Abhishek Rungta

Growth gets everyone excited. But it needs investment of cash, time, and effort. The extent of these investments depend upon the number of variables involved (and the possible values of these variables) in the growth strategy. Lesser variables mean higher probability of success, and hence low-cost growth, as it becomes easy to identify the point of failure, and hence quick iterations.

Let’s take three most common dimensions: growth by hiring new sales people, growth in new geographies (or market), and growth through new product introduction.

When adding more sales people, it is best to deploy them in the geography and product that are working well for the business. This enables us to quickly get the talent up to speed, and qualify the capabilities.

And, when adding new product, we can keep the variables limited by deploying our best sales people in the geographies where business have got great results. This will enable us to quickly iterate on product features, as it is most likely that the product design has not been suitable.

Similarly, when adding new geography (or market), it is ideal to go to the new geography with the suitable existing product and sales talents who have worked well in most geographies. This gives us more confidence that the problem is originating due to the new geography (e.g. demand in the given geography, competition, product adaptation required to succeed in the new geography).

The moment you change two variables, the permutation and combination of possible values of these two variables can increase the complexity exponentially. Hence to reach the right combination may need a lot more investment.

For example, if you are launching a new product with a new sales team, you never know if the problem is with the sales team members or the product design. And you may keep changing both team members and product features without enough confidence, hence bleeding resources.

If you want to get best ROI on your growth investments, change one variable at a time, measure the outcome, critically evaluate the outcome, and explore options for the variable; till you achieve the desired result.

Fast growth at “all cost” can actually be very expensive.

Which dimension are you experimenting at this point in time? What has been your learning? If you had any interesting anecdote affirming the above structure (or otherwise) do share!