When should you strive for radical innovation?

Innovation is a word that gets thrown around so much in the technology world I feel it has become misinterpreted. CEO’s like to tout their innovative teams in public while they push their employees into maximizing their short-term profits. This inevitably leads to failure because while they are using their engineers to A/B test colors in order to increase profits 1%, a startup spent their engineer’s time on a radical new product ten times better than theirs.

How did this happen? Well, the startup most likely found out some crucial piece of information that when extended from an existing product, fulfills some market needs that the company initially missed out on. These extreme jumps in product value are where true leaps in innovation come from, just like when Apple released the first iPhone and Google introduced the PageRank algorithm. The reason these products were not created by anyone else was because their innovative features were not common knowledge before then. Google took the search engine concept and found that one of the most statistically significant determinants of page quality were the links to that page. Apple realized that phones were just lightweight internet connected computers contained in ones pockets, and then created a phone that exemplified their inherit versatility more than the current solutions. Both of these companies took previously existing products and creating their own versions of them that provided way more value to the consumer.

The problem with radical innovation in some companies is that they tend to give up resources on it once a good enough solution is found. This is bad due to the ever-increasing pool of knowledge our society is creating that can be used to formulate better solutions. Not only is societies total knowledge pool increasing, but as the company’s solution is used and becomes more publicized, more potential competitors are enlightened about their solution and thus even grounded in their attempt to find radically more valuable solutions. Therefore, the longer a company has remained stagnant on their current solution to a problem, the more likely it is that someone else will find a much better solution.

The answer lies in balancing ones company's short term goals, with the long term risk that someone else will blow them away. Another key point is to be sure one is solving the correct problem. Phone companies pre-iPhone were creating slightly different versions each other's products when Apple re-imagined the phone as a tool to connect a person constantly with the internet. They designed their phone around this concept with a fully functional web browser, unlimited data, and a massive screen to view it through. This stagnation and poor problem identification is pretty easy to see in many products today and I have recently blogged about how I think review sites, which have been around for a decade, are currently missing out on much more potential value.

Review sites are underutilized

Recently, review websites sparked my curiosity and I’ve decided a few of my insights on them (don’t want to give away all my ideas). Review sites use crowdsourcing to try a form a more accurate picture of the general public's perception of that company. They generally do this by allowing users to input a comment about the place, and a 1-5 rating of it that is usually displayed as an overall average and a bar chart. This provides value to the customer by allowing them to use the rating to discern the overall picture, while still being able to read up on personal tidbits of what a person’s perception of the company is.

Some of the problems currently with review sites are: the companies are incentivized to pay for reviews to cheat the system in order to increase their personal revenue, users are biased in their reviews, there may not be enough information to accurately judge the company, and reviewers may not know what they are talking about. Fortunately, most of these problems have pretty good solutions waiting to be implemented.

One of the most important things review sites can do to increase their value to customers is to increase the number of reviews. This is due to the law of large numbers that states that the average value of an experiment performed a large number of times will regress towards the expected value as more trials are performed. This means that as the number of reviews goes up, it becomes more likely that the average rating reflects the company’s actual public perception. This will not only drown out the random occurrences, but also will disincentive the companies from trying to pay for reviews because as the number of reviews goes up, the effect of each bought review goes down, thus decreasing the return on investment for buying reviews (assuming the cost of buying reviews stays the same).

To achieve an increase in reviews, one can apply a basic law of economics that states that people respond to incentives. A great example of this is Amazon Vine. Because reviews give a product more credibility, companies are incentivized to give away free products when they are first released in order to encourage users to buy that product in the future. Amazon Vine reviewers are incentivized to write good reviews because they know if they do, they can get free stuff. Thus, the middle man (Amazon Vine), experiences almost no cost to get more reviews that increase the credibility of its products and therefore increases it’s sales. This can be applied pretty easily to review sites through things like gift cards or other rewards for writing an in-depth review. Now to maximize the value created for the review sites, the optimal reinforcement schedule would probably be variable-ratio. Examples of this type of reinforcement are slot machines, which reward people inconsistently while they are usually losing money. This leads to a high, steady response rate, and because it is still somewhat random, it could be influenced by the quality of a review, thus promoting more valuable reviews.

While having lots of reviews is helpful to generalizing a company. Review sites still suffer from reviewer bias and ignorance. Fortunately, statistics has solved both of these problems through the standard score, which represents the number of standard deviations a data point lies above or below the mean. This can be applied to review sites where Z = (x - μ)/σ where Z is the standard score, x is the raw user score for the company, μ is the mean of the user’s scores, and σ is the standard deviation of the user’s scores. The reason this is helpful is because if a user gives every place he visits a 5 star review, it does not really add any value in comparing companies and therefore should not be taken into account for the company's rating. But, if a user gives out mostly 3 star reviews, a few 4 stars, and sparingly gives out 5 star reviews, then we know that the businesses with a 5 star review must be pretty good. Not only does the standard score help compare companies, but it makes it much harder to fake reviews because if a reviewer’s reviews do not follow the normal bell curve, then they are most likely a fake.

These are but a few my ideas on the underutilized value review sites can offer. Hopefully, they will pick up on these and improve themselves, or hire me for an internship/job to do it for them :).