Customers today, whether B2B or B2C, demand a personalized experience. They expect a choice of engagement channels and flexibility to access information or connect with companies at any time, from any place, using any device. But as businesses adopt newer technologies, systems and channels to support the changing needs of customers, the customer data becomes fragmented.
For example, customer data may consist of the profile information, demographics, omnichannel interactions, e-commerce transactions and analytical insights. This information is scattered across CRM, marketing automation, financial, logistics, support and business intelligence systems. Additional data sources include social networks to understand customer preferences and sentiments, data from connected devices as well as data purchased from third-party data providers.
Companies realize that, in order to deliver the best customer experience and create relevant engagement, they need to process data from various sources and make informed decisions. They recognize the need to understand the complete customer journey and are becoming more customer- rather than product-centric. Customers are becoming more sophisticated and tone-deaf to one-size-fits-all messaging and promotions. They look for messages that are relevant to them. To deliver personalization at scale, a deep customer understanding with accurate and complete customer information is a must.
Such digital transformation requires you to have data management capabilities that are responsive to the customer in real time, and help identify the right engagement, with the right message and at the right time. Unfortunately, traditional master data management has not delivered on the promise of meeting the needs of organizations in this new age of the customer. We need to think beyond traditional master data management and into the realm of modern data management where we not only think of “golden records” of customer profiles but also about their relationships, their past interactions and transactions. We need to process these to glean deeper insights and help customer-facing teams with intelligent recommended actions.
Ensuring data reliability involves blending the data from all internal, external and third-party sources to create complete customer profiles. Modern data management platforms provide connectivity to ingest data from all sources in any format. For third-party data sources, that includes Data as a Service capabilities through which data-driven customer 360 applications can subscribe to such sources, search based on any available attribute and enrich the internal customer information.
The next step is to blend the data together and create a single-source-of-truth about all B2B accounts and customers. The data from all sources is matched and merged using various survivorship rules. Data owners decide what will be the surviving sources for each profile attribute. Next-generation modern data management platforms also use machine learning to identify hidden match rules.
Multiple high-velocity data streams make ensuring data quality hard. It requires ongoing data stewardship including matching, merging and cleansing of the data. Modern data management supports high-volume data management with cleaning, matching, merging, unmerging and verification capabilities while maintaining full audit trails, history and data lineage, where compliance is required.
Such consolidated and accurate customer data is then provisioned to all operational and analytics systems. A single source of truth of complete customer data ensures consistent customer experience across channels and functional groups.
Just consolidating customer information is not enough. Discovering relationships between people, products and places is fundamental to understanding customers. For consumers, you want to know the many-to-many relationships they have with products, channels, stores, family members, friends and devices.
Retailers, for example, want to bundle customers into households. This is only possible if you know the relationship between various customers and locations. Understanding relationships is important in account-based B2B marketing, as well. Marketers must understand the organizational structure of the account, key influencers, places and products of interest. Modern data management platforms leverage hybrid data stores with graph technology to uncover relationships between all entities like contacts, accounts, products and places. With this information, marketers can design very specific campaigns and offers for the accounts.
Furthermore, using an advanced analytics environment, like Apache Spark, data teams can uncover hidden relationships or gather other interesting insights about relationships. Business owners can find the key influencers, understand product penetration and get to the key member of procurement committee. This information helps them get to the right person at the right time, with the right offer.
Navigating the complex organizational hierarchies in large accounts can be tricky. It takes a lot of effort and resources to figure out the business units, their locations, key contacts and product penetration across the account. Companies use third-party data sets such as Dun & Bradstreet data to acquire hierarchy information, but that information may be limited to the legal structure. Sales need hierarchies that can provide information about product penetration, competitive coverage, credit risk roll ups and business value across the account. Graph capabilities can help in creating personalized hierarchies that provide a roll-up of such contextual information for account planning and execution.
Once you have all customer information, complete with relationships, you can visualize the information as contextual data-driven applications. A reliable data and relationship foundation allows data-driven applications to provide relevant and consistent information for sales, marketing or support. The information is utilized for accurate segmentation, campaign design or to run analytical models like customer value or churn propensity and includes such insights in the data-driven application.
Relevant insights, delivered in the context of a user’s role and objective is essential for marketing success. Modern data management provides capabilities for near-real-time analytics and recommends next-best actions using predictive analytics and machine learning. You can determine trends in customer preferences, the effectiveness of marketing programs, the business value of an account and the influence of a contact. Armed with this information, marketers can provide personalized information and offers to the customer, using the right channel of engagement, at the right time, delivering superior customer experience.
Data clean-up is not a one-time job. It’s an ongoing undertaking that requires collaboration from all operational and customer-facing teams. Data-driven customer applications must incorporate an easy way to collaborate via tools like discussion threads and voting to gain more information about a client, and keep it current. Business users may also need to use structured processes to source additional information from a data stewarding group, flag suspected bad data or request data updates. Bringing all teams together in a collaborative workspace helps build a sound customer engagement strategy and supports high data quality.
As customer data volume, variety, and sources increase, managing data will continue to challenge business and IT teams. To make sense of vast customer information and ensure that all operational and analytical systems are using accurate data, you need to establish a reliable customer data foundation.
Blending data from all sources, cleansing and curating it, and discovering relationships provides a better customer understanding and enables personalized engagement. Agile modern data management allows organizations to build reliable customer views and ensures that, as more information sources, systems and channels are introduced, those can quickly be included to create a fuller customer picture.