What is Data Orchestration?
Data orchestration takes siloed data from different data storage sources and combines and organizes it to make it digestible for data analysis tools. Data orchestration gives organizations the ability to automate and simplify data-driven decision making.
What is the impact of Data Orchestration?
Marketing Personalization
Personalization drives customer loyalty. In fact, according to research from a Harris Poll survey sponsored by Redpoint Global, 37 percent of consumers will stop doing business with a brand that fails to offer a personalized experience. Marketers need large amounts of data to create these customized journeys for customers and potential buyers. However, lots of data can easily become unmanageable, unorganized, and therefore, unusable.
Data orchestration helps marketers improve the efficacy campaign data, especially as data requirements evolve. Data orchestration collects and organizes data from a variety of sources, including intent searches, website visits, ideal customer profile information, online conversions and brand-specific searches and activities. That data is then categorized and segmented across customized and predetermined online identifiers, personas and purchasing paths.
The result is hyper-personalized buyer and customer journeys that reach the right people at the right time and in the right context. Unorganized and unusable data is a big waste of money for businesses. On the flipside, when data is orchestrated and used to create truly customized marketing paths for your audiences it can lead to much higher revenue generation, customer acquisition, and retention.
Revenue Generation
Expanding on the last point, let’s talk about data as a revenue center. Revenue attribution is a top priority for businesses, but without the right technology, direct attribution in marketing can get murky. This issue also impacts advertising and marketing ROI calculations as well. Effective data orchestration drives better attribution and gives businesses much better insight into what marketing activities are the most effective at driving revenue.
Digital marketers are able to run much more effective campaigns with data orchestration technology. And, just as importantly, they have the visibility and attribution to understand whats working and what isn’t and make corrections when needed. Data orchestration technology handles campaign execution, audience reach, personalized content and automated A/B testing. Marketers can now launch broader and more efficient campaigns that reach the total addressable market.
How Does Data Orchestration Work?
Marketers are always looking for the right moment to interact with a customer. Data and data models are half the battle on this quest for the right moment. Data orchestration completes the picture. Data orchestration technology ensures data flows where and when it’s needed.
The right technology will cleanse, merge, match, and transport data. It will also feed the data into tools for model building, training, and assessment and into every task linked with orchestration. Marketers need this technology to achieve their personalization ambitions. Passing data around in spreadsheets creates missed opportunities and increases the possibility of stale, misused, or lost data.
Use Your Data
The relationship between the marketer and the customer is in constant motion. Interaction points are determined a few different ways:
- Marketer’s preference: Example: I want to sell more of item X
- Customer preference: Example; I’m going to abandon a shopping cart to shop in-store
- Business or machine learning rules select audiences, determine cadence, and plan the next step dependent on the last step.
The heart of data orchestration is measuring and acting on each of these preferences as they relate to one another in real time. This requires having the right data at the right time and the right place.
Customer engagement is an ongoing process, not a destination. Breathe new life into models or create new models in line with changing objectives. This will also ensure that data orchestration is in place to guarantee that customer data is not left behind.
Seize Your Opportunity
The orchestration engine must adapt and recalculate audience, channel, and action. The engine is choosing content, measuring response, and interacting with many small decisions about a customer during a precise moment of their journey.
The importance of feedback is illustrated with an examination of the infinite permutations of a set of actions in a customer journey. Did a customer open an email? How did the customer respond? How soon after opening the email did the customer visit a physical location? Did they post a photo on Instagram? Data orchestration decides the next action to take so that every interaction meets the customer where they are in a buying journey.
The right technology feeds customer response data back into the orchestration engine in real time to ensure that brands stay in sync with the consumer. With the right data at the right time and in the right place, marketers end guesswork that happens when working with stale data or old data models.
Bring it All Together
Retraining machine learning models to stay in tune with a customer is necessary, but is only one part of the closed-loop data cycle. The marketers’ intent is another component that requires a continual data refresh. Real time product availability will also determine engagement.
Machine learning models represent the intersection between customer desires, marketer intent, and organizational constraints. Each requires continuous retraining to make sure the right data is in place. This data orchestration keeps pace with a dynamic customer journey. It also ensures that a journey is not derailed by ignoring or devaluing one part of the intersection.
The combination of desires, intent, and constraints offers a broad canvas for experimentation. Because the customer journey is open-ended, permutations on the journey may seem infinite. Select a short, well-defined path for testing and optimization to allow marketers to manage a specific outcome. Feed results back into the orchestration engine to test a new model’s effectiveness. Results will also inform decisions to tweak or reboot a model to explore a new path.
Learn more about the right tools for data orchestration here.
