Nearly every multichannel retailer wrestles with increasingly competitive and fast-moving markets, and implementing a dynamic, highly responsive pricing solution is critical to competitive success. With thousands of SKUs in multiple channels with different competitive dynamics and promotion strategies, it’s clear computers using the right technology will do a better job of pricing than human beings. Throwing more people at the problem juggling more lines in a spreadsheet just doesn’t scale.
But few of us have the deep pockets and armies of data scientists of dynamic pricing leaders like Amazon. For the rest of us, here are some pragmatic considerations as you assess, select and implement dynamic pricing.
ROI Drives The Business Case
The core of any pricing technology decision is a commitment to driving revenue, margins and profits with better insights into pricing parameters and shopper behavior at an extremely granular level — and in real time. There’s a compelling ROI case. With the right solution used correctly, in fact, you can expect ROI in a matter of months.
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Vendor credentialing is key as well — look for a long-term partnering mentality and a highly credible installed base, a track record, implementation methodology and performance in markets dominated by highly transactional categories. For organizations that want to focus on the core competency of executing dynamic pricing well, not on having IT manage and maintain pricing software, a SaaS solution is appealing. The SaaS model also drives down deployment and maintenance costs and man-hours.
Doing your homework up front minimizes risk and uncertainty and gets you to ROI more quickly.
The Technology Is Important — But Don’t Overlook The Science
Dynamic pricing needs to be strategic: meet market fluidity, accommodate the importance of price transparency to consumer, and deliver pinpoint precision in pricing. A solution should have the technical ability to adjust to rapidly changing market and shopper trends as well as the analytical ability to focus only on those items and those competitors that are relevant. Surgical precision lets your organization win competitively where it counts. Dynamic pricing that lets users constantly test and validate multiple parameters with granularity supports an agile but rigorous business culture.
Don’t limit your thinking to just managing prices on the Web — basic rules management technology has been largely commoditized. The secret is in adding science for insights into how we price things and what price changes will mean to us. Consider the ability to support daily or even intraday price changes with highly targeted pricing recommendations, along with automated workflows to support seamless execution.
Ideally you marry battle-hardened rules application with a long and aggressive investment in science that leverages self-tuning models and predictive analytics to future-proof your competitive edge. Without a mechanism to capture cause and effect, you just have super-administration.
The goal is to learn how your markets are working and to optimize your business performance given those conditions. That means being smarter about how every action you take has an impact and knowing in advance exactly what that impact will be — that’s what makes the demand modeling/forecasting and predictive analytics so important.
Do The Groundwork For A Successful Culture Shift
Leading a team from spreadsheets to science-based software means being a change agent, actively engaging both the executive team and the pricing and merchandising teams. Help the pricing team see how this frees them up from time-intensive price administration activities to sophisticated decisions that respond to the intelligence of pricing software — and ultimately make you win in your markets.
If you think in terms of delivering breakthroughs while shedding unproductive old habits, dynamic pricing can productively shake things up while letting the team focus on their core competencies and on the health of the business.
Usability Matters
All that positive change agent groundwork won’t matter in the end if you don’t select a highly usable product that is accessible to business and executive users, not just analysts and scientists. The pricing and merchandising teams should be able to easily set and update business rules around promotions, seasonality, competitive prices, KVIs, categories and elasticity. Transparency is important too, including context around price recommendations and the ability for business users to easily tune parameters and execute what-if scenarios.
It will take some time for the team to fully grow into the capabilities of a demand-responsive analytics platform. Given time and training, the team will evolve into the science; no one starts out as a power user. Essentially it’s a change in your muscle memory around pricing. But it’s exciting and rewarding to see your teams begin to tap into the potential of the solution and respond with increasing sophistication to the intelligence of the software.
A Rewarding Journey
Taking on a project as transformative as dynamic pricing can be daunting. But adopting and leveraging pricing science is a true differentiator for you against market players stuck in a beat-the-competition pricing approach, or who just focus on data collection and rules management. Done right, dynamic pricing gives you incredible insights into the economics of your business, in real time and at an extremely granular level, to transform your business agility and pack a powerful bottom-line punch.
Girisha Chandraraj is Senior Vice President and Chief Digital Officer, heading e-Commerce, digital strategy, and capabilities, for Essendant. He also serves as COO for Essendant division CPO Commerce LLC. Formerly Chandraraj was Executive Vice President for Blick Art Materials, where he ran its direct business (e-Commerce and catalog), and senior Vice President Of Marketing, Strategy and Merchandising for Broder Bros. Corp., a Bain Capital portfolio company.
Jeff Moore is Chief Science Officer for Revionics and has more than 13 years of price optimization experience. Before joining Revionics in 2008 as Director of Research, Moore held architecture and science management positions leading demand forecasting teams at SAP. Moore was a researcher and systems engineer at pricing pioneer Khimetrics, designing solutions for Markdown, Promotions, and Replenishment as well as developing core demand modeling and forecasting science.
