Pre-Churn Outreach: Re-Engaging At-Risk Customers

In the ecommerce landscape, customer churn is a persistent challenge. Each lost customer represents a significant hit to your revenue and a missed opportunity for long-term growth.

While win-back campaigns for lapsed customers are valuable, preventing churn before it happens is a far more strategic and cost-effective approach. This is where pre-churn outreach comes into play.

Pre-churn outreach involves identifying customers who exhibit early warning signs of potential churn and proactively re-engaging them with targeted messaging and offers. By recognizing and addressing these warning signs early on, you not only minimize customer losses but also foster stronger customer relationships and maximize lifetime value (LTV).

This article will dive into the strategies and tools needed to identify at-risk customers, craft compelling win-back campaigns and utilize data to anticipate customer needs. By the end, you'll be equipped with a proactive churn prevention strategy that protects your bottom line and strengthens your customer base. 

Identifying At-Risk Customers

Preventing customer churn starts with recognizing the warning signs. Your customer data is a treasure trove of information but it's important to know which signals truly matter.

These churn indicators can be the difference between retaining a valuable customer and losing them to a competitor:

• Declining Purchase Frequency: Customers who once bought from you regularly but are now spacing out their purchases could be losing interest. Analyze purchase patterns to identify this trend early on.

• Reduced Website Activity: A decrease in browsing time, page views or session duration can be a red flag. It might indicate dissatisfaction with your products, difficulty navigating your site, or a waning interest in your brand overall.

• Negative Feedback or Complaints: Pay close attention to feedback across channels. Negative reviews, complaints filed with customer support or even frustrated social media posts can all be early warning signs of churn.

• Changes in Engagement: Have your email open rates taken a nosedive? Are customers not clicking on links or engaging with your social media content? These are all signs that their interest is waning.

By monitoring these key indicators, you'll gain useful insights into which customers are most likely to churn. With this knowledge, you can tailor targeted outreach to address their specific needs and concerns, hopefully re-engaging them before they drift away.

RFM Analysis: A Powerful Segmentation Tool

RFM (Recency, Frequency, Monetary Value) analysis is a customer segmentation technique that evaluates customers based on their past purchase behavior. It's a simple yet effective way to gauge customer value and loyalty and it can be particularly useful in identifying those at risk of churning.

Here's how it works:

• Recency (R): How recently did the customer make a purchase? Customers who haven't bought in a while are more likely to churn.

• Frequency (F): How often does the customer make purchases? Frequent buyers are generally more loyal and less likely to churn.

• Monetary Value (M): How much has the customer spent in total? High-value customers are a priority for retention efforts.

By assigning scores to each of these factors, you can create customer segments that reflect their likelihood to churn. For example, customers with low recency, low frequency and low monetary value scores would be considered high-risk for churn.

RFM analysis is a powerful tool on its own, but when combined with other churn indicators (like the ones mentioned earlier), it becomes even more effective.

Predictive Analytics: Going a Step Further in Churn Prediction

While RFM analysis is a valuable starting point, predictive analytics takes churn prediction to the next level. By leveraging machine learning algorithms and statistical models, predictive analytics can analyze vast amounts of customer data to identify patterns and predict future behavior with greater accuracy.

By incorporating variables like customer demographics, website activity and even customer service interactions, predictive analytics models can create more nuanced churn probability scores. This allows you to target your re-engagement efforts even more effectively, focusing on the customers most likely to respond.

LTV.ai is a great tool to use in this regard, as it automatically segments your customer list based off factors like customer demographics, lifecycle touchpoints, RFM analysis and overall brand engagement using AI data crawling models. 

Crafting Compelling Win-Back Campaigns

Once you've identified your "at risk" customers, it's time to win them back with a tailored approach. Remember, generic messaging falls flat – personalization is your secret weapon to re-engage lost interest.

Segment for Targeted Outreach

Don't blast the same message to every potentially churned customer. Instead, leverage your RFM analysis and any additional churn indicators to create targeted segments.

This allows you to tailor your messaging and offers based on their individual behavior and needs.

Email Campaigns: The Art of Re-Engagement

Here's the playbook we recommend:

• Subject Lines that Demand Attention: Spark curiosity, urgency, or personalization:
  • "We Miss You, [Customer Name] – Here's a Special Offer We Made For You"
  • "Is Everything Okay? We Haven't Seen You in a While, [Customer Name]"
  • "[Customer Name], [Product Name] You Viewed is Almost Gone!"
  • "[Customer Name], This Will Go Great With Your [Last Purchased Product]

• Targeted Messaging: Acknowledge their absence and address potential reasons for their lapse. If they expressed a specific concern in past feedback, demonstrate that you've listened and taken action.

• Relevant Solutions: Offer tailored solutions and incentives that resonate with their interests. Recommend products they'll actually love or those that will go well with their last purchased product, not just generic best-sellers.

• Clear CTAs: Tell them exactly what you want them to do – "Shop Now", "Redeem Your Discount" or "Tell Us What You Think."

LTV.ai implements this personalization and more at scale, across your customer list, to increase brand's owned channel sales by 10-25%.

SMS Campaigns: Short, Sweet, and Personal

• Concise & Impactful: SMS messages need to be brief and attention-grabbing.
  • "Hey [Customer Name], we've got a surprise for you..."
  • "Don't miss [discount code] – it ends soon!"
  • "New arrivals you'll love what I picked out for you, [Customer Name]! Check it out..."

• Optimal Timing: Experiment to find the best time for your audience. Afternoons and early evenings are often effective.

• Clear CTAs with Links: Short, trackable links are essential for mobile optimization.

Omnichannel Approach

Maximize your impact by weaving together email, SMS and other relevant channels (push notifications, social media).

The key is to create a cohesive experience, most make the mistake of bombarding customers with repetitive messaging. Instead, each touchpoint should offer something new or build upon where you left off. 

Here's an example of one of our Omnichannel Approach's at LTV.ai:

1. Email: Send customer a personalized product recommendation with a personalized promo code ("LISA15")
2. SMS: Follow up to ask if they saw the email, what they thought of the product recommendation and letting them know their code will expire soon 
3. Email + SMS: Follow up again to offer other recommendations and remind them their coupon will expire very soon, while inviting any feedback. 

Using Data to Anticipate Customer Needs

Proactive churn prevention involves more than just reacting to warning signs, it's about anticipating customer needs before they become problems.

By harnessing your customer data, you can turn insights into action, re-engaging customers at the right time with the right message.

Predictive Analytics: Your Churn Crystal Ball

As discussed above, predictive analytics uses machine learning algorithms to analyze customer data and forecast future behavior. In the context of churn prevention, this means identifying customers likely to lapse even before they show clear signs of disengagement.

Think of it as a proactive alarm system. By spotting patterns in past churned customers' behavior, predictive models can flag those currently exhibiting similar trends. This early warning allows you to:

• Launch pre-emptive win-back campaigns: Reach out with personalized offers, relevant content or even a simple "check in" email to rekindle interest.

• Address underlying issues: Analyze the reasons behind predicted churn (eg. dissatisfaction with a specific product category) to proactively address them.

• Refine your overall retention strategy: Are certain customer segments more prone to churn? Use this data to adjust your approach and tailor your messaging.

Customer Feedback Loops: The Voice of the Customer

While predictive analytics offers powerful insights, don't neglect the value of direct feedback. Regularly collect and analyze customer feedback through surveys, reviews, and social media. This will give you a deeper understanding of their needs, preferences, and pain points, informing more effective re-engagement campaigns and future product development.

For more efficient review gathering, reach out to each customer individually from a customer support member, to ask direct questions about how their experience or purchase was. LTV.ai takes this hyper-personalized approach towards feedback gathering.

Behavioral Analysis: Uncovering Hidden Patterns

Track customer behavior across your website, email interactions and social media engagement. Look for changes in patterns that may indicate disengagement, such as:

• Decreased email open rates

• Reduced website visits

• Fewer interactions on social media

By monitoring these trends, you can identify opportunities for proactive outreach before a customer churns.

How Ministry of Supply used AI to re-engage "At-Risk" customers

A leading menswear brand, Ministry of Supply, was having issues with the relevance of their emails and thus not being able to truly connect with and convert all of their customers, despite detailed segmentation and triggers.  

To combat this, they tested out LTV.ai to see how the addition of AI into their marketing stack would impact their repurchases, customer loyalty, customer satisfaction and LTV. The results were better than expected... 

The Goal?

The overarching goal was to re-engage customers that hadn't purchased in 180+ days but had made at least 1 purchase with the brand historically.

These at-risk customers were incredibly valued individuals that had been lost by Ministry of Supply and they wanted to show them they cared and wanted them back. 

LTV.ai's Strategy?

LTV.ai sent a sequence of 5 text-based emails from a "brand ambassador" dedicated to these lapsed customers, mentioning variables like their physical location and previous feedback and offering personalized product recommendations based on their previous purchases.

These emails also featured dynamic coupon code usage, meaning customers only got coupon codes if they were proving incredibly difficult to re-engage. When coupons were offered, they were hyper-personalized ("AMY15" for 15% off) and varying levels of discounting was testing for different customer segments (some got 10% off, while others got 25% off). 

LTV.ai's AI also held conversations with customers over email, to nurture the relationship and help guide them towards the next purchase that would be perfect for that one individual. 

The Results? 

LTV.ai's impact was impressive:

🟢 $0.91 revenue per unique lapsed customer

🟢 +9.93% conversion rate

🟢 11.59% of total brand revenue driven by LTV.ai

🟢 20x ROI

Apart from this, LTV.ai also: 

🟣 Sent hyper-personalized text based emails, with personalized product recommendations, that customers loved

🟣 Held 100's of AI driven conversations over email to nurture customer purchases

🟣 Only offered coupon codes when necessary to maximize margins

🟣 Gathered tons of feedback directly from customers to stop future churn before it occurs

Full case study here.