Attrition is a top issue for banks, and for good reason.

Transcription

1 e-book How to Alert to Potential Attrition for Banks Enabling real-time alerts to decrease banking customer attrition

2 Attrition is a top issue for banks, and for good reason. The opportunity cost of falling behind the competition is extreme. Over half of customers have opened or closed at least one product in the past year and nearly as many, 40%, plan to do so in the coming year. Each of these customer represents a new business opportunity for a competing bank or financial service provider. Global Consumer Banking Survey 2014, Ernst & Young 2 Copyright 2015 NGDATA

3 Getting to the bottom of customer attrition In order to get ahead and stay ahead of the attrition issue, banks must focus on the customer relationship. Banks need to increase engagement with the appropriate messages, products, and services their customers have come to expect in this data-driven world. Not to forget, a happy customer is a loyal customer. 3 Copyright 2015 NGDATA

4 Be Proactive Notwithstanding the bank s effort to nurture relationships, some customers might still have attrition potential. The bank can either adopt a reactive or pro-active attrition management approach. Adopting a reactive approach, the bank waits until the customer contacts the bank to cancel their relationship. If the bank is on the ball, they may then offer the customer an incentive, for example a rebate, to stay. Adopting a proactive approach, a bank tries to identify customers who are likely to leave well ahead of their possible attrition and targets them with retention actions. Proactive attrition management is preferred over reactive management as the former is likely to have lower incentive costs (because the incentive may not have to be as high as when the customer has to be bribed not to leave at the last minute) and because customers are not likely to negotiate for better deals once they have decided to leave. Lily supports banks with proactive attrition management. It is clear that banks don t want to lose customers, especially high value customers. And, determining who those customers are - with the highest potential to leave, ahead of time is key. 4 Copyright 2015 NGDATA

5 What is Attrition? Attrition is the action of a customer to leave a bank by dropping all products and closing all services. Banks need to also consider that customers do not always actively terminate their bank relationships, but might still have a few products/services and be quite inactive. A bank customer could also leave partially (i.e. partial attrition), by closing or not renewing some, but not all products/services. Knowing these types and focusing on which to address, when, or first, will help with the project of decreasing attrition most effectively. 5 Copyright 2015 NGDATA

6 Which attrition is most effective to address? Depending on the customer s motivation to leave, he or she can be classified into: Involuntary When a customer no longer has the finances to continue doing business with the bank or can no longer afford the product; customer dies; customer relocates and no longer continues the relationship with the bank Voluntary: When a customer consciously decides to stop doing business with the bank Rotational: Customer drops a product and purchases another product within the same bank to obtain better conditions (e.g. if you become a customer, you receive a promotional offer, take the company up on the offer and close the existing product in order to qualify for the new offer. This is a fake new customer by opening the closed account with the new deal that was offered) Customer constantly (partially) switches between companies depending on where he can get the best conditions (e.g. bank customer transfers money from savings account to competitor, having an interesting savings account offer) Addressing those who are likely to leave voluntarily will be the most effective and advantageous for the business. 6 Copyright 2015 NGDATA

7 What are the most likely attrition predictors? Attrition traditionally has been about the why and what. To be more accurate and effective, it needs to also include the when and how. Traditional socio-demographic slow-changing metrics - product ownership - subscriptions - age behavioral Advanced rapidly-changing metrics - usage - service interaction & consumption ADVANTAGE: ADVANTAGE: 7 Copyright 2015 NGDATA

8 Let s look at the process and how Lily makes a difference Define the problem Collect the data Explore the data Prepare the data Build the models Validate the models Deploy and update the models 8 Copyright 2015 NGDATA

9 Define the problem To get to who has the most potential to leave, you start with defining the problem, which is of utmost importance. Banks need to choose a scenario with the most value to the company high value customers whose attrition could be easily averted, with proper knowledge and clear implementable actions. Say for example reducing attrition rates of high value tech savvy, upwardly mobile customer by 5%. Clearly stating the goal will give explicit direction, and make the most difference to the business. Say for example mobile app users are considered high value customers. Using Lily s Set configuration the marketer can monitor his business-relevant churners - by creating the Set of Valuable mobile customers, specifying the condition on the DNA metric segnr=1 and the DNA metric totalmobileappconsultationsper120days>0 as shown in Figure 1. Figure 1: Example of creation of the Set of Valuable Upwardly Mobiles. 9 Copyright 2015 NGDATA

10 Collect the data Taking action by collecting the data can be intensive. For each problem, the Data Scientist must usually search for the most useful data for that project. If he is looking into attrition, then he will try to find the data usage, complaint, invoice issues, etc. - then map the required functional data sources. However, with Lily, the data is collected from all the data sources at once, done once, or added over time as the data source becomes available. The determination of relevant data is unnecessary because building the DNA from all the data available is done independent of the specific problem/opportunity that has been defined. This data is also loaded for all customers, (not as sample data), in real time, and is continuously available for the Data Scientist - enabling a substantial time and effort savings. For example, a new inter-institution transfer interaction is contributing to a usage DNA metric wiretransferfrequency but also to a potential attritionrelated DNA profile that is relative to the specific customer s attrition date by using a dynamic data frame selecting the attrition date DNA metric as the reference time for the data frame. Building such customerspecific and dynamic - time lines is a task that is typically very hard to accomplish with conventional analysis software. 10 Copyright 2015 NGDATA

11 Explore the data Once all the data is together, the Data Scientist will begin exploring the data - mainly toward better understanding potential predictors of attrition. Suppose that those who request support 2-4 times recently have been more likely to leave, vs. users who have phoned in just 0-1 times. Using Lily Explore for the Set of Valuable Mobile Customers, the Data Scientist can then visually assess whether customers who had 2-4 support requests in the last 30 days are indeed more attrition prone. This would then point to totalsupportcallsper30days as a good predictor of attrition. Likewise using Lily Explore the Data Scientist is able to exclude potential attrition drivers that seem to have no value for predicting attrition in that particular Set. For example, a decrease in the number of cash withdrawals is typically a good indicator of a customer who gradually makes his second bank his primary bank. Exploring the trendcashwithdrawalper60days for the Set of Valuable Mobile Users, the Data Scientist might view that there is little to no difference in this metric for the people likely to leave versus those likely to stay. Hence, the trendcashwithdrawalper60days is excluded as a potential attrition driver for the Valuable Mobile attrition model. Exploring the data for potential attrition drivers is facilitated in Lily through the Explore function but is not really required. Using Lily Customer DNA metrics, Data Scientists do not need to know their data before building their models, as in the past. Lily determines a myriad of attrition drivers to automatically deliver the best predictors of attrition from the hundreds of DNA metrics available. 11 Copyright 2015 NGDATA

12 Prepare the data Preparing Data consists of configuring new DNA metrics, which might be attrition drivers. Detecting who will leave earlier requires tracking the customer more closely using short-term predictors in addition to long-term predictors. Lily s timeline functionality allows Data Scientists to easily aggregate interactions over various short and long-term windows. For example the data scientist can easily profile the customer s online account consultations per 7 days instead of the default 60 days, as used by the existing DNA metric totalwebconsultationsper60days by just changing the aggregation window from 60 days to 7 days. Moreover, Lily will automatically track the change in this online account consultation behavior by calculating the trend and acceleration for the metric. After all, a good attrition predictor is often not based on the absolute value of a DNA metric (e.g. 2 web consultations in the last 7 days) but on how the customer changes with regard to this metric; i.e. the dynamics on this metric (e.g. a drop from 5 web consultations over a 7 day period to only 2). Once the Data Scientist has all desired attrition predictors he creates a data frame in Lily which is the input data for estimating a model. As customers leave at different dates, Lily supports dynamic data frames in which the predictors are relative to each customer s attrition date. In Lily it is easy to create a DNA profile that is relative to the specific customer s attrition date by using a dynamic data frame selecting the attrition date DNA metric as the reference time for the data frame. Building such customer-specific and dynamic - time lines is a task that is typically very hard to accomplish with conventional analysis software. 12 Copyright 2015 NGDATA

13 Build the models Building Models can be done as always in analytical tools, and easily integrated into Lily. Where Lily shines is in shortening the time it takes to implement the model operationally. Usually, the Data Scientist writes in applied code (SAS, SPSS, etc.) to return attrition scores in real time or daily. Code of the Data Scientist is then given to the IT person, who translates to C or Java code (lower level). All of this analytic code translation takes time. The implementation of the model in Lily is much quicker. And most importantly, in Lily, you can see the attrition score over time. Once the models are incorporated, Lily can easily find those who will leave, set alerts to attrition, etc., in and easy to use environment. 13 Copyright 2015 NGDATA

14 Validate the models Validating Models is more powerful with Lily as you can see how the model applies to real-time data for the most stable and accurate model results. Additionally, most modeling does not account for trending, which can be the missing link for attrition prediction. In the past, trending has pretty much been impossible. Calculating the trend score, however, in Lily, is a piece of cake and incredibly powerful in determining attrition. 14 Copyright 2015 NGDATA

15 Deploy and update the models Deploying and updating models is an easy, ongoing and dynamic capability in Lily. The result of an attrition model is either a score expressing the probability of attrition, or a flag indicating whether the customer will leave or not. Lily gives an ongoing trend score that can be used to alert actions to avoid the attrition, rather than a one variable 0 or 1 not or likely to leave. Lily also delivers these trend scores so that preventative actions can take place well before the attrition may occur, vs. alerting when it is too late to do anything effective to change the person s mind. Marketers, analysts and data scientists are better able to address the potential of attrition with more detail on the reason that a customer entered the danger zone, as well as allowing them to set an attrition score well in advance of them leaving - to help them address the attrition in a timely manner. Additionally, having real-time updated attrition scores give more details, and the ability to know why, at that moment, a customer might leave in the future. Knowing information on the most recent hours, vs. the last month gives marketers, analysts and data scientists a greater jump on the issue of attrition. Based on why they have entered the attrition danger zone, customers are ranked on their attrition probability and those in the top-x% are targeted with preventive actions. 15 Copyright 2015 NGDATA

16 Through its breakthrough technology - Lily Enterprise gives banks 5 key advantages to better address the attrition issue call center ATM ERP 3rd party data IVR M2M payment system PoS branch social mobile web product catalog order management CRM ecommerce file system campaign management 16 Copyright 2015 NGDATA

17 Lily Enterprise gives banks 5 key advantages 1 More easily gather customer data with little formatting, from multiple data sources From behavioral interaction data, payment, and transaction data, to factual information pulled from CRM and Data Warehouses. Lily provides marketers with unique and comprehensive customer profiles, or Customer DNA. With more than a thousand realtime metrics per individual customer, including determining the highest value customers and their propensity for attrition, based on a predictive attrition model, derived from the same information. Some metrics include: file system social media campaign management ATM 3rd party data branch mobile ERP CRM PoS web product catalog order management ecommerce payment system M2M IVR mobile apps call center usage balance, transactions, credit and debit purchases, etc. impressions complaints in last 60 days, number of incoming requests, etc. socio demo age, gender, income, etc. relationship customer satisfaction, etc. value primary financial services provider, etc. product total active products, total active loans, etc. competition share of wallet, etc. Customer data aggregated to Lily 17 Copyright 2015 NGDATA

18 Lily Enterprise gives banks 5 key advantages 2 Implement an attrition model in Lily on the aggregated data Banks can then continuously see attrition propensities, without the need to restart the process each time to determine who might leave next. 18 Copyright 2015 NGDATA

19 Lily Enterprise gives banks 5 key advantages 3 Better understand customer behavior Not only take into consideration dynamically updated scores for individual customer metrics at each moment of the relationship, but also attrition score trends over time to gauge the specific reason for attrition, and exact urgency and action - reacting to the potential attrition alert. Sharon Room day week month OCT Lily alerts for increased a ri on risk customer removes mul ple products from por olio WIN-BACK PERIOD 11 NOV 05/09/ /12/2014 customer leaves if no ac ons have been taken in advance Lily A ri on Score (con nuous) Win-back sensi vity Por olio size (weekly) 10 0 SEP 14 SEP 21 SEP 28 SEP 14 OCT 05 OCT 12 OCT 19 OCT 26 NOV 02 NOV 09 NOV 16 NOV 23 NOV 30 DEC 07 DEC 14 DEC Copyright 2015 NGDATA

20 Lily Enterprise gives banks 5 key advantages 4 Compare customer behavior Create comparisons with Sets of similar customers for example high value customers in order to compare the individuals behavior with a similar Set, or group members - to drive personalized, timesensitive actions. 20 Copyright 2015 NGDATA

21 Lily Enterprise gives banks 5 key advantages 5 Timely Alerts Quickly alert the marketing campaign management system, branch information system, call center, IVR, etc. in order to ensure the most appropriate attrition preventative actions are taken in a timely manner. My Alerts! 10% HIGHEST TOTAL DEBT TO TOTAL! ASSETS RATIO > 0.55 % CHANGE IN SHARE OF WALLET > 2% TREND ATTRITION PROBABILITY > 5% ACQUISITION RATE < 3% Set: CA + Credit first two products customers Alert state: ac ve Number of alert ac va ons: 1 Number of days since alert ac va on: 64 Set: Affluent customers aged 35 to 55 Alert state: ac ve Number of alert ac va ons: 1 Number of days since alert ac va on: 35 Set: A ri on Prone 10+ tenure customers Alert state: normal Number of alert ac va ons: 2 Number of days since alert ac va on: 136 Set: High-Value Prospects Alert state: normal Number of alert ac va ons: 0 Number of days since alert ac va on: Not relevant 21 Copyright 2015 NGDATA

22 Putting Attrition Prevention into Practice A Fortune 500 bank was interested in decreasing attrition among its high value customers. To do so, they realized the need to better understand the actions of their customers, as they occurred and over time as trends. They wanted to lower their attrition percentage and realized the need to look at all of their data, instead of just a sample of the total data. The bank added Lily to their environment and quickly started to aggregate all their customer data from their interaction store ATM, PoS, branch, call center, IVR, mobile, M2M, payment system, web, mobile apps, social media, etc.; and from their entity store CRM, FRP, product catalogue, campaign management, ecommerce, order management, file systems and 3rd party data. Their data scientists did not have to sample data, use only long term (3 month+ old information), nor find the most appropriate data for a one-time exercise, but worked with data that ensured the best Customer DNA for this attrition project and an ongoing attrition program. The bank was able to also create a High Value Customer Set based on predefined metric scores, which made up the individual Lily allows banks to be more relevant what customer experience is all about. Customer DNA. With this in place, they were able to see the customer behavior over time trends and set alerts to trend thresholds that might lead a High Value customer to potentially leave. These trend alerts were a more powerful way to deliver more predictive actions. Once a customer met a trend threshold, based on their behavior or lack of behavior an alert was sent to the most appropriate outbound systems for action, based on the situation. An action, or actions, then took place to begin to avert the attrition. 22 Copyright 2015 NGDATA

23 Discover how you can begin to more effectively address and decrease attrition at your bank. For more information on Lily, visit or contact us at or Connect with us Twitter Facebook Vimeo