Customers interact with businesses through multiple channels – and are incessantly bombarded via email, social media, direct mail and broadcast media. To keep up with the ever-changing landscape for creating and keeping customers, businesses need to leverage real-time customer data to drive targeted and personalized cross-channel marketing. By taking the intelligence gained about any prospect or customer – at any point in the relationship – and leveraging it in their ongoing marketing dialogue, organizations are able to better target and engage their consumers by making all interactions seamless and relevant.
Given that the data and information about customers is typically spread across multiple customer-facing systems, leveraging all of the data in a unified fashion across all marketing and sales channels can be elusive for most companies. Each marketing channel and each customer-facing system’s platform has its own infrastructure, its own database, and its own information management. The integration of these various data sources – especially customer relationship management (CRM), enterprise resource planning (ERP), marketing automation and social media – is key to rising above the noise, achieving effective cross-channel marketing and driving business growth.
In this slideshow, Scribe founder Peter Chase outlines the seven obstacles to integrating customer data across various sales and marketing applications, as well as ways to overcome them.
Click through for seven data obstacles to cross-channel marketing and ways to overcome them, as identified by Scribe founder Peter Chase.
Obstacle #1: Divergent data models
Databases are structured to suit the application they are created for, so there are a lot of dissimilarities. Databases in CRM systems are more normalized than most because they store data in many related objects with complex relationships, and certain vertical markets – insurance, as an example – have incredibly complex models. Marketing automation systems are much less complex as they are designed for high-volume processing and real-time execution, while email-centric marketing systems are list-based models. So how can organizations logically fit all of these differing data structures together?
Solution: Logically map your integration up front at the object level and identify any differences that need to be reconciled in the integration process.
Obstacle #2: Data quality
Data quality refers to the completeness and accuracy of the data elements present in each record. Bad or missing data can plague integration processes by creating processing errors that then require manual intervention to repair. Data problems become viral through integration, propagating from application to application. Ultimately, they result in escalating costs and bad customer experiences.
Solution: Assess a reasonable sample of records from each application and manually verify the accuracy and completeness of the records to get an understanding of any issues. You can then build assessment and correction right into your integration processes by including rules that perform data validation checks and resolve any issues through predefined processing rules.
Obstacle #3: Duplicates
Duplicates can be introduced at any entry point of data in the system, and are a particularly thorny issue for company or contact records. Duplicates can cost companies money and make them look bad – for instance, if a single recipient receives four of the same email – but having people scan across multiple records in a system to fully understand the customer is time consuming and kills user adoption of your applications.
Solution: Develop processes and procedures to achieve excellent data quality by consistently cleaning and standardizing data in all systems. Building duplicate checking rules into the process and using advanced matching services is important.
Obstacle #4: Data relevance
Don’t let unnecessary data clutter applications or destroy productivity. Minimize the data exchanged between applications to strip out the noise. Excess and unnecessary data storage, especially in the cloud world, can be very costly as many SaaS providers charge extra for excess data storage.
Solution: To ensure data relevance, start with the end objective in mind. What data is going to drive the actions within each specific application that are essential to the needed ROI? Prioritize the records and fields based on the answer to that question and strip out everything else.
Obstacle #5: Performance
Time is money, and the underlying concern behind performance issues is how long it takes to get things done that will produce value. Latency (response time) is an issue in real-time integration scenarios and typically involves only one or a few records, while throughput (time window) is an issue in large batch-integration scenarios.
Solution: Outline latency or throughput requirements at the outset of any integration project. Minimize the amount of data processed by leveraging change data capture – integrating only new data or data that has changed, thereby filtering out any unnecessary records. To shorten response time, leverage bulk integration APIs whenever possible and consider multi-threading – concurrently processing multiple data feeds by a target application.
Obstacle #6: Rate of change
Innovate, don’t imitate. Change is inevitable so organizations cannot afford to be locked into today’s way of doing things by rigid solutions. Flexibility and control over the integration processes are key.
Solution: Expect and plan for the fact that you will need to frequently make modifications to accommodate new opportunities and situations. Make sure integration solutions are iterative – growing and expanding with changing needs without disrupting the business.
Obstacle #7: IT dependence
Most IT departments are overworked and overburdened. Traditional approaches to integration have involved intense programming so getting an integration project resourced and scheduled can take seemingly forever. By the time it is finally completed, business needs have changed.
Solution: Empower line-of-business users with an easy-to-use platform. Use an integration platform that provides a visual design environment, mapping out integration processes without the need to write any software code.