Retailers experiment with transformation to platform model
An increasing number of (traditional) retailers are already experimenting with transforming to platform models. A logical consequence of the fact that the place you take as a retailer within the total distribution chain has become more important online. But it creates other challenges, because the transition from a less flexible business model to a network orchestration model is often very capital-intensive at first.
For retailers who are active within the platforms, the distinction in proposition becomes even more important. Platforms that are strongly focused on standardized products can push the price down very quickly. As a retailer, competing primarily on price within platforms will therefore not be sufficient in most cases.
With the rise of tools like Google Optimize , VWO , and Optimizely , most advertisers have implemented A/B testing by now. But when you look at the number of advertisers that have embraced qatar telegram data data-driven optimization across their entire business, the numbers are still very small.
In particular, the largest online parties in the world optimize their entire business model in an almost scientific manner, and also have the most data at their disposal. To a large extent, this also explains why large parties are becoming increasingly larger, and small parties are struggling to achieve the same growth percentages.
Optimizing only on conversion rate is no longer enough. For the right tests you should look at more variables. You can also test with prices and propositions and find the right combinations in that area.
By experimenting with different business models, you can come up with radically new product combinations. For example, look at how Amazon earns money from its Kindle, because experiments quickly showed that the lifetime value of a customer increases exponentially after purchasing a Kindle. In this way, the company does not necessarily have to earn money from the sale of the hardware itself, but knows that it has earned back a sale after x period. Such combinations may seem logical now, but are often found as a result of a large number of data-driven experiments that are unleashed within a company.
