How can you measure customer retention success with marketing automation metrics?

CRR is a key metric, but here's how to go deeper with automation to spot issues BEFORE they cause churn: Look for Behavior Changes, Not Just Outcomes Automation makes it easier to track leading indicators, not just lagging ones: Engagement Drop-Off: Are they opening fewer emails, downloading less content, etc.? A red flag even if their subscription is still active. The "Shifting Interest" Alert: Did their content consumption switch from product-focused to support-focused? May mean they're struggling to get value. Time Between Actions: Are they going longer and longer between site visits? May need re-activation before they fully lapse. Proactive intervention with offers and outreach is more effective than trying to win back lost customers.

Measuring customer retention success with marketing automation metrics involves tracking various KPIs that indicate the effectiveness of your efforts in retaining customers like Customer Churn rate, customer lifetime value, Repeat business, NPS, engagement metrics, feedback etc. By regularly monitoring and analysing these metrics, you can assess the effectiveness of your customer retention efforts and make data-driven decisions to improve retention rates over time.

- One way to measure customer retention success with marketing automation metrics is through customer lifetime value (CLTV). This metric measures the total value a customer brings to a company over their relationship. - Another metric is the customer churn rate, which measures the percentage of customers who stop doing business with a company over a certain period. Engagement metrics such as open and click-through rates on email campaigns can also help gauge customer interest and satisfaction. By tracking these metrics over time, businesses can better understand customer retention rates and adjust their marketing strategies accordingly.

CRR, CLV, CSAT and NPS are all great metrics for predicting and tracking customer retention. These are not Marketing automation metrics. Marketing automation metrics included customer engagement through email opens and clicks, page views and form fills. You can use a marketing automation system to build out scoring models that trigger off of those activities and those can roll into a Customer Engagement Score or a Customer Health Score.

Regularly calculate your CRR to track how well you're retaining customers over time. A B2B company finds its CRR has improved from 80% to 85% in a year, indicating better customer loyalty likely due to enhanced service quality.

Customer Lifetime Value (CLV) is the king. It tells you how much cash a customer brings in over time. High CLV means happy wallet, low CLV means churn city. Think of it like this: CLV equals (Average Customer Spend) multiplied by (Retention Rate) divided by (Churn Rate). Basically, high spenders who stick around are your golden goose. Learned this the hard way consulting for a B2B company obsessed with lead count. They bragged about tons of leads, but their CLV sucked. We switched gears, focused on high-value leads, and guess what? CLV went through the roof. Marketing automation's a tool, not a magic trick. Use it to find your best customers and keep them happy.

Use CLV to forecast revenue from a customer over their entire relationship with your company. By introducing upselling campaigns via marketing automation, a company sees a 20% increase in CLV, showing that customers are spending more over time.

In my experience, Customer Lifetime Value (CLV) is key to value your customers and at the same time to find new opportunities to grow your business. A customer with a high CLV is a loyal customer that trust in your brand, services and solutions. TRUST here is a very important matter. Keep that trust! Invest in a good relationship with them (communication channels) In the other hand, those customers who have a low CLV, they become an opportunity for Sales & Marketing, to offer them new services that they may not know.

This is the opposite of retention rate and measures the percentage of customers lost over a specific period. It's calculated by dividing the number of customers lost during a period by the total number of customers at the beginning of that period, then multiplying by 100. Churn Rate = (Lost Customers / Total Customers) x 100

CLV is a fundamental metric for measuring customer retention success within a marketing automation framework. Unlike single purchase metrics, CLV quantifies the total value a customer brings to your business over the course of their relationship. To measure it, consider average order value, purchase frequency and customer lifetime. By tracking CLV, you can gain insight into the long-term profitability of your marketing efforts. Prioritising strategies that increase CLV - such as personalised nurture campaigns, loyalty programmes or exceptional support - drives repeat business and maximises customer value.

In B2B marketing, we need loyal customers who recommend us, and that's where CSAT comes in. It's a simple score based on customer happiness. High CSAT means they love your product and will stick around. Low CSAT? Time to fix things. I learned this the hard way, chasing likes that didn't translate to sales. But with CSAT surveys, we listened to customer pain points and addressed them. Retention soared, and guess what? Happy customers became our best salespeople through recommendations.

In B2B business needs ACTIVE customer who highly appropriate your product and offerings. a high csat indicates that your customer churn rate and growth base.

Le CSAT ou encore le NPS (Net promoteur score) en France est un indicateur puissant pour mesurer la satisfaction client. Mais attention, il est important de le coupler avec d'autres indicateurs. Car ce dernier à lui seul peut être biaisé. Quoi qu'il en soit, faire de la satisfaction client une priorité est un gage pour booster le levier de la recommandation (Levier puissant)

Implement regular CSAT surveys post-interaction to gauge customer satisfaction levels. After automating follow-up emails to collect CSAT scores, a firm notices a consistent score above 4 out of 5, indicating high satisfaction.

The Best Data is derived from a community aspect, where the product is so Good we don't mind negative and positive feedback as long as its more detailed. What are they unhappy about, - The Purchase Process - The product - The price point - The Service - The Follow up time - How often our prices change. 1- 5 stars aint enough.

NPS measures customer loyalty and satisfaction. Satisfied customers are more likely to stick around. and how much time they recommending.

Measure NPS to understand the likelihood of your customers recommending your business. Post-integration of a new customer support tool, a company’s NPS jumps from 40 to 55, showing improved customer loyalty.

This should be part of your regular product marketing strategy, including this sort of scoring in interviews with current customers, beta users, etc. Based on the other questions you ask in these cases, you can assess the following: 1) What do customers like most? 2) What surprises customers most? 3) What feature do customers use most often? 4) Where should the product improve?

The NPS measures customer satisfaction. Satisfied customers are more likely to stay with a company - which has a positive effect on CLV (Customer Lifetime Value)! Regularly ask your customers about their satisfaction and recommendation rate and work on customer support if the results are not ideal

Something that could be tracked in your Marketing automation tool but other survey tools offer higher levels of sentiment analysis to help you to aggregate the feedback received in your NPS questionaires. Once captured this data can be fed back to your CRM and MAP to inform customer email segmentation.

Set up your event tracking, and capture the relevant Metadata. Meaning? What was the action? (Click, view, skip, scroll, open, interact, participate, respond, engage, download) What was the medium? (Video, pdf, text, image, interactive) What was the content? (Topic, subject, category) Who did this? (Cust category, demographic, behavioural segment) When did they do this? What brought them to this? (Campaign, mail, ad, internal promo) Now that you have all this, look for patterns. What combos work? What jumps out, what's a spike on the graph (for good or bad). Take the top 5 in each. Can you figure out the backstory, why did this happen? Expand the good, fix the bad. Repeat.

Don't Just Track Engagement, Decode It Automation can do more than count clicks; it can reveal what drives them: Segment CES: Do your most engaged customers share a trait (role, industry, etc.)? This tells you who to model your ideal customer experience from. The "Hot Streak" Indicator: Sudden spike in CES? Try to pinpoint what triggered it (new content drop, successful onboarding, etc.) so you can replicate it. Compare CES to Outcome: Most engaged customers also have the highest LTV? Great! If not, there's a disconnect between the experience they want and the one you're delivering. CES shouldn't be about celebrating the folks ALREADY in love with you - it's about understanding HOW to create more of them.

Engagement scores are critical and require sales + marketing alignment. Also recommend having point boosters and removing points based on engagement, as well as leveraging customer tagging. Ideally you're tracking UTM codes from converted events in your CRM.

The Customer Engagement Score (CES) gives you the lowdown on just how much your customers are vibing with your business. It's all about tracking their moves—opening emails, clicking on stuff, downloading goodies, hitting up webinars, you name it. Each action gets scored, painting a picture of who's really digging what you're offering. A high CES? That’s gold. It means your customers are dialed in, likely to stick around, and ready to spend more. But a low CES? That’s a red flag, indicating your customers might just be browsing, not buying, and could be on their way out the door.

A really important one to track to not only track engagement but also to track the impact of that engagement as well as ensuring that you are not bombarding your customer with requests for things like references. Having a collated view of birth decision maker and user engagement is also key. Tracking adoption of training, utilisation and product sentiment for users. For decision makers tracking CSM engagement, referencability and company sentiment in an aggregated view would be an ideal state.

CHS is a great metric, but here's the CMO angle on how to make it a powerful early warning system: Focus on the "Why" Behind the Numbers A low CHS is obvious...the value is in pinpointing the root cause FAST, so it doesn't spread. Automation helps here: Correlate with Content Consumption: Struggling customers all hitting the same help docs, bypassing your shiny features? Points to an onboarding issue. Sudden Shifts Matter: Did their use pattern change AFTER a new release? Could be a UI issue frustrating them, not a lack of overall product need. Tie to Sentiment: Combine CHS with NPS surveys to see if low usage is about apathy, or active anger. Very different fixes needed! Pairing CHS with the RIGHT behavioral data makes you proactive.

Utilize CHS to monitor customer satisfaction, usage patterns, and overall relationship health. A SaaS provider uses CHS to identify at-risk accounts early, leading to a targeted intervention strategy that reduces churn by 10%.

Actually there is also the customers 'emotive' score as there is a wider spectrum of more detailed emotions they feel at various stages throughout the various customer experiences they encounter. If you can correlate this with 'churn' rates you can start preventing churn. Also take a closer look at employee experience as often there is a correlation between a happy employee experience and a 'happy' customer. Take a closer look at how you can ensure that the customer requirement is 'one and done' at the first point of contact is a good first start. Avoid cusomers getting into the 'loop of doom' this requires a closer look at communication channels and getting rid of cumbersome back office processes. Some pointers can be using ASM and ACC.

Combine lead scores with grading (job title, firmographics, basically if the lead matches your ideal customer profile). Then it's time to build CRM reports/dashboards with this data for sales and marketing reps, managers, and executives.

Combine these metrics to get a comprehensive view of customer retention and identify areas for improvement. Continuously refine your measurement criteria and tools to align with evolving customer expectations and business objectives, ensuring your retention strategies remain effective.

These include metrics like email open rates, click-through rates, website visits, and social media engagement. Higher engagement levels typically correlate with better customer retention.

Cognitive dissonance is the doubt that often comes after the purchase about the correctness of the purchase. Therefore, after-sales support is no less important than the sale itself. Customers often ask themselves whether they have been "talked into" something, especially after spending more. Give your customers a good feeling and then convince them with performance