Maximizing Profits by Finding the Tipping Point for New Technologies
When to invest in new technology is an important question for investors trying to maximize returns. According to 2019 research from top consulting firm Bain & Company, the concept of tipping points holds the answer for when to invest in new technologies and when to wait for another better opportunity.
The article, titled Tipping Points: When to Bet on New Technologies, outlines four key tools to accurately forecast tipping points and use these forecasts to maximize profits [Appendix 1].
The first tool is the experience curve (e-curve), which plots the cost of production on the y-axis against the experience that producers gain as they make more and more units of the product on the x-axis. The graph is a downward-sloping curve that demonstrates decreasing cost of production as more units of the product are produced. This relates to the graph of network effects [Appendix 2] described in class. We discussed in class how one way to increase adoption is to decrease price such that the adoption rate rises above z’ and will therefore increase up until z’’ in the graph below. This experience curve demonstrates how increasing the number of units produced decreases cost, which enables the producer to decrease the price (p) presented to customers. The lower price (p) means that customers gain more net value when they purchase the good, ceteris paribus. In the end, this increased value drives more consumers to adopt the new technology.
The second tool is the elements of value, which is a hierarchy of functional, emotional, life-changing, and social impact elements that provide value to the consumer upon adoption of the new technology. As we see the new technology add new features that provide unique elements of value to consumers, we expect to see adoption increase and even push adoption past the tipping point. Aside from decreasing price as described above, increasing the presence of these elements of value in the product is another way to increase the adoption of the new technology above z’ in the graph listed below.
The third tool is the adoption curve which graphs new product share of sales (%) on the y-axis and time on the x-axis. Itshows an S-shaped curve that begins with low adoption for a period of time, reflects a sharp increase in adoption in the middle of the curve, and then plateaus at the top of the curve. The adoption curve corroborates the network effects graph listed below. The graph below reflects how there are few consumers willing to buy the product at the beginning when few other consumers have purchased the product. It shows how adoption increases rapidly between z’ and z’’ as more people learn about the product and the value gained from purchasing the product dramatically exceeds the price paid. As the value and price converge again, fewer and fewer new customers purchase the product and the adoption rate converges back to z’’. These changes in the rate of adoption in the network effects graph from class mirror the aggregate S-shaped adoption curve from the Bain source.
The last tool is barriers and accelerators of the adoption curve. This tool highlights that the adoption curve is not fixed or stagnant. It can be shifted upward by accelerators like government subsidy and additional technological advancements that decrease the cost of production, or it can fall downward because of barriers like regulation that increase the cost of production or otherwise make the value of the product to the consumer decrease. This mirrors how in class, we discussed that the network effects graph can also be modified to become more favorable for the new entrant after successful marketing campaigns or become less favorable because of negative press coverage.
Taken together, these four tools acknowledge and underscore the presence of tipping points in determining when a new technology will take off and become a lucrative choice for investors. This concept of tipping points comes directly from the study of networks, crowds, and markets and applies findings from this field of study to tangible investment and business outcomes.
Appendix 1: Four Tools to Forecast Disruption [Bain]
![Appendix 1: Four Tools to Forecast Disruption [Bain]](https://blogs.cornell.edu/info2040/files/2021/11/Four-Tools-to-Forecast-Disruption-Bain.png)
The image describes and visualizes the four tools used by Bain to predict the tipping point for new technologies.
Appendix 2: Network Effects Graph
The graph visualizes an adoption curve with network effects.
Graph taken from Easley, David, and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010.