Many executives and investors assume that it is possible to use customer data capabilities to gain a competitive edge. This is because it is assumed that the more customers you have, the more data you can gather, and that data can then be analyzed with machine learning tools to offer better products and services that attract even more customers.
When you collect even more data, you can eventually marginalize your competitors in the same way that businesses with sizable networks often do. That is the way most business executives think, but more often than not this assumption is wrong. In most situations, people grossly overestimate the advantage that data offers.
The cycles generated by data-enabled learning may look similar to those of regular network effects, wherein an offering, like a social media platform, becomes more valuable as more people use it and ultimately garners a critical mass of users that shuts out competitors.
But practically speaking, regular network effects last longer and tend to be more powerful. To establish the strongest competitive position, you need them and data-enabled learning. However, just a few companies are able to develop both.
Nevertheless, under the right conditions, customer-generated data can help you build competitive defenses, even if network effects are not present. In this whitepaper, you will understand some of the conditions to making data competitive and how to evaluate whether these concepts apply to your business.
There are a lot of questions that are often extensively asked to enhance business data knowledge. Some of these questions include:
- How much value is added by customer data relative to the stand-alone value of the offering?
- How quickly does the marginal value of data-enabled learning drop off?
- How fast does the relevance of the user data depreciate?
- Is the data proprietary—meaning it can’t be purchased from other sources, easily copied, or reverse-engineered?