Best Practices

10 Triggers to Prevent Customer Churn

Akshaye Badiger

November 14, 2023

6 min read

Graphic introducing Alert Triggers to Retain More Customers

You've seen it happen — that moment a loyal customer suddenly seems less engaged, their visits become sporadic, and then they're gone. It's frustrating, isn't it?

Studies say that It costs up to 7 times more to acquire a new customer than to retain an existing one. Customer churn can completely derail any business’s growth. But often, companies take a reactive approach - waiting for clear signs of churn before scrambling to retain customers.

This hands-off strategy is high risk. By the time you see customers leaving, it’s already too late.

The key is being proactive. With intelligent alerts triggered by changes in customer behaviour, you can get ahead of churn before it happens. This gives you the opportunity to re-engage customers and prevent revenue loss.

In this article, we’ll explore how advanced alerting helps you take a proactive approach to customer retention. You’ll learn:

  • The limitations of traditional churn dashboards
  • How smart alerts provide an early warning system
  • Custom queries to target high-risk customers
  • Proven tactics to re-engage customers and prevent churn
  • How platforms like Locale enable proactive retention

Let’s get started.

Understanding Churn

Before diving into proactive retention strategies, let’s briefly define churn and how it’s calculated.

Churn rate measures the percentage of customers you lose in a given period. It's calculated as:

(Customers Lost Over a Period/Total Customers in that Period)

For example: To get a monthly churn rate, you'd look at the number of customers lost over the past 30 days divided by total customers at the beginning of that period.

Churn varies significantly by industry, but a few benchmarks are:

  • SaaS: 5-7% monthly
  • E-commerce: 15-25% monthly
  • Media/Entertainment: 1-5% monthly

Knowing your churn rate and how it compares to your industry average is key. An uptick may signal problems ahead.

The Pitfalls of Traditional Churn Dashboards

Many companies rely on churn dashboards to understand customer retention. These dashboards visualize past churn rates and segment users by attributes like plan type or location.

While these dashboards offer useful historical insights, they have three key limitations:

  1. Reactive, not proactive: Dashboards need to be manually tracked and analyzed. This can lead to churn signals falling through the cracks
  2. Limited data sources: Most dashboards only incorporate basic metrics like canceled accounts or missed payments. They lack a holistic view.
  3. Delay in taking action: Dashboards require a lot of time searching for issues. Companies need to react to churn signals fast. Dashboards add unnecessary friction to the entire process.
  4. No way to track if action was taken: With dashboards, the way people go about co-ordinating and tracking resolutions is through phone calls, WhatsApp chats, Slack threads. Needless to say that this results in nobody being sure of what’s being done to prevent churn.

To reduce churn proactively, you need forward-looking alerts, not backward-looking dashboards. Real-time signals across customer touchpoints are essential for early warning signs.

The Need for Advanced, Cross-Platform Alerting

While native alerts in CRM tools like Hubspot or analytics platforms like Mixpanel serve a purpose, they often miss critical signals in the subtle battle against churn.

These tools are great for flagging obvious issues like missed payments, but churn is rarely that simple.

The true signs of a customer on the brink of churn emerge from an analysis of behavioural patterns across all the touchpoints in your business.

Early signs of churn include negative mentions, decreasing product engagement, changes in purchase patterns, and more

No single data point tells the whole story and to make decisions, you need a comprehensive view. Native alerts often fail to provide this integrated perspective because they are limited to siloed datasets within each app. As a result, churn risks go undetected until it's too late

Why Use SQL for Churn Alerts?

While easy-to-use, drag-and-drop analytics tools have their place, SQL offers strategic advantages:

  • Custom queries match your data schema and business logic.
  • High-performance processing of large datasets enables enterprise scalability.
  • Unified workflows from insights to alert-driven actions.
  • Predictive analytics reveal customer signals not visible otherwise.

The flexibility and sophistication of SQL make it the right choice for in-depth analysis of complex customer profiles.

Is your data stack ready for churn alerts?

The SQL queries in this article assume you have a structured database like:

  • Users - Customer attributes and metadata
  • Interactions - Customer engagement events over time
  • Transactions - Purchases, subscriptions, and payments
  • Support - Service tickets and communication history

Your implementation could vary and you may have to modify these queries based on your schema.

Identifying and Acting on Churn Triggers

Now let's look at 10 such triggers that you can monitor with cross-channel alerting to uncover customer risks before they escalate:

Stalled Onboarding Alert

This identifies users who recently started but did not complete onboarding.

  • Trigger: Users not completing onboarding flows and setup processes.
  • Data Source: App analytics like Mixpanel, product usage data
  • Ideal Followup Action: Reach out to guide users through onboarding and offer assistance in completing key setup steps.
  • Sample Query:

SELECT user_id  
FROM onboarding_data  
WHERE completion_status = 'incomplete'  
AND start_date < CURRENT_DATE - INTERVAL '7 days'

Inactive User Alert

This identifies users who have not logged in or been active recently.

  • Trigger: Customers/users with no activity in the past 30 days.
  • Data Source: Analytics tools like Amplitude, Mixpanel, product usage logs
  • Ideal Follow-up Action: Email/message inactive users with re-engagement campaigns like new features or promotions.
  • SQL Query:

SELECT user_id  
FROM activity_logs  
WHERE last_active_date < CURRENT_DATE - INTERVAL '30 days'

Subscription Downgrade Alert

This catches customers downgrading from higher-tier plans.

  • Trigger: User downgrades their subscription plan/level
  • Data Source: Subscription system like Stripe or Zuora
  • Ideal Followup Action: Contact the user to understand why they downgraded and offer incentives to re-upgrade.
  • SQL Query:

SELECT customer_id, old_plan, new_plan  
FROM subscriptions  
WHERE new_plan < old_plan  
AND change_date BETWEEN CURRENT_DATE - INTERVAL '30 days' AND CURRENT_DATE

Cart Abandonment Alert

This identifies users who left items in their carts without completing purchases.

  • Trigger: Added cart items without checking out.
  • Data Source: E-commerce platform data like Shopify.
  • Ideal Followup Action: Email users with cart reminders and offer discounts to complete purchases.
  • SQL Query:

SELECT user_id  
FROM cart_events  
WHERE event_type = 'cart_add'  
AND checkout_date IS NULL  
AND cart_add_date < CURRENT_DATE - INTERVAL '3 days'

Support Ticket Spike Alert

This identifies users opening significantly more tickets than their typical volume.

  • Trigger: A much higher number of support tickets opened recently compared to the past average.
  • Data Source: Customer service platform like Zendesk.
  • Ideal Followup Action: Proactively reach out to resolve their issues before frustration escalates.
  • SQL Query:

SELECT user_id, COUNT(ticket_id) AS ticket_count  
FROM support_tickets  
WHERE opened_date BETWEEN CURRENT_DATE - INTERVAL '30 days' AND CURRENT_DATE  
GROUP BY user_id  
HAVING ticket_count > user_avg_tickets

High Churn Area Alert

This identifies regions with higher-than-average churn rates.

  • Trigger: Churn rates exceeding a threshold percentage in a defined region.
  • Data Source: CRM, subscription data.
  • Ideal Followup Action: Analyze regional differences and improve localization.
  • SQL Query:

SELECT region, SUM(churn_value) AS total_churn  
FROM customers  
GROUP BY region  
HAVING total_churn > regional_avg_churn


Lead Scoring Drop Alert

This catches new leads whose engagement score drops below a threshold.

  • Trigger: Lower lead score compared to initial signup.
  • Data Source: Hubspot, Salesforce
  • Ideal Followup Action: Re-engage leads with tailored messaging and offers.
  • SQL Query:

SELECT lead_id, initial_score, current_score  
FROM lead_scoring  
WHERE current_score < initial_score - score_threshold


Feature Usage Change Alert

This identifies changes in usage patterns of key product features.

  • Trigger: >15% decrease in usage for critical features compared to the previous month.
  • Data Source: Posthog
  • Ideal Followup Action: Dig into reasons for reduced engagement and improve features.
  • Sample SQL Query:

SELECT user_id, feature,  
CURRENT_MONTH_usage AS current,  
PREVIOUS_MONTH_usage AS previous  
FROM feature_metrics  
WHERE CURRENT_MONTH_usage < PREVIOUS_MONTH_usage * .85


Profile Incompleteness Alert

This finds accounts with partially filled profiles.

  • Trigger: Significant profile fields blank after 1 week of sign-up.
  • Data Source: CRM user records.
  • Ideal Followup Action: Prompt to complete profile through in-app messaging on Intercom.
  • Sample SQL Query:

SELECT user_id  
FROM user_profiles  
WHERE SIGNED_UP_DATE < CURRENT_DATE - INTERVAL '7 days'  
AND (address IS NULL OR phone IS NULL)

Downgrade Alerts on Enterprise Accounts

This catches enterprise/high-value customers who have downgraded their plans.

  • Trigger: Downgrade from enterprise/high-value plan tier.
  • Data Source: Stripe, CRM.
  • Ideal Followup Action: Send Slack notification to #sales channel asking AEs to reach out immediately and provide incentives to re-upgrade.
  • Sample SQL Query:

SELECT customer_id, old_plan, new_plan  
FROM subscriptions  
WHERE old_plan = 'enterprise'  
AND new_plan <> 'enterprise'  
AND change_date BETWEEN CURRENT_DATE - INTERVAL '30 days' AND CURRENT_DATE

Now that we’ve covered sample queries to detect churn, acting on these signals requires a control tower to manage automated retention workflows.


The Need for a Centralized Churn Response System

To fully realize the value of predictive churn alerts, customer success teams need a solution that serves as a control tower - a centralized platform that oversees and coordinates your churn response workflows when triggered by alerts.

The key functions of this control tower include:

  • Centralizing the orchestration of retention campaigns triggered by alerts.
  • Enabling easy configuration of conditional, multi-step workflows.
  • Integrating context from alerts to inform follow-up actions.
  • Providing visibility into workflow progress and results.
  • Automating repetitive tasks like creating & assigning tickets or sending reminders.

Without centralization, responding to alerts becomes chaotic:

  • Team members receive alerts outside their workflows.
  • Important signals may be missed or mishandled.
  • There’s no centralized playbook driving systematic responses.
  • Lack of action history leads to redundant or missing efforts.

A dedicated churn response platform solves these challenges with:

  • Workflow automations that are triggered by the alerts.
  • Coordination and accountability across teams.
  • Ability to track issues to resolution.
  • Optimization of retention results over time

Centralize your Churn Response with Locale (TBD)

Platforms like Locale make the orchestration and automation needed to optimize churn response much simpler teams who want to take control without depending on data and engineering bandwidth.

Locale provides the unified control tower needed to optimize churn workflows. With Locale, you can:

  • Centralize alerts and response workflows in one single platform.
  • Build automated, multi-channel playbooks triggered by alerts.
  • Sync context from alerts into follow-up actions.
  • Built-in escalations and reminders
  • Gain visibility into workflow performance over time.
Illustation depicting how quickly users can connect their data source to quickly setup alerts on Locale's platform

Locale ties together disparate data and systems into a centralized engine powering your retention campaigns.

Conclusion

Predictive churn alerts enable proactive customer retention, but only with the right workflows. Without centralized monitoring, signals fall through the cracks.

Reducing customer churn has a dramatic financial impact. With Locale, you can respond to predictive signals quickly across channels before customers leave.

The future of customer retention is personalized and proactive. With the right platform, that future is now.

Still not sure? Talk to one of our retention specialists today.

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