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 5 types of data, including demographic, transaction, environmental, behavioral and social data.
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).
Customer transaction data aids another CRM best practice which is to determine profiles and traits of high contribution customers and then identify other customers with the same or similar characteristics, but not yet in the top contribution segment. By extending or replicating the messaging, promotions, interactions, loyalty program, nurture marketing campaign or other engagement techniques shown to be successful with like peers in high contribution segments, companies can systemically move customers from lower to higher contribution tiers.
Transactions aren't just financial data. Customer activities produce transactions that reveal customer sentiment and are indicative of future customer behaviors. For example, customer inquiries or complaints regarding service fees or misleading statements are highly correlated with subsequent customer churn. With properly designed CRM software, these types of customer transactions can be captured and considered in context of other customer attributes or events and create alert notifications with recommended actions when the data suggests that inaction will result in a negative outcome.
Customer Environmental Data
The 5% of companies that leverage their customer 360 degree views to systemically grow their businesses generally append their customer profile records with environmental data, sometimes called economic third party data. For consumer accounts this may include data such as profession, education, personal income, family size, household income, home value, disposable income, net worth, economic affluence and even merchant records such as retail purchases or travel expenses.
This third party data can be used to create more telling customer profiles, link consumer relationships, establish house-holding, and more accurately align company products to customers.
An environmental data best practice is to append prospect and lead records with third party data (i.e. profession, consumer disposable income, house-hold income, retail purchase histories, etc.) for improved segmentation, targeting and sales pursuits. Richer account profiles can further be applied to sales algorithms to better determine which prospects and leads deserve increased investment, and which don't. The result is improved customer acquisition conversions and reduced sales and marketing spend.
Environmental data brokers include Acxiom, BlueKai, atalogix, eBureau, Epsilon, Experian, IRI, Neilson and V12 Group. Several data providers have information bundles designed for different industries.
Customer Behavioral Data
Businesses can harvest large volumes of prospect and customer data in order to improve customer intelligence by appending what is generally stale and static demographic and transactional data with real-time and dynamic behavioral data. This improved customer intelligence dramatically enhances customer segmentation and profile records with attributes and dimensions that more accurately predict intent and demand, and enable advanced segmentation techniques such as micro-targeting and campaign triggers. Additionally, this improved segmentation permits companies to deliver better messaging and offers that are more personalized, relevant and timely; all characteristics that significantly improve customer engagement and offer conversions.
Each time a prospect or customer visits your website, uses your mobile app or interacts with your social networks he or she is leaving digital footprints that can be harvested to understand their intent and behaviors. CRM software can be used to track and correlate these digital footprints, identify patterns such as products of interest, score the level of interest, and link these interests and scores to the customer profile record in the CRM system. CRM alerts can then be sent to client account managers or the data can be used for highly specific nurture marketing campaigns.
Customers are far better defined by their behaviors than their demographics. Demographics are explicit data while behaviors are implicit data. For example, explicit data such as age and income only indicate how interested the company is in the customer. Implicit data such as the number of website visits to a product webpage is far more powerful as it can show how interested the customer is in the company.
Increased behavioral data deepens the understanding of customer preferences, more accurately identifies interests and purchasing patterns, and enables more precise customer segmentation. The challenge with customer behaviors is that they may change quickly. Therefore, it's essential that behavioral responses are captured in an automated fashion and integrated in real-time to the customer profile.
Customer behaviors can help companies understand each customer's product of interest, buying intent, channel and communication preferences, and even when a customer is about to defect.
As customers become more prolific in social channels, companies can listen and act upon that data for engagement and relationship building purposes.
Social data permits businesses to understand the sociological attributes for each customer. Knowing what each customer 'Likes', retweets or comments on creates a highly specific customer social graph.
When social attributes are appended to the customer profile in the CRM system and used in customer segmentation and persona mapping, the organization is in better position to deliver more personalized messaging, offer higher fit products and deliver services that influence loyalty.
360 Degree Customer View Capabilities
When customer profiles and customer segmentation include an integrated mix of demographic, transaction, environment, behavior and social data attributes, organizations achieve several powerful capabilities, such as:
- Improving engagement and relationships with the most valuable customers
- Systemically migrating low or marginally profitable customers to become more profitable
- Altering product promotions, levels of service or other factors that decrease the number of unprofitable customers
- Calculating Customer Lifetime Value by segment
- Measuring the costs to acquire, serve and retain customers for each customer segment
- Experimenting with combinations of customer data from each category to determine what type of messaging best resonates, which products and services best align with customer interests and which offers result in the highest conversions
- Understanding customer expectations and calculated propensity to purchase products, including personalized, bundled or customized products
- Recognizing what channels each customer segment or each customer prefers to communicate
- Learning where to reduce costs by understanding low value channels and services
- The machine learning and dynamic algorithms in several CRM software applications such as Salesforce Einstein and Azure Machine Learning can leverage customer data for predictive analytics. For example, in business to business industries machine learning can calculate lead and opportunity scores as well as predictive sales forecasts. In consumer industries, machine learning can calculate intelligent up-sell and cross-sell recommendations for each customer at different points in time as well as predict customer attrition (churn). Predictive capabilities can also answer more tactical questions such as — will you earn a higher return from marketing to fewer high value customers or more mid-value customers?
These capabilities highlight the strategic value of finely tuned customer segmentation and appending each CRM customer profile record with the five types of customer data that reveal each customer's DNA in a rich 360 degree customer view.
How much customer data do you need? Enough to deliver personalized and engaging customer experiences and achieve company performance objectives.