MailChimp is a marketing service designed to improve your email campaigns through easy-to-design, automated, and personalized emails. Some of the features that MailChimp uses to increase campaign performance include drag-and-drop email templates, automated product suggestions, follow-up emails based on customer actions and revenue reports. These can help refocus marketing campaigns to provide the customer experience that is most likely to translate into sales.
As an advanced, high-speed data analytics platform, Mode helps you understand the information in your data warehouses. As a hybrid solution Mode lets (1) data teams customize queries and visualizations in Python, SQL, or R; and (2) empowers non-tech-savvy users to dive into visualizations and explore live-updating reports to get the data they need.
Bring all your MailChimp data to Amazon Redshift
Load your MailChimp data to Google BigQuery
ETL all your MailChimp data to Snowflake
Move your MailChimp data to MySQL
Bring all your Mode data to Amazon Redshift
Load your Mode data to Google BigQuery
ETL all your Mode data to Snowflake
Move your Mode data to MySQL
Monitor replies to your email campaigns based on a number of filters, including which campaigns have unread messages, which replies are part of a specific campaign and the dates that the replies were sent on. This allows you to more carefully track customer responses to your marketing efforts so that you can integrate that data into your marketing analytics efforts.
Track an array of analytical data about your MailChimp campaigns, including how many emails have been sent, whether they were delivered or bounced and how the recipients interacted with them (whether they opened them, forwarded them, subscribed, unsubscribed, etc). These reports can inform future campaign design by providing you with deeper analytics about which campaigns were most successful and why.
Retrieve data about your email campaigns, including the recipients, the type of campaign and how many emails have been delivered since the campaign began. You can then use this information to closely monitor the success of each campaign, which can be especially useful for those that are designed to test for marketing effectiveness, such as A/B campaigns.
Create a list of contacts that are subscribed to your email campaigns and define details about the campaigns themselves (like the email address that the emails will send from). Then, you can request metrics related to that list, such as how many emails have been delivered, how many subscribers the list has and how many people have unsubscribed since a campaign began.
Create targeted, automatic campaigns that will send emails in response to time-based and activity-based triggers. You can also use this endpoint to retrieve information about a specific automation, including how many emails have been delivered, what the defined triggers are and who the recipients are.
Mode's SQL editor features a schema browser to explore the tables and columns of your connected data sources. It also has an autocomplete function to assist with query writing, a query history to browse and reuse old queries, and advanced logic tools for creating loops, IF statements, and more.
The technology behind Mode's analytics solution is Helix, a high-performance in-memory data engine that combines modern drag-and-drop business intelligence tools with a code-first (SQL, Python, R) data science platform to offer an instantly responsive user experience. Mode streams query results into the Helix data engine to reduce database load, provide quick responsiveness, and give users the ability to visually navigate as much as 10 gigabytes of data.
Powered by SQL, Python, and R, Mode dashboards display automatically-refreshing metrics without the need for proprietary tools. Customize your dashboards the way you want, with on-brand reporting and interactive features to facilitate collaboration with others across your organization.
Mode features native Python and R Notebooks. This allows you to query in SQL, send the results to a dataframe in R or pandas, and conveniently access them in the platform's native Notebook. At the same time, a sidebar provides tips, shortcuts, and documentation to help you get the most of these features.