Category: Marketing

Software and Solutions for Marketers

Software & Solutions for Marketers is the final installment in our Marketers’ Guide to Data Management and Analytics series. Throughout this series, we’ve covered major terms, acronyms, and technologies you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments.

In case you missed them, you can access part one here, part two here, and part three here.

In this last section, we will cover various aspects of software and solutions for marketing, including:

  • The differences between the cloud and on-premise (on-prem) solutions
  • Customer data platforms (CDP)
  • Custom development (custom dev)

Cloud vs. On-Prem

Cloud

Also known as “cloud computing,” the cloud is a global network of software and services that run over the internet on someone else’s server, as opposed to running locally on your computer or server.

Why It Matters for Marketers:

  • Get the flexibility your business needs. Today’s marketing teams are mobile, require a variety of working schedules, and are often spread across geographies and time zones. Cloud-based software and services are accessible by any device with an internet connection, quick to set up, and reliable to access, regardless of the user’s location or device.
  • Deliver the level of service your customers expect. Hosting your website or e-commerce business on the cloud means your site won’t get bogged down with high traffic or large data files. Additionally, hosting your data in the cloud reduces the amount of siloed information, empowering teams to work more seamlessly and deliver a higher quality, more personalized experience to customers.
  • Spend your money on campaigns, not infrastructure. While many softwares are sold with on-premise or cloud options, the cloud-native options (tools such as Snowflake, Azure, AWS, and Looker) enable marketers to use these technologies with little to no reliance on IT resources to maintain the back-end infrastructure.

Real-World Examples:

Most marketing organizations use cloud-based applications such as Salesforce, HubSpot, or Sprout Social. These cloud-based applications allow marketing users to quickly and reliably create, collaborate on, and manage their marketing initiatives without being tied to a single location or computer.

On-Prem

On-premise or on-prem refers to any software, storage, or service run from on-site computers or servers.

Why It Matters for Marketers:

Most marketing software is run on the cloud these days. Cloud solutions are faster, more dynamic, and more reliable.

So why would a business choose on-prem? Today, there are two main reasons a business might still have on-prem software:

  1. The company is in a highly regulated industry where data ownership or security are big concerns.
  2. The company has legacy on-prem solutions with massive amounts of data, making the switch to cloud more challenging.

However, many of these companies still recognize the need to update their infrastructures. On-prem is harder to maintain and has reduced up-time as glitches or breaks are fixed at the speed of IT teams. What’s more: on-prem solutions can bottleneck your insights and ability to deliver insights at scale.

With this in mind, even companies with more complicated situations can use a hybrid of cloud and on-prem solutions. By doing this, they migrate less sensitive information to the cloud while keeping more regulated files on their own servers.

Real-World Examples:

In marketing, it’s likely that most data will be in the cloud but if you’re working with a client in a highly regulated industry, like government or healthcare, you might have some on-premise data sources.

Healthcare companies have patient privacy regulations like HIPAA about how customer data can be used, including marketing campaigns. In this case, an on-prem solution might be a better alternative to protect patients’ rights.

Customer Data Platform (CDP)

A customer data platform is a software solution that synthesizes customer data from various sources to keep them in sync with each other. CDPs often additionally offer the ability to send this data to a database of your choice for analytics.

Why It Matters for Marketers:

CDPs allow your various tools (such as your CRM, Google Analytics, and e-commerce systems) to stay in sync with each other around customer data. This means if you change a detail about a customer in one system, everyone else sees this update come through automatically without any manual updating.

Real-World Examples:

CDPs make it really easy to create quality account-based marketing (ABM) campaigns. CDPs deliver a persistent, accurate, and unified customer base, making it easy to use data throughout the ABM campaign.

For example, selecting and validating target accounts uses data from across your entire organization. Once pulled into the CDP, you can perform analytics on that data to identify the best accounts to go after. You will have thousands of attributes to better understand which customers are more likely to purchase.

One note: CDPs do not usually tie these customers and their information to other subject areas like products, orders, loyalty, etc. They are also not meant for analytic use cases. If you are doing deeper, company-wide analysis, you might want a data warehouse.

Custom Dev

Custom development, or custom dev, is a term that refers to any application or solution developed to satisfy the requirements of a specific user or business rather than for general use.

Why It Matters for Marketers:

Even the best out-of-the-box software or solutions are designed to overcome the challenges of a broad user base, providing functionality that only satisfies generalized needs. Custom dev solutions address your specific business needs in a way that gives you a competitive advantage or reduces the amount of time spent trying to make a generic software match your unique needs.

Real-World Examples:

One retail company was receiving flat files from a monthly vendor report that were hard to integrate with the rest of their reports. This made it challenging to get the deeper insights their marketing team needed to make informed omni-channel decisions.

As there were no tools available in the market with a connector to their system, a custom dev solution was needed. An application was created to automatically take in these flat files from the vendor so the marketing team could receive new data without the lengthy request and ingest process that relied heavily on IT resources. This enabled the marketing team to easily target the same customer across channels by using personalized campaigns that aligned with purchasing habits and history.

Another example of custom dev is the implementation of automated customer touchpoints. Adding features that trigger events based on business rules is a great way to personalize your customers’ experience. For example, you could create a rule that emails customers a coupon for their most frequently purchased product when they haven’t made a purchase in the past six months.

Throughout this Marketers’ Guide to Data Management and Analytics series, we hope you’ve learned about the different tools to manage, integrate, analyze, and use your data more strategically to get the most out of your investments. Please contact us to learn how we can help build and implement these various solutions, so you can better understand your customer base and target your customers accurately.


Analytics and Insights for Marketers

Analytics & Insights for Marketers is the third in a series of our Marketers’ Guide to Data Management and Analytics. In this series, we cover major terms, acronyms, and technologies you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments.

In case you missed them, you can access part one here and part two here.

In this post, we’ll explore:

  • Business intelligence (BI)
  • Real-time analytics
  • Embedded analytics
  • Artificial intelligence (AI)
  • Machine learning

Business Intelligence

Business intelligence refers to the process in which data is prepared and analyzed to provide actionable insights and help users make informed decisions. It often encompasses various forms of visualizations in dashboards and reports that answer key business questions.

Why It Matters for Marketers:

With an increasing number of marketing channels comes an increasing amount of marketing data. Marketers who put BI tools to use gain essential insights faster, more accurately define key demographics, and make marketing dollars last.

Marketers without access to a BI tool spend a disproportionate amount of time preparing, rather than analyzing, their data. With the right dashboards in place, you can visualize observations about customer and demographic behaviors in the form of KPIs, graphs, and trend charts that inform meaningful and strategic campaigns.

Real-World Examples:

Your BI dashboards can help answer common questions about more routine marketing metrics without spending hours preparing the data. In a way, they take the pulse of your marketing initiatives. Which channels bring in the most sales? Which campaigns generate the most leads? How do your retention rate and ROI compare over time? Access to these metrics and other reports can shape the big picture of your campaigns. They help you make a measurable impact on your customer lifetime value, marketing RPI, and other capabilities.

Real-Time Analytics

Real-time analytics utilizes a live data stream and frequent data refreshes to enable immediate analysis as soon as data becomes available.

Why It Matters for Marketers:

Real-time analytics enhances your powers of perception by providing up-to-the-minute understanding of buyers’ motivations. A real-time analytics solution allows you to track clicks, web traffic, order confirmations, social media posts, and other events as they happen, enabling you to deliver seamless responses.

Real-World Examples:

Real-time analytics can be used to reduce cart abandonment online. Data shows that customers abandon 69.57% of online transactions before they are completed. Implementing a real-time analytics solution can enable your marketing team to capture these lost sales.

By automatically evaluating a combination of live data (e.g., abandonment rates, real-time web interactions, basket analysis, etc.) and historical data (e.g., customer preferences, demographic groups, customer click maps, etc.), you can match specific customers to targeted messaging, right after they leave your site.

Embedded Analytics

Embedded analytics is the inclusion of a business intelligence functionality (think graphs, charts, and other visualizations) within a larger application (like your CRM, POS, etc.)

Why It Matters for Marketers:

The beauty of embedded analytics is that you do not need to open up a different interface to visualize data or run reports. Integrated BI functionality enables you to review customer data, sales history, or conversion rates along with relevant reports that enhance your decision-making. This enables you to reduce time-to-insight and empower your team to make data-driven decisions without leaving the applications they use daily.

Real-World Examples:

A member of your marketing team is reviewing individual customers in your CRM to analyze their customer lifetime value. Rather than exporting the data into a different analytics platform, you can run reports directly in your CRM – and even incorporate data from external sources.

In doing so, you can identify different insights that improve campaign effectiveness such as which type of content best engages your customers, how to re-engage detractors, or when customers expect personalized content.

Artificial Intelligence

AI is the ability for computer programs or machines to learn, analyze data, and make autonomous decisions without any major contributions from humans.

Why It Matters for Marketers:

Implementing AI can provide a better understanding of your business as it detects forward-looking data patterns that employees would struggle to find – and in a fraction of the time. Additionally, marketers can improve customer service through a data-driven understanding of customer behavior and with new AI-enabled services like chatbots.

Real-World Examples:

Customizing email messaging used to be a laborious process. You’d need to manually create a number of campaigns. Even then, you could only tailor your messages to segments, not to a specific customer. Online lingerie brand Adore Me pursued AI to mine existing customer information and histories to create personalized messages across omnichannel communications. As a result, monthly revenue increased by 15% and the average order amount increased by 22%.

AI chatbots are also making waves, and Sephora is a great example. The beauty brand launched a messaging bot through Kik as a way of engaging with their teenage customers preparing for prom. The bot provided them with tailored makeup tutorials, style guides, and other related video content. During the campaign, Sephora had more than 600,000 interactions and received 1,500 questions that they answered on Facebook Live.

Machine Learning

Machine learning is a method of data analysis in which statistical models are built and updated in an automated process.

Why It Matters for Marketers:

Marketers have access to a growing volume and variety of complex data that doesn’t always provide intuitive insight at first glance. Machine learning algorithms not only accelerate your ability to analyze data and find patterns, but they can identify unforeseeable connections that a human user might have missed. Through machine learning, you can enhance the accuracy of your analyses and dig deeper into customer behavior.

Real-World Examples:

One Chicago retailer used a centralized data platform and machine learning to identify patterns and resolve questions about customer lifetime value. In an increasingly competitive landscape, their conventional reporting solution wasn’t cutting it.

By combining data from various sources and then performing deeper, automated analysis, they were able to anticipate customer behavior in unprecedented ways. Machine learning enabled them to identify which types of customers would lead to the highest lifetime value, which customers had the lowest probability of churn, and which were the cheapest to acquire. This led to more accurate targeting of profitable customers in the market.

That’s only the beginning: a robust machine learning algorithm could even help predict spending habits or gather a customer sentiment analysis based on social media activity. Machine learning processes data much faster than humans and is able to catch nuances and patterns that are undetectable to the naked eye.

We hope you gained a deeper understanding into the various ways to analyze your data to receive business insights. Feel free to contact us with any questions or to learn more about what analytics solution would work best for your organizational needs.


The Data Supply Chain for Marketers

In this blog post, we’ll explore the basics of the data supply chain and why they matter for marketers, including:

  • Data ingestion
  • The differences between ETL and ELT
  • Data pipelines
  • Data storage options

If you haven’t read Data 101 for Marketers, start with that blog post here.

Data Ingestion

Data ingestion is the first step in any analytical undertaking. It’s a process where data from one or many sources are gathered and imported into one place. Data can be imported in real time (like POS data) or in batches (like billing systems).

Why It Matters for Marketers:

The process of data ingestion consolidates all of the relevant information from across your data sources into a single, centralized storage system. Through this process, you can begin to convert disparate data created in your CRM, POS, and other source systems into a unified format that is ready for real-time or batch analysis.

Real-World Examples:

Marketing teams pull data from a wide variety of resources, including Salesforce, Marketo, Facebook, Twitter, Google, Stripe, Zendesk, Shopify, Mailchimp, mobile devices, and more. It’s incredibly time-consuming to manually combine these data sources, but by using tools to automate some of these processes you can get data into the hands of your team faster.

This empowers marketers to answer more sophisticated questions about customer behavior, such as:

  • Why are customers leaving a specific product in their online shopping carts?
  • What is the probability that we’ll lose a customer early in the customer journey?
  • Which messaging pillar is resonating most with customers in the middle of the sales funnel who live in Germany?

Image 1: In this image, three source systems with varying formats and content are ingested into a central location in the data warehouse.

ETL vs. ELT

ETL and ELT are both data integration methods that make it possible to take data from various sources and move it into a singular storage space like a data warehouse. The difference is in when the transformation of data takes place.

Real-World Examples:

As your business scales, ELT tools are better-equipped to handle the volume and variety of marketing data on hand. However, a robust data plan will make use of both ELT and ETL tools.

For example, a member of your team wants to know which marketing channels are the most effective at converting customers with the highest average order value. The data you need to answer that question is likely spread across multiple structured data sources (e.g., referral traffic from Google Analytics, transaction history from your POS or e-commerce system, and customer data from your CRM).

Through your ETL process, you can extract relevant data from the above sources, transform it (e.g., updating customer contact info across files for uniformity and accuracy), and load the clean data into one final location. This enables your team to run your query in a streamlined way with limited upfront effort.

In comparison, your social media marketing team wants to see whether email click-through rates or social media interactions lead to more purchases. The ELT process allows them to extract and load all of the raw data in real time from the relevant source systems and run ad-hoc analytics reports, making adjustments to campaigns on the fly.

Extract, Transform, Load (ETL)

This method of data movement first copies data from the original database into the target system and then converts the data into a singular format. Lastly, the transformed data is uploaded into a data warehouse for analytics.

When You Should Use ETL:

ETL processes are preferable for moving small amounts of structured data with no rush on when that data is available for use. A robust ETL process would clean and integrate carefully selected data sources to provide a single source of truth that delivers faster analytics and makes understanding and using the data extremely simple.

Image 2: This image shows four different data sources with varying data formats being extracted from their sources, transformed to all be formatted the same, and then loaded into a data warehouse. Having all the data sources formatted the same way allows you to have consistent and accurate data in the chart that is built from the data in the data warehouse.

Extract, Load, Transform (ELT)

Raw data is read from source databases, then loaded into the database in its raw form. Raw data is usually stored in a cloud-based data lake or data warehouse, allowing you to transform only the data you need.

When You Should Use ELT:

ELT processes shine when there are large amounts of complex structured and unstructured data that need to be made available more immediately. ELT processes also upload and store all of your data in its raw format, making data ingestion faster. However, performing analytics on that raw data is a more complex process because cleaning and transformation happen post-upload.

Image 3: This image is showing four different data sources with the data formatted in different ways. The data is being extracted from the various sources, loaded into the data warehouse, and then transformed within the data warehouse to all be formatted the same. This allows for accurate reporting of the data in the chart seen above.

Data Pipeline

A data pipeline is a series of steps in an automated process that moves data from one system to another, typically using ETL or ELT practices.

Why It Matters for Marketers:

The automatic nature of a data pipeline removes the burden of data manipulation from your marketing team. There’s no need to chase down the IT team or manually download files from your marketing automation tool, CRM, or other data sources to answer a single question. Instead, you can focus on asking the questions and honing in on strategy while the technology takes away the burden of tracking down, manipulating, and refreshing the information.

Real-World Examples:

Say under the current infrastructure, your sales data is split between your e-commerce platform and your in-store POS systems. The different data formats are an obstacle to proper analysis, so you decide to move them to a new target system (such as a data warehouse).

A data pipeline would automate the process of selecting data sources, prioritizing the datasets that are most important, and transforming the data without any micromanagement of the tool. When you’re ready for analysis, the data will already be available in one destination and validated for accuracy and uniformity, enabling you to start your analysis without delay.

Data Storage Options

Databases, data warehouses, and data lakes are all systems for storing and using data, but there are differences to consider when choosing a solution for your marketing data.

Definitions:

  • A database is a central place where a structured and organized collection of data can be stored in a computer that is accessed via various applications such as MailChimp, Rollworks, Marketo, or even more traditional campaigns like direct mail. It is not meant for large-scale analytics.
  • A data warehouse is a specific way of structuring your data in database tables so that it is optimized for analytics. A data warehouse brings together all your various data sources under one roof and structures it for analytics.
  • A data lake is a vast repository of structured and unstructured data. It handles all types of data, and there is no hierarchy or organization to the storage.

Why It Matters for Marketers:

There are benefits and drawbacks to each type of data structure, and marketers should have a say in how data gets managed throughout the organization. For example, with a data lake, you will need to have a data scientist or other technical resource on staff to help make sense of all the data, but your marketing team can be more self-sufficient with a database or data warehouse.

Database

Benefits:

Without organization and structure, the insights your data holds can be unreliable and hard to find. Pulling data from various source systems is often time-consuming and requires tedious and error-prone reformatting of the data in order to tell a story or answer a question. A database can help to store data from multiple sources in an organized central location.

Real-World Example:

Without databases, your team would have to use multiple Excel sheets and manual manipulation to store the data needed for analysis. This means your team would have to manually match up or copy/paste each Excel sheet’s data in order to create one place to analyze all of your data.

Data Warehouse

Benefits:

A data warehouse delivers an extra layer of organization across all databases throughout your business. Your CRM, sales platform, and social media data differ in format and complexity but often contain data about similar subjects. A data warehouse brings together all of those varying formats into a standardized and holistic view structured to optimize reporting. When that data is consolidated from across your organization, you can obtain a complete view of your customers, their spending habits, and their motivations.

Real-World Example:

You might hear people say “enterprise data warehouse” or “EDW” when they talk about data. This is a way to structure data that makes answering questions via reports quick and easy. More importantly, EDWs often contain information from the entire company, not just your function or department. Not only can you answer questions about your customer or marketing-specific topics, but you can understand other concepts such as the inventory flow of your products. With that knowledge, you can determine, for example, how inventory delays are correlated to longer shipping times, which often result in customer churn.

Data Lake

Benefits:

A data lake is a great option for organizations that need more flexibility with their data. The ability for a data lake to hold all data—structured, semi-structured, or unstructured—makes it a good choice when you want the agility to configure and refigure models and queries as needed. Access to all the raw data also makes it easier for data scientists to manipulate the data.

Real-World Example:

You want to get real-time reports from each step of your SMS marketing campaign. Using a data lake enables you to perform real-time analytics on the number of messages sent, the number of messages opened, how many people replied, and more. Additionally, you can save the content of the messages for later analysis, delivering a more robust view of your customer and enabling you to increase personalization of future campaigns.

So, how do you choose?

You might not have to pick just one solution. In fact, it might make sense to use a combination of these systems. Remember, the most important thing is that you’re thinking about your marketing data, how you want to use it, what makes sense for your business, and the best way to achieve your results.

Hopefully this information has helped you better understand your options for data ingestion and storage. Feel free to contact us with any questions or to learn more about data ingestion and storage options for your marketing data.


Marketing Dashboard Examples for Data-Driven Marketers

In recent years, marketers have seen a significant emphasis on data-driven decision-making. Additionally, there is an increased need to understand customer behavior and key metrics such as ROI or average order value (AOV). With an endless number of data sources (social media, email marketing, ERP systems, etc.) and a rapidly growing amount of data, both executives and analysts struggle to make sense of all the available information and respond in a timely manner.

A well-developed marketing dashboard helps companies overcome these challenges by organizing data into digestible metrics and reports that update automatically. Dashboards provide intuitive visuals that unlock the business value within your data quickly and without the manual effort of getting pieces of information from multiple places. Below, we have outlined four ways that dashboards benefit the entire marketing team, from the executives to the analysts.

Download Now: Sales and Marketing Dashboard Lookbook

1. Dashboards centralize all of your key information in one place.

Dashboards combine all of the essential information that would typically be found across various reports from disparate systems such as your CRM, ERP, or even third-party reports. Questions such as “How does our average order value compare to this time last year?”, “Which marketing channels are driving the most new customers?”, or “What is our marketing ROI?” can be quickly, consistently, and reliably answered without waiting on IT to put the information together or spending your department’s working day going to each software to download a new report and then marry them together. Instead, you can simply refer to a dashboard that highlights key statistics.

2. Dashboards automate manual processes and ensure reliable and consistent reports.

Marketing analysts often spend more time wrangling, cleaning, and validating data for a report than they do gleaning insights from it. Even after these reports are created, it’s difficult to ensure the metrics will remain consistent each time they are delivered to executives. Dashboards are typically built using one source of data that has predefined metrics and inputs, to ensure the reports remain consistent. They can automatically refresh data on a set schedule or surface it as it’s collected. Not only does this create more time for marketing analysts to focus on creating campaigns and incentives based on these insights, but it also allows executives to make decisions using accurate and consistent data points.

This dashboard highlights the trend in a selected metric, including its predicted future value, allowing marketers to quickly pivot when current campaign ROI is trending downward. It also allows the marketing department to demonstrate ROI to the company as a whole, which often leads to an increase in marketing budget.

This dashboard highlights the order activity associated with experimental products to allow the merchandising team to pivot quickly when a new product isn’t working, or it can allow the marketing team to increase advertising dollars if a new product isn’t getting enough attention.

3. Quick and reliable understanding of customer behavior paves the way to a stronger customer relationship.

Dashboards consolidate and visualize the story your data is telling. This often reflects the reality of how customers interact with your brand and clearly points out new trends.

For example, creating a picture that combines key pieces of information across systems, such as how many orders a customer placed and the value of those orders from your ERP with click analytics for that customer from your email marketing platform, allows you to identify like-customers who may respond to similar incentives. These customer profiles can then grow and change over time as you gather more data, leading to insights that allow you to more efficiently target the audiences who are more likely to convert based on that incentive. It also cuts down on the number of incentives or touchpoints you put in front of a customer who isn’t interested in that particular part of your business. Less spam for your customers and more conversions for you.

This dashboard quickly highlighted which customer segment was more engaged when the brand pushed social/email/web content, giving the marketing department the perfect focus group on which they could test new ideas.

This dashboard provides an executive-level overview of marketing performance.

This dashboard shows the comparison in purchase behavior across loyalty and non-loyalty customers. Our clients use dashboards like these to inform when they should incentivize customers to participate in a loyalty program or jump to the next tier and when the loyalty program is actually costing them more money than it’s yielding.

This dashboard highlights the time between orders across your customer base. Say, for example, you have a set of customers who place an order every six weeks, but they haven’t returned for 10. Our clients automate the discovery of these customers and send that email list to their email service provider (ESP) to automatically re-engage that customer base.

4. Dashboards are a gateway to advanced customer analytics.

With your analysts no longer consumed by manually building out reports, you’re on your way to identifying strong use cases for machine learning (ML) and predictive analytics. This form of advanced customer analytics covers a wide variety of use cases.

A common marketing use case for ML is predicting customer lifetime value (CLV/LTV) prior to investing marketing dollars on acquisition by matching a potential customer to the profile of existing customers that either yield high net profit or end up costing your business money. Another great marketing use case for data science is predicting the probability of conversion for a specific campaign or promotion based on a customer or customer segment’s previous behavior with like-campaigns. Your branding and merchandising teams may want to focus on identifying products that would yield a higher profit or increase in orders as a bundle instead of being sold individually.

Regardless of the use case, your dashboards will put you in a strong position to have a more targeted and therefore effective data science use case.

This is an example of a marketing dashboard that helps better understand customer demographics.

This dashboard gives marketers a place to test theories on customer demographics that would yield the highest LTV for a specific campaign.

Implementing strategic, goal-oriented dashboards significantly improves your marketing efforts at all levels. They provide analysts with the ability to spend their time acting on information rather than searching for and cleaning up data. More importantly, they enable executives to make informed decisions that ultimately increase ROI and ensure marketing budget is spent on impactful efforts.

2nd Watch has a vast array of experience helping marketers create dashboards that unlock valuable insights. Contact us or check out our Marketing Analytics Starter Pack to quickly gain the benefits listed above with a marketing dashboard specialized for your company.


Data Clean Rooms: Share Your Corporate Data Fearlessly

Data sharing has become more complex, both in its application and our relationship to it. There is a tension between the need for personalization and the need for privacy. Businesses must share data to be effective and ultimately provide tailored customer experiences. However, legislation and practices regarding data privacy have tightened, and data sharing is tougher and fraught with greater compliance constraints than ever before. The challenge for enterprises is reconciling the increased demand for data with increased data protection.

The modern world runs on data. Companies share data to facilitate their daily operations. Data distribution occurs between business departments and external third parties. Even something as innocuous as exchanging Microsoft Excel and Google Sheets spreadsheets is data sharing!

Data collaboration is entrenched in our business processes. Therefore, rather than avoiding it, we must find the tools and frameworks to support secure and privacy-compliant data sharing. So how do we govern the flow of sensitive information from our data platforms to other parties?

The answer: data clean rooms. Data clean rooms are the modern vehicle for various data sharing and data governance workflows. Across industries – including media and entertainment, advertising, insurance, private equity, and more – a data clean room can be the difference-maker in your data insights.

Ready to get started with a data clean room solution? Schedule time to talk with a 2nd Watch data expert.

What is a data clean room?

There is a classic thought experiment wherein two millionaires want to find out who is richer without actually sharing how much money they are individually worth. The data clean room solves this issue by allowing parties to ask approved questions, which require external data to answer, without actually sharing the sensitive information itself!

In other words, a data clean room is a framework that allows two parties to securely share and analyze data by granting both parties control over when, where, and how said data is used. The parties involved can pool together data in a secure environment that protects private details. With data clean rooms, brands can access crucial and much-needed information while maintaining compliance with data privacy policies.

Data clean rooms have been around for about five years with Google being the first company to launch a data clean room solution (Google Ads Data Hub) in 2017. The era of user privacy kicked off in 2018 when data protection and privacy became law, most notably with the General Data Protection Regulation (GDPR).

This was a huge shake-up for most brands. Businesses had to adapt their data collection and sharing models to operate within the scope of the new legislation and the walled gardens that became popular amongst all tech giants. With user privacy becoming a priority, data sharing has become stricter and more scrutinized, which makes marketing campaign measurements and optimizations in the customer journey more difficult than ever before.

Data clean rooms are crucial for brands navigating the era of consumer protection and privacy. Brands can still gain meaningful marketing insights and operate within data privacy laws in a data clean room.

Data clean rooms work because the parties involved have full control over their data. Each party agrees upon access, availability, and data usage, while a trusted data clean room offering oversees data governance. This yields the secure framework needed to ensure that one party cannot access the other’s data and upholds the foundational rule that individual, or user-level data cannot be shared between different parties without consent.

Personally, identifying information (PII) remains anonymized and is processed and stored in a way that is not exposed to any parties involved. Thus, data sharing within a data clean room complies with privacy policies, such as GDPR and California Consumer Privacy Act (CCPA).

How does a data clean room work?

Let’s take a deeper dive into the functionality of a data clean room. Four components are involved with a data clean room:

#1 – Data ingestion
Data is funneled into the data clean room. This can be first-party data (generated from websites, applications, CRMs, etc.) or second-party data from collaborating parties (such as ad networks, partners, publishers, etc.)

#2 – Connection and enrichment
The ingested data sets are matched at the user level. Tools like third-party data enrichment complement the data sets.

#3 – Analytics
The data is analyzed to determine if there are intersections/overlaps, measurement/attribution, and propensity scoring. Data will only be shared where the data points intersect between the two parties.

#4 – Application
Once the data has finished its data clean room journey, each party will have aggregated data outputs. It creates the necessary business insights to accomplish crucial tasks such as optimizing the customer experience, performing reach and frequency measurements, building effective cross-platform journeys, and conducting deep marketing campaign analyses.

What are the benefits of a data clean room?

Data clean rooms can benefit businesses in any industry, including media, retail, and advertising. In summary, data clean rooms are beneficial for the following reasons:

You can enrich your partner’s data set.
With data clean rooms, you can collaborate with your partners to produce and consume data regarding overlapping customers. You can pool common customer data with your partners, find the intersection between your business and your partners, and share the data upstream without sharing sensitive information with competitors. An example would be sharing demand and sales information with an advertising partner for better-targeted marketing campaigns.

You can create governance within your enterprise.
Data clean rooms provide the framework to achieve the elusive “single source of truth.” You can create a golden record encompassing all the data in every system of records within your organization. This includes sensitive PII such as social security numbers, passport numbers, financial account numbers, transactional data, etc.

You can remain policy compliant.
In a data clean room environment, you can monitor where the data lives, who has access to it, and how it is used with a data clean room. Think of it as an automated middleman that validates requests for data. This allows you to share data and remain compliant with all the important acronyms: GDPR, HIPPA, CCPA, FCRA, ECPA, etc.

But you have to do it right…

With every data security and analytics initiative, there is a set of risks if the implementation is not done correctly. A truly “clean” data clean room will allow you to unlock data for your users while remaining privacy compliant. You can maintain role-based access, tokenized columns, and row-level security – which typically lock down particular data objects – and share these sensitive data sets quickly and in a governed way. Data clean rooms satisfy the need for efficient access and the need for the data producer to limit the consumer to relevant information for their use case.

Of course, there are consequences if your data clean room is actually “dirty.” Your data must be federated, and you need clarity on how your data is stored. The consequences are messy if your room is dirty. You risk:

  • Loss of customer trust
  • Fines from government agencies
  • Inadvertently oversharing proprietary information
  • Locking out valuable data requests due to a lack of process

Despite the potential risks of utilizing a data clean room, it is the most promising solution to the challenges of data-sharing in a privacy-compliant way.

Conclusion

To get the most out of your data, your business needs to create secure processes to share data and decentralize your analytics. This means pooling together common data with your partners and distributing the work to create value for all parties involved.

However, you must govern your data. It is imperative to treat your data like an asset, especially in the era of user privacy and data protection. With data clean rooms, you can reconcile the need for data collaboration with the need for data ownership and privacy.

2nd Watch can be your data clean room guide, helping you to establish a data mesh that enables sharing and analyzing distributed pools of data, all while maintaining centralized governance. Schedule time to get started with a data clean room.

Fred Bliss – CTO Data Insights 2nd Watch 


Data 101 for Marketers

In Data 101 for Marketers, we’ll cover the basics of data you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments. This includes:

  • The definition of data
  • Different types of data
  • What matters most for marketers
  • Examples of marketing data
  • The benefits of marketing data management

What is Data?

Definition:

Data is any piece of information that can be used to analyze, manage, or connect with your buyers. Data is often stored in various systems throughout your organization such as your website or email marketing tool.

Why it matters for marketers:

At the most basic level, data can be used to communicate with customers. As a marketing organization matures, the need to access, analyze, and leverage data becomes more critical.

Two main Types of Data in Marketing: Structured Data vs Unstructured Data

There are two main types of data, structured and unstructured. Each contains valuable insights about your buyers. When they are combined, your marketing team can create greater context for data and expand the depth of your analysis.

Structured Data

Definition:

Structured data is highly organized, formatted, and searchable data that fits neatly into a field in a data table. This data gives you a basic understanding of who your customers and prospects are. It’s also known as quantitative data.

Examples:

An example of structured data in marketing is data stored in systems such as customer relationship management (CRM) tools, enterprise resource planning (ERP) software, or point of sale (POS) systems. It includes information like:

  • Names
  • Dates
  • Phone numbers
  • Email addresses
  • Purchase history
  • Credit card numbers
  • Order numbers

How it is used:

Structured data is the data you use to connect with and understand your customers and prospects at the most basic level.

The information is used in:

  • Email communication in your CRM or marketing automation tool
  • Tracking of inbound and outbound sales, marketing, and service touchpoints through your CRM
  • Website and content optimization for search engine optimization (SEO)
  • Purchase history analysis

Real-world examples:

Example 1: Gmail uses structured data from your flight confirmation to provide a quick snapshot of your flight details within the email.

Image Source: litmus.com

Example 2: Your marketing automation software uses structured data to pull customer names for customized email campaigns.

Unstructured Data

Definition:

Unstructured data is any data that does not fit into a pre-designed data table or database. This data often holds deeper insights into your customers but can be difficult to search and analyze. It’s also known as qualitative data.

Examples:

Unstructured data is relevant and insightful information about your customers and prospects from a variety of sources such as:

  • Email or chat messages
  • Images
  • Videos or video files
  • Contracts
  • Social media posts
  • Survey results
  • Reports

How it is used:

Unstructured data, often combined with structured data, can be used to find deep insights on customer or prospect behavior, sentiment, or intent such as:

  • Understanding buying habits
  • Gaining a 360 view of the customer
  • Measuring sentiment toward a product or service
  • Tracking patterns in purchases or behaviors

Real-world examples:

Social media data has a huge impact on businesses today. Social listening is used as a way to gain deeper insight about your customers and what they think of your business. They might comment, post their own user-generated content, or post about your business. All of those highly valuable data points are unstructured or qualitative in nature but provide a deeper dive into the minds of consumers.

Data Sources

Definition:

Data sources are the origin points of your data. They can be files, databases, or even live data feeds. Marketing data sources include web analytics, marketing automation platforms, CRM software, or POS systems.

Why it matters for marketers:

Each data source holds a fragment of a story about your customers and prospects. Often these data sources come from siloed systems throughout your business. By combining various data sources, you can uncover the full narrative and get a 360 view of your customers and prospects.

Making use of new technology to aggregate and analyze data sources can reduce marketing dollars and time spent on multiple softwares to piece together the data you need for your daily questions or analysis.

Real-world examples:

CMOs and marketers are increasingly being asked to justify marketing spend against KPIs. This can be challenging because a lot of marketing activity is, by nature, indirect brand-building. However, that doesn’t mean we can’t get better at measuring it.

It isn’t an easy task, but centralizing your marketing data sources actually makes it easier to prove ROI. It cuts down on reporting time, enhances the customer experience, and makes it easy to use insights from one channel to inform another.

For example, customer service data can make a huge difference for the sales team. If a customer emails or calls a customer service rep with a complaint, that issue should not only get tracked in the service rep’s software but in the sales representative’s system as well. That way, when the sales rep calls on that customer again, they have the full history of service and/or repairs made, potentially making it easier to retain or upsell that customer.

We hope you found this intro into data management useful. Feel free to contact us with any questions or to learn more about marketing data solutions.

Want better data insights and customer analytics?

2nd Watch’s Marketing Analytics Starter Pack provides an easy way to get started or expand your current marketing reporting and analytics capabilities.