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360 View How to Design a CRM 360 Degree Customer View

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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.

Customer DNA

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.

Social Data

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.

Customer DNA sources

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 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. End

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Customer behavioral data 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.

 

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