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Managing Customer Preferences Using Graph Databases

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Read more about author Tejasvi Addagada.

Managing customer preferences is one of the many aspects of managing customer journeys. Customers interact with organizations across a myriad of digital channels, and being mindful of preferences can increase the trust associated with customers. Read on for how graph databases can help manage these customer preferences.

Data privacy and protection policies across geographies make it imperative that customer preferences are collected and personal data processed as per the preferences provided. For example, you may need to see how personal data moves from the moment the customer opts in to the time when it gets deleted. Having this lineage of personal data enables the organization multi-fold in personalizing communication to customers.

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Consent from customers can be strictly related to a purpose such as marketing, personalized messages, or offers. Each purpose can be associated with one or more data processes that generate value by storing, processing, applying, or sharing data. Data processing as a life-cycle stage of personal data can be as simple as deriving age from date of birth.

According to an Accenture report, two-thirds of customers will share personal information with brands. But, this can happen only in exchange for some kind of value. For a well-planned marketing campaign to work, you would require at least 70% of your customers to provide consent to receive relevant content. This stresses that the right customers are chosen based on their interests, and their likes and interests are translated through a preferential reach. However, without a good marketing strategy and content, customers can opt-out of your communication, which can impact your intent to further reach out for marketing.

Most customer preferences are diverse in the way they are collected and stored:

  • Varied products and forms within the same division in an organization
  • Divisions having similar purposes and processed as well as basis for processing
  • Distributed purposes like marketing, analytics, offers, etc.
  • Diverse platforms of servicing like website, mobile, internet banking, bots, branches, etc. 
  • Storage locations including cloud and on-premise, to name a few
  • Different systems including channels, core banking, master data, etc.

We can look at a variety of preferences that customers can have on purposing their personal data. Here are a few scenarios:

  • A customer wants to receive new offers on credit cards with their private email address.
  • A customer wants to receive a phone call whenever a new servicing update has happened, like an address getting updated.
  • A customer is interested in their personal data being analyzed to provide personalized marketing over a mobile app.
  • A customer wants to receive a phone call when additional documentation is required on a loan application. However, because the customer is only 15 years old, you need the consent of a parent.
  • A customer is comfortable having their email addresses being shared with life insurance providers to receive the best returns.
  • A customer is interested in being contacted about any change in interest rates on personal loans over any means of communication.

A company often collects consent from multiple customer-facing channels, including websites, mobile apps, physical forms, contact centers, and branches, to name a few. Customer experience across different channels to support the same product is now necessary when you advertise your products and support your clients across multiple channels (e.g., social media, SMS, chatbots, etc.). Maintaining a single view of multiple product preferences through all these channels and journeys further complicates the situation.

Graph databases assist in naturally arranging these relationships between customers, their product holdings, preferences, consents across products, related households, channels, etc.

Here are a few questions that can be answered easily by a customer consent management database with ease and short turn-around time:

  • Customer-provided consent for a purpose after opting out on the first one
  • Purposes for which consents have been provided by the customer
  • Most consents are provided by customers through which channel?
  • Customers inclined to a purpose like marketing for which products/channels
  • Customer house-holding grouping for preference
  • Who are the customers and their identifiers who have given consent for a product and a purpose?
  • When has customer provided consent for data and analytics purpose – through onboarding or servicing journeys?
  • Which customers and their relationships have provided consent to marketing?

Fast lookups drive the efficiency of the data protection and privacy engineering team, especially when the customers are around millions, and the products extend to a few hundred, while customers are being serviced across close to 100 channels. It’s all about the single and dimensional views of preferences across customers and their traversal for product preferences that make a difference in improving customer communication and building data trust.

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