What is Data Ingestion? Why Context Matters in a Data Ingestion Strategy for CX

Data ingestion refers to importing data to store in a database for immediate use, and it can be either streaming or batch data. With an increasing number of data sources and types, businesses are challenged with ingesting and processing data fast enough to support business goals.

For marketers, meeting this challenge via data ingestion is increasingly a key requirement to creating a unified customer profile and competing on customer experience.  A unified customer profile entails obtaining and linking all possible data points into an underlying customer record: Internet-of-Things data from a connected device, fitness data, mobile data, web streaming data, social media data, and any other behavioral data. The unified profile – single view of the customer – combines all structured and unstructured data, first-party data, second-party data and third-party data, and data from both marketing technology and enterprise systems.

Why Data Ingestion is Only the First Step in Creating a Single View of the Customer

Businesses sometimes make the mistake of thinking that once all their customer data is in one place, they will suddenly be able to turn data into actionable insight to create a personalized, omnichannel customer experience. Certainly, data ingestion is a key process, but data ingestion alone does not solve the challenge of generating insight at the speed of the customer.

If delivering a relevant, personalized customer engagement is the end goal, the two most important criteria in data ingestion are speed and context, both of which result from analyzing streaming data. Layering context at the point of a customer interaction allows marketers to move at the speed of the customer to deliver relevant content in real-time.

Every brand has a vested interest to reduce their share of the roughly 69 percent of online users who add a product to their shopping cart but never complete the purchase. A brand that knows how much research went into an abandoned item, knows how long an item remained in the cart before abandonment, or knows that the item was abandoned one day after a customer purchased a similar item at a physical store has a fair approximation of the customer’s intent. The single view of the customer is the only viable way to follow and influence the customer journey in its entirety.

Relevance and Real-Time, a Winning Combination

Companies generate the next generation of insights by matching and cleaning data as it’s being ingested. When data precision is managed in lockstep with data ingestion, an accurate context is the end result. Running a data matching process after the fact is like trying to put toothpaste back in the tube, and the opportunity to provide a customer with a next-best action for their unique customer journey may have passed.

With data quality ensured, analysis of behaviors and patterns results in being able to form a next-best action recommendation for a customer at the right cadence. Consider the customer with the abandoned shopping cart. If everything we knew about the abandoned item was connected to a continuously updated customer record, we could recommend a next-best action relevant for the customer in the perfect cadence of their path-to-purchase. This could take the form of a mobile app message letting the customer know that a cart fulfillment will trigger a discount coupon for the last item they searched for on the ecommerce site.

If a customer cancels a shopping cart filled with leisure wear and the brand learns through social media that the same customer just canceled a Caribbean cruise to book a ski trip, the brand could proactively shape the customer journey at the next interaction with offers for apparel suited for a colder climate.

These examples help illustrate why the real-time component is crucial. Without it, capturing streaming data loses its value because it sheds context – which is what will ultimately deliver relevance to the customer. With context, analyzing streaming data is really about providing marketers with a window into customer intent. Endless data types and data sources means an endless number of triggers that can analyzed to gauge customer intent along the path-to-purchase.

Data ingestion is an important first step toward creating a unified customer view, but ingestion without context does not provide marketing organizations with the tools they need to succeed in today’s competitive marketplace. With the right technology and skills in place, a marketer can listen for, capture, and layer context to all customer signals, ultimately providing the hyper-personalized experience that customers now expect.

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