As your organization moves toward digital transformation, how do you manage end-to-end customer and product lifecycles?
Whether you’re in a cloud or hybrid environment, chances are, you’re going to end up with customer data in a relational database.
Enterprises have been migrating to fast, scalable cloud data warehouses because they make product and customer data more readily available for analysis — and empower IT to respond faster to business needs. With a powerful, petabyte-scale relational data warehouse like Amazon Redshift, it’s easier to do cohort analysis and tie retention to specific behavior and value you’ve provided your customers.
And whether you’ve totally migrated to cloud data warehousing or are operating in a hybrid cloud/on-prem environment, there’s often still a barrier to line-of-business users accessing customer data. When contextual customer data like product usage is locked into databases that marketing, BizOps, sales, and customer success can’t access without extra work from IT, then the data is way less actionable — and customer experience often suffers.
In most organizations, extracting actionable customer data is an involved process. Without added technology layers for integration and orchestration, you generally have to extract data from all those systems, normalize it, and upload it into each platform (ETL) or make do with a piecemeal view of your customer lifecycle.
Without another layer of analytics that ties the involved entities and process improvements together, it’s super-difficult to answer a simple question like, “What impact did this new sales/marketing/CS process have on business objectives?”
With the Usermind database connector, you can act on signals from any connected system, and unify or move data between those systems. Our database connector also supports Postgres, Microsoft SQL Server, Oracle Database, and for this example, we’ll talk about Amazon Redshift.
With Usermind, you can use product usage and other data stored in your Redshift instance to inform marketing campaigns in Marketo or Eloqua — or sync existing sources of customer data into Salesforce or a billing system like Zuora — without coding, queries, or custom integration work.
With bi-directional integration to most systems — and field/object mapping between systems — you can manage data in unprecedented ways with Usermind.
With the Usermind database connector, you can implement logic to automate workflows and update data in any connected system — without coding. This means even non-technical team members can extract important product data like usage metrics from your Redshift instance, and apply it to the appropriate system for marketing automation, transactional email, CRM, help desk, communication, or billing.
Basically, this integration lets your business team access existing data in Redshift, and use it to facilitate and personalize cross-functional journeys like automated customer onboarding — without wrangling queries, running extensive ETL processes, or uploading CSVs.
With this integration, you can implement relatively complex, technical processes without having to monopolize engineering resources. IT’s time gets freed up to tackle bigger, value-driving initiatives, and the team running the customer-facing initiative can analyze the processes and take immediate action to improve them, from one platform.
Because Usermind also provides a data warehouse of clean, normalized records from all the connected systems, you can use Usermind’s data directly in your business intelligence tool, rather than trying to manage complex ETL processes and analysis between heterogeneous systems and disparate data structures.
Let’s say we store product analytics in our Redshift instance. If we use Redshift to store this data, then we can easily access it in Usermind journeys. This alone cuts out the effort of querying or extracting data from a database, then delivering it to whomever manages the customer-facing systems — who would then need to upload it and manage processes in those systems.
Here are a few examples of things we could do with Redshift data in Usermind:
As a customer success manager, I want to personalize the customer experience using product usage data stored in Amazon Redshift. When a customer takes a specific action within my SaaS product, I want to append that user behavior to their Salesforce record, add them to an ongoing marketing automation campaign.
Specifically, let’s add any customer who completes onboarding to a Marketo campaign, update their Salesforce record with that data, and just for good measure, send a Slack notification to their account manager.
1. Select tables and views to use as entities in Usermind
First, you’ll connect your Redshift database to Usermind by authorizing the connection with your login and selecting the database you want to use in your journeys.
Then, you’ll select which tables and views you want Usermind to continually fetch. Don’t worry — we’ll also fetch the associated metadata.
Then, you’ll browse the schema and then map the data and metadata to corresponding fields in other systems, so that we associate the appropriate records with each customer, and you can build journey logic around it.
2. Build a rule or journey
Then we’ll build the logic in Usermind. Here’s how easy it is to make a complex rule around completed onboarding in our product:
We can quickly validate this data in preview mode before publishing it to our customers.
Once systems are connected and data is mapped between systems in Usermind, it only takes a few minutes to orchestrate complex journeys across the full customer lifecycle — and no queries or code.
3. Analyze those processes and export data from the customer data store
Usermind analytics show a customer (or partner or product) as it passes through the stages of the journeys you’ve defined. You can perform cohort analysis to see what drives business impact, and see where in the funnel customers are getting stuck.
We also provide a data store that automatically stores versioned records as the defined entities travel through a journey in Usermind.
Basically, you define the milestones, criteria for matching, and actions that go into a journey, and we capture all the involved history and changes over time.
Because this aggregated customer data is normalized between systems, it’s ready to pump directly into a business intelligence tool like Tableau or to head straight into your reporting.
So everything gets a whole lot easier: building out a new business process around previously siloed data — and answering the critical question, “What impact did this process have on business outcomes?”