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The Danger of Pursuing Customer 360 View | @CloudExpo #IoT #M2M #BigData
The Customer 360 View is a relic of the old-school Business Intelligence and data warehousing days
Mar. 12, 2017 01:00 PM
One of the best parts of my job is talking to a wide variety of customers across a wide variety of industries at a wide variety of different points on their big data journey. I’ve recently had several customer engagements where the client’s top business initiative is creating a Customer 360 View. Danger, Will Robinson!! I think the Customer 360 View business initiative is both dangerous and distracting; it is dangerous because it gives organizations a false goal to pursue, and it is distracting because it diverts the organization’s resources from more actionable and financially rewarding business initiatives.
The Customer 360 View is a relic of the old-school Business Intelligence and data warehousing days. Hate to be so harsh, but for many organizations, Customer 360 View was created as an artificial goal for organizations that could not move beyond the Business Monitoring stage with their data and analytic investments (see Figure 1).
Figure 1: Big Data Business Model Maturity Index
The Customer 360 View business initiative was created as a substitute for the hard data analytics or data science work necessary to understand and quantify your customers’ behaviors, propensities, tendencies, inclinations, preferences, patterns, interests, passions, affiliations and associations. The Customer 360 View business initiative lulls organizations into a false sense of accomplishment that seduces organizations to invest scarce data and analytic resources on pulling together any and all customer data. Unfortunately, there are two significant issues with the Customer 360 View:
- The Customer 360 View data is not actionable. While leverage data visualization techniques can help to flag potential problems in the data, the data in of itself is not actionable until you apply analytics, and you don’t know what analytics to apply until you know what customer-centric business problem or opportunity the organization is trying to address.
- Not all customer data is off equal value. One does not know which data is most important until you know what customer-centric business problem or opportunity the organization is trying to address.
Yea, I hate the Customer 360 View as a business initiative.
Identifying and Prioritizing Customer Use Cases
Let’s expand on the efforts that organizations have invested in their Customer 360 View by identifying, qualifying and prioritizing the decisions that the organization is trying to make about its customers (and pre-customers or prospects). In order to determine what data (and ultimately) analytics are most important, the organization must first determine which customer-related decisions – either decisions being made by the organization about the customer or decisions being made by the customer – are most important.
We recommend that organizations start with an envisioning exercise to identify, validate, justify and prioritize those decisions. The envisioning process focuses on identifying and brainstorming the decisions that are being made about customers across all the different business functions (e.g., Sales, Marketing, Services, Customer Support, Product Development, Finance, Operations). The process will yield a set of decisions that we then group into use cases or common subject areas (see Figure 2).
Figure 2: Grouping Decisions Into Use Cases
For example, the following customer-centric use cases might come out of the envisioning exercise:
- Improve customer profiling
- Improve customer behavioral segmentation
- Improve prospect targeting effectiveness
- Improve customer acquisition effectiveness
- Increase customer activation (after acquisition)
- Improve customer likelihood to recommend (LTR)
- Increase customer social advocacy
- Improve customer cross-sell / up-sell effectiveness
- Increase customer shopping cart margins
- Monetize customer events (e.g., vacations, anniversaries, ski trips)
- Monetize customer life stages (e.g., births, graduations, weddings, death)
- Increase customer satisfaction
- Reduce customer attrition
After we have identified, validated and vetted the use cases with the different business stakeholders, we then leverage the Prioritization Matrix process to prioritize the customer use cases based upon business value and implementation feasibility over next 9 to 12 months (see Figure 3).
Figure 3: Prioritization Matrix Process
Building Actionable Customer Analytic Profiles
Once we know upon what use cases to focus (after prioritization), we can begin to:
- Identify and collect the data in the data lake necessary to support the prioritized customer use cases, and
- Identify and collect the analytics necessary to support the customer use cases using Customer Analytic Profiles.
Analytic Profiles are structures (models) that standardize the collection, application and re-use of the analytic insights for the key business entities at the level of the individual human (e.g., customer, patient, doctor, student, teacher parolee, mechanic) or individual physical object (e.g., cars, buildings, jet engines, airplanes, locomotives). See Figure 4.
Figure 4: Customer Analytic Profile
We will build out the Customer Analytic Profiles one customer use case at a time, ensuring that 1) we are focusing the organization’s scarce data and analytic resources on those use cases offering the optimal business potential, and 2) that we have a big data architecture in place (data lake and analytics tools with Analytic Profiles) to capture, refine and share the data and customer analytics across multiple customer use cases.
Creating “Customer Actionable View”
Instead of a feel good Customer 360 View, we have created actionable customer analytics that are focused on supporting the organization’s key customer initiatives and leveraging the data lake and Analytic Profiles to ensure that the resulting data and analytics can be captured so that they can be leveraged across multiple use cases (see Figure 5).
Figure 5: Foundational Customer Data & Analytics
Now, isn’t that better – and more actionable – than just collecting any and all customer data?
By the way, I am going to be teaching this process at Strata + Hadoop World in San Jose on Tuesday, March 14th. I running a three and a half hour workshop titled “Determining the economic value of your data.” If you sign up, show up ready to work (bring your work gloves and work boots!). I think you’ll find the simplicity of the process illuminating!
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