We have been discussing the Customer 360 framework for a while. You can refer to our earlier write-ups here and here.
Once you have understood the need for a comprehensive view of the customer, the next logical question is “How does one go about it?”


A Customer 360 architecture is typically built in 4 phases:

    • Create Global Unique Identification or GUID for each guest
      The First Phase focuses on unique identification of guests. This is commonly referred to as a Global Unique Identification or GUID. This provides the ability to integrate information across brands, regions, loyalty programs etc. For some businesses, Household ID may be equally relevant as the most granular level of data integration. With existing customers and especially loyalty program members, it is quite easy to track information at a GUID level. However, this can be quite tricky with New Customers and Prospects. What comes in handy is fuzzy logic algorithms, which use nontraditional data points such as GPS, IP, Zip+4 etc.


    • Integrate Disparate (Internal) Data Sources
      The Second Phase in the development of a Customer 360 architecture involves the Integration of Disparate Data Sources. The intent here is to create a wide angle lens by bringing together data points that already exist, but are separated by organizational or technological silos. This includes purchase transactions, customer service, loyalty, campaign, T&L (test & learn), and web, loyalty, fulfillment etc. to name a few. By putting together pieces of information about the customer that reside across departments of the organization, a more complete picture begins to emerge. This needs to be linked to other business databases like marketing & media, promotions, revenue management etc. to understand the cause and effect relationships between actions taken by the business and resulting impact on customer behavior.


    • Bring in Third Party Data
      The Third Phase addresses the seemingly obvious, but often missed, dimension of understanding the Customer beyond the brand’s own ecosystem using Third Party data. Painting a picture of the customer based entirely in his/her interactions with the company can create an incomplete and myopic view – especially with new customers and competitor loyalists who happened to stumble in just once or twice. This includes understanding the level of engagement the customer has with the category in general. While this is easy in the case of financial services (e.g. shared data across brands in credit reports), it’s relatively more complex in other industries where competing brands do not share any data e.g. travel and hospitality. Leading practitioners have attempted to overcome this problem by using proxies – e.g. frequent flier status, credit card data etc. Others have attempted to develop predictive profiles of category engagement. This is done by collecting category engagement data for a sample of customers using primary research, mobile GPS panels etc. These category engagement profiles are then projected onto the entire customer database using advanced analytics. It is also useful to develop an understanding of the customer outside of the category. This is especially useful with customers who are new to the category itself. These data points include Adjacent Category, Demographics, Geography, and Lifestyle etc.


    • Deliver Actionable Insights
      The Fourth and Final Phase leverages the all of the above mentioned information to deliver Actionable Insights that help generate Incremental Revenue and Operational Efficiencies.
      This is done by Predicting Future Behavior and more importantly how we can Influence it. Broadly, this has 3 dimensions.The first is Propensity – will Jack buy a six pack of beer today?
      The second is Affinity – given that jack bought a beer, is he now more likely to buy a bag of potato chips?
      The third and most important is Persuasion or Influence – will a discount change Jack’s decision?


We will be discussing more details on this subject as part of our webinar on March 12, 2015 on “Driving Success through Data Collaboration in Hospitality Industry” (click here to attend).
Authored by – Dr. Anil Kaul – CEO and co-founder Absolutdata.

Authored by Dr. Anil Kaul – CEO and co-founder Absolutdata  .