Importance of customer churn prediction in the financial services industry
In a business environment of increasing competition and digital disruption, customer retention is becoming increasingly important for services companies. However, there is a gap in addressing the retention challenge, as 85% of customers report that companies could do more to retain them. [i]
Effectively predicting and minimizing churn can lead to significant cost savings. However, current customer retention processes tend to be more reactive than preventative.
Customer retention presents significant challenges in the financial services industry. Companies are facing competition within existing established players, and also competition from the booming Fin Tech sector. With increased options, customers are also getting more savvy. In essence, the cost to acquire customers have significantly increased, along with the risk of losing customers.
American Express, as a financial services provider and a network provider, is challenged in multiple dimensions of customer retention. Amex’s customers are both card holders and merchants.[ii] On the consumer end, the credit card industry is notorious for churn[iii], and JPMorgan Chase’s launch of Chase Sapphire Reserve further pressures Amex. On the other end, merchants are increasingly seeking providers’ help on identifying customer purchasing behaviors. For Amex, effectively managing churn is not only crucial to its consumer business, but can also be a key competitive advantage for the network business.
Amex’s approach to address customer churn through predictive analytics
Amex has already started preparing itself to use machine learning algorithms for various parts of its business. In 2010, AMEX upgraded from traditional database technology to a Hadoop infrastructure to work better with machine learning algorithms.[iv] They implemented data platforms and NoSQL databases to enable large-scale machine learning applications.[v] Amex employed machine learning techniques for a wide range of use cases, most notably in fraud detection.
Amex has also gained traction in the customer churn prediction use case. Through its vast amount of historical transactions, Amex has created a machine learning model to forecast potential churn. It uses 115 variables that define customer behaviors, and it believes that it can identify 24% of Australian accounts that will close within the next four months.[vi]
In the merchants business, American Express launched Amex Advance in November, 2017. American Express Advance is a predictive analytics platform for business clients, which aims to provide customized services for merchants to understand their customers’ behaviors.[vii]
Overall, Amex is continuing to harness the predictive analytics power for its own businesses, and transforming the predictive capabilities into products for its merchants.
Recommendations for next steps
As Amex continues the journey of predictive analytics, data availability and quality are crucial factors. Amex needs to focus on both the quantity and quality of collection of payments data. To build a full picture of customer purchasing behavior, it needs to venture into new areas. Its corporate investments arm has already invested in digital payments and digital concierge startups to further build data around customer behavior. Amex needs to integrate these different pieces of data with its existing database. It can also try to collect richer data around customer purchases, by asking consumers to volunteer more information about the purchase. For example, Google and Facebook often ask questions about a recent behavior to gather more data.
In addition, human judgment is required to truly understand the causation of risk elements and address the root causes. Amex needs to hire high caliber data scientists who will work cross-functionally with the business side to make sense of the “at-risk” rankings for customer churn. At the same time, research has shown that there is a difference between targeting “high-risk” customers and customers who are likely to react well to retention campaigns. [viii] With this distinction, the company can effectively segment high risk customers based on (1) the root cause of churn risk, and (2) the probability of reacting well to retention campaigns.
As other financial services firms also invest in machine learning and advanced analytics, how can American Express differentiate themselves? Should American Express continue to build its predictive analytics in house, or should it leverage third party solutions that are specialized in the area? With increased scrutiny on data privacy, and EU’s GDPR coming into effect, what are the limitations American Express can face in terms of building the full picture of the customer behavioral patterns?
[i] Ascarza, Eva. “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions” https://www.hbs.edu/faculty/Publication%20Files/ascarza_et_al_cns_17_e08d63cf-0b65-4526-9d23-b0b09dcee9b9_538a6ea6-a480-4841-b9f0-a87be24989ba.pdf
[ii] Forbes. “Inside American Express’ Big Data Journey” https://www.forbes.com/sites/ciocentral/2016/04/27/inside-american-express-big-data-journey/#443621e73d89
[iii] Gerdeman, Dina. “A Smarter Way to Reduce Customer Defections”. https://hbswk.hbs.edu/item/a-smarter-way-to-reduce-customer-defections
[iv] Bernard Marr & Co. “American Express: How Big Data And Machine Learning Benefits Consumers And Merchants” https://www.bernardmarr.com/default.asp?contentID=1263
[vi] HBS Digital Initiative. “American Express : Using data analytics to redefine traditional banking” https://digital.hbs.edu/platform-digit/submission/american-express-using-data-analytics-to-redefine-traditional-banking/
[vii] MarTechSeries. “American Express Launches Amex Advance Personalization Services” https://martechseries.com/predictive-ai/ai-platforms-machine-learning/american-express-launches-amex-advance-personalization-services/
[viii] Ascarza, Eva. “Retention Futility: Targeting High-Risk Customers Might Be Ineffective” https://www.hbs.edu/faculty/Publication%20Files/ascarza_jmr_18_783d54d4-e548-41ed-b1d7-8a180f1ae85a.pdf