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 Chuck Schaeffer How Financial Services Leaders Are Succeeding with Big Data

 

Achieving the Elusive 360 Degree Customer View

Social Data

Social data is one of the most common forms of big data. As consumers become more prolific in social channels, financial services firms have the opportunity to listen and act upon that data for engagement and relationship building purposes.

Social data permits financial service organizations to understand the sociological attributes for each customer. Knowing what each customer 'Likes', retweets or comments on creates a highly specific customer social graph.

Customer DNA View

When social attributes are appended to the customer profile in the CRM system and used in customer segmentation and persona mapping, the financial services institution is in better position to deliver more personalized messaging, offer higher fit products and deliver services that influence loyalty.

As an example, FSIs that have paved this social data path have discovered that high net worth individuals have unique affinities that can be used to increase engagement. For example, high net worth individuals are frequently foodies that visit online food channels and shop at organic supermarkets. Consider how advertising, nurture marketing campaigns, product placement, relationship conversations and customer service could be more tailored using this one simple social data element, and then extrapolate to hypothesize how learning more specific social attributes could personalize the customer experience.

Social network data can go beyond improved customer engagement. Companies such as Zest Finance and Kreditech are using social data to score customer credit. Other social data aggregators such as LendUp and Lendo provide loan services based on social data.

When customer profiles and customer segmentation include an integrated mix of demographic, transaction, environment, behavior and social data attributes, FSIs achieve several powerful capabilities, such as:

  • Improving relationships with the most valuable customers
  • Systemically converting marginally profitable customers into highly profitable customers
  • Altering product pricing, levels of service or other factors that decrease the number of unprofitable customers
  • Measuring customer worth and calculating Customer Lifetime Value by segment
  • Measuring the costs to acquire and retain customers for each customer segment
  • Experimenting with combinations of customer data from each category to determine what type of messaging best resonates, which products and services best align with customer interests and which offers result in the highest conversions
  • Understanding customer expectations and calculated propensity to purchase products, including personalized, bundled or customized products
  • Recognizing what channels each customer segment or each customer prefers to communicate
  • Learning where to reduce costs by understanding low value channels and services
  • Predictive capabilities to anticipate and avoid customer churn or answer questions such as — will you earn a higher return from marketing to fewer high value customers or more mid-value customers?

These capabilities highlight the strategic value of finely tuned customer segmentation, appended with the five types of big data customer attributes that deliver each customer's DNA is a rich 360 degree customer view.

Customer DNA

Once you have an intelligent 360 degree customer view, you can use that information with big data techniques that improve sales, marketing and customer service.

Next: Big Data for Financial Services Sales >>

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When social attributes are appended to the customer profile in the CRM system and applied to customer segmentation and persona mapping, the financial services institution is in better position to deliver more personalized messaging, offer higher fit products and deliver services that influence loyalty.

 

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