“Just 9.99 euros.” Price optimization has been practiced ever since someone discovered that changing the price can yield better results. With technological advancements, the possibilities for optimization have multiplied. Nowadays, it’s possible to offer the best possible price to the customer in all scenarios. This applies to B2B business as well, where competitor pricing information isn’t generally available. In this article, we spoke with Joonas Ollila.
We will talk with you today about optimization. What is it really about?
Optimization is all about finding the best possible option from a myriad of different choices. Simply put, it’s about seeking the most efficient way to achieve goals within set parameters. Technology enables us to sift through virtually unlimited options, including those that might elude human detection.
Tell us a little bit about yourself?
I help clients find solutions to a variety of problems. My background is in applied mathematics. I wanted to be in a role where I could make the broadest possible impact, which is why I ended up in IT consulting. I also enjoy how this isn’t a zero-sum game; in the best-case scenario, everyone involved wins. For instance, if we consider sales, optimizing the price can lead to the best possible price from both the seller’s and the buyer’s perspectives.
Additionally, by leveraging technology, it can be done in such a way that the salesperson hardly needs to spend any time on it, and the customer doesn’t have to wait around for an offer.
What should business decision-makers know about optimization?
Decision-makers usually have a pretty solid high-level understanding of their organization’s goals and the methods to achieve them. These methods, such as sales, marketing, customer service, and human resources, require their own unique processes. It often comes as a surprise to corporate executives that all these areas can be significantly enhanced by automating parts of the process through an optimization model.
The results that come as a surprise are often the achievable outcomes. It’s not uncommon for optimization in certain areas to increase efficiency by 5-10 percent. The principle of optimization is simple, but the technical implementation, with its data pipelines and algorithms, is best left to the experts.
What can optimization be applied to in organizations?
Optimization can be applied to a wide variety of things, but here I’ll showcase a few different examples.
Let me start with a very everyday example, which really opens up the practical side of optimization. Probably all of us have baked gingerbread cookies at some point and faced the challenge of how to arrange the cookie cutters on the dough in such a way that minimizes waste and maximizes the number of cookies for the oven. It’s the exact same scenario in industry when placing components on a circuit board or cutting parts from a steel sheet.
In organizations, optimization works in theory in much the same way, but things are far more complex and there are many more moving parts.
Let’s take workforce planning as an example. First, we need to understand what kind of jobs we actually have, meaning what needs we are meeting with our workforce. After that, we plan the shifts based on what needs to be accomplished during the day. It sounds mechanical, but that’s really what it is in reality.
Optimizing this aspect benefits everyone involved. For the individual employee, the advantage lies in the ability to efficiently size their workload. There’s no need to twiddle your thumbs or try to do too much. For the supervisor, it provides assistance in planning. From the senior management’s perspective, transparency is increased, and the predictability of labor needs improves. Above all, the greatest impact is seen in productivity growth, as optimization allows more to be done with the same resources.
Another classic and prime example is the production environment. Production lines always have significant money tied up, so it’s crucial that the machinery operates efficiently. These production lines are generally complex assemblies that you shouldn’t try to manage based on gut feelings or Excel spreadsheets.
The third example is the delivery of food orders, which is a classic optimization problem. How can you, say, deliver 20 food shipments as quickly as possible and with the shortest route choices? In the bigger picture, the most crucial factor, of course, is that the fewer vehicles used for deliveries, the better.
From the organization’s perspective, we can also examine which food orders are the most profitable and guide ordering behaviors, for example, with pricing. If I place an order between 3-4 PM today and my neighbor happens to make an order at the same time, we can be encouraged through pricing strategies to continue placing our orders simultaneously in the future.
Price optimization sounds interesting. How is it done in practice?
Price optimization is built on the foundation that we have data on customer reactions to different prices. There are won and lost deals, along with information on what products or services were included in these deals. Therefore, we can deconstruct these won and lost deals to start searching for the optimal price based on these components.
One example could be the supply of IT equipment. Larger firms often order their supplies in bulk. Imagine wanting, say, 700 laptops, 200 mice, and 100 monitors. IT equipment suppliers frequently receive such large orders. Buyers, too, frequently send out requests for proposals to multiple suppliers. What’s more, the situation is always in flux. Some items are more readily available than others.
As a supplier, I’m well aware of the availability situation, and based on historical data, I know that Customer A has purchased at a certain price and Customer B has refrained from purchasing at a different price. By leveraging this information, we can explore price elasticity. For example, we can offer Customer C a 5% discount if we are 90% certain the proposal will be accepted and that we will still achieve a sufficient margin on the deal.
Here’s how we acquire the optimal price for a deal. Calculations can account for several predefined factors, such as inventory status, the likelihood of the sale, and the margin in this example. You definitely shouldn’t start these calculations simply based on intuition. In Finland, there are still many B2B companies making good profits that do not optimize their pricing.
What if the productivity of these companies could be enhanced by, say, five percent? With optimization, pricing decisions become much faster and more accurate. The more complex the request for proposal, the more crucial it is to nail the best possible price. As a seller, I wouldn’t want to settle on a price based on scant preparation and back-of-the-envelope calculations. It would be great to have a tool in the toolkit that could automatically provide the right price for every situation.
Are there any other benefits to price optimization that we haven’t covered yet?
One definite added benefit is that information flows much faster. Especially in large sales organizations, not everyone has the time to discuss with each other. Optimization also allows for quick responses to changes.
For instance, if competitors have reduced prices within a certain customer group, optimization models enable a swift response to such changes. Optimization keeps us consistently on the pulse of market happenings. On the consumer side, the competitive scenario is different since prices are public.
We too have developed pricing robots aimed at optimizing prices in real-time, based on price data extracted from various sources. In the B2B world, prices are often not public, so pricing decisions are made based on customer reactions. By the way, as a little curiosity, the consumer-facing practice of setting prices ending in .99 is one of the oldest applications of price optimization in the world.
Someone once realized that by marginally lowering prices, sales happen in a totally different way. In a sense, the B2B sector has started to resemble the consumer market in that if one supplier responds to a request for quotation within a day and another takes a couple of days, the train might have already left the station. Some companies have gone as far as allowing customers to chat with a bot to get a preliminary price estimate, after which a salesperson provides a binding quote.
This certainly poses challenges for the chatbot, as obviously, as a customer, I think that the price estimate given by the chatbot is the price at which I can close the deal. This puts the seller in a tricky situation if the chatbot’s price estimate is off the mark.
You are specifically specialized in optimization. What are your customers typically like? What kind of implementations do you make for them?
A typical client of ours is a fairly large Finnish company. We’re talking about maybe the top 1000 businesses in Finland size-wise. Some clients just want us to supply something like a price optimization model and they handle the technical implementation themselves.
For some companies, we deliver a comprehensive optimization package that includes, for example, model building, practical implementation with data pipelines, and reporting. When it comes to technologies, it doesn’t really matter which IT environment we are implementing the optimization in.
In the lightest implementations, we’re talking about preliminary assessments and offering recommendations. These are usually about two-week projects. If you want some real software, like an analytics module, such projects typically span a couple of months. For broader solutions, we’re looking at projects lasting 4-5 months, tackled by a multi-person team.
Our clients are typically fairly large companies. The reason for this boils down to the sheer amount of data, its quality, and resources. If the goal is to increase efficiency by 5% and it requires a development project spanning a few months, smaller businesses often simply do not have the capacity for such an endeavor. It’s also clear that the outcomes multiply when we’re talking about a large scale or, for instance, large volumes. We’ve also collaborated with numerous startups. They generally want to refine a specific area to the absolute max, seeking to carve out a competitive edge in the market with that focus.