There is no shortage of advice on what metrics a startup should be tracking at different stages of growth, and what the limitations are of these metrics. See for example YourStory’s earlier articles on Valuation and Profitability, GMV, Product Psychology, and even Startup Purpose.
One of the key measures of a startup’s skills, capability and progress is its performance as a learning organisation. The field of knowledge management (KM) offers useful indicators of how a startup should become an effective learning organisation as it figures out its product-market fit, features, customer solutions, and business model. Here are the five key types of metrics entrepreneurs should add to their performance dashboards: activity, process, knowledge, people and business.
These are a necessary but not sufficient reflection of traction – also described as technology metrics or even vanity metrics. Measures such as web traffic, app downloads, footfalls and online interactivity fall in this category. If you have low performance in these basic ‘entry-level’ metrics, you better go back to the drawing board and start over again – but even high performance in this phase is only the first step towards success.
This is where the action really begins: are you able to improve a certain core business process or activity for your customers, or for your business model? This could be lowering costs of acquisition, higher efficiency in marketing, better price comparison services, higher sales rates, and so on. These should be tied to specific problems and pain paints of your B2C or B2B target consumers.
This is where you are actually building best practices (as well as worst practices to avoid!) in your startup processes, gleaning valuable customer insights and profiles, filing patents, refining checklists for different customer segment engagement, and so on. As explained by Eric Ries in his classic Lean Startup (see my book review), startups are companies who are ‘specifically geared to product and institution building in a context of extreme uncertainty, based on innovation accounting and build-measure-learn cycles.’
Knowledge metrics are focused on validated learning from customers about business prospects (the most vital function of a startup). Startups should become adept at not just knowing their domain but learning and experimenting efficiently in this domain. The repository of tested and validated hypotheses – and the speed of crafting such quality learnings - are part of the success fabric of a startup, particularly in a hyper-competitive ecosystem.
These are semi-quantitative metrics which assess the level of satisfaction of your customers, employees, managers and business partners. How well are you performing in the LIFE metric (Little Innovation From Everyone)? How are you measuring progress against attrition and poaching? What is the empowerment and aspiration level of your team?
External metrics on the people side include how satisfied your customers are with the product performance, price points, and after-sales service. In ecosystem-based innovation (see my book review of Wide Lens Innovation), level of comfort and trust among your channel partners is also an important people metric.
These are some of the most important metrics a startup must measure itself up against. Investors, especially, will look at customer retention rates, up-selling and cross-selling, profit margins, and competitive moats. These measures are cascaded from the previous types of metrics.
There is no end of advice startups will get on such metrics, and this may seem bewildering at first – but clarity will emerge once you categorise metrics and expectations by sector. For example, retail sector startups (such as grocery delivery) will require large investments up front even before launch, B2B enterprise sector startups may also need relatively large investments early on, whereas B2C digital startups may be able to show some validation and revenues even before needing funding (see YourStory articles on boot-strapped startups and valuation dynamics).
Many of these issues will be discussed at TechSparks 2016. For example, there will be practical in-depth case studies of startups at different scale stages such as Paytm, RazorPay, FreeCharge, Zendesk, HelpChat, HelpDesk, Zerodha, FusionCharts, BookMyShow, Directi, Zoho, MyDala, MapMyGenome and Ola.
Investor insights on their priorities of metrics will be provided by Sequoia, Signal Hill, Aspada, Blume, Axis Bank and Swan Angels. There will also be extensive coverage of these tips on metrics in YourStory articles – but for first-hand insights and networking, be ensure to enrol for TechSparks here!
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- Lean Startup
- eric ries
- knowledge management
- Product/market fit
- technology metrics