Detecting customer churn is essential for every subscription business. Once you acquire customers you must make sure they are engaged and successful with your product, as well as supported by your teams.
When detecting customer churn, focus on:
- High-value customers who have churned
- High-value customers at high risk for churning.
Identifying these two segments will highlight who your customer success team should reach out to, and in which areas. Remember that by preventing churn from happening, you are keeping existing customers happy, which research shows is many times more profitable than drumming up demand for new business.
Questions to Ask
Focusing on product usage and support will help you get the most value out of your fused data. Specifically, you’ll want to ask:
- How do churned customers use my product compared to non-churned customers? How do customers at risk for churning compare to non-churned customers?
- How does support for churned customers compare to support for non-churned customers? How does support for customers at risk for churning compared to non-churned customers?
The Value of Fused Data
Under normal circumstances, to answer these questions I would have to wrangle data from different database shards, copy-paste data, export and stitch .csvs, create workflow diagrams, and join tables. Oftentimes, ingesting and preparing data is so much work that no sooner than the data has the data been prepared than is it already stale. Or support is managed in a separate system and tough to align with CRM data. HelpScout lacks an Account object, and even for support systems, such as Zendesk or FreshDesk, which have this object, the accounts in the support system are managed separately from the accounts in the CRM.
With a Fused Database, tickets, contacts, and companies are automatically linked together, so reports can be generated at the click of a button. All my data can feed any dashboard, create relationships that didn’t exist, and I can reuse queries as many times as I want.
For example this report, using a tool like Metabase I can look at:
- Account data from Salesforce, which includes renewal date & ARR per customer;
- Ticket data, specifically unresolved incidents, from HelpScout.
Before diving in, I’ll first quickly familiarize myself with the object list in my Fused Database, either by running a SHOW TABLES query or looking at the fused records from a GUI. These fused data tables can be viewed alongside raw data as well.
In my Fused Database, for example, here are just some of the tables I can query to detect customer churn:
- Activity Company Links
- Activity Contact Links
- Activity Opportunity Links
- Activity Ticket Links
- Company Contact Links
- Company Opportunity Links
- Company Ticket Links
- Contact Opportunity Links
- Contact Ticket Links
And raw data from Gotoweb, HelpScout, HubSpot, and Salesforce.
One way to detect churn with fused data is looking at the incident rate (tickets / time). Using the object list above, I can see more than just a customer’s experience with my business — I can see entire company’s, and improve that account’s experience based on what I learn.
For example, I could use the company_ticket_links table to tie support cases to accounts. Below you’ll see the average number of tickets per month and the tickets per account. This unified view allows me to compare which accounts might be more prone to churn based on their relative incident rates.
One thing to consider here is choosing a time frame — either a quarter or a year — which corresponds to a certain KPI metric (e.g. Monthly Recurring Revenue vs. Annual Recurring Revenue). Whatever your time frame, make sure it relates to the seasonality of your business (e.g. renewing a monthly magazine subscription offers 12 times a year to churn, whereas annual subscriptions offer just one).
Building on this example, we could run many other types of analyses using fused data. For instance, if I had defined Intercom events, I could send Intercom product data to Fusion and link Activities to Contacts or Companies using their corresponding links tables.
Using a different example, we might imagine that my company was a CMS, and a key milestone is how many website and landing pages my customers create. With fused data, I might discover that churned high-value customers only create 3 of these, when my goal is 5 or 10. I could now see, through the magic of holistic analytics, all the different ways that customers have (or haven’t) engaged with my product, and take action to fix these high-churn areas.
Today, getting started with your trusted data source is simple. Here’s how it works:
1. Connect your data sources. Extract your data from SaaS applications such as Marketo, HubSpot, Salesforce and NetSuite. In just a few clicks.
2. Fuse your data. Automate data matching, de-duplicating, resolving data conflicts and modeling object relationships for more real-time reporting and dashboards.
3. Warehouse your data instantly. Automatically access your database via a cloud data warehouse.
4. Feed analytics & BI tools. Let your cloud data warehouse feed SQL input into tools you are using for visibility and analytics including Tableau, Microsoft PowerBI, Amazon QuickSight, MetaBase, and more.