Single Customer View Demystified

Today’s customers expect to be known and cared about by companies. While modern online experiences can customize web page layouts and provide customer service representatives with a customer’s purchase history, the future of highly personalized experiences can be seen in the idea of a single customer view.

There are many perspectives on what makes a true single customer view (SCV), and one of the most common misconceptions is that an SCV is either a piece of software or an off-the-shelf product. Rather, SCV is not only a product but is actually a process that your data must follow to make it ready and suitable for marketing, analytics and insight. Through the extraction, transformation and load process, SCV should take all the data you hold concerning your customers and put it through an automated journey of matching, cleansing and enhancement.

More businesses than ever are interacting with their customers through multiple online channels, which is resulting in enormous amounts of data being created and stored in the digital space. According to an IDC study, consumers created 1.8 zettabytes of information in 2011, which will multiply approximately 50 times by the year 2020. With so much information available, it is up to companies to take the correct steps in collecting, organizing and analyzing it effectively.

Why Single Customer View?

SCV gives companies the chance to understand customers with better data to know who they are, where they are, what they do and, eventually, what they want. An online experience that is able to create a strong single customer view can provide tailored experiences similar to what can be seen in the best in-person experiences, such as a restaurant predicting and preparing the order of a faithful customer long before he or she orders it.

Better data creates better insights concerning a company’s target audience. When applied appropriately, this leads to:

• Better Marketing - Derive interest based on pages visited, articles read and keywords searched to cater to audience interest and send more relevant promotions.
• Better Sales - Create complete profiles with historical data to help sales people identify needs, budgets and timing for customers to more effectively close deals.
• Better Support - After purchase, customers may encounter issues, report bugs or look for FAQs. This data can be gathered to form a proactive approach to solve issues and provide better training and documentation to solve issues before they arise.
• Better Retention - Proactively track trends and data concerning what creates unhappy or happy customers to create better experiences and boost upselling and renewing for long-term customer relationships.

In the end, these all add up to a better customer experience.

The Benefits of Single Customer View

While companies should provide many well-designed online touchpoints to their customers in order to provide them with the best online experience possible, such as online chat and e-commerce checkout options, businesses should also see every point as a chance to connect and learn about individual consumers and interact with them in meaningful ways.

A successful single customer view is created by collecting all information related to your customers and providing a 360 ̊view for each person. These types of customer information can be pulled from touchpoints and include:

• Individual Customer Identities & Demographics
• Browsing Activities
• Preferences
• Sentiments
• Customer Support Team Interactions
• Firmographics
• Sales Interactions
• Event Attendance
• Social Engagements
• Transactions

While each type of data can give specific insights into a customer, they provide a robust view of individuals when combined as an SCV.

Four Steps for Aggregating SCV Data

Single customer view can act as a single source of truth for your business concerning your customers, which can aggregate data from mobile apps, websites, portals, databases, CRM, emails, social ads, e-commerce and customer service. In order to properly aggregate data, follow these four steps:

1. Extract Data from Different Sources - Target data from mobile apps, websites, portals, emails and social media. This data can be collected from e-commerce, as well.
2. Cleanse and Standardize - After extracting, convert data into a common format so it’s easier to analyze, especially if it is coming from different global regions.
3. Merge Identities with PII - Information about the same person should be merged together using Personal Identifiable Information (PII), which includes combinations of emails, addresses and government-issued IDs to help identify individuals with user consent and the ability to remove if requested by customers.
4. Model and Index - Collected data should be readable and cleanly organized in a unifiable schema so it can be used effectively by data teams.

Once information has been properly gathered through data visualization and analysis, the unified data can be used to:

• Create Profiles, Dashboards and Reports - Helps keep track of trends and customer behaviors.
• Calculate KPIs - Creates conversion rates, engagement scores, customer lifetime value and customer satisfaction scores.
• Support Machine Learning - Feeds data into models to create sentiment and interest analysis or create a prediction model to identify the most valuable customer segments.
• Improve Workflows and Audience Targeting - Provides data to workflows like audience targeting, campaign managers and AB testing to improve them.

The ETL Pipeline

Creating a data pipeline with the ETL (Extract, Transform and Load) process can help your business better collect, organize and analyze customer data to gain the most benefits from your SCV efforts. Before you start creating an ETL pipeline that can result in a single customer view, ask yourself the following questions: Where are your data sources? What do you want to measure and analyze? What are your business processes and how is data being collected? How can SCV creation be a company-wide effort?

This process can help you successfully approach a customer data pipeline and help you properly analyze it for the creation of a Single Customer View. The ETL pipeline consists of the following elements:

• Extracting data from different sources, both structured and unstructured.
• Transforming the data by cleaning, filtering, validating, merging and applying business logic.
• Loading the transformed data into a queryable data warehouse ready for visualization and analysis.

By approaching your data through an ETL pipeline, you can have greater assurance that you will be creating accurate insights upon which to base your marketing and online experience strategies. The result can be more satisfied customers, a higher return on investment and long-term customer relationships for your business.