Data has become the cornerstone of modern business. No business can operate without some level of this intangible building block, but most are wary of embracing too much of it. There was a period where Big Data reigned supreme, and a fanatic dedication to data for data’s sake could justify even a seven-figure business expenditure. Today, businesses are increasingly focused on actionable insights. As a Solutions Engineer responsible for proposing projects to clients based primarily on the delivery of data, I’ve seen this firsthand. Now, executives and decision makers mandate their teams answer the question “what will I actually do with all this data?” before agreeing to countersign an agreement.
Committing to harvesting massive amounts of data without a plan on how it will be used to improve business functions is unwise. A fitting analogy: in college I ordered 5 tons of sand dumped in our college house parking lot to build a massive sandbox for a beach party. After a successful and memorable event, I realized “what am I going to do with all this sand!”
Just like my ill-fated sandbox implementation, businesses can easily accumulate vast amounts of data without a clear plan for utilization. QL2 provides competitive intelligence to its customers. That intelligence is, predictably, valuable sets of data that our customers extract insight from. The data files that we push to customer end points can be anywhere from five hundred to five hundred thousand rows of pricing data! That volume of data requires tactical consideration. When I get on scoping calls with potential customers, it’s vital that they have a plan for how they will use our data. If the plan is to have one revenue manager named Dave run .csv files through pivot tables and vlookups, I start having flashbacks to shoveling sand into trash cans for a week straight. A future state process like that is destined for failure. Trying to run large quantities of data through one extraction mechanism means that, unless Dave is a master data scientist and can write SQL queries in his sleep, he’ll be lucky to get even 5% of the potential value out of a dataset before the next one comes rolling in.
My recommendation to customers is to always consider a solution that automates insight extraction. And, if their organization is ready for it, enable pricing updates that are published automatically based on our data. A Revenue Management System (RMS), can accomplish both of these things. Revenue management solutions enable data analysis at scale using machine learning or AI models. A really good RMS is able to tie multiple streams of data together to paint a holistic picture. For example, most businesses that I work with have clear visibility of their inventory, whether that be available hotel rooms or cars for rent over a specific period. When they combine that data with competitive market data, their published prices can factor in the vital nuances of demand, supply, and competitive positioning. Weaving multiple data sources together is possible to do manually, but having a dedicated tool ensures that you are getting maximum value out of each data set, and not just collecting data for data’s sake.
There are pitfalls to watch out for with a Revenue Management System implementation. Simply purchasing one of the various solutions on the market will not automatically increase revenue by double digits. An RMS starved of data is useless. Most deployment cycles for these tools can take upwards of a year because a business has to connect their various systems and data lakes to the RMS. Prices cannot be effectively managed if an RMS doesn’t know the whole story. The least impactful RMS I have seen ingested only the business’ cost and inventory into pricing proposals. Buying a solution that is only trained to assess a variation of cost-plus pricing model is an absolute waste of money. Revenue management systems function at their best only when competitive intelligence is a foundational piece.
The true potential of RMS solutions is unlocked when integrated deeply with all parts of a business and the key drivers of price. They are enormously valuable even when providing price suggestions that revenue analysts have to accept before they are published. Behind one suggestion are magnitudes of historic and market data aggregation. However, the most powerful RMS solutions are those that can automatically adjust prices based on real-time data. While this can be intimidating for pricing teams used to having full control, there is considerable precedent for doing this. Look no further than Amazon. The ecommerce behemoth reportedly earns nearly one million dollars per minute, with automated price updates occurring, on average, every 10 minutes! The path to total competitiveness and market capitalization is through price automation, and a capable Revenue Management System is required to navigate that path.
Don’t just let the sand pile up without a plan. Simply collecting information isn’t enough, you need actionable insights to optimize your business. QL2 provides valuable competitive intelligence data, but for consistent results, you need a system to turn it into actionable strategies. Whether you are looking to bolster the power of your existing RMS through a competitive data source or are just starting to explore the market for an RMS solution, I would be happy to provide my expertise and recommendations. Shoot me an email at cschneider@ql2.com to start a conversation!
