Search this site

From Predicting Market Response to Testing Commercial Decisions Why pricing and promotion intelligence may need to move beyond simply predicting what happens next. Prediction only gets us so farMost pricing technology is trying to answer a perfectly reasonable question: What is likely to happen?If we change the price, how will demand respond? If we run a promotion, what lift might we get? What is the likely revenue or margin impact? All useful questions. But when you are actually making the decision, there is usually another question sitting behind them: What would happen if we made a different decision? What if we changed the price by 5% instead of 2%? What if we did not run the promotion? What if we changed the offer completely? That is a different problem. Prediction tells us what is likely to happen. Decision intelligence needs to help us think through the alternatives. History has its limits Of course, much of what we know about a market comes from history. We look at what we did, what the market did in response, and try to learn from it. But there is an obvious limitation. Historical data tells us what happened after the decisions we actually made. It cannot directly tell us what would have happened if we had made a different decision. There is another complication. We never see the market perfectly. We can observe prices, sales, promotions, competitors and customer behavior. But things like willingness to pay, buyer confidence, substitution pressure or changing market sentiment are much harder to see directly. So the challenge is not simply having more data. It is understanding enough about the market to make a better decision. Building a useful representation of the market That is what led us to the idea of a Virtual Market Surrogate. The term may sound technical, but the thinking is fairly straightforward. If we cannot observe the entire market, can we build a useful representation of the parts of the market that matter to the decision we are trying to make? That representation can bring together what we can observe with what we can derive, model and infer. The objective is not to build a perfect digital copy of the market. That is neither realistic nor necessary. The objective is to understand the market well enough to support the decision at hand. And as new decisions are made, the market responds. Those responses give us new information. Change a price and something happens. Run a promotion and something happens. Make an offer and the customer accepts, rejects or behaves differently. Each response tells us a little more. From predicting outcomes to testing decisions This is where the idea becomes interesting for us. If we can build a richer understanding of the market and keep learning from how the market responds, we can start moving beyond simply forecasting what happens next. We can begin to evaluate alternative decisions against that understanding of the market. In simple terms: Understand the market. Test the alternatives. Make the decision. Learn from the response. That is what we mean by Virtual Market Surrogate — and an important part of how we are approaching the future of pricing and promotion decision intelligence at Gestalt and Price-It-On®. __________________________________________________________________ Gestalt has filed a provisional patent application covering aspects of the Virtual Market Surrogate technology.

Why a scenario should be able to change as the decision starts changing the market around it. The limitation of a static scenario Most of us have used some form of scenario analysis. Change the price. Estimate what happens to demand. Compare the outcomes. Simple enough. But there is an assumption hidden inside that approach: that the same response relationship continues to apply throughout the scenario. In the real world, that may not be true. A price increase, for example, does more than change the price. It may move you from being competitively priced to being at a premium. Demand may then slow. That may change inventory pressure, buying momentum or some other part of the commercial environment. So after the first step, you may already be in a different situation from the one you started with. That raises an obvious question: Should we still be using the same response model? Letting the scenario adapt That is the basic thinking behind Context-Adaptive Scenario Simulation. Instead of assuming one response model applies from beginning to end, the scenario keeps reassessing what is happening. You make an action. That action changes the context. The market responds. That response may change the context again. And if the context has changed enough, the response relationship used for the next step should change too. In simple terms, the scenario does not just ask: “What happens next?” It also asks: “Given what just happened, are we still using the right way to think about the next response?” That may sound like a small change, but it can make a big difference. Two pricing decisions may look quite similar after the first step, but they can lead to very different paths as demand, inventory, competitive position or other conditions start to change. Moving beyond simple what-if analysis This changes the way we think about scenarios. Instead of only asking: “What happens if we make this change?” we can also ask: “Where could this decision take us, and how might the market behave as we move along that path?” That feels much closer to how commercial decisions actually play out. Markets do not stand still while a decision works its way through. Actions change conditions. Those conditions influence the next response. And that response can change the situation again. So the intelligence used to evaluate a decision should be able to adapt as well. That is what we mean by Context-Adaptive Scenario Simulation — and another part of how we are approaching the future of pricing and promotion decision intelligence at Gestalt and Price-It-On®. ______________________________________________________________ Gestalt has filed a provisional patent application covering aspects of Context-Adaptive Commercial Scenario Simulation with Dynamic Response Intelligence Resolution.

Why the market’s response to price depends on what else is happening at the time. The problem with a single elasticity number We have all seen elasticity expressed as a number. Increase price by a certain amount and demand is expected to move by another amount. It is useful. But does the same number really apply every time? Think about it. The same product can behave very differently when demand is strong versus weak. Customers may respond differently when you are priced below competitors rather than above them. A price increase may work when inventory is scarce, but produce a very different result when you are sitting on excess stock. Season matters. Competition matters. Inventory matters. Customer behavior matters. So instead of asking: “What is the elasticity of this product or segment?” Perhaps the more useful question is: “How is the market likely to respond to this price change, under the conditions that exist right now?” Context changes the answer That is the basic thinking behind Multi-Dimensional Contextual Elasticity. The idea itself is simple. Customers do not respond to price in isolation. They respond to price in a situation. That situation could include demand, competitive position, inventory, season, channel, customer behavior, where the product is in its lifecycle and several other factors. And these factors don't necessarily act independently. Take something as simple as being priced above the competition. Is that good or bad? The answer depends. If demand is strong and inventory is scarce, being slightly above the market may not matter much. If demand is weak, inventory is building up and the season is coming to an end, exactly the same price position could matter a lot. Same price position. Very different response. That is why we don't see contextual elasticity as simply adding a few more variables to a traditional elasticity model. The more important shift is to understand the combination of conditions surrounding the pricing decision. Moving beyond the average Historical data is still where much of the learning starts. But there is a catch. If we combine observations from very different market conditions, we can end up with an average response that is mathematically correct — but not particularly useful for the decision we are making today. The opportunity is to get closer to the response that is relevant now. In simple terms: Same action. Different context. Different response. That is the thinking behind Multi-Dimensional Contextual Elasticity, and it is an important part of how we are thinking about pricing and promotion decision intelligence at Gestalt and Price-It-On®. __________________________________________________________________ Gestalt has filed a provisional patent application covering aspects of Multi-Dimensional Contextual Elasticity and Commercial Response Modeling.

“Computers are useless. They can only give you answers.” Pablo Picasso spoke these words in 1964, a time when the computer was barely more than a large machine, but his words still ring true today. Technology is only as powerful as the humans adopting and applying it. We leverage technology to optimize every part of our businesses and operations today. Digital technologies create the possibility for efficiency gains and strong customer engagement. Organizations use many tools to automate and connect and thus become more efficient, including customer relationship management, enterprise resource planning, dashboards, etc. However, limited digital capabilities of the workforce often result in technology only being partially utilized. It’s not just about usage of digital tools; people must be digitally skilled enough to be able to comprehend the vision behind digital initiatives, understand their positive impact, and execute to it to realize the gains. In order to truly transform to a digital enterprise, people within the business must adopt a digital-first mindset. A mindset where one instinctively looks for digital solutions first for any opportunity or challenge. A mindset with which one works through technology rather than aided by technology. A mindset that leads one to explore, adapt, and apply new tech. Such a mindset leads to speedier operations where employees often collaborate with others and act in real time rather than wait for action either due to sequential workflows or traditional communication methods. It helps drive higher engagement and transparency, facilitates collaboration, and increases data-driven decision making. It incorporates innovative technologies into business processes. It leads to a digital way of working. The top companies understood this quite early, and despite being considered leaders of the technology industry, continue to invest in digitally upskilling their workforce. Many of the world’s top companies have all come to the same conclusion: the future of work is rapidly changing, uncertain at best, daunting and unmanageable at worst. Many workers believe they either lack the specific skills needed to fulfill their current roles or do not have the means to obtain the training needed to make them viable candidates in the future. As such, ever since the first industrial revolution in the 18th century, technology has always necessitated acquiring newer skills. What’s different this time? What makes these companies continuously invest in the digital upskilling of their employees? They realize that the magnitude of reskilling that is required now is enormous. Moreover, the new technology cycles are increasingly getting shorter, necessitating more frequent skill refreshment to keep up and stay relevant in this rapidly changing digital environment. The advent and adoption of artificial intelligence, machine learning, the Internet of Things, robotics, and automation is impacting every job in every sector. It is nearly impossible to predict the nature of future jobs due to rapid evolution. It is best that companies prepare their workforce for the future of work by priming their employees to develop the right mindset for continual technical upskilling. So how do you develop a digitally skilled workforce? We all know simple technology training can only go so far; it seldom leads to a lasting change in people’s way of working. The following steps can help achieve exactly that: 1. Develop the imperative and motivation for reskilling While employees are generally well aware of digital changes taking place in an organization, the traditional way of working ensures that employees wait for IT department to drive the change through systems and associated training rather than proactively learn themselves. Most employees do not realize the power they have to amplify the gains from digital technologies many times over with an increase in digital acumen. The workforce needs to see that the digital disruption is creating a burning platform, which is equally as applicable to their jobs and to their skills as it is to their industry and their organizations. Since the year 2000, 52 percent of Fortune 500 companies have either gone bankrupt or have been acquired, essentially ceasing to exist. Those that continue to thrive are digitally native companies, or the companies that have successfully adopted digital like those indicated earlier in the article. Employees need to realize that proactivity in reskilling is a prerequisite to staying afloat in an ever-changing market. 2. Focus on the mindset, which is formed with experience and not just knowledge A digital mindset is one with which you instinctively look for a digital solution to any problem first. To develop this, we must rewire brains to think differently than years in the traditional world have taught us. This rewiring can organically happen over many years by conscious effort, or it can be accelerated by simulating various digital workplace experiences in immersive learning worlds. Such an experiential learning approach enables the subjects to learn, practice, and exercise the digital-first mindset and behavior. 3. Develop a continuous and effortless learning rhythm Technology will continue to evolve at an increasingly rapid pace and it is important that employees fall into a regular rhythm of learning. The ability to unlearn and relearn should almost become second nature, almost as seamless as it is for the current high school generation. 4. Maintain the focus on core technology Mindset is extremely important, but let us keep our eyes on the prize. There is no digital without technology. The foundation must be laid out by a strong focus on usage of technology, so that employees can seamlessly use digital workplace tools to do their job. They should be able to work through technology to collaborate, communicate, search for and create content, and apply information and data to be efficient in their jobs. 5. Set the ecosystem to develop a digital culture and a digital way of working Culture is formed with everyday practices, and everyday practices in turn are set through leadership’s behaviors. Company leaders should institutionalize methodologies and processes like design thinking, agile way of working and data-driven decision making which help form new work habits in the organization, thus leading to a digital way of working. Existing employees of a company are best positioned to drive transformation within it, as they understand the company intrinsically; they already know its strategy, purpose, strengths, and weaknesses. Arming them with an understanding of the technologies the transformations hope to leverage and helping them adopt a digital-first mindset is the best shot any organization has at staying competitive. These experienced employees, with plenty of institutional knowledge, once upskilled and supported till they become unconsciously skilled digitally, can position the organization to win in a continuously evolving digital era. Rajesh Makhija WAM12 is the founder & CEO of GoGestalt, a revolutionary digital workforce development company, and is an executive director at McorpCX, a top customer experience strategy, services and solutions company. Previously, he was the CEO of Mphasis Wyde and Eldorado, both Blackstone Group companies.