Transforming Big Data into Big Value in the New World of Modern Commerce

By Valerie Howard, Senior Industry Solutions Manager, PROS, Inc.

Big Data is one of the most buzzed about words in business. Marketers use data to learn customer preferences. NASA uses Big Data to visualize space missions. Politicians use data to predict the outcome of elections. Insurers leverage data science to calculate risk. The examples are endless. Despite the proliferation of technology and our access to data, the question comes down to this: Are organizations leveraging this valuable asset where it matters most – boosting the bottom line? Surprisingly, the answer is pretty straightforward – not as much as they could be.

In this age of Big Data, analytics are a powerful tool in supporting the new world of modern commerce, where the customer buying experience has forever changed. And where transparency is the name of the game. But many professionals are still trying to make heads or tails of that data to support their revenue growth. In fact, experts say four out of 10 companies leave money on the table due to a lack of modern approaches like dynamic pricing.1 No matter what the product or service, organizations need to tap into data science to better inform pricing, help sales teams sell smarter, improve profit margins and drive revenue – it’s just a matter of putting the right processes in place. Most enterprises lack the data and analytical tools needed to adequately evaluate their deal pricing, but there are ways to address these issues so businesses can come out on top.

Building the right infrastructure  

While there is no “quick fix” solution, if enterprises are willing to dedicate time and resources to better streamline and understand their data, they are the ultimate beneficiaries in the long-term. Establishing a company culture that is rooted in turning data into actionable insights requires an operational investment, but the return will be tenfold.

Dynamic pricing science, or running a business on facts, algorithms and context-aware, machine-guided learning, is the lynchpin that connects Big Data to the bottom line. Rather than setting prices based on hunches, this model uses real-time market data to better inform pricing decisions, correctly anticipate supply vs. demand needs, and analyze actual buying behaviors and the real factors that influence customer purchase decisions. More often than not, the answers are surprising.

Responding to Data in Real-Time 

The first step in the dynamic pricing science model is recommending pricing, based on real-time market data. There are a number of factors that can impact pricing and production. In the technology space for instance, companies that use certain parts like batteries to power their devices, such as hover boards and cell phones, have run into various malfunction issues. Their challenges have ultimately impacted sales and the bottom line, forcing these companies to shift their product and pricing strategies. The acceleration of technology has created a new era of Modern Commerce, where customers now expect immediate responsiveness and an uninterrupted buying experience. With the support of dynamic pricing science, companies can better meet the needs of their customers by providing quick and accurate price quotes that end exhausting back-and-forth negotiations. Responses are fast, which creates a far better buying experience. 

In addition to market variation, keeping a pulse on supply vs. demand data is an integral piece of the modern commerce puzzle. Enterprises can use data science to price in real-time based on demand, and also predict demand in order to properly manage fulfillment. The main goal of any business is to supply customers with a product, service or solution, which means these resources have to be available for consumption to boost revenue and profits. Using integrated data such as inventory algorithms enables companies to prepare for unexpected bumps in the road such as commodity shortages or spikes in pricing. By having insight into supply and demand, companies are able to reduce overhead and create a strategic fulfillment plan.

Calculating Customer Satisfaction

While it’s important to assess market-moving factors and trends in supply vs. demand, one of the most important areas to focus on is the end customer. Humans can be unpredictable, but analyzing data patterns when it comes to a customer’s price sensitivity and need for a product – or even their budget and willingness to pay — can offer invaluable insight. With this data, sales professionals can offer personalized pricing based on the scientific understanding of a customer’s preferences to create a personalized buying experience. Dynamic pricing science aims to provide guidance on identifying the right price, in the right amount of time and on the first try. Customers are no longer to endure a protracted negotiation: They want prices that enable them to get what they need quickly. This level of precision can only be achieved by companies that have a solid understanding of how to turn Big Data into big value.

Big Data, machine learning and sophisticated analyses can be applied across divisions in a company to improve business functions and supply chain management, increase the bottom line and foster client relationships. The trick for enterprises is to optimize data in a way that supports the overall business strategy. Most companies have this data available; it’s simply a matter of streamlining and assessing patterns. Big Data and dynamic pricing science are here to stay, and will only grow in sophistication in the years to come. Now is the time for enterprises to incorporate a dynamic pricing strategy if they want to stay nimble and competitive in today’s market.

1Gartner Predicts 2016, Simon-Kucher Global Pricing Study 2016