Dynamic pricing & its role in hyper-personalized consumer market

Over the past year, as homebound customers placed online orders for everything from yoga mats and laptops to groceries and soap over the past year, numerous people were reminded of how simple it is to compare shops on the internet. With just a few clicks, a buyer can determine which retailer sells a particular item at low prices. And because the shift to e-comm is expected to continue even in the post-pandemic era, pricing will become an increasingly crucial competitive tool for retailers. Dynamic pricing is poised to become one of the core abilities that sets winners apart in the retail landscape of the future.

Simply put, dynamic pricing is the (complete or partial) automated adjustment of prices. The rapidly increasing data of online shoppers worldwide are responsible for bringing transition in the current customers' behavior. In the present scenario, customers prefer to shop at places that offer cost-efficient options and a convenient mode of shopping at the same price.

With the ongoing boom in online shopping, implementing dynamic pricing has never been more critical than it is right now. Dynamic pricing is when an organization changes its pricing to match demand and supply. For instance, Uber's base fares are typically less than a taxi, but prices go up when a cricket game lets out and demand spikes. You may have to pay more, but you can always get a car when you need one -- and more drivers show up at the stadium knowing there are better fares.

As people leave and availability opens up again, the price goes back down. Prices are required to make sense within an increasingly competitive landscape, and your business' pricing model needs to be ready to adapt to fluctuations in customer demand and purchasing behaviors. The ability to make quick, informed action around pricing has a massive impact on overall profit margins.

Traditionally, pricing in retail was set based on static price rules that utilized a limited amount of data inputs (e.g., conversion rates, cost base, etc.) With this approach, massive amounts of essential data – transaction and non-purchase data – went underutilized, which could inform smarter, more agile pricing decisions.

In today's hyper-fast, highly competitive retail landscape, data-based dynamic pricing strategies harness the power of this consumer data and use it to drive pricing decisions. The explosive growth of big data and its potential for developing machine learning and Artificial Intelligence approaches to pricing strategies has unraveled new opportunities for intelligent pricing solutions. ML technology takes dynamic pricing to the next level. It can process many massive data sets and consider various influencing factors to predict price changes.

Implementing these technologies enables dynamic pricing algorithms to train on inputs -- transactions, external data -- and understand patterns. AI can find ways humans cannot see. As a result, an AI dynamic pricing engine can operate at a much more granular level than a pre-internet rules-based engine, where humans have to understand and anticipate what might happen.

Today, thanks to AI and ML, retailers can more readily get a robust view of what both customers and competitors are doing at any given moment, as well as a better sense of the reasons and influences behind their buying behavior. The sheer quantity of data online customers generate enables new, better-informed strategies to drive customer happiness and company profitability.

Now let's explore the technical side of Dynamic pricing; whatever kind of data it is, it's not fruitful until and unless it's not analyzed correctly. Hence, one of the eminent ways is to use the advanced dynamic pricing tools to analyze Data and settle down on the right price for the products to be sold.

Earlier also, the pricing of products and services was determined through algorithm methods. But the methods and patterns of the traditional algorithms were much different than the current advanced algorithm process.

The traditional algorithm worked on the if/then method and applied only to a limited amount of data. But as the amount of data increased, handling them with the traditional algorithm method seemed quite impossible. This was when the advanced algorithm based on machine learning came into existence. This particular method can automatically consider a wide range of factors to offer the most efficient and concrete pricing strategy that will be fruitful for the business house.

Data can be segregated into different types that ultimately help in dynamic pricing optimization.

Internal Data: The internal data of any business firm consist of several elements, and these are:

• Product Attributes are product ID, pricing, size, expiry date, and other details.
• Inventory levels that describe the current inventory and further related details
• Transactional Data consists of conversion data, price history, buyer information, and similar options.

External Data: Similarly, the external data that will help in pricing includes a vast number of elements, and these are:

• Pricing data of the competitors
• Days such as weekdays, weekends, holidays, special days, and others depending upon the products and services
• The trend of a particular region
• Data related to the weather and the season of a location

Now, it is quite interesting to see how exactly different data influences the data pricing algorithm. There are some specific factors related to data that influence this algorithm to offer proper pricing.

1. Data Quantity

Big Data and Data Pricing Algorithms through machine learning depend upon each other quite closely. It was mentioned earlier that the machine learning system was introduced to manage Big Data. But another point that cannot be missed is that machine learning can work efficiently only when provided with a massive amount of data. Hence, machine learning works efficiently to offer better data-pricing algorithm results when the data amount is enormous.

2. Data Quality

When we talk about data quantity, this does not mean that you can provide any quality of data to get results. The data provided has to be of high quality to get relevant results. The data provided to machine learning should be complete, clean, and consistent. Often non-availability of quality data leads to issues such as machine learning not being able to calculate the data and adequately offering poor performance.

To manage complex data, everything has to be done in a proper system. Following the appropriate steps surely help in managing the complexities during dynamic pricing.

Collection of Data

Clear and complete data can be collected regularly with the help of proper technologies. Today, the availability of different APIs and other systems is quite helpful in managing the correct data regularly. This data has to be escalated to the next levels on time so that the activities going on in a chain format continue smoothly.

Processing of Data

While the collection of data can be hectic, processing the same data can be even more exhausting. The raw data is filtered first to get rid of the errors, and then it is finally stored in the cloud warehouse, from where feeding the data to the algorithm system becomes much more convenient.

Inputting of Data

Now comes the essential role of data scientists. These individuals will create features of the processed data according to the company for which the data has to be used. Proper quality data input is essential for effective collaboration amongst the workforce. Take inventory management for instance, when you input the current data only, then suggest when a particular product is available or out of stock, and the entire team can see the changes happening.

Analysis of Data

Dynamic pricing is attained only when the data is understood and analyzed in the best possible way. Of course, analyzing such huge data manually can be a difficult task. While data plays a significant role in the process, not understanding it may spoil the entire effort of getting and processing the quality data. Therefore, machine learning is there to analyze even the smallest data feature. Therefore, machine learning is there to analyze even the smallest data feature, saving time and making the process much more efficient and convenient.

Polestar Solutions proudly works with clients to introduce new, data-driven processes and improve pricing strategies using advanced analytics. Our analytics consulting approach can transform organizations by seizing data's value and empowering decision-makers with analysis and insights to make better decisions at organizational level.

While the suggestion to implement these new processes can seem overwhelming, Polestar solutions are focused on making sure that clients realize the value. Our blend of technical and functional expertise with our keen business acumen ensures your organization maximizes business value creation with every initiative.

Our analytics practice capabilities focus on giving our clients competitive benefits, from robust self-service analytics and data management platforms and to rich visualizations that fuel actionable insights.

Are you ready to unlock revenue by leveraging your data to create a proven pricing strategy? Get in touch today!