Subscribe to DSC Newsletter

The Danger of Pursuing Customer 360 View

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?

Views: 1940

Tags: Analytics, Big, Customer, Data, Relationship, Science

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Lynne Mysliwiec on March 13, 2017 at 9:56am

Also, there are only three revenue-generating activities you can do as a marketer in order of easiest to most difficult:
1. Convince someone who is an active customer to purchase something more (strategies: cross sell, up sell, renew - anti attrition/loyalty)
2. Convince someone who has never purchased from you to make a purchase (advocacy, acquisition)
3. Convince someone who left you (lapsed customers) to return and purchase again (win back)

Comment by Lynne Mysliwiec on March 13, 2017 at 9:44am

"TThe Customer 360 View business initiative was created as a substitute for the hard data analytics or data science" -> No, it wasn't. It was created BECAUSE computers and programming allowed data to be aggregated in a way that allowed companies who market directly to their customers to market more efficiently through analytics. Data aggregation is only done when there is a business payoff. Most companies don't do the work unless a) the data exist  b) there is an expectation of positive ROI.

I agree that there can be diminishing marginal returns for some customer data (where the cost of acquisition prohibitive -- example: competitive intelligence and wallet info). However, without aggregation of data across channels, you are limited in your ability to provide the right incentives to the right customers for incremental behavior. Without data aggregation and the despised "360 degree view" you are doomed to waste acquisition offers on existing customers (and often to your detriment as a company since companies often provide the best incentives to "new" rather than ongoing customers).

If you are going to pursue a lifecycle strategy, it's helpful to know WHO is in which part of the lifecycle and how profitable they are.

Don't get me wrong - for CPG companies who distribute to wholesalers and who do not directly market to consumers, your suggestion describes the ONLY data they get.

However, for companies where consumers provide their identities to you along with their transactions it's actually FOOLISH to ignore the information when you can invest marketing dollars MUCH more efficiently by targeting the right people at the right time.

Also (at least in the United States), information on age and income and home value and home ownership and family composition is readily available as an overlay to aggregated data and need not be placed in an expensive "other" category.

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service