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?

The Data Supply Chain: Data Ingestion

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.

The Data Supply Chain: ETL

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.

The Data Supply Chain: ELT

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.

The Data Supply Chain: Data Pipeline

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.

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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.

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Get to Know ALTR: Optimizing Data Consumption Governance

ALTR is a cloud native DSaaS platform designed to optimize data consumption governance. In this age of ever-expanding data security challenges, which have only increased with the mass move to remote workforces, data-centric organizations need to easily but securely access data. Enter ALTR: a cloud-native platform delivering Data Security as a Service (DSaaS) and helping companies to optimize data consumption governance.

Not sure you need another tool in your toolkit? We’ll dive into ALTR’s benefits so you can see for yourself how this platform can help you get ahead of the next changes in data security, simplify processes and enterprise collaboration, and maximize your technology capabilities, all while staying in control of your budget.

How Does ALTR Work?

How Does ALTR Work?

With ALTR, you’re able to track data consumption patterns and limit how much data can be consumed. Even better, it’s simple to implement, immediately adds value, and is easily scalable. You’ll be able to see data consumption patterns from day one and optimize your analytics while keeping your data secure.

ALTR delivers security across three key stages:

  • Observe – ALTR’s DSaaS platform offers critical visibility into your organization’s data consumption, including an audit record for each request for data. Observability is especially critical as you determine new levels of operational risk in today’s largely remote world.
  • Detect and Respond – You can use ALTR’s observability to understand typical data consumption for your organization and then determine areas of risk. With that baseline, you’re able to create highly specific data consumption policies. ALTR’s cloud-based policy engine then analyzes data requests to prevent security incidents in real time.
  • Protect – ALTR can tokenize data at its inception to secure data throughout its lifecycle. This ensures adherence to your governance policies. Plus, ALTR’s data consumption reporting can minimize existing compliance scope by assuring auditors that your policies are solid.

What Other Benefits Does ALTR Offer?

ALTR offers various integrations to enhance your data consumption governance:

  • Share data consumption records and security events with your favorite security information and event management (SIEM) software.
  • View securely shared data consumption information in Snowflake.
  • Analyze data consumption patterns in Domo.

ALTR delivers undeniable value through seamless integration with technologies like these, which you may already have in place; paired with the right consultant, the ROI is even more immediate. ALTR may be new to you, but an expert data analytics consulting firm like 2nd Watch is always investigating new technologies and can ease the implementation process. (And if you need more convincing, ALTR was selected as a finalist for Bank Director’s 2020 Best of FinXTech Awards.)

Dedicated consultants can more quickly integrate ALTR into your organization while your staff stays on top of daily operations. Consultants can then put the power in the hands of your business users to run their own reports, analyze data, and make data-driven decisions. Secure in the knowledge your data is protected, you can encourage innovation by granting more access to data when needed.

As a tech-agnostic company, 2nd Watch helps you find the right tools for your specific needs. Our consultants have a vast range of product expertise to make the most of the technology investments you’ve already made, to implement new solutions to improve your team’s function, and to ultimately help you compete with the companies of tomorrow. Reach out to us directly to find out if ALTR, or another DSaaS platform, could be right for your organization.

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