Pitch-perfect marketing—on the scale of Big Data—is about more than ingesting and unifying customer data. To achieve masterful results, you need the ability to orchestrate everything from emails to social media to events marketing, all from a single, accurate source. You also need the ability to surface insights and act on them faster than humanly possible—and you need to do this all at scale.
What Is Customer Data Orchestration?
Customer data orchestration refers to connecting data across devices and channels, analyzing that data to form insights, and acting on those insights. For example, you might:
- Analyze—Observe that customers who visit a certain page on your site also visit your retail outlet
- Insight—Posit that an email with a redeemable coupon could boost the amount they spend
- Action—Send emails with coupons to a customer segment matching the initial observation
To close the loop, once an action is taken, you can track purchasing patterns and report on results. Some Customer Data Platforms (CDPs) can even do real-time interaction management and orchestration, which makes it possible to market to people while they’re shopping, or even to gamify shopping, as some Japanese malls have done.
Why Does Data Orchestration Matter?
Most marketers have at least a piece of the data orchestration process in place—they’re collecting data and analyzing it automatically, but often, they aren’t able to automatically trigger actions that execute on other martech platforms. Speed, accuracy, and efficiency all suffer in such a siloed environment. Here are four big reasons why brands should integrate customer data orchestration into their marketing plans and infrastructure.
1. The Customer Journey is Omnichannel
Consumers interact with brands through multiple channels. For instance, a buyer goes on social media and visits a brand’s page. Another link leads them to a website. After a few days, they visit the company’s physical store. The same customer might also sign up for an e-newsletter, leave items in an online shopping basket, and talk to an agent over the phone.
All together, these touchpoints generate a great deal of data. When a company has the means to stitch the data together and act quickly and automatically on insights, it can engage in the right way, even within a narrow window of opportunity.
2. Customers Demand Personalization
Personalization is table stakes when it comes to reaching customers today. They are more likely to open emails addressing them by their first names, respond to campaigns providing a solution to their specific needs, and become advocates of brands that understand their unique identity.
Furthermore, customers expect brands to know where they are in the buying process. For example, a customer who’s already bought a dishwasher will feel annoyed to receive further advertisements for the same product in the future.
With customer data orchestration, it’s easy to efficiently deliver the right personalized messaging at the right time, and at scale.
3. Customer Experience is Crucial
No matter how good a product is, customers won’t develop loyalty to a brand unless the buying process has been a good experience. And the key to a seamless customer experience is processing and acting on data, quickly and correctly. When customer data is used to create real-time buyer profiles, it becomes easy to determine who the customers are and what they’re looking for at any particular moment. Then your customer data platform can automatically send the right messages for each trigger.
4. Marketers Are Drowning in Data
The volume of data that comes into a business is simply too high for any marketing team to thoroughly process manually. You’re likely missing key insights and opportunities if you’re relying on manual analytics—doubly so if you’re responding to insights manually.
In addition to the sheer amount of data, it is constantly changing. For example, the moment a customer buys a product, the whole buyer profile changes. When marketers aren’t able to keep up with these changes, they can’t approach customers with the most compelling messaging.
With customer data orchestration, however, companies can set rules for data processing and subsequent actions, and let the CDP handle the rest.
Customer Data Orchestration Step-by-Step
You can start building your data orchestration workflow in four steps:
1. Collect Data
The first step is to connect your customer data streams to your CDP. Notice I didn’t say to migrate or copy the data; for data orchestration to work, you need real-time, live data. Since the data stream is constantly refreshed, you won’t have to deal with outdated or duplicated data.
The more data your CDP has to work with, the more complete its customer profiles will be.
2. Sanitize and Consolidate Data
Although a CDP has similarities with DMPs and CRMs, this step highlights the difference between the three technologies. A CDP stitches together cross-channel and cross-device data, including personally identifiable information, into one real-time view of a customer.
In this step, a CDP continually goes through the following processes, guided by artificial intelligence (AI) and machine learning (ML) algorithms:
- Ingests and stores data: It’s not all about personal data from customers. A good CDP also stores unstructured data like customer reviews and website visits, CRM data, and loT data. This makes it easier to create accurate customer profiles.
- Identifies matching data across channels: A CDP can collect similar customer information across different channels. These include identical names and addresses on different platforms and storing an individual’s varied identifiers. In some cases, external directories are also used.
- Cleanses data: This entails removing old leads, clearing duplicate or corrupt records, and cleaning outdated information. With the right CDP, it’s possible to have this done in real time, so customer profiles are always accurate.
3. Create Profiles and Enrich with Third-Party Data
A CDP unifies all the data relating to the same person—including third-party data—to create a single, holistic customer view.
This automated data enrichment enables deeper insight than you might get with just your company’s first-party customer data. For instance, you might discover that 80% of customers who visit your online store all share a particular hobby, which could inform how ads are placed and which copy to use for which segment.
4. Analyze for Insight & Act
Now it’s time for the CDP to identify trends and surface insights. For example, Wish used its CDP to drive a personalized shopping recommendation engine. The CDP was able to process data from social media, the web, and more to determine which products were likely to appeal to specific customer segments. Then it took action, customizing product recommendations based on the data.
CDPs Help Companies Orchestrate Excellent Customer Experience
The massive amount of data available to marketers holds the potential to make our marketing more relevant, personalized, and effective at scale. But right now, much of a company’s customer data is underused or misused—stuck in silos that give marketers an incomplete picture of each customer. By the time we figure out where the customer is on their journey, they’ve already moved on.
Customer data orchestration helps solve the data problem, moving marketers closer to real-time action on the touchpoints that matter most. With the right CDP as the foundation, your tech stack—and all its many parts—starts playing like a flawless symphony.