When it comes to CRM systems, companies can choose to process customer data in real time or batches. As a marketer, it’s important to understand the differences between real-time and batch CRM data and how each can be used effectively.
This article tackles what real-time and batch CRM processing looks like, along with key benefits and strategies for leveraging each approach in your marketing campaigns.
Real-time vs. batch-based CRM data processing: How do they differ?
Your involvement in choosing the CRM system’s data processing approach may be limited, but it significantly affects your work.
Real-time means the martech stack, including the CRM platform being used, can collect customer data in real-time and you can use such data in real-time for customer messaging and activations.
On the other hand, batch processes collect customer data over time (i.e., over one day, one week, one year), ingest data points and unify them under customer profiles. Afterward, you can leverage them in your campaigns.
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How CRM data processing influences marketing campaigns
Real-time CRM data activations
This approach involves using the latest customer information and promptly responding to it, especially in terms of customer behavior. It’s ideal for companies that need to swiftly address actions like abandoned shopping carts or customize website content based on recent customer navigation. It is also beneficial for online customer care teams.
To support real-time CRM use cases, you should have an ongoing, relevant stream of behavior data through your digital properties (i.e., websites, apps, ecommerce purchases, email communication, etc.) that can also be leveraged to communicate with customers in real-time.
You also need content that aligns with the current customer behavior you’re monitoring. It’s crucial toto avoid having real-time customer data but stale content.
For instance, in ecommerce, you can create (and test) the cart abandonment messages and work closely with the customer care team to prepare messages and understand questions customers may have about products or services.
Batch-based CRM data
When it comes to batch-based CRM data, you can leverage both the latest behavior and customer self-reported attributes (i.e., name, address, preferences, occupation, company name, and interests) instead of focusing and reacting mostly on the latest customer behavior.
Depending on your martech stack and data operations practices, you may even be able to use historical data for CRM activities. Think of B2B companies with long buying cycles and complex sales processes that can take months and involve multiple people.
For these companies, having a more detailed picture of these customers (or prospects) may be more relevant than just having the last customer behavior.
Consider which customer data points will be used for CRM and activation purposes and which channel. Will it use self-reported customer data such as name, occupation, and the latest email marketing? Maybe some CRM channels will use some customer self-reported data (name and interests), while others may use past customer behavior or a mix of both (name and website pages visited in the last week).
Combining real-time and batch-based processing of customer data
Many companies use a combination of real-time and batch processes for handling customer data in their CRM systems. This approach helps them prioritize recent customer behavior for tasks like online customer support. Simultaneously, they can utilize a more comprehensive view of the customer for other CRM activities requiring a detailed understanding of their profile.
Unlocking the potential of real-time and batch-based CRM data
If you are unsure how customer data is being collected and prepared to be used throughout activations and campaigns, reach out to your technical / data teams to get a better picture. When/if you are already familiar with this, the next step is to determine how each type of customer data (and how this data can be used) fits into the CRM / customer activation strategy.
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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.