Data-enabled learning has long been known as one of the virtues of having a large customer base, and an asset that can make any entity have an unbeatable edge regarding the competition. The more customers you have, the more data you can gather, and with the use of machine-learning tools, can have you ultimately offering a better product.
The Harvard Business Review is a pretty competent source for marketing-related studies and content, and they recently made the bold claim that while very valuable, sometimes the collective power of data has been overestimated. What’s viable is that an offering such as a Facebook platform instantly gains value as more people use it and can shut out competitors with strength in numbers.
But when in practice, regular network effects have proven to last longer and be more powerful when stacked up to raw data. If your brand wants to establish the absolute strongest competitive stance, both data-enabled learning and regular network effects are ideal.
Things Have Really Changed That Much?
Companies that have made their living by gleaning and putting data to use have been around for some time. Credit bureaus and those who aggregate information such as Bloomberg, Thomson Reuters, and LexisNexis have been completing the task on a large scale, but have not necessarily taken the time to look closely and mine the data to bring more value to their service.
Since the incorporation of surveys and focus groups were costly and ate up a lot of time, it was only possible to acquire data from a batch of customers that was a bit on the smaller side. You could get to the heart and soul of the customer’s wants and needs, but during decades such as the 1980’s, it was just more difficult to scale up and be efficient.
When cloud-based technology arrived on the scene, firms were now able to process data quicker. The ways in which customers communicated, social media posts, GPS location, and overall internet surfing behaviors were all up for grabs. After algorithms analyzed this massive amount of information, the ways in which a company provided their offerings could be adjusted automatically to benefit and profit from these findings.
Innovation and the Moat Around the Castle
There definitely IS a strong competitive advantage achieved by data-enabled learning, and the Mobileye brand is one good example. They are the leading provider of advanced driver-assistance systems, paving the way for an autonomous future such as collision prevention. Testing data is very essential to improving accuracy, and for them, it has worked to raise it to 99.99%.
Smart TVs have now included software that will offer up personalized recommendations for shows or movies based on one’s past habits. Even though it seems very futuristic and predictive, consumers haven’t caught on quickly yet. The willingness of the customer to finally break out their debit card and pay is the strongest deciding factor, and when you hold this in regard, you begin to deal with a “depreciation rate” of data.
The Unparalleled Quality of Authenticity
Possessing consumer data with few or no substitutes is crucial for the success of data management. The well-known music streaming service Pandora benefits greatly from making its improvements within the AI realm difficult to replicate. The more frequently a user listens to their stations and rates songs up or down, the better the service can tailor selections to that specific user.
The design improvements that can take place are not easily imitated by a rival, because they are deeply tied to the Music Genome Project, which helped to categorize millions of songs based on some 450 attributes. In contrast, many coordinating calendars and office productivity apps have been able to compile worthy information on consumers as well.
Some insights from customer data change at lightning speed, and interfaces such as Google Maps were admittedly outright copied by Apple Maps. Real-time user data was a big boost as far as Google Maps is concerned because it affected the ability to predict traffic and suggest the most sensible routes. Rapid learning cycles can make it very difficult for competitors to catch up because what current customers want is going to be most sought-after data.
Even products that tend to lean on the mundane side such as clothing are now becoming smart and can react to weather changes and vital signs. Once these products are in the hands of the consumer, data-enabled learning can help to personalize more options.
The All-important Vision of the Future
In the years ahead of us, customer data will no doubt improve the way a product and entire brand can meet needs quickly, therefore accelerating in the profit margin domain. The most successful models will be those that are built on the principles of regular network effects such as Facebook, and reap the benefits of data-enabled learning, as Amazon has so successfully proven over the past 3 years alone.
A full-service agency with full advertising knowledge of both important spheres can assure that your entire marketing plan reaches the right people, keeps them engaged, and can eventually even predict what they will do next. Here at Farfetched Studios, we will devise a plan for your unveiling and digital introduction, cultivate leads for the future, blog about what’s important to your followers, and constantly track how the major players in the search engine sphere reacts to your newfound presence!