What’s a data strategy for me?
It's like having a map before a road trip, without it you'll end up in a data desert, eating data dust and asking for directions from data strangers.
Because you can't just wing it with your data, you have gotta have a plan, from startup to enterprise, or else you'll be in a world of hurt like Billy Madison on his first day of school 😄
If you are reading this post - it means that you are in the right direction to establish future data capabilities for your team/company, and it’s never too late, even if you are in the post-growth stage - there is still much to do.
So.. let’s unlock the secrets to measuring and driving success for your SaaS B2B growing company with these top metrics for aligning your data strategy and company goals for product market fit, land grab, territory expansion, increasing the install base, and finding new revenue sources
I’ll focus on the growth stage in this post and address enterprise data strategy in the next part of this post.
In the early stages of a startup company, the focus should be on building a solid data foundation that can scale as the company grows. This includes:
Setting up robust data infrastructure
Implementing data management and governance practices
Developing a data-driven culture within the organization.
As the startup begins to grow, the focus should shift to leveraging data to drive decision-making and business growth. This includes developing advanced analytics capabilities, such as predictive modeling and machine learning, as well as building out data-driven products and services.
In terms of budget and effort, the majority of resources should be allocated to building a strong data foundation in the early stages, with a gradual shift toward leveraging data to drive business growth as the company matures. As the company becomes an enterprise, efforts should be focused on optimizing and automating data-driven processes, and integrating data from multiple sources.
An early-stage data strategy plan for a startup company should include the following key elements:
Data infrastructure: A plan for setting up robust data infrastructure that can scale as the company grows. This includes selecting and implementing data storage and management solutions (read this for further explanation), as well as planning for data backup and disaster recovery.
Data governance: A plan for implementing data management and governance practices to ensure data accuracy, integrity, and consistency. This includes developing data quality standards, data lineage and metadata management, and data security protocols.
Data-driven culture: A plan for developing a data-driven culture within the organization, including training and education programs for employees, and establishing data-related roles and responsibilities.
Data analytics: A plan for developing basic data analytics capabilities, such as data visualization and reporting, and identifying key performance indicators that will be used to track the company's progress.
Data integration: A plan for integrating data from different sources and systems, such as CRM, ERP, and marketing automation systems, to gain a holistic view of the business.
Data security and compliance: A plan for ensuring data security and compliance with relevant regulations and standards.
** You probably saw that I haven’t mentioned something like “Plan your data science function/project based on…”, my philosophy says- first build, format, clean, and design your data, and later, run ML on top of it (read that for further explanations)
The data strategy plan should be flexible and adaptable as the company grows and evolves and should be reviewed and updated regularly. Additionally, it's important to be realistic in terms of budget and resources and prioritize the most essential data initiatives that align with the company's business goals.
In this part, I choose to focus on the first SaaS metrics you should care about, as they aim to measure what is critical for early-stage/growth companies. The list includes:
Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR): These metrics measure the revenue that a company can expect to receive on a recurring basis, and are important for tracking the growth of the business.
Customer Acquisition Cost (CAC) and Lifetime Value (LTV): These metrics measure the cost of acquiring new customers and the revenue generated by those customers over the course of their lifetime, respectively. This can help to determine the product-market fit and the efficiencies of customer acquisition channels and tactics.
Net Promoter Score (NPS): This metric measures customer satisfaction and loyalty and can help a company understand how well its product is resonating with customers.
Retention rate: This metric measures how well a company is retaining its customers over time. High retention rates indicate that customers are satisfied with the product and are likely to continue using it.
Churn rate: This metric measures the rate at which customers cancel their subscriptions or stop using a company's product. High churn rates can indicate that a company has a problem with customer satisfaction or retention.
Sales funnel conversion rate: This metric measures the percentage of leads that convert into paying customers and can help a company understand the effectiveness of its sales and marketing efforts.
Product usage/engagement: This can help understand how customers are using the product, and identify areas where the product is succeeding or falling short so that the company can improve the product and drive user engagement.
Market share: This metric measures the company's share of the total market for its product or service. It can help a company understand how well it is doing relative to its competition and identify areas where it can expand its reach. ** advanced team
Gross Margin: it provides insight into the company's pricing and cost strategies, and how well the company is managing its costs, which can be an indicator of the sustainability of the company's growth and a valuable metric for identifying cost savings opportunities.
It is important to note that the above metrics are not exhaustive, the company should continuously monitor and evaluate the effectiveness of its data strategy, and track the metrics that are most relevant to its specific goals and objectives.
Hope that by now - you have a vision of how your data strategy should look like.
In the next posts, I’ll focus on the enterprise data strategy plan and the other parts of the plan (like data-driven culture and data analytics).
Stay tuned!
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