Embedded analytics (customer-facing metrics)

Embedded analytics is often used in a B2B context to help 3rd-party customers gain a better understanding of relevant data.

This comes with its own unique compliance challenges, but when done correctly, it can massively boost engagement, provide better insight into customer needs and permit 3rd-party customers to become more data-informed.

What is B2B embedded analytics?

Within this context, a software provider sells data software or insights to a business, which can subsequently be embedded in that business’s own software to provide its users with data insights.

In the case of B2B2B there would be other users further down the line

What use cases does embedded analytics allow?

B2B product analytics

If your customers can show their own customers high-quality product analytics data, this can strengthen their value proposition as well as your own. Embedded data can help users understand their own product usage and clearly demonstrate the value of the product.

Demonstrating the value of your product to your customers is closely followed by churn prediction and prevention. Once you know which variables create a high-value customer, as well as an at-risk customer, you can design recommendations and tailored messaging to ensure they are getting the most out of your platform. 

Next best action

After you know how many users are navigating through your platform and using your features, you can start to get proactive. It’s common for users to get frustrated if they don’t know a feature exists or if they have to constantly re-enter the same information. With ‘next-best action’, you can use their behavior to suggest the next feature or pre-fill a form based on their recent interactions.

Partner portals

The ability to create a ‘partner portal’ for product analytics can be a powerful differentiating factor in an often crowded B2B market. 

Many partner organizations are eager to further understand their shared users’ behavior, and are often willing to pay for such insight. This is particularly relevant in ecosystems with many companies offering complementary value propositions, such as the Modern Data Stack.

Competitor benchmarking

It is also possible to create comparisons between organizations, or groups of organizations, that help them to understand the performance of their tool compared to their competitors. 

For example, an organization might see a particular performance metric that can be offered as a regional benchmark within a geographical area.

Common challenges with embedded analytics

1. Compliance

Compliance presents challenges on two fronts. Firstly, in terms of data governance, such as preventing the wrong person from seeing the wrong data. Secondly, in terms of gaining explicit user consent, any 3rd-party users will need to understand exactly who has access to their data under GDPR rules (as well as other regulatory frameworks).

2. Separating and managing data

Centrally stored data sets need to be well managed in order to grant the right organizations and users access to the right data. While this closely relates to compliance, it is also important for showing users the information most relevant to their context. Achieving this means creating very well-understood data sets and closely managing their usage. 

How Snowplow can improve your embedded analytics

Snowplow helps organizations to collect better behavioral data – i.e., second-by-second records of user activity on their sites or apps. We provide pre-made data product accelerators, so you can find value quickly; these can be customized to create very tailored embedded analytics dashboards for your customers.

One of the main strengths of Snowplow is the flexibility of this data capture, as the data definitions can be customized to the needs of any business context. This allows B2B companies to pass on the most relevant data to their own users, with a level of accuracy and granularity not available with other tools.  

Check out one of our modeled data sets to see the ~130 out-of-the-box event contexts offered. As you can see, this data is complete and highly structured. The fact that our data sets are thoroughly documented and well understood helps you to effectively and compliantly share parts of your data sets with your customers.

We also deploy our tech completely in your own cloud environment, so it’s fully private. This means Snowplow has no access to the sensitive data that you may collect on your own customers and helps you to effectively manage your data compliance.

To test this for yourself, try Snowplow for free