With the increasing rate of cart abandonment and the onset of crumbling cookies, it is imperative to invest in churn analysis and understand customers better
How strong is your customer retention rate? While retention is more important than acquisition is debatable, it holds enough truth. Consider this: A churn rate over 10 per cent indicates trouble, and two of the most dangerous outcomes of churn are financial instability and the risk of losing competitive reputation.
A Harvard Business report highlights that a five per cent increase in customer retention causes a 25-95 per cent increase in business profits. It indicates that 65 per cent of a brand’s business stems from existing customers making a high churn rate a cause of worry.
It’s essential to keep track of every customer who unsubscribes, cancels, or even abandons a cart. Experts recommend analysing the common factors among abandoners to derive insights and identify trends and opportunities for improved sales and ROI.
The problem remains that many marketers struggle with a holistic and clear understanding of their customers despite hoarding data and available technology. However, an effective method to shape a new behavioural shopping trait and coax customer loyalty is to analyse customer moments of truth — the first-time experience moments in a consumer’s buying journey that have a notable impact on them.
It’s not possible to eliminate churn, and that’s alright. When churn rates are higher than your brand or industry average, it has to be a top priority. With the increasing rate of cart abandonment and the onset of crumbling cookies, one of the best methods to understand why customers do or don’t purchase brand goods begins with churn analysis, which involves a set of metrics that can make or break a business.
Making the most of churn analysis
From increased profits, better customer experience, increased customer retention, and service/product optimisation, the benefits of churn analysis are nothing short of an effective business success strategy. It’s imperative to keep pace with churn analytic metrics, including customer churn rate, retention rate, customer health score, customer engagement rate, satisfaction score, and NPS score.
To gain the most from customer churn analysis, it’s best to monitor it regularly. High-performing brands usually set a below-industry-average target churn level, track their metrics over time, use a KPI scorecard, and act to address issues before they become adverse trends. According to Ayşe Gedikli, ecommerce Director, Evidea, “A good CRM tool is expected to lower the churn and increase loyalty.”
There are several tried and tested methods and tools for churn analysis, including calculating KPI over timeframes and trending those results.
Companies with ERP systems can develop churn analysis reports using prebuilt dashboards or other automated reporting tools. But other businesses require some manual number-crunching techniques.
Experts believe the best use of company resources is increased recurring subscription revenue. Consider the competitors Netflix and Hulu. According to an analytics report, Netflix had lost about nine per cent of its subscriber base over 12 months compared with about 50 per cent churn at Hulu. Hulu’s top priority has to be to learn its customer pain points and reduce its churn rate.
Churn risk scoring
Additionally, as churn analysis can help identify factors related to customers leaving, insights can be used as a predictive force to recognise at-risk customers. There’s more than one way for churn risk scoring, and brands use the ones that best suit them based on needs and resources.
While RFM Segmentation is one of the standard approaches, there are two other common churn risk scoring strategies. Classifier-based prediction involves training a supervised algorithm for greater granularity than the RFM method. Here, every customer holds a predicted unique value based on their data.
Experts advise data to be appropriately prepared with binary data variables to train the model. Although this approach provides a churn risk score per customer, it can easily corrupt the dataset used for training. Sometimes, external stakeholders find it hard to interpret it, too.
The other method is to use survival-based models. While it was initially used to study population lifespans, the process expanded into other fields, including medical and business.
Survival models allow one to analyse time-to-events in populations where the events have not yet been observed. Similarly, businesses need not wait for customers to churn and instead use unobserved data to estimate and model churn behaviour.
Kaplan-Meier is a simple survival regression model that only requires two data elements – duration and event. While the former identifies the total active time of a customer, the latter is binary and indicates if the customer has churned or is still there. While survival regression models allow the user to regress additional features (demographic, product usage) as we do with a classifier-based model. There are various types, and a simple regression model is Cox’s proportional hazard model.
Triggered Calls To Action (CTA)
One of the most effective processes to reduce churn is investing in triggered calls to action (CTA), a nudge to potential customers at the top of the conversion funnel. After a successful churn analysis, brands should use the acquired insights to fuel their triggered CTAs.
Is the product good enough? Is it worth the money? Does it solve my problem? Doubt is one of the biggest reasons that make a customer back out at the last minute.
Experts believe a CTA trigger can eliminate that doubt in testimonials, sale signs, and text boxes explaining the product or service. When a CTA answers important customer queries, it pushes them to click. Additionally, adding a content upgrade to the existing sales funnel, providing guarantees through returns and exchanges to build trust, and adding social proof are other effective CTA triggers.
Churn trouble across industries
Experts recommend brands predict churn by analysing and gathering actionable insights from operational and survey data by creating a single data lake. With technologies like artificial intelligence (AI) and machine learning (ML) supporting these processes, brands across industries can better understand customers and how to incentivise them.
The healthcare sector has one of the highest churn rates, ranging from 15 -20 per cent. Here, as insurance differentiation margins are slim, the patient experience becomes the most crucial factor in decreasing churn. Care quality can be addressed using operational and survey data.
Retaining customers in the telecom industry is also challenging and stressful as risking the loss of CLV can directly affect the overall business valuation. Fighting massive marketing budgets, brands need to be proactive about gathering customer feedback at every touchpoint. They should leverage software tools to analyse what customers are doing, lean on big data to extract insights, and predict what customers want.
Talking about the financial sector, the paperless onboarding process impacts the churn. With unprecedented digital growth, the process has to be simple yet personalised. “In the UAE, almost every record is digitised. All one requires is the biometrics of a customer. The fulfilment of receiving the product as soon as possible is equally important,” said Gino George, head of customer analytics and AI at First Abu Dhabi Bank.
Meanwhile, the retail industry finds it easier to use data to reduce churn. They can analyse customer data to understand which customers spend the most, what they buy, and how often they buy at specific stores. Figuring out pain points becomes easier, and loyalty programs, user log-ins, and even weather forecasts in the buyer’s location can be used to personalise recommendations and predict what people will buy.
As the issue of churn is massive, particularly in the world of SaaS, Paddle recently acquired ProfitWell, which has a set of tools to provide analytics to companies that sell their products via subscriptions. ProfitWell offers a full suite of services catering to SaaS businesses, including retention and pricing analytics that they would need to run their subscription businesses and reduce churn.
According to a report, 44 per cent of brands have heavy acquisition strategies, and only 16 per cent of them focus on reducing churn. It’s high time the numbers change. In the age of technologically driven solutions and customer-centric business strategies, brands across industries must build customised systems and invest in churn analysis to keep customers satisfied and loyal.
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