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Chuck Schaeffer How to Design Your 360 Degree Customer View

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The often cited but seldom realized 360-degree customer view is a holistic customer profile record that captures five types of customer data from across channels and systems, aggregates that data to understand what's important to customers, and applies those customer insights to deliver personalized and engaging customer experiences as well as achieve company performance objectives.

Sound easy? It's not. According to Gartner, fewer than 10% of companies have a 360 degree customer view, and only about 5% are able to use this view to systemically grow their businesses. Here's what that 5% know.

A 360 customer view does 3 things.

First, it drives customer intelligence. A 360 view displays customer insights that show how to solve for the customer, delight the customer, upsell and retain the customer, and deliver relevant, personalized, contextual and predictive Customer Experiences.

Second, it enables predictive analytics. A 360 view captures each customer's interaction history and calculates an outcome for each event. For example, outcomes may be customer satisfaction or dissatisfaction related to a product sale or call center incident, or the acceptance or rejection of a sales proposal or marketing offer. Based on sum of these calculations for the customer, or the customer segment, the system can recommend the highest probability actions for marketing, sales or support scenarios. Sample recommendations may include Next Best Action (NBA) for an account plan or Next Best Offer (NBO) for a marketing campaign.

Every customer interaction should contribute to a business performance goal – such as an increase in customer share, loyalty or retention. Customers are not homogeneous, so company actions or inactions with customers must be designed, measured and reported by customer type or segment in order to achieve high confidence patterns that can be modeled and scaled for predictable results. For example, measuring the outcomes of advertisements, marketing offers, sales proposals, cross-sell offers, loyalty program promotions or customer service responses for each customer segment allows the company to adjust and direct its messaging and actions with greater specificity for improved and predictable outcomes.

Similarly, when the CRM software detects an initial pattern – such as a trend showing certain types of customers positively responding to an up-sell or cross-sell offer – that offer can be quickly scaled among like type customers. Or at a customer specific level, the CRM system can identify the Next Best Offer based on the offer acceptance rates of similar customer types. With each customer's acceptance or rejection of every offer the system is updating and adjusting the NBO algorithm. The ability to learn and predict customer responses to company actions will increase new customer acquisitions, customer share and customer retention.

Third, it prescribes customer alignment. A 360 view maps each customer into customer segment(s) to allocate resourcing and align business processes based on customer contribution or other business drivers. For example, a company may deliver high touch customer support (with entitlements, Service Level Agreements, etc.) for high contribution customers and self-service support for low contribution customers. Defining business processes by customer type or segment is extremely effective in growing revenues and margins from high contribution customers and lowering costs to serve for low or negative margin customers.

The below illustration shows how companies may use segmentation to align services and business processes.

In the above example, customers are segmented from most to least profitable. Identifying customers that contribute negative profits to the company creates an opportunity to plug those profit leaks. Reducing costs to serve these customers creates an alternative to discontinuing these customer relationships.

There are numerous other benefits of a 360 customer view, such as cross departmental information sharing and business process orchestration. For example, sales may be wise to defer a new promotion to a customer waiting on a critical customer service response. Similarly, customer service may be wise to refer a customer incurring a challenge to sales when an additional product or service would resolve that challenge. When designing cross-departmental customer interactions, it’s important to remember that customers expect a unified and seamless experience regardless of the company department they engage.

Customer Data Types

Customer segmentation is an essential best practice in customer relationship management. However, a common mistake in customer segmentation is to group customers by their upside potential to the company and without regard to what these customers want from their suppliers.

Engaging customers in a one to one fashion at scale is best accomplished by creating finely tuned customer segments and then further appending customer profiles with demographic, transaction, environmental, behavioral and social data.

360 Customer View

Customer Demographic Data

Demographic data such as customer type, size, industry and location provide the initial basis for customer segmentation. While demographics are a common starting point, they are relatively stagnant and not good predictors of customer behaviors or contribution to key performance measures such as revenues, costs, profits and lifetime value. To better engage customers for these business drivers, companies must take the next steps of further appending customer profiles with transaction, environmental, behavioral and social data.

Customer Transaction Data

It's no secret that most companies allocate their scarce time and resources across customers regardless of customer contribution. A CRM best practice is to append customer profiles with financial transaction data in order to reallocate effort and investment to customers based on their contribution to the organization. The fastest method to an uplift in margins and profits is to invest the bulk of the company's focus and services toward the most profitable customers.

Financial transaction data may include sales, returns, referrals and costs to serve and can thereby be used to identify several key performance indicators, and answer critical questions such as:

  • What ~20% of customers generate ~75% of margins and profits
  • What ~20% of customers deliver ~80% of the referrals that result in new sales?
  • What ~5-10% of customers contribute negative profits?

Companies often take a stepping stone approach to applying financial transaction data to customer profile records, beginning with sales and expense transactions, then aggregating the data into performance measures such customer profitability and RFM (Recency Frequency Monetary) analysis, and then finally calculating longer-term customer metrics such as Customer Lifetime Value (CLV).

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When you can capture cause and effect relationship patterns and model that customer intelligence, you can predictably prescribe which actions to which customers will grow customer relationships, loyalty and customer share.


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