Customer-centric companies are winning. Studies show that companies that excel at customer experience achieve 5 percent to 10 percent higher revenue growth than their competitors and that customers are willing to pay more for a premium brand experience.
In today’s hyper-connected world, companies are interacting with their customers in countless ways, from ads, emails, in-product, support and more. In fact, a recent Zendesk survey suggests companies create an average of 29 data touch points per month, up from an average of 18 just two years ago, that is data waiting to impact revenue.
While leveraging Fivetran helps centralize data from scattered sources, data access is only the first step towards a 360-degree view of your customer. You need to transform raw data points into informed insights to improve customer outcomes.
Get insight-ready tables with our free data models
Transforming data into analytics-ready tables is a time and resource intensive process. That’s why Fivetran offers our robust library of free dbt data models to accelerate your time to insights.
We’ve previously discussed the value of data models, but several new data models and improvements help turn centralized data into insights-generating tables. These improvements are across all of your customer-centric data types.
Understand all of your customer-marketing touchpoints
Marketing is how brands speak with their customers. It involves a complex effort across channels, platforms and tactics. That’s why our Fivetran analytics engineering team built our popular Ad Reporting data model to help companies aggregate their cross-channel marketing into one 360-degree view. This enables marketers to understand where to invest their budget to delight customers and grow their return on ad spend.
Our new union schema functionality helps brands leveraging multiple accounts on the same platform get a complete view of their performance. With this functionality (available for many popular data models like Ad Reporting, Amazon Ads, Facebook Ads, Google Ads, Quickbooks, Shopify, Stripe and more) you can aggregate and unify data across various connectors of the same source without deploying multiple data models or manually creating union logic. This reduces development time and lets you spend more time analyzing and optimizing your customer-centric marketing.
In order to expedite time to insight even further, we’ve recently released new Quickstart data model compatibility for some of your key marketing sources like Twitter Organic, Mailchimp and Marketo, along with many others including: Greenhouse, Iterable, Amplitude, Klaviyo and Mixpanel.
Optimize your customer experience
Marketing is only one way that customers experience your brand. You likely build entire customer and prospect journeys in your CRM system — like Salesforce — that are essential to understanding how prospects evolve to valued customers.
To help you better utilize this crucial Salesforce data, we have released two important data model updates.
Many companies have custom logic, called formula fields, written in their Salesforce instance. These formula fields include calculations, classifications and data manipulations that help identify a customer, their lifetime value and nurture strategy. These fields are integral for customer engagement success. Unfortunately, getting these insights out of Salesforce was previously a cumbersome task.
With our new Salesforce Formula Fields Quickstart data model, accessing and analyzing this custom logic is easy. With a few clicks, you can unnest and take action on these insights.
Our Salesforce data model also includes a number of output models that build on top of Salesforce history tables. These history tables track the changes in customer stage and status overtime, letting you analyze what touchpoints led target customer groups to take your desired actions. Leverage these insights to optimize the customer journey accordingly to time to value and increase engagement.
Understand the impact of improved customer outcomes on financial performance
Understanding cost through financial reporting is a critical part of understanding the impact of improved customer outcomes. Due to regulations and scrutiny, generating financial reporting is a cumbersome process. For that reason, many companies use Stripe for their standardized reports on business-critical accounting and month-end reconciliation.
Unfortunately, these pre-built reports live within Stripe’s UI and are not easily exported or replicated with fidelity — until now.
With the updates to our Stripe data model, you can replicate critical Stripe reports in your data warehouse without manual effort, streamlining your financial processes, increasing accuracy in your accounting and ultimately improving Stripe ecosystem performance.
We replicate the following reports:
- Activity.itemized.2
- Balance_change_from_activity.itemized.3
- Payouts.itemized.3
- Ending_balance_reconciliation.itemized.4
Automate critical business reporting to meet the needs of customers
For many companies, SAP is the backbone of critical business processes that affect customer outcomes. However, SAP reporting is very challenging to construct.
Whether you’re trying to analyze your financials, business operations or inventory logistics — you need access to clean, analytics-ready data.
That’s why we built our new SAP data model. This model provides replication of extractor reports that you’d see within SAP’s UI. This saves time for your data team and removes the need to rebuild necessary reporting from scratch.
Quickly visualize customer insights
If you’re looking for a quick way to visualize Customer 360 insights, our analytics engineering team has developed dashboards using Streamlit that directly leverage the end models of the Zendesk and Netsuite data models. These dashboards include the following reports:
- Ticket metrics
- Assignee metrics
- SLA metrics
- Financial executive dashboard
- Balance sheet report
- Income statement report
You can explore these apps and their possibilities for free by viewing the published Streamlit app with Dunder Mifflin test data to showcase the visualizations. From there, you can easily fork the Streamlit app yourself and connect it to your own data.
Don’t worry, we have custom data sources covered too
We understand that you own custom data sources that are integral to your customer business. This can include product, user experience, segmentation or any other proprietary data source that you might store in a database.
While we don’t have a pre-built data model for these sources, we can help you orchestrate, manage and visualize your custom dbt data models with our Fivetran Transformations. Our Fivetran Transformations for dbt Core has helped countless businesses reduce data latency and computational costs by running custom data transformations upon successful load of new data in their destination.
Our new dbt Cloud™ integration coming in January 2024 will soon make doing so even more seamless.
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