Unique businesses collect unique data, so it’s important to take time before and during integration to consider what specific data means for your organization.
The meaning of “customer” isn’t always straightforward, and it’s imperative for involve.ai to understand your organization’s definition.
For example, perhaps you serve corporations which include many subsidiaries. In this case, we would typically recommend establishing parent and child accounts, where subsidiary health can be measured and predicted individually, with roll up capabilities for the parent accounts. This will allow you to get granular in your data, or identify which child accounts have gaps in the data.
Or, perhaps your organization offers short-term trials for prospects. In such instances, you would need to decide whether the data you are collecting for those accounts is something you want impacting your overall model.
Your Data Science Consultant and Customer Success Manager can help you think through the pros and cons of different approaches, but the decision will ultimately be yours and you will need to determine exactly which field(s) you will want pulled into the dashboard under “Customer Name” and from which data sources.
Active / Churned
The involve.ai model works by assessing how different data points have historically impacted churn. It’s therefore essential to define:
What “churn” means within your customer base
Where it is measured
When it takes place
This will ensure you pull or calculate the correct metric in the involve.ai dashboard. Do you consider revenue churn (down-sell), contract cancellation, or failure to pay? At what point do you determine that it crosses the line (Notice of cancellation? End of contract? Each FY?)? Is the information better pulled from a CRM or financial system of record?
Ensuring the involve.ai models are looking at what your team believes to be the correct metric for churn ensures that they correctly predict likelihood of churn going forward.
Many organizations offer different products and services. It can be complex to set this up in the involve.ai platform, but also delivers immense value, as each of your product lines may have distinct leading indicators of churn and upsell.
You need to define which products and services to include in your demographic data, and if they use different systems of record (often this is the case) it’s important to ensure a common identifier, so that the involve.ai platform can recognize which customers are using multiple products and how their behaviors differ and impact health for each. If you don’t know where to begin, your Data Science Consultant can help!
Companies define and track revenue in different ways. Are you more focused on Annual Contract Value, Monthly Recurring Revenue, Annual Recurring Revenue, or something else entirely? Understanding how you measure and defining where the information is hosted and how it is updated will ensure involve.ai calculates revenue risks and opportunities in a way that is most meaningful for your organization.
Resources and roles
Consider the people on your customer teams. How many and which roles support which customers? Thinking through whether and how you want this presented on your dashboard will allow you to filter for which accounts have, for example, a Customer Success Manager, Account Manager, or other assigned consultants and experts. This may come in helpful if you are trying to balance your CSMs’ time between accounts with low and high needs, or to determine individuals’ capacity when account planning, or even to determine whether having certain roles assigned to accounts correlates with higher health.