identity model or identity table. This is a structured framework that captures and organizes key customer information such as email, phone number, and user ID. A good identity model must include one stable identifier per customer—that is, a piece of data such as a user ID, that travels consistently across all instances of that customer’s behavior.

Identity resolution isn’t just a one and done thing: marketers’ everyday needs to personalize messaging across channels throughout the entire customer lifecycle means the identity model needs to be re-validated any time there is a significant change to the underlying data, like the addition of a new data source or identifier.

The three tell-tale signs of a broken identity model

Not validating your customer identity table on a regular basis can lead to issues down the road that have huge implications for your customer experience and marketing ROI. Most often the symptoms of a broken identity table are felt by the marketing team, and the people with whom they are attempting to communicate. These can appear in the form of:

• Customer complaints: Marketing often receives complaints from customers that are benign in nature – but when the complaints are due to broken experiences – such as, customers receiving multiple copies of the same email in one day, or customers receiving out-of-context offers via email or SMS – your identity table could be the culprit.
• Missing or incomplete profiles: If your customer support team reports not being able to find customers within their systems, or seeing multiple profiles for one person or none at all, and having to ask questions like “could it be under another name or phone number?” your identity table could be broken.
• Mis-sized segments: If, in the process of building customer segments, your marketing team reports audience sizes that are way larger or smaller than they intuitively know they should be, or those segments are missing individuals that marketers know should be in there – your identity table could be to blame.

There are a number of downstream effects from poor identity hygiene, not the least of which is the impact on customer experience when people get wrong or out-of-context emails or miss receiving messages that they should.

Other downsides include inefficient use of your marketing spend and security and compliance risks. But perhaps the costliest one is that in the long run, your internal teams across the company may lose confidence in the data.

Master data management for validating your identity table

Most data professionals are already familiar with using master data management to validate their identity model, among other things.

Master Data Management (MDM) is defined as a comprehensive approach to managing and maintaining an organization’s critical data assets. It involves creating a unified, consistent, and accurate view of master data, which typically includes customer, product, vendor, and other key data domains.

In the same vein, an MDM platform supports the implementation and execution of these strategies. These provide capabilities to integrate data from multiple sources, cleanse and standardize the data, resolve conflicts and duplicates, and create a single trusted version of the data.

Identity resolution is particularly relevant to MDM – when dealing with customer data, where multiple records may exist for the same individual or when trying to link customer data with external reference data sources.

The thing is, master data management takes a bit time to implement – regardless of if its design and implementation is led by an outside agency or internally. This poses issues for marketers who need access to rich and accurate customer profiles to support their marketing strategies now.

Introducing IdentityQA – a faster solution for fixing broken identity models

MDM may be a worthwhile investment for your data organization, but while you’re waiting for that strategy to stand up, we believe strongly that brands – and the marketers powering them – should not have to wonder if their identity model is correct or not.

That’s why we recently launched IdentityQA, a Snowflake native app now available in the Snowflake App Marketplace.

With IdentityQA, you can test your identity table for free – right within your Snowflake cloud data warehouse. IdentityQA offers a free, easy solution to validate your identity table or identify the root cause of identity problems.

Simply download and run the app from Snowflake’s new native app marketplace, and immediately you’ll be able to spot inconsistencies, duplicate records, and incomplete information that may be impacting the accuracy of your identity table.

For more information or to download IdentityQA, visit https://get.simondata.com/identityqa/.

The pandemic forced many (if not all) brick and mortar brands to make the move to digital.

Some brands did it right. Others, not so much.

The biggest differentiator was in data and content.

Many of the organizations who weren’t fully accustomed to the digital world approached audience segmentation wrong. They did not delve fully into their data to understand who would actually use their online platforms.

Those who got it right knew who to market to, and most importantly, how to use the right content to do so.

This is one of the many things we discuss in this week’s episode of data unlocked. Simon Data CEO, Jason Davis, sits down with Mike Mothner, the founder and CEO of Wpromote.

Mike started his career back in 2001, when digital marketing was still in its infancy. Even when advertisers were skeptical of this new industry, Mike followed his vision and founded his own company, Wpromote.

Since then, he has been at the head of Wpromote, a performance marketing agency focused on leveraging the powerful combination of best-in-class marketing experts with proprietary technology to drive transformative growth for their clients.

They have worked with some of the biggest brand names in the world, such as Whirlpool, TransUnion, Frontier Airlines, and Jack Black Skincare.

In this episode, Mike and Jason discuss Wpromote’s work, what a winning digital marketing strategy needs, why brick and mortar brands should go digital, how the pandemic affected brick and mortar brands all around the world, and more.

Are you ready?

Let’s dive in.

For some time now, Snowflake and the larger cloud data warehouse (CDW) industry has made waves in the business world, transforming the way businesses operate, connect with customers, and measure success. And yet, even as more organizations bring on CDWs, marketers continue to be left out of the conversation.

This is due to the fact that most marketing technologies—including traditional customer data platforms (CDPs)—are not built on cloud data warehouses, nor are they designed to work with them. As a result, marketing teams and the organizations they serve are faced with an unwinnable situation: if they’re using a CDW, accessing that data for marketing requires purchasing a tool that serves data teams only—like reverse ETL (rETL) tools or “composable” CDPs—or, purchasing tools that serve marketers only, such as customer experience platforms or marketing clouds. This leaves sensitive customer data in a duplicative, fractured, unsafe, and unusable (for the rest of the organization) state.

Today we say “no more!” to that forced dichotomy and offer a better way.

Simon Data is excited to announce that we now offer the first fully Snowflake-connected CDP through our new connected segmentation feature—built to serve the needs of data teams, while enabling non-technical marketers to build customer segments directly within Snowflake, without writing a single line of SQL.

Say goodbye to long wait times for data and living in CSVs

Traditionally, B2C marketers have built customer segments in two ways: by hacking together the fragmented data that lives within their own marketing systems, or they turn to engineering teams to source data directly from their CDW. In either situation, marketers require significant data engineering support to source, extract, transform, scope and size these audiences, as well as actually build the lists for use in downstream marketing channels.

The subsequent back and forth wrangling can cause delays and some very real headaches on both ends.  Marketing continues to be limited by the fact that most of its data is decentralized outside of the rest of the business’ data architecture, which not only prevents them from achieving their personalization goals, but also introduces some serious security and compliance issues. Data folks report feeling like glorified order takers—endlessly writing and rewriting ad hoc SQL queries to support marketing needs.

And thus the divide between marketing and data teams continues to grow.

Enter Simon Data: The CDP for B2C businesses running on Snowflake

Accomplishing a fully-connected deployment of our CDP was no mean feat and required us to take our no-code segmentation tool—something that’s already known and loved by marketers across hundreds of B2C brands—and rebuild it directly in Snowflake.

Many data leaders already see the benefits of providing their marketing teams with data at their disposal. Those benefits are both self-serving and altruistic: doing so frees up the engineering team to focus on other priorities, and marketers enjoy access to the data they need to launch the hyper-personalized campaigns of their dreams.

The additional value of a fully-connected deployment is that sensitive customer data never leaves Snowflake until it’s ready to be used in downstream marketing tools. This means Simon now offers consistent and cutting-edge security architecture, broadly available business logic, no risk of data fragmentation, flexible data syncing latency, and most of all, simplicity.

In short: we’re giving marketers the keys, but data leaders are still the ones who determine how far they can drive.

The fastest and easiest CDP deployment–ever.

Deploying Simon takes mere hours and can be accomplished in just a few simple steps.

1. Share your Snowflake schemas with Simon – Grant Simon access to one customer identity table with a unique customer identifier, plus any other customer property tables (one to one with customer) or event tables (many to one with customer) that can be combined with the customer identity table.
2. Match relevant customer tables – Use Simon’s Schema Builder to associate the relevant customer tables. Your attributes from the provided tables will immediately surface within the segment builder.
3. Build your customer segments – Using the customer attributes, build your customer segments using Simon’s easy and intuitive no-code user interface. Your data will remain in Snowflake until you activate it in a marketing end channel, such as your email service provider or SMS tool.

Connected Segmentation solves a problem that reverse ETL can’t

Reverse ETL tools don’t measure up for two reasons – first they really only shuffle data around between tools and systems. Second, they are built for data engineers, not marketers. This means members of your team continue to be tied up servicing marketing requests when they could be tackling more pressing data challenges.

At its core, we built Connected Segmentation as part of our bigger mission to drive a healthier and more dynamic relationship between data and marketing teams – one that’s truly symbiotic rather than codependent. Not only are marketers now able to size and build their segments on their own, the output adds even more value to the data ecosystem – as segments are built and put to use, the resulting engagement data flows directly back into Snowflake for further use and iteration.

Ready to get started?

If you’re interested in learning more about Connected Segmentation and our broader fully Snowflake-connected CDP, request a demo today.

Attending Snowflake Summit? Our team will be on-site at Snowflake Summit offering a limited number of free 60-day trials. If you’d like to learn more stop by our booth #1952-D and speak with a member of our team.

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Good data has the potential to be a company’s greatest asset. This is why so many organizations have turned to tools like Cloud Data Warehouses and Customer Data Platforms to ensure organizations have the ability to collect, access and deploy data within their organizations.  

But of course, if your efforts start and end with purchasing new technology, well you’re missing the point. And therefore, missing out on all the value your data could be offering you.

This is where a good CDP strategy comes into play.

You see, implementing a CDP requires way more work than just selecting  software. It’s a collaboration between people, processes, and technology. And, if done right, that collaboration can have excellent results.

This is exactly what we discuss in today’s episode.

In this week’s episode of Data Unlocked, Jason sits down with Aric Zion, CEO of Zion & Zion.

Aric is an entrepreneur with decades of experience in the marketing field. He has several international experiences, including positions with Intergraph, where he was based in The Netherlands, and additional assignments with Nortel Networks where he was based in Tokyo, then London.

Today, he is at the head of Zion & Zion.

Zion & Zion is a full-service agency with a fully-staffed CDP practice. They pride themselves on meeting their clients at what they refer to as “the intersection of strategy and creativity.” Some of their biggest clients include Sun Health, Goodwill Industries, and Walmart.

In this episode, Aric and Jason discuss Zion & Zion’s work, how CDPs can transform businesses, the key dimensions required to build an effective CDP strategy, and more.

Are you ready? Let’s dive in.

Today, Simon Data is proud to announce the launch of our free native app on Snowflake: IdentityQA.Launching today with the public announcement of Snowflake’s brand-new Native App marketplace is IdentityQA from Simon Data, an easy, free way to diagnose customer identity models for clients using Snowflake.

IdentityQA, a free Native app by Simon Data, gives Data Engineers an easy way to quickly surface errors in their customer identity table. Customer identity is the lifeblood of a healthy customer experience, from sales, marketing, and customer support.

But, before IdentityQA, there was not an easy way for data engineers to check the accuracy of their identity table. Often, QAing a broken identity table meant hours of manual work by data engineers.

A little background on identity resolutionIdentity resolution is the foundational element for modern digital marketing. Defined as the process of attributing customer behavior and interactions across all touch points, platforms, or channels to a single unified customer profile. Identity resolution is powered by a customer identity model, a structured framework that captures and organizes key customer information such as email, phone number, and user ID.

Without a reliable identity model, marketers are forced to engage with their customers in generic batch and blast messages, and risk sending the wrong message, to the wrong person, at the wrong time.

For data engineers, the first step towards solving the identity resolution problem is moving customer data into a centralized cloud data warehouse. But, getting all of your data in one place is just the beginning – you need a solid identity model to unify that data in an organized and consistent fashion.

But the question remains: how solid is your identity model? How can you be sure of its accuracy? For a while, there was no way to independently verify the quality and accuracy of your identity model. And while your marketing team may be complaining about duplicate customer profiles, incomplete and inaccurate data, and fragmented customer views (all telltale signs of an incorrect identity model) isolating the issue remains challenging for data engineers.

That is, until now.

With IdentityQA, data engineers can…Run comprehensive identity analysis: IdentityQA provides a comprehensive analysis of your existing identity model, enabling you to identify flaws and weaknesses in your customer profiles. IdentityQA uncovers inconsistencies, duplicate records, and incomplete information that may be hindering your marketing team’s efforts.Access real-time insights: Use IdentityQA to stay up-to-date with your customer profiles and continuously monitor and analyze identity data within your Snowflake instance. This ensures you have the most accurate and complete customer information at your disposal and empowers your marketer team to execute highly personalized campaigns with confidence.Make data-driven improvements: Armed with actionable insights from IdentityQA, you can implement changes and optimizations to enhance the quality and integrity of your customer data.Integrate seamlessly: IdentityQA seamlessly integrates with your existing Snowflake infrastructure, leveraging its robust capabilities. There’s no need for additional data transfers or complex integrations. Simply install IdentityQA from Snowflake’s native app marketplace and start uncovering valuable insights and pinpointing issues right away.How IdentityQA worksWe built IdentityQA using Snowflake’s new native application framework. The app works by enabling Snowflake users to perform quality assurance on their own data and identity model. Our app does not build an identity model; rather, it requires you to indicate a pre-built identity model and assumptions to validate.

Inputs to the application include:

Source table that contains relevant identifiersIdentifier names & data typesConstraint definitions, for example:email & user_id are 1:1iuser_id should be uniqueWithin minutes of running IdentityQA, you will see a set of Snowflake tables that can be manually inspected, as well as a QA dashboard that allows you to understand if your assumptions hold true.

Given the constraints configured in the input, the output will show you which tests passed or failed, and provide examples of data that’s invalid and can be cleaned up.

Think of IdentityQA as an identity “sandbox.” You can use the output to inform any changes that should be made to your existing identity model, or you can run it retroactively after making changes to your identity model. We also recommend periodically running the app to ensure the underlying data, such as email addresses and phone numbers, pass quality checks

IdentityQA offers benefits to both data engineering and marketing users.  It grants data engineers a useful starting point for correcting an identity model that they may not even know is broken. And marketers will no longer need to wonder if the data powering their communications and marketing experiences is correct or not. They will be able to trust the data in their customer profiles and use it to engage with audiences thoughtfully, in context and in a hyper-personalized manner.

How to get started It is crucial to invest in your identity model to ensure accurate customer profiles, personalized experiences, and effective marketing campaigns. Regular monitoring, data quality checks, and leveraging advanced technologies like IdentityQA can help mitigate these risks and improve the overall effectiveness of your identity model.

If you’re interested in learning more about IdentityQA, visit our listing in the Snowflake native app marketplace.  To get started, simply install the free app and follow the step-by-step instructions

As a data leader, you understand the critical role that data plays in driving successful marketing. But you probably also know that traditional centralized approaches to data management often creates bottlenecks and hinders the agility and innovation required for marketing in today’s business environment. This blog post explores how data engineering leaders can effectively integrate marketing teams into the data mesh framework, empowering them to leverage data as a strategic asset and propel their organizations forward.

What is a data mesh?

The data mesh paradigm, introduced by Zhamak Dehghani in 2018, offers a decentralized approach to data ownership and governance. Instead of relying on a centralized data lake, the data mesh brings data ownership to individual domains, such as marketing. This new approach challenges the centralized data management model by treating data as a product and promoting decentralized ownership, offering teams more autonomy, flexibility, and agility in leveraging data for their distinct domains.

For data leaders, embracing the data mesh means inviting marketing teams to take charge of their own data. This shift not only alleviates any marketing burden on data teams but also enables marketers to access and use data with greater agility and efficiency.

Integrating marketing into the data mesh framework offers huge benefits for organizations

First and foremost, the data mesh empowers marketers with autonomy and flexibility, enabling them to leverage data effectively for campaign optimization, customer segmentation, and personalized messaging. This leads to faster campaign launches, increased efficiency, and vastly improved marketing outcomes.

By decentralizing data ownership, the data mesh encourages cross-domain collaboration. Marketers can share their data products with other teams, such as product development or customer support, which can provide valuable insight into customer preferences, mindsets, and behaviors. This not only broadens the ROI of marketing activities but can also drive product innovation, support the creation of better machine-learning models, and enhance the overall customer experience.

Moreover, integrating marketing into the data mesh paradigm allows for continuous experimentation. Marketers can leverage data to fine-tune their campaigns, optimize audience targeting, and uncover new growth opportunities. This iterative approach, powered by data mesh principles, promotes a culture of innovation and positions marketing as a strategic driver of business success that goes far beyond revenue.

How to bring marketing into the data mesh

Bringing marketing into the data mesh framework requires a collaborative effort between data engineering and marketing teams, but the first major hurdle is to establish a culture of data ownership among marketers, empowering them to become data product owners. Here are seven strategies that we’ve seen work to accomplish this:

1. Promote data literacy: Organize training sessions or workshops to enhance marketers’ data literacy skills. Educate them on basic data concepts, data analysis techniques, and data interpretation. By increasing their data literacy, marketers will feel more confident and empowered to work with data.
2. Collaborate on data modeling: Work with marketers to define data schemas, data structures, and key metrics relevant to marketing initiatives. By involving them in the decision-making process, data engineers can ensure that the data model aligns with marketers’ needs and priorities.
3. Enable self-service access: Empower marketers with user-friendly, self-service tools that allow them to query, visualize, and analyze data independently. These tools should be designed with a marketer-friendly interface and require minimal technical expertise to use effectively.
4. Collaborate on data governance guidelines: Work with marketing to establish data governance guidelines that balance data access, privacy, and security. Define roles and responsibilities, data usage policies, and consent management processes. By involving marketers in these discussions, data engineers can foster a sense of ownership and responsibility for data governance.
5. Create data documentation: Develop a centralized data catalog or dictionary that documents available datasets, their definitions, and usage guidelines. Encourage marketers to contribute their knowledge and insights to this documentation. Regularly update and share this information to promote transparency and collaboration.
6. Foster cross-functional collaboration: Create forums or communities of practice where marketers can interact with data engineers, data scientists, and other stakeholders. Encourage knowledge sharing, brainstorming sessions, and collaborative problem-solving.
7. Celebrate successes and share wins: Recognize and celebrate instances where marketers leverage data successfully to drive positive outcomes. Share success stories across the organization to inspire others and reinforce the importance of data ownership. Highlight the impact of data-driven decision-making on marketing initiatives and overall business performance.

By implementing these strategies, data engineers can foster a culture where marketers feel empowered to take more ownership of their data, develop data products, and thus, be able to contribute to the data mesh – in theory. But in actuality, this is only half the battle.

Technology remains a huge blocker in getting marketing engaged with the data mesh. Traditional data architectures often lack the flexibility and scalability required to accommodate the diverse data needs of marketing teams. Siloed marketing technology platforms-yes, even those all-in-one marketing clouds-further compound the problem by creating data fragmentation, and make it hard for data to integrate seamlessly across other marketing channels and tools, as well as the tools used by the broader organizations. This hinders marketers from accessing a holistic view of customer data and leveraging it effectively.

Are you leading the charge on the data mesh?

To enable marketing to contribute to your data mesh, it is essential to establish a modern data architecture that supports real-time data integration, ensures data quality, consistency and governance, and enables seamless integration within marketing and across the organization.

Download our white paper “From Silos to Synergy: Harnessing Marketing’s Potential in Your Data Mesh Journey” to learn more about the modern data architecture and access more strategies for data mesh.

Simon’s re-platforming into a connected application is an innovative foray into using the Snowflake Data Cloud for marketing. Our approach here is dramatically different from that of some other customer data platforms (CDPs), and yet is straightforward in its architecture – and an almost obvious next step for our business.

Leveraging Snowflake’s connected app ecosystem has allowed us to achieve a simpler, more efficient architecture with various security and latency benefits as a result. Data leaders who support marketing understand the challenges of elevating their marketing teams with the data at their disposal.

In this post, we’ll outline how we leveraged Snowflake’s connected application model to build our segmentation tool and “Activate” capabilities on our client’s Snowflake infrastructure, with the ability to deploy audiences directly into their Snowflake instance.

As a quick intro, my name is Asher Woodbury and I lead the data team here at Simon Data. I’m passionate about building data tools that are simple and composable – especially when they have a less technical user base. I love making complex problems easier to understand, and the best way you can do that with your products is to architect them to be straightforward and logical. For the past few months, my team has been building a tool that is built in just that way: “Connected Segmentation.”

Snowflake + Simon Data = The evolution of marketing

Simon’s mission has always been to bridge the gap between marketing teams and their data. Our platform is one of the most powerful CDPs out there, unlocking the hidden value of our clients’ data and allowing them to leverage it in highly personalized experiences. That power, however, has come at a cost. As we’ve expanded the set of functionality our product supports, our platform has become complex and our data pipelines to centralize that data into our optimized infrastructure have become heavyweight.

Platform complexity presents a real roadblock to bringing marketing teams closer to their data. Many of our clients have their own intricate data models under the hood. To be their window into that data requires a platform that simplifies, not complicates. A tool that can solve difficult marketing problems isn’t enough. Our clients need to be able to use that tool in a self-serve manner, communicate the contours of the solutions they’ve built internally, and – most importantly – evaluate their success to inform future solutions.

And this is happening within a macroeconomic environment that requires our clients to do more with less – and so they expect the same of their CDP. This puts a primacy not only on the capabilities or elegance of our product but also on its efficiency. Our clients need our platform to be more powerful, faster, and simpler all at once.

That left us with some big questions: How can we provide an even more powerful tool for our clients, but deliver it to them in a dramatically simpler way? How can we do it in a way that allows marketing organizations to not only use the data assets of departments across their business but also contribute their expertise back? And how can we do it efficiently and securely?

We’ve rebuilt our best-in-class marketing segmentation tool on Snowflake, such that it:

1. Deploys audiences as views into your Snowflake instance
2. Can sync these audiences at low latency to downstream marketing channels.

Customer segmentation enabled by a no-code, marketer-first UI

Our Connected Segmentation tool offers a no-code, marketer-first UI so non-technical users can easily create audiences. This allows marketers to leverage customer attributes and their event histories to build highly-specific audiences quickly and easily – all powered directly by tables and views in your Snowflake instances. Because Connected Segmentation doesn’t depend on replicating data out of Snowflake, you (and, more specifically, your security teams) can maintain full control over your data.

Centralized segmentation logic in your Snowflake instance

Audiences created via this segmentation application are then deployed back to your Snowflake instances as views. This serves to centralize business logic back into your Snowflake instance rather than having it live solely within Simon’s application. You can manage, own, and share that business logic however you see fit. Simon becomes the vehicle for your marketing teams to contribute to their business’ data mesh, by defining and socializing audiences business-wide, rather than solely utilizing those audiences for marketing.

Downstream activation of customer data

Our “Activate” product enables marketers to sync those audiences to downstream marketing channels. Where Connected Segmentation defines those audiences directly on your data, Activate can sync any changes to those audiences as fast as the underlying data changes.

Given the macroeconomic climate and the focus on efficiency, fast moving data needs to be acted on quickly to maximize each marketing dollar spent and re-processing slow moving data repeatedly needs to be avoided. Thus achieving not only low latency, but also latency flexibility across use cases, is critical. By executing on top of Snowflake (which already provides several layers of caching to increase performance), you can determine the frequency with which you want each audience to be evaluated.

Why Snowflake was the obvious choice for Simon

Simon and Snowflake have worked in close partnership – operating with a shared vision of enabling data-driven organizations by making the Data Cloud the central source of truth. Lourenco Mello – Product Marketing Lead, Solutions @ Snowflake says:

^{“Simon’s Connected Segmentation feature offers the best of both worlds in CDP implementation: data teams get the composability they need, while marketing teams benefit from a no-code Segmentation UI that’s built with their workflows in mind. The benefit to Snowflake clients is clear: With Simon on board, Snowflake customers can more widely access and use the data in their own Snowflake account along with data from other parts of the business, and activate it in their desired marketing channels." — Lourenco Mello Product Marketing Lead, Solutions @ Snowflake}

Additionally, Snowflake’s separation of storage and compute allows us to operate on our clients’ infrastructure with no risk of our platform sideswiping their production systems, and gives our clients granular control over the resources dedicated to our platform.

Critically, that separation also allows Simon to operate directly on our clients’ data – we don’t need to ingest or store that data internally, so clients’ security teams’ have complete control over our access to their data.

This symbiotic relationship between Simon and Snowflake platforms enables our organization to serve the needs of both data teams and marketers. Thus, empowering companies to usher in a new standard of highly personalized customer experiences at scale.

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The scene is all too familiar: Another marketing data request comes in through Jira. After a few back and forth conversations with the submitter, you eventually align on the exact data they need and are able to write the SQL query to pull it all together.

You’re nearly ready to hit send and move on to the next item on your list, when an update to the ticket arrives…actually, now that they think about it, marketing needs to change their original request.

In fact, not only do they need their original list of loyalty program members with a minimum spend of $300 in a particular product category, who rank in the top 5% in terms of customer lifetime value – but they also need to know, how many (if any) of those customers have a high email open rate in the last three months AND have not visited the website in the past 30 days. And if that audience is large enough, they’re going to want that list as well.

But do you even have that data? Can you be sure that data is up to date from all possible channels? What emails did marketing send during that three month time frame? Time to go back to marketing…

Does this headache sound familiar? For many of our clients, it was their reality for a long time.

Too often there’s a gap between what marketing needs and what data teams can readily provide in a timely way. And it’s easy to pinpoint the villain: most marketing tech and data tools don’t make it easy for non-technical marketers and technical data teams to collaborate, thus resulting in these infamous data silos and bottlenecks we all know too well.

But the stakes are high. Markets—and customers—move fast and a days-long delivery time for a request like the one outlined above could be the difference between making or missing a window of opportunity for additional revenue.

Unpacking the marketing-data request process

The data request process is nuanced – and its complexity depends on your business size, structure, the people and teams involved, and your data and marketing infrastructure. That said, after working with hundreds of consumer brands over the last decade, we feel confident that we can accurately summarize the process into four distinct stages:

Stage 1: Defining requirements

The data request process begins when marketing identifies a segment they want to target, and then identifies which data, attributes, events and conditions define that segment. This includes specifying the type of data, timeframe, granularity, and any specific filters. Accurately articulating these requirements is critical to ensure the data team understands their exact needs.

Stage 2: Submitting the request

Once the segment is defined, marketing submits their request to the data engineering team. This step involves documenting and submitting the request through a formal channel, such as a project management tool like Jira, or a dedicated data request system.

Stage 3: Assessing the request

Upon receiving the request, the data team evaluates its feasibility and complexity. They assess the availability of the requested data, potential technical challenges, and the required effort for extraction, transformation, and delivery. The evaluation stage helps determine the time and resources required to fulfill the request.

Stage 4: Delivery

After the assessment, the data team proceeds with extraction, transformation, and delivery. They retrieve the requested data either directly from their cloud data warehouse or from various sources, apply necessary transformations or calculations, and deliver the finalized dataset to the marketing team in their desired format. Voila!

Understanding where things go wrong

So, let’s be honest: the data request process almost never runs that smoothly and far too many data engineers thus find themselves mired down in writing ad hoc SQL queries, managing APIs and running ETL processes for what seems like an endless string of marketing requests. Consequently, on the other side, marketers feel like they’re being held hostage by the time between request and ultimate delivery of the data.

There are, of course, a few common culprits for these workflow issues.  

​​Bottleneck 1: Lack of Clarity

One of the biggest blockers in the data request process arises when marketers are unable to clearly define their requirements. The resulting ambiguous or incomplete requests lead to misunderstandings, delays, and/or multiple rounds of revisions that will have all parties seeking out the nearest wall to bang their head against.

To overcome this, we have a few suggestions. First, an easily accessible and up-to-date data catalog or data dictionary enables marketing users to browse and discover what customer attributes and fields are readily available to them, and eliminate the incidence of repetitive data requests.

Data dictionaries have been around for a long time and exist in many formats. Some organizations build and maintain them in an internal wiki. Some are content with a simple spreadsheet. Most of our clients find value in using the data dictionary that’s built directly within our CDP, as it prevents them from having to reference multiple tools to build segments. Regardless of how the dictionary is built, it should be a resource that is owned and maintained equally by both marketing and data teams with each entry including the definition, key fields, use cases, and acceptable values.

Aside from referencing a dictionary, marketers should also invest time to precisely articulate their needs by providing examples or use cases when submitting their requests. This is one area where, yes, collaborative discussions with your team and marketing will help ensure mutual understanding and alignment. After all, you’re a data engineer not an order-taker.

Bottleneck 2: Limited resources and competing priorities

Another reality check: Data teams are in high demand and your work supports many departments across the entire company, not just marketing. If limited resources and competing priorities are getting in the way of fulfilling marketing requests, this is where the presence of savvy leaders on both marketing and data teams is key.

These leaders do the work of ensuring both groups are aligned with the greater business priorities. They can also introduce joint initiatives and practices such as regular alignment meetings (even if just quarterly) to strengthen the relationship between the two teams. Marketing leadership can also help instill the practice of providing clear justifications for the urgency of their team’s data requests.

Opening the lines of communication between teams will also help with negotiating timelines and managing expectations for individual data requests. A shared Slack channel, standing check-ins and even incorporating a 15-minute intake meeting after receiving a request are all meaningful touch points to consider introducing.

Bottleneck 3: Technical challenges and complex data and marketing ecosystems

Even in today’s business environment, where data is king, we still see businesses operating with their marketing and customer data scattered across multiple systems, databases, and platforms. In addition to some obvious governance and security issues, this also introduces all sorts of technical challenges to data engineers tasked with integrating and transforming data from diverse marketing sources.

The onus here is on marketing, who must work closely with your team to understand the underlying data architecture, provide assistance in data preparation, and commit to bringing on marketing tools and technology that fit into the larger picture of your data infrastructure.

Connected Segmentation: a better marketing request process

Ultimately, we’ve found that the best way to streamline the process of getting data to marketing is to eliminate the data request process completely.

Our brand new Connected Segmentation feature leverages a native integration between our CDP and Snowflake’s cloud data warehouse (CDW). With this new feature, Simon enables non-technical marketers to build customer segments directly within Snowflake, all without writing a single line of SQL.  

With Connected Segmentation, the results of a given query reside right in the CDW. This means if marketers need to make changes to a segment, they are able to do so without needing any additional support from your team. In addition to this self-service quality, Connected Segmentation offers additional benefits to both data and marketing teams, including:

• Better security and governance: Connected Segmentation solves the issues introduced by decentralized marketing data by bringing the task of building customer segments into the data warehouse, rather than in separate marketing tools and workflows.
• Better analytics integrations: Because Simon now integrates with all the other tools built on the CDW, such as business intelligence tools like Looker or Tableau, these technologies can now work together to drive incredible insights, and enable marketers to change course quickly if there are performance issues.
• A new (and actual) single source of truth for all business data: Simon’s fully Snowflake-Connected CDP establishes a true, single source of truth for your organization: your cloud data warehouse.
• Access to new marketing data products: Connected Segmentation creates a direct feed of marketing campaign data–events and engagement data, and performance analytics–right back into the data warehouse. This open back and forth flow of data enables marketing to contribute to your data mesh, and leverage additional value from the data their work produces.

In practice: a Snowflake + Simon CDP success story

Prior to Simon, Travel+Leisure Co, the world’s leading membership and leisure travel company, was plagued by heavily siloed data. To overcome this challenge, the company used ETL processes to bring data into an on-premises database and managed data in batches. While that worked for a while, the process required a ton of maintenance, and importing data in batches prevented the team from being able to serve experiences in real time.

Enter Simon and Snowflake. Today, Travel+Leisure centralizes its data in Snowflake and leverages Simon’s marketer-friendly UI to deploy data models that prioritize messaging based on customer intent, and unify their 1:1 personalization efforts across all channels. Travel+Leisure’s marketing team now enjoys a significantly more simplified process for building segments–which now update automatically and in real-time. They can aslo seamlessly deliver cross-channel campaigns to those customer groups – across their website, digital ads, email, call center, and direct mail channels. Finally, the company now can leverage a continuous flow of data back and forth between its marketing channels and the CDW.

All of those points speak to a tighter integration between data and marketing, but it’s also important to know that within a year of implementing Simon and Snowflake, Travel+Leisure reported over $350k in incremental revenue.

It’s time to go from order takers to strategic partners

It’s true that with a better data request process, marketing and data teams can start their conversations from the same page and at a higher point of departure and they can have higher value interactions starting from a shared understanding. But do you even need the process after all?

Having a CDP sitting between marketing and the data warehouse not only alleviates bottlenecks in the flow of data to and from marketing, but opens doors for data and marketing teams to have better conversations.  And, after all, isn’t the goal to partner with marketing to solve complex business problems?

Connected Segmentation is a time creator – enabling more time and resources for your team to do the work you love: building models, uncovering insights, asking more and better questions, and bringing ideas to marketing.

So, what are you waiting for?

Click here to learn more about Connected Segmentation and request a free trial.