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 Chuck Schaeffer How Financial Services Leaders Are Succeeding with Big Data

 

Achieving the Elusive 360 Degree Customer View

Customer Transaction Data

It's no secret that many FSIs allocate their scarce time and resources across customers regardless of customer contribution. A financial services best practice is to append customer profiles with transaction data in order to update customer segments based on financial measures and reallocate effort and investment to customers based on their contribution to the organization. The fastest method to an uplift in margins and profits is to invest the bulk of the FSIs focus and services toward the most profitable customers.

Transaction data should be used to find the Pareto principal along several key performance indicators, and answer critical questions such as:

  • Who are my highest value customers, or what 20% of customers generate ~80% of margins and profits?
  • What 20% of customers deliver ~80% of the referrals (that result in new sales)?
  • What 5-10% of customers contribute negative profits?

Financial services firms often take a stepping stone approach to applying purchase transaction history to customer segmentation, beginning with RFM (Recency Frequency Monetary) analysis and then advancing to persona based customer profiles.

Another best practice to grow customer share and increase lifetime value is to increase the size of the consumer's initial purchase. Financial services research shows that the size and type of a customer's first purchase is predictor of their of their lifetime value to the organization. However, comparing this research to customer trends illustrates an interesting quandary. Consumers are churning their banks and insurance companies at an increased pace. Additionally, the percentage of consumers who never procure additional financial services products is increasing. Techniques to increase the consumer's initial purchase – and thereby grow customer share and lifetime value – include personalized recommendations, discounts or promotions on cross-sell or up-sell products, and membership invitations to loyalty programs. Increased consumer engagement during this initial time sensitive purchase will improve a number of financial performance measures. Further, applying this initial purchase transaction to customer segmentation and customer journey mapping can provide additional value to downstream business processes.

A third financial services best practice related to customer transactional data is to determine profiles and traits of high contribution customers and then identify other customers with the same characteristics, but not yet in the top contribution segment. This later group can then be moved to customer segments, nurture marketing campaigns, loyalty tiers or other engagement techniques to grow their contribution to their like peers.

Transactions aren't just purchase or sales data. Customer activities can produce transactions that are indicative of future customer behaviors. For example, customer inquiries or complaints regarding bank services fees or misleading statements are highly correlated with subsequent customer churn. These types of customer transactions should be captured in a CRM system that can consider the data in context to other customer attributes or events and create alert notifications or suggested actions when the data suggests that inaction will result in a negative outcome.

Customer Environmental Data

Building upon customer demographics collected as part of the KYC and onboarding processes, leading financial services firms are purchasing environmental and economic third party data such as profession, education, personal income, family size, household income, home value, disposable income, net worth, economic affluence and merchant records such as travel expenses and retail purchases.

This third party data is a type of big data and can be used to create more telling customer profiles, link consumer relationships, establish house-holding, and more accurately align financial services products to customers.

An environmental data best practice is append prospect and lead records with third party data (i.e. profession, consumer disposable income, house-hold income, retail purchase histories, etc.) for improved segmentation, targeting and sales pursuits. Richer account profiles can further be applied to sales algorithms in order to better determine which prospects and leads deserve increased investment, and which don't. The end result is improved customer acquisition conversions and reduced sales and marketing investment.

Environmental data brokers include Acxiom, BlueKai, atalogix, eBureau, Epsilon, Experian, IRI, Neilson and V12 Group. Several data providers have information bundles designed for the financial services industry.

Customer Behavioral Data

FSIs have the opportunity to harvest large volumes of prospect and customer data in order to improve customer intelligence by appending what is generally stale and static demographic and transactional data with real-time and dynamic behavioral data. This improved customer intelligence dramatically enhances customer segmentation with attributes and dimensions that more accurately predict intent and demand, and enable advanced segmentation techniques such as micro-targeting and campaign triggers. Additionally, this improved segmentation permits banking, insurance and financial markets companies to deliver better messaging and offers that are more personalized, relevant and timely; all characteristics that significantly improve customer engagement and offer conversions.

Each time a consumer visits your website, uses your mobile app or interacts with your social networks he or she is leaving digital footprints that can be harvested to understand their behaviors. Packaged software can be used to easily and inexpensively track and correlate these digital footprints, identify patterns such as products of interest, score the level of interest, and link these interests and scores to the consumer profile record in the CRM system. CRM alerts can then be sent to client account managers or the data can be used for highly specific nurture marketing campaigns.

Customers are not homogenous and are far better defined by their behaviors than their demographics. Demographics are explicit data while behaviors are implicit data. Explicit data such as age and income only indicate how interested the FSI is in the customer. Implicit data such as the number of website visits to a particular product webpage is far more powerful as it can show how interested the consumer is in the FSI.

Increased behavioral data deepens the understanding of customer preferences, more accurately identifies interests and purchasing patterns, and enables more precise customer segmentation.

The challenge with customer behaviors is that they may change quickly. Therefore, it's essential that behavioral responses are captured in an automated fashion and integrated in real-time to the customer profile.

Customer behaviors can help FSIs understand each consumer's product decision making; channel and communication preferences; level of financial self-direction; risk tolerance; or when consumers are about to defect.

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Demographics are explicit data while behaviors are implicit data. Explicit data only indicates how interested the FSI is in the customer. Implicit data is far more powerful as it shows how interested the consumer is in the FSI.

 

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