Have you ever reviewed online customer ratings or feedback prior to buying a product? If you’re like most consumers, this type of research has now become a standard part of your purchasing process, especially if you’re considering a big-ticket item. For example, a couple planning a vacation can readily view customer ratings on different hotels in the area before finalizing their plans. Similarly, a runner can easily review feedback from other athletes before making an expensive shoe purchase.
Thanks to the prevalence of consumers providing online feedback, customer ratings have become a fairly reliable resource for gauging customer experience. It has also provided companies with a deluge of customer experience data that was not available in such abundance just a few years ago.
It is well known that superior customer experience positively influences future demand, and this higher demand often translates into premium prices. But how many service providers, manufacturers or retailers can adjust their pricing models based on this type of customer experience data? It turns out very few.
What’s the difficulty?
Some years ago, there was simply not enough customer experience data available for reliable analysis. Now, with the advent of social media and the internet, this information is readily available at our fingertips. The challenge, however, is that rating trends change often. It’s not possible for humans to update pricing models quickly enough to keep pace with how rapidly the market is changing.
Unlocking the value of structured and unstructured data
Service providers, manufacturers, distributors, and retailers who can harness customer ratings feedback on their products or services put themselves in a position to command premium prices in the market. This i translates into higher revenues and profit margins, which is the goal of every for-profit business.
This is becoming even more pertinent as companies are flooding the market with new products to keep up with changing consumer preferences. Now when a new product is launched, social media-based sentiment can provide insight into how the new product is going to perform in the market, even before point-of-sale data starts rolling in.
New technology ushers in new opportunities
Today, decision and data science algorithms can sift through structured data (such as customer ratings) and unstructured data (customer comments), and predict the impact those ratings, trends and social sentiment will have on future demand. For instance, receiving consistently higher customer ratings on a specific hotel should allow that hotel to charge a little more for its services. Lukewarm social sentiment on a newly launched electronic device may allow a company to direct inventory in its distribution network differently before the device hits stores and suffers massive markdowns.
Machine-learning algorithms that can optimize product and service pricing are already available in the market today. Additionally, technology for incorporating structured and unstructured data into predicting customer demand, and automatically integrating that demand with price optimization, is in advanced stages of research in the labs of leading supply chain software companies, who see the value of applying machine learning to the subject of demand shaping and price optimization.
The ability to marry machine-learning-based data analysis with inventory and replenishment policies will also allow manufacturers, distributors and retailers to more profitably tailor and deliver products and services to their customers. Technology using structured-data-based insights have been commercialized already, whereas unstructured-data-based insights are going through intense research and will likely be available in the market soon.
To learn more about how machine learning can power your digital supply chain, read the first part of this blog series.