With the advent of AI-enabled customer data analytics, banks can now segment customers objectively and quantitatively based on dynamic predictions of expected future value, from each individual customer. This Customer Lifetime Value (CLV) is dynamic, and not static, as is the predicted value from each customer at any point in time.
Commonly used customer segmentation methods are based on income, demographics, behaviour, and personas. These are ineffective and subjective. For example, some high-income customers only have low-value transactional products with your bank, while their high-value products, such as loans, are with another bank. Therefore, they are not high CLV customers to your bank. However, these crude segmentation methods are what banks have had to resort to until now, in the absence of easy and superior AI-enabled customer data analytics.
Driving Segmentation for Increased Profits
It is now time to unleash the power of AI on customer data analytics to segment customers individually, objectively, and quantitatively on CLV to maximize profit.
The starting point is gaining a 360-degree view of customers, especially to avoid double-counting (or multiple-counting in some cases). This can be achieved by unifying customer data (transactional, behavioural, and demographic) to achieve unique Customer 360-degree profiles. Personalization, which relies on accurate Customer 360-degree profiles, is the key to growing CLV.
CLV is the present value of future (net) cash flows associated with a customer:
- A prediction and forward-looking concept, not to be confused with (historic) customer profitability.
- Complete profitability analysis should be carefully implemented in accounting for costs, discount factors, etc. This requires collaboration with Finance. However, revenue is a good proxy for profitability and can enable you to avoid complex cost apportionment at a customer level.
- For any current and yet-to-be acquired customers representing future cashflows, CLV is still based on a prediction, as there is no definite way of knowing the actual number of periods they will remain customers, how many purchases they will make, and so forth.
Determining Intrinsic Value
One of the key methods of valuing a bank is intrinsic value, which is defined as the discounted value of future earnings. CLV contributes to customer-based corporate valuation and is aligned with intrinsic value. In fact, you can argue that aggregate Bank CLV contributes the most to a bank’s intrinsic value. In addition, CLV enables marketing and finance to have a common quantitative KPI (Key Performance Indicator).
Maximizing Profit by Making Informed Investments
CLV is also important because you can maximize profit by making informed investments in marketing, sales and customer service. It achieves the following:
- Dictates allocation of marketing resources
- Shows what your customer acquisition spend should be
- Helps you find the actual value of your customers beyond statistics like NPS (Net Promoter Score), engagement, clicks, etc.
- Helps evaluate campaign ROI (Return on Investment)
- Helps prioritize customer metrics that contribute to customer-based corporate valuation
- Sanity check for Sales forecasts and budgets. Common Sales forecasts and budgets are based on volume (especially the number of Accounts opened) and lack sufficient consideration of quality, such as CLV. The race is not for the most Accounts but for the most value from the customer portfolio.
Understanding Low and High-Value Customers
CLV distribution is dominated by lower-value customers. Low-value customers make up the largest proportion. This more realistic view also lines up with the famous “80:20 rule” – that is, the bottom 80% of customers bring in only 20% of the total value.
In fact, the key finding from the Proof of Concept we did with a major Bank in Nigeria, is that the High CLV segment was 64 times more valuable than the low CLV segment. Although high CLV customers only constituted 5% of the sample of 100,000 customers, their value was 64 times that of low CLV customers, who constituted 70% of the sample.
It’s important to understand the inherent characteristics that make up high-value customers. The paradox, though, is that the more you obsess over acquiring the best customers, the more you need to retain and develop lower-value customers, to help balance out your business. The diversity of any market is such that customers come in all CLV sizes. The key performance challenge is for the bank to maximize profit from the diverse market. This especially means that the cost of acquiring and servicing low CLV customers should not exceed their CLV.
Once you have predicted CLV at an individual customer level, you may align customer acquisition, retention, and development accordingly:
- Use your understanding of the inherent characteristics that make up your existing high-value customers, to acquire more high-value customers. Let your customer data analytics with AI guide you as opposed to using intuition
- Deploy customer retention and development tactics to maximize CLV
- Differentiate customer experience on CLV
- Personalize and differentiate experiences across the entire customer journey (marketing, sales and customer service) with CLV
Addressing Existing Challenges
Meanwhile, banks face the challenge of solving common banking challenges such as:
- Customer Service
- Increasing Competition
- Digital Transformation
The following are some key high-level insights in banking around segmentation, marketing, selling and customer retention:
- With the bottom 80% of customers bringing in only 20% of business value, it is crucial to identify your most valuable customers.
- A relevant high-impact recommendation is up to 50 times more likely to trigger a purchase than one that’s low impact.
- According to Forbes, it can cost five times more to attract a new customer, than it could to retain an existing one. Increasing customer retention rates by 5% increases profits by 25% to 95%.
- With the top five Global Banks averaging 50 percent of sales in digital channels, it is crucial for financial institutions to use the power of technology to drive digitization.
Superior customer data analytics, now available with AI, enable predictions on CLV, product recommendation, customer churn, and channel recommendation. In fact, you can conceive and create predictions with AI, on any business opportunity (use case) that involves customer data analytics.
4 Key Steps to a Bank-Wide Adoption Strategy
1 – Implement a CDP (Customer Data Platform), such as Microsoft Dynamics 365 Customer Insights, to deliver a fully automated Enterprise-grade solution. “Large-scale Data Science projects are 10% Data Science knowledge and 90% working with large data sets and architecture knowledge,” says our Head of AI, Peter Reid. Therefore, a CDP is a foundation on which superior customer data analytics with AI are based.
2 – Implement a CLV Predictive Model with Machine Learning.
3 – Align with the CRM (Customer Relationship Management) solution consisting of Marketing, Sales and Customer Service.
4 – Adopt CLV, as a KPI, Bank-wide, at every level, to translate strategy into action and measure performance accordingly.
CLV Use Case for the Bank Distribution Model
An innovative CLV segmentation use case bank in Africa is providing input into the bank’s Distribution Model:
- Selectively adapting the size, location, and team of the branches to the new market context.
- Resetting over-the-counter offerings to reinforce the differentiation of the service model by segment.
- Strengthening service excellence for the most valuable segments.
- Reducing unit cost to serve mass market customers, by encouraging the migration to digital channels.
Leadership should take an active role in supporting and driving this strategy bank-wide. CLV, as a KPI, can be aggregated and trended at every level (Bank, State/Province, Branch, Relationship Manager, etc.) to translate strategy into action.