How to Trial Salesforce Data Cloud with a Pilot Project

Rather than dive in all guns blazing, the smart approach is to start small with a Salesforce Data Cloud trial, using a pilot project. By testing Data Cloud on a manageable scale, you can validate its value, fine-tune your setup, and build confidence before rolling it out more widely. 

In this post, we’ll explore how a startup might combine two basic data sources - sales and support - to test campaign impact using a straightforward ‘Batch Data Transform’. We’ll walk through the steps, recommend metrics to track, and explain how to scale once you see results.

Data Cloud is powerful, but it’s not a plug-and-go solution. It thrives when you feed it with the right data, shape it using tools like Batch Data Transforms, and align everything to your business goals, whether improved sales performance, better customer service, streamlined operations, or reliable Salesforce marketing attribution.

For startups and smaller teams, diving straight into a full rollout risks wasting time and budget, even though Enterprise and Ultimate Sales Cloud customers can access a free account - any allocation of your team’s time is essentially a financial investment, too!

A pilot project allows you to explore a focused, meaningful use case, see real-world results, and iterate without overcommitting. Think of it like a test drive, you wouldn’t buy a car without checking that it suits your daily needs.

A pilot also helps you identify whether Data Cloud is the right solution for your specific challenges. It’s easy to assume it can solve every data issue, but that’s not always the case. Testing early lets you find out what works, before making a larger commitment.

Let’s take a simple example: a subscription box company with basic sales data (orders, amounts) and support data (tickets, resolutions). They want to see if bringing these together can improve a re-engagement campaign targeting lapsed subscribers. Rather than integrating every system, from web analytics to email and social, they begin with just two. It’s low-risk, easy to manage, and gives them a clear sense of what Salesforce Data Cloud can deliver. If it works, they scale. If not, they adjust course without overspending.

Here’s how to run a pilot like this using Batch Data Transforms, Data Cloud’s tool for processing data in scheduled batches. It’s simple to manage and powerful enough to demonstrate real impact.

1. Pick your data sources

Start with two systems you already use, perhaps a sales CRM like Sales Cloud, and a support platform like Zendesk. For our startup example, ‘Sales Data’ includes customer names, emails, order date, and totals. ‘Support Data’ covers names, emails, ticket counts, and resolution statuses.

At this stage, keep it simple. This is a proof of concept, not a comprehensive solution. Check both datasets for shared fields, such as email addresses, so you can link records later.

2. Bring data into Data Cloud

Connect both systems to Salesforce Data Cloud using its ingestion tools. Think of this as piping the data into a central workspace. Sales and support records land as Data Lake Objects - containers that store raw data. You don’t need to pull every field, just what’s needed for your test. In this case, customer details, order history, and support interactions.

Once configured, this can be refreshed on a schedule: daily, weekly, or whatever suits your timeline.

3. Unify with a Batch Data Transform

Use a Batch Data Transform to join these datasets. In the drag-and-drop interface, create a transform that matches records where emails align. Choose which fields to keep: order totals and dates from sales, ticket counts, and statuses from support.

The result is a new Data Lake Object, let’s call it ‘Customer Snapshot’, that combines relevant information into a single view. For example, Jane Doe might show £150 spent, two orders, and one unresolved support ticket. Set this to run weekly for now. Real-time updates can come later.

4. Map to a unified profile

Next, map the Customer Snapshot object to a custom Unified Data Model Object (DMO). This gives structure to your fields. For instance, ‘Total Spend’ becomes a currency field, ‘Ticket Count’ a numeric value. Think of it as a lightweight version of a Unified Individual Profile, ready for testing and campaign activation.

5. Test with a campaign

Push this DMO to Marketing Cloud (or your chosen marketing automation platform) to run a small test campaign. The startup might target lapsed subscribers; anyone without an order in the last 90 days. Thanks to the support data, you can use personalisation:

“Missed us, Jane? We’ve fixed that issue you flagged. Here’s 10% off your next box.”

Send this to a test segment (say, 100 customers) and monitor the response.

Once the pilot is live, it’s time to measure. Focus on clear, actionable metrics linked to your campaign objective.

For the startup example:

• Response rate: How many recipients clicked or replied? Compare this to past campaigns that didn’t use unified data.

• Conversion rate: Did any lapsed subscribers return? Even five conversions from 100 emails is a strong early result.

• Support follow-ups: Did customers with resolved tickets engage more? Spotting this trend could guide future targeting.

• Time saved: How long did this setup take versus manually pulling and merging data? Hours saved = operational value.

Record these results in a spreadsheet. If response rates jump from 5% to 15%, or conversions go from 1% to 5%, that’s a clear signal that Data Cloud is making an impact. Even modest improvements can justify further exploration.

What success looks like: The startup’s results

Let’s say the campaign runs for a month. Of the 100 emails sent, 12 customers click through, and four resubscribe, adding £200 in revenue. Their previous manual campaigns took a full day to build and brought in just one sale.

This time, the setup takes two hours. They see a 12% response rate, up from 5%. More importantly, they learn that subscribers with resolved support tickets are twice as likely to return. That insight alone is worth building on.

Once the Data Cloud trial proves itself, you can scale confidently:

• Add more data: Introduce website visits, PPC clicks, and email engagement to enrich the profile. With enough sources, you can start using Identity Resolution, too.

• Refine the unified profile: Filter for active subscribers or calculate custom metrics like lifetime value.

• Activate across channels: Use Data Actions to alert your sales team about high-value churn risks or sync to a loyalty platform.

• Test real-time transforms: If batch updates can’t keep up, explore Streaming Transforms for faster insights.

• Keep it simple: Start with two sources and one clear objective.

• Check your data: Mismatched fields or typos can prevent accurate joins.

• Set a clear timeline: 2-4 weeks is enough to see results without dragging out the process.

• Track everything: Document setup time, issues, and outcomes. This will help justify the next phase.

Salesforce Data Cloud has the potential to transform the way you manage and activate customer data. But you don’t need to dive in head-first. A pilot project, like our example combining sales and support data, lets you experiment with Batch Data Transforms, gather insight, and build momentum with minimal risk.

Even small wins, such as improved campaign performance or time saved on manual tasks, can be the first step towards something bigger.

And remember, if you’re a Salesforce customer with Enterprise Edition or above, Data Cloud is available to you at no extra cost.

Thinking of giving it a try? Contact MarCloud for expert support and your best chance at first-time success. As the leading consultancy for Marketing Cloud and Data Cloud, we’ll help you plan a pilot that delivers real insight, fast.