Businesses have implemented many point solutions to run their processes efficiently. However, these point solutions store data in silos, creating an unstructured environment which actually hinders effectiveness due to inaccuracy and speed
Inside this unstructured data environment, you will see point systems to manage their customer data, customer marketing and engagement (Marketing Automation platforms spanning email management, social campaigns, live chat, mobile engagement, loyalty tools and other), Commerce Systems (Point-of-Sale systems, E-commerce platforms), along with Intranet systems (Employee Collaboration, Document Management, Enterprise Content, Process Workflows), Supply Chain Management (For product lifecycle management), Enterprise Resource Planning and Financial Systems (Invoicing, Billing, Accounting)
Today you will see that some of these point systems have been consolidated to become platforms with feature aggregation. This solves data isolation to an extent but still does not draw a holistic view. Marketing Automation, Behavior Analytics and CRM are still offered as three different systems. With this, the challenge of unifying web behavior data with CRM or marketing automation data is not picture perfect. Marketing Automation platform has evolved to bring together Web/Email/Social marketing features but that would not be enough to understand the complete behavior of a single customer.
Without bringing data together, businesses cannot successfully implement effective data analytics, which is an integral part for process optimization to make accurate decisions. Department data stored in silos with no relationship with the other makes it extremely hard to drive business intelligence, and intelligence driven over siloed data can be very inaccurate.
Today, due to inaccurate decision making, the business users get wrong insights.Some of these insights are translated to customer strategies, which puts customer engagement processes to risk. With customers constantly looking out for quick answers to their queries, intelligence cannot be latent and irrelevant. Real-time intelligence which can translate to customer strategies with less human intervention is the need of the hour.
Be it a personalized customer communication; or insights on how much discounts can be offered to a particular customer based on current NPV, accuracy in intelligence is a must to improve effectiveness.
Accuracy problem can be solved using the unified architecture which clearly builds data relationship between every data attribute gathered by all kinds of business applications. Coupling this with a data-aware system can solve the problem of data disorganization, latency, and redundancy. With the unified structure, you can organize your data between customer and non-customer fields and quickly extract customer data from the rest of the business data.
The deck below illustrates the gaps in the traditional solutions proposed to achieve single customer view and also elaborates on how a unified system can bridge these gaps and solve data problems for good.