The Difference Between Integration and Orchestration is Customer Context

Blog Post
June 13, 2017

Customer expectations have changed. Consumers expect seamless, relevant experiences with brands, regardless of the device or channel they happen to be on. In particular, B2B customers expect meaningful communications that are appropriate to their current stage in the customer journey and their relationship with your company.

As customer expectations evolve, the systems required to support omnichannel customer experiences continue to multiply. Most companies are using some combination of database, CRM tool, marketing automation, email service provider, web analytics, call center technology, customer support platform, billing system, and custom-built technology solutions to manage their customers’ data and interactions with them.

Often, each touchpoint in the customer’s journey is tracked in a single system of record or engagement. For example, a call from a salesperson may get logged in the CRM — but not the call with customer support a few weeks later.

Unfortunately, the success of a single touchpoint, section of the funnel, or stage in the customer journey doesn’t guarantee an overall successful business outcome. A shoddy customer service experience can derail a customer’s relationship with a brand — and when compounded with a poorly timed promotional email, it may be enough to drive that customer to unsubscribe or abandon the product or service.

Applying customer context is critical — at every stage of the journey

Customers create more signals than ever before — of interest, purchase intent, propensity to churn, and other key indicators that impact your business. At every touchpoint, customers are giving you valuable data. But optimizing a single touchpoint won’t have the same impact as optimizing the end-to-end customer experience.

To keep up with these shifts in consumer expectations and deliver a context-driven customer experience, you need the ability to do four key things:

1. Listen for customer signals in any system, across channels

Point-to-point integrations can’t support the complexity of customer context. To get the full picture of your customer, you need bi-directional, adaptive integrations between all systems that support their journey and interactions with your company.

2. Create a single, 360° view of the customer

To create a single view of the customer’s historical interactions and current stage in the customer journey, all integrated signals and data and actions must be unified in a customer data platform (CDP). With a connected record of a customer’s history and current context, you can power complex orchestration based on their activity, inactivity, past behavior, and real-time signals.

3. Orchestrate journeys based on customer context

Firing off stateless campaigns based on a single trigger criteria won’t cut it in today’s landscape. Customer experience should be orchestrated around the customer’s individual context — both historical interactions and current state.

To do this, you need an abstraction layer that pulls business logic out of individual siloed systems of record, and provides a connective tissue for end-to-end customer journeys across systems, channels, and functional teams within your business. With an abstraction layer, contextual data is available in every connected system — resulting in a more relevant customer experience, and smoother handoff of customers between teams like sales and customer success.

4. Build a feedback loop of analytics and optimization

Once you’ve gained the capability to orchestrate complex, omnichannel customer journeys based on context, it’s important to validate whether or not they’re driving the desired business outcomes.

Just as signals, customer data, and actions must be stitched together for a complete picture of the customer, analytics should also span those cross-system and cross-functional processes. This capability helps turn customer journey insights into immediate action across each of the connected systems of record, engagement, and data.

With these combined capabilities, experimentation at scale is possible, and data science teams can build better models to predict conversion, churn, and other customer behaviors.

As companies move beyond optimizing individual moments and focus on end-to-end customer journeys, customers will experience more seamless, relevant interactions.