Customer churn costs a lot to a business. It is not just the recurring revenue that a company loses with a customer leaving. Other costs that come along include lost upselling and referral opportunities, impact on goodwill, weak competitive positioning and reduction in business valuation.
Customer retention has assumed a greater priority in the business world today. Due to growing customer expectations, technological evolution and the recent pandemic, retention leaders are facing a mighty challenge like never before. Furthermore, traditional approaches to build and maintain customer loyalty appear inadequate to meet today’s retention goals.
Disadvantages of Traditional (Reactive) Retention Approaches
Reactive retention involves addressing a problem after the customer has expressed it. Generally, the decision to cancel the subscription is not sudden. It is based on accumulated factors and experiences across the customer lifecycle over a period. Hence by the time company realizes the churn risk, it is already too late and efforts to retain the customers prove to be more difficult, expensive and sometimes futile.
In about 30-50% of cases, customers do not call before cancelling. They just stop paying and switch to a competitor. When a customer calls to cancel, it leaves the company with fewer options to reverse the decision. This often results in a significantly higher cost of marketing.
How Proactive Retention Helps
Advances in predictive data science and machine learning have made it possible to leverage massive volumes of structured and unstructured data, including customer interactions with call center and service teams, to predict which customers are more likely to cancel, what the cancellation drivers might be and how you can retain those customers.
Let us look at the key advantages proactive retention has over the traditional, reactive retention:
Bigger audience of at-risk customers
Proactive retention expands the focus or retention effort from those who call to cancel or don’t pay, to every customer still in contract. Thus, the audience size is significantly bigger as your predictive models keep a daily track of predicted risk for every customer.
Better understanding of causes of churn
Using natural language processing (NLP) and machine learning models, you can analyze your customer data pool to understand what your customers are saying. Based on this, you can detect the effort, intent and sentiment of each customer, and flag those customers who exhibit at-risk behaviors. This enables businesses not only in personalizing their retention effort but also in addressing the root causes driving long term churn trends.
Detecting and addressing customer concerns at an early stage results in a lower cost of retention. Since it is when the retention risk is low and customers generally call to inquire and not complain, you do not need an expensive last-minute offer to make a customer stay. Instead, low-cost interventions are enough.
For example, using historical customer data and interactions, you can predict net promoter score (NPS) for every customer. You can then personalize offers for them and improve the ROI on your marketing dollar, simultaneously reducing the marketing expense and increase NPS.
Additionally, it widens the window for value enhancement to encourage customers to use the products optimally and derive the best value.
Combining a proactive model with prescriptive analytics, you get granular risk insights at the level of every individual customer. This beats other methods, like customer surveys, in understanding customer behaviors and risk indicators at scale. The models automatically assign a risk score to every customer, segment customers by risk level, and figure out how they will respond to your retention offers across multiple touchpoints. Marketing teams can use this intelligence to create personalized offers for every at-risk customer and deliver those offers through marketing and care channels.
Risk-based automation enables routing user queries or concerns to the right service representative. It allows integrating predictive scores with IVR. Based on the risk score, it automatically transfers a normal service call from an at-risk customer to a team of empowered, proactive retention agents. This automatic call identification and routing not only reduces the waiting time for customers but also increases customer saves dramatically.
Prescriptive guidance to frontline
A proactive model facilitates the better performance of frontline staff and branch locations due to better risk-awareness. When they have all the information about customers along with their risk profile and tailored retention offer, the conversion rate skyrockets.
In addition to the above, going proactive can:
- Improve retention-based branch performance
- Plan service routes based on geospatial risk analysis
- Monitor and analyze cancellation trends
- Improve CSAT by predicting NPS for every customer
- Reduce cost to serve as customer lifetime extends
- Drive continuous improvements in product and service
Being proactive not only gives enough room to design an effective retention plan but also provides insights into the behaviors, expectations and loyalty drivers of customers. Companies with a proactive retention strategy have been witnessed to gain 8-10% retention annually. This is noteworthy, considering that just a 5% increase in retention rate can lead to a 25% increase in profit. With retention being the key to the long-term business growth, proactive retention can prove a sustainable competitive advantage in an uncertain environment where customer preferences are always evolving
By: Raviteja Sidda