External vs Internal Data: What to Use When Discovering & Measuring New Opportunities
In this article, we will discuss how internal and external data sources should be utilized to guide product strategies.
Confident data analysis is essential for how companies make decisions. In particular, for product or service managers, a combination of internal and external data sources can be used when making product strategy related decisions. Each species of data is optimally used to answer different questions related to how a product or service can evolve to meet customer needs and revenue growth.
Below we break down what internal and external data is, when each should be leveraged, and provide examples of instances of how they can guide future iterations of products.
Internal data refers to the sources that come directly from a company’s own systems, making that information highly specific in its relevance. Internal data presents additional responsibilities, requiring the management of capturing, formulating, and distributing that data.
At its most impactful, internal data can provide insights related to a company’s current practices, and can reveal opportunities for more efficient practices or growth. Internal data should be a source of truth in how customers behave and how they should be engaged. For example, stadium attendance data may indicate that fans who arrive at the stadium one hour before a game starts will spend 3X more on concessions than fans who arrive later. This insight can then be addressed through new strategic initiatives to get fans on-premise earlier, and empower fan engagement teams to justify those investments.
When applied, internal data can also be used to optimize certain processes and operations. For instance a team may be tracking renewal rates of season tickets and find that customers who are directly contacted more than two times during a season are more likely to renew the following year. This might lead the team to change their outreach strategy to prioritize premium-priced ticket buyers. Internal data is also necessary to establish benchmarks for organizations, enabling them to compare present results to their own historical data.
External data refers to sources that measure industry or an even broader scope of changes that may affect a product’s features, operations or business model. Studying these sources is called external analysis or environmental analysis, and is a key component of product strategy. External data is not typically focused on a single company, but it can be highly specific to an industry, type of product/service, or segment of audience.
External data sources have a range of applications to businesses and their product strategies including: understanding trends, integrating new product features, creating messaging, establishing industry benchmarks, gaining new revenue streams, anticipating shifts in demand, and limiting risk. Overall external data analysis should be performed to guide businesses adaptations to market and audience changes.
Through environmental analysis, product or service managers can begin to see how external factors, such as trends or competitor features, impact what consumers value from the products and services they utilize. An example of this analysis could come from measuring the interest in metaverse environments as indicated through reporting from a platform such as Google Trends. If product or service managers see a significant growth in metaverse-related searches, that might be an indication of opportunities to develop new offerings or implement a metaverse channel strategy.
Of course there are many challenges to using external data, both business and technical. The right partners need to be established with data-providers having to prove the quality of their data. External data often needs to be purchased, with more tailored data requests earning a premium rate. However, even free resources, such as Google Trends or the US Census Bureau Business Builder, are informative measurements that can help product or service managers identify trends and broader changes to user behaviors and habits that can uncover new revenue sources.