Read Time: 13 minutes

Reality Check: Choosing and Deploying the Right Price Optimization Solution

Alex Hoff< Alex Hoff June 9, 2017

In my previous look at “How to Optimize Pricing to Drive Margins and Customer Experience”, I noted that price optimization capabilities are becoming more and more common across many industries. I also observed that the companies with price optimization capabilities are finding both significant financial returns as well as improved sales processes that result in an improved customer experience.

In this article, I will explore the types of optimization tools that are available for organizations, a few of the most common challenges they face when deploying them, and how they can overcome these challenges.

Price optimization software solutions have a reputation for being a pretty significant investment, yet they have almost unbelievable returns. The average payback is just 12 months.

From Airlines to B2B

The theories and math behind most forms of price optimization have actually been around for quite some time, typically in textbooks on economics or marketing. Until modern computer systems made working with large amounts of data practical, however, these methods were limited to special projects conducted by consultants and data scientists, usually on a small set of products or situations.

In the 1980s and 1990s, airlines and some other travel companies began using a type of price optimization often referred to as Yield Management or Revenue Management. In the early 2000s, retail and consumer product companies began using commercial price optimization software solutions to find the optimal shelf price, promotional prices, and even markdown or clearance prices, using complex algorithms with multiple price-demand elasticity measurements.

READ MORE: Winning with Price

In the mid-2000, a new batch of price optimization solutions designed for the various business-to-business industries became available.

In B2B industries, all of the leading price optimization software solutions first focus on identifying fine-grained “pricing segments” – segments of their business where willingness-to-pay tends to be similar. Then they utilize some sort of an algorithm to identify the ideal target price for any situation.

Because of the inherent differences in certain sectors, price optimization software vendors tend to concentrate on one main sector like retail/consumer, travel & hospitality, and B2B industries.

Benefits and Challenges

Price optimization software solutions have a reputation for being a pretty significant investment, yet they have almost unbelievable returns. Supply chain specialist AMR Research, which is now part of Gartner, looked at over 100 projects and found that the average payback is just 12 months.

A rough estimate of the investment required of a US$1 billion business unit would be software subscriptions of around US$500,000 to 700,000 per year, and one-time implementation costs of around US$500,000.

This may sound like a big investment, but when you consider the fact that Deloitte measured an average of 3.2% revenue lift from price optimization projects, that’s US $32 million in benefits – usually considered an annual gain – on a one-time investment of US $500,000 plus around US $600,000 in annual subscription.

But, as always, there are challenges. The three common ones are overreliance on price optimization software, getting carried away with the data science, and failure to get buy-in from key departments such as Sales.

Don’t Rely on Software Alone

One fundamental challenge or mistake that often occurs when deploying price optimization solutions is that the client naively believes that a software tool alone will fix the company’s pricing deficiencies.

As in other business disciplines such as marketing, sales, operations, or supply chain, pricing is a combined function of people, strategy, business processes, and, yes, tools. A great software solution that’s deployed with little thought about how to align that tool to a company’s pricing strategy or whether the organization can take advantage of its insights will not achieve its full potential for ROI.

To overcome this challenge, do at least a basic diagnostic or benchmark of your organization’s pricing capabilities. This diagnostic would assess all areas around strategy, process, people, tools, and so on.

There are several management consulting firms such as Simon-Kucher & Partners, Bain & Company, PwC, Deloitte, and others that do relatively short projects to fully assess an organization’s capabilities and recommend a holistic plan for moving forward.

Whether or not you do a diagnostic up-front, make sure to ask questions of your vendor(s) and your internal team leads to make sure they have aligned all aspects of your pricing capabilities, beyond just the software tools themselves. And remember, some software solutions will implement those consultant recommendations better than others.

Probably the most common challenge I’ve seen in price optimization projects is the failure to secure support and buy-in from any or all of the key departments and leaders in an organization.

Don’t Get Carried Away With the Data Science

In the past, practical limitations meant that that marketing could create only one set of target prices for their organization. Or maybe they could set several target prices, based on their customers’ size, geography, industry, etc.

But with massive computing power at their disposal, some companies have actually gotten carried away with what data science can do – simply because they can.

I once spoke with a building-products distributor who lamented that after deploying one price optimization solution, they were given (by the vendor’s PhDs) over 41,000 pricing segments.

Another company said that they started with 6,000 segments. In hindsight, they feel they could have gotten as much benefit, or maybe even more, with 600 or even just 60 segments. Just because you can, doesn’t mean that you should.

In some cases, a statistical model can identify segments so granular that no mere mortal can comprehend what the segment really is, and therefore it’s of no practical use. Popular buzzwords such as “machine learning” and “artificial intelligence” further encourage total reliance on letting algorithms and data science take over.

READ MORE: How Oil and Gas Companies Can Stay Afloat in a Volatile Market

But those who have real-world experience with practical deployments almost universally say that starting with incrementally better pricing (as opposed to multifarious pricing across thousands of segments) already yields large benefits. The point of diminishing returns comes quickly as pricing is made more and more complex.

In many cases, there are key business dynamics that defy quantification (like the relative competitiveness of a market). And yet an optimization model that ignores these factors is inadequate.

Often, basic managerial rules and expert input (properly vetted, of course) are as valuable as the data science or algorithms that are running against the data inputs.

So start with ‘incrementally better’ – and make sure to leverage qualitative inputs, not just the quantitative inputs.

Secure Support From Key Departments

Probably the most common challenge I’ve seen in price optimization projects is the failure to secure support and buy-in from any or all of the key departments and leaders in an organization.

Too many times, a price optimization initiative is led by a pricing leader who “gets it.” And yet, if the key people in the organization who are instrumental in utilizing or using the optimized pricing are not on board, the project is likely to fail.

When I was first thrust into a pricing role in 2003, I inherited a project where the optimization tool was working fine, but the business leaders who were supposed to be utilizing it were ignoring its input and optimizations. It took a large investment of time, lobbying, etc. to eventually garner the support of those business leaders before we started achieving the expected ROI (and more).

Our organization had underestimated the need for buy-in and support form key parts of the organization. This was in retail and consumer goods, but the same challenge occurs in B2B organizations as well.

Some vendors essentially say: “Send us your data and we will send back the optimized prices.” This black-box approach simply doesn’t work.

The typical story is that a pricing team is able to convince the right person that they need more sophisticated pricing tools, and they fail to sell the concept internally – especially with sales. The project often crashes at take-off or soon thereafter.

The way to address this challenge is first to acknowledge – especially in B2B industries – that pricing is usually a part of sales effectiveness or commercial excellence. And while – with the help of some new technology – the pricing may be “optimized”, if the sales team can’t understand and then later defend the pricing, it will be ignored and the sales team will return to pricing by their instinct.

Your price optimization approach (and tool/solution) should be relatively easy to understand and explain. Some vendors essentially say: “Send us (or our system) your data and we will send back the optimized prices.” This black-box approach simply doesn’t work.

Your sales teams (or whoever the key business users are in the last mile of pricing) should be part of the process from the beginning to make sure they are on board and supportive.

The takeaway: While price optimization technology solutions are now widely available across many industries, and the case for tremendous ROI is sound, there are some common challenges that companies face when seeking to achieve those fantastic ROIs.

The good news is that with some sound change management and project management practices, many organizations are able to ensure they achieve the value these types of solutions are capable of delivering.