In Part 3 of this series, we explored the need for large organizations to adopt new approaches to product development in order to stay competitive. We examined the ‘what’ and ‘how’ of the “Build” phase of the Lean Startup “Build-Measure-Learn” loop. Now, we look at Measuring and Learning.
Is your organization struggling to derive intelligence from your data? Are there systemic roadblocks to gaining customer insights? You are not alone.
In Capgemini Consulting’s report, Cracking the Data Conundrum: How Successful Companies Make Big Data Operational, it was noted that only 27% of executives we surveyed described their big data initiatives as ‘successful’. Additionally, the “lack of strong data management and governance mechanisms, and the dependence on legacy systems, are among the top challenges that organizations face”.
Lean startup offers an approach to address these issues. It requires strict attention to customer data, specifically how they are responding and interacting with a product or service. Organizations that adopt lean startup principles are better able to correctly measure customers and adapt their products to meet their demands. This is the key to “Measure and Learn” of the “Build-Measure-Learn” loop.
The lean startup approach enables quick responses to customer needs and requirements. Once a Minimal Viable Product (MVP) has been created, it must be tested with the customer. To set this test, the company must use metrics that matter, and capture the data that allows designers and product developers to make informed decision about changes.
A common example here is A/B testing used in email direct marketing. For example, a marketer may send two different email subject lines out to a small subset of recipients to determine which headline receives the best open rate. This data is then used to send the best headline to the remainder of the recipients.
The customer and their contextual engagement with the product is the key. With the email, they are providing feedback to the senders directly in the context from which they consume the product (the email).
Context is valuable because the customer acts as they would normally. Outside of context, while the customer may think they are acting normally or honestly, they provide inauthentic responses. This approach is known as contextual inquiry research.
Would a focus group on the best sale promotion tagline for an email get the best results? Unlikely, as the research is out of context.
Large companies often face the challenge of not knowing what their customers think or want. However, they still have endless servers full of data. This is a symptom of measuring too much of the wrong things. In this case, less is more. But the “less” must matter.
This is where the lean part of lean startup provides the relentless focus on the customer. Consider the lean point of view to help guide your approach to connecting with the customer:
- Always have a hypothesis: Only measure against key research questions that will improve your product or relationship with your customer
- Only feature level is actionable: Broad and vague customer feedback won’t deliverable actionable information or product improvement
- Direct is favored over indirect: Seek data directly from the customer when possible to derive the most useful information
- Measuring in context provides real insight: Use contextual inquiry research methods to obtain honest, authentic feedback on your product
With customer data in hand, organizations should utilize it to make decisions that improve their product or service in direct relation to what has been tested and measured. The key is to make the decision based on data and to make them quickly.
A more difficult aspect for a large company is how to become a learning-based organization that values data driven insights and can make decisions quickly. Often, the bureaucracy (endless lists of stakeholders) can fatally slow the decision making process and in turn hinder the realization of a lean startup organization.
To combat this bureaucracy, consider the following tactics.
- Codify key decision-making principles, rooted in data
- Empower groups to take risks and make educated mistakes (caution: do not encourage risk-taking and then punish resulting failures)
- Minimize stakeholders during the early development and MVP stages
The data collection approach in lean startup hints at an important cultural component to becoming a learning organization. The key words are “hypothesis” and “actionable”.
All hypotheses have the possibility of being right or wrong. It could mean failure. Accepting and learning from failure is a key cultural principle for organizations wishing to pursue lean startup. This poses a challenge to large companies that are accustom to punishing and avoiding failure, especially high risk industries such as Pharmaceuticals and Life Sciences. Changing an entire organization to this mindset can be daunting and slow.
Large companies need to cut through the red tape and implement metrics that matter and use that data to become a true learning organization that values risk-taking. As a potential strategy, choose a strategic department or development group in which the culture can be changed more quickly. Start the change there. This can serve as an example for the entire company, from which the culture can spread.
The next and final article in this series examines the Innovation Center as a vehicle to deliver the promises of lean startup.