In this video, Greg Poirier, Founder & President of CloudKettle, answers the question:

What’s the difference between Salesforce Data Cloud and a Data Warehouse?

 

Transcript:

Hi, I’m Greg Poirier, President & Founder of CloudKettle. One of the questions we often get from our clients is: How is Salesforce’s Data Cloud different from a data warehouse?

There are many ways, but one of the most meaningful differences for Marketers is that Data Cloud gives us the ability to action data in near real time. So let’s look at how Data Cloud makes this possible:

First let’s talk about Identity Resolution.

Often, marketers can have their hands tied, because while we have several databases that include customer data,  we can’t combine them into a single identity. Greg Poirier is in the email database and can’t be combined with Greg Poirier in the Direct Mail database and Greg Poirier in our Product database. If Marketing wants to run a campaign to individuals using data points from all three of  those databases, it may take weeks for a data team to build the audience, if it’s possible at all. Data Cloud, however, is built with identity resolution in mind. It allows us to create a single representation of an individual, and sometimes more importantly, a household. We finally have this actionable unified profile of a  “human” that is a single person, but using different data from different platforms.

So now let’s talk about segmentation.

We have an individual, we can also build custom and reusable segments made up of individuals. We can  pool people into groups like customers: lapsed customers, new customers, high data users, young adults etc. And we can use those segments without recreating them every time. But that’s not all. We can also nest them. So once a segment has been perfected,  we can also create a subset of that segment with specific criteria – for example people under 30,  who are customers, and have high data usage. Perfect audiences for perfect messaging.

We often think about Sales and Marketing efforts as being targeted towards audiences.  As we just discussed, Data Cloud facilitates this for Marketers, by allowing for identity resolution and the easy creation of reusable and nestable audiences. This in turn, allows for calculated insights.

Consider this the evolved form of lead scoring. Historically, calculating things like a lead score, on the fly, in a data warehouse has been difficult. As the name implies, data warehouses are places to store data – not action data. In Data Cloud, we can take the data we are ingesting from multiple platforms, like our website, Marketing Cloud, Sales Cloud, backend product databases, and enrichment solutions and score people in near-real time, and then also trigger actions based on those scores.

So for example, a Recency, Frequency, Monetary model (often called an RFM) can look at which customers have spent recently, who spends frequently and who’s spend is changing and then kick off campaigns in Marketing Cloud based on those calculated insights. Similarly, a score that considers how heavily the client is using the platform based on logins   and data consumption could trigger an upsell opportunity being created in Sales Cloud or, if a combined set of data shows a customer is at risk of churn, automatically create a winback case in Service Cloud – so that Customer Success intervenes.

So what is the main takeaway here? With Data Cloud – the ability to identify individuals through unified profiles,  and then create custom and reusable segments, opens up opportunities to  create Calculated Insights. We can define and create multidimensional metrics to understand  customer behaviour and preferences, and then take action on those insights in near-real time.

Thanks for watching. If you found this helpful, check out our YouTube channel or our CloudKettle blog.