What if I said you could make more money by doing less work?
Despite nearly all competitive sellers using algorithmic repricing software, many don’t realize that there’s much more to optimizing prices than setting a minimum and maximum price and letting the repricer go to work. Most repricers are highly sophisticated and will maximize your Buy Boxes given the parameters set forth, but how do you know the parameters you set are optimal?
It’s widely reported that more than 80% of all purchases today on Amazon go through the Buy Box. It’s also generally accepted that the strongest determining factor in winning the Buy Box is price. Conventional wisdom would say that offering the lowest price, or close to it, to obtain the most Buy Boxes gives you the best chance of being successful on any marketplace.
Thus, it’s easy for merchants to find themselves reducing their minimum prices and chasing top-line revenue in an effort to claim more Buy Boxes and sell at scale, hoping that despite smaller margins they make up for it in volume. But this chase often results in one thing for merchants: razor-thin margins or even losses leading to potential self-destruction. And so, the million-dollar question becomes:
How do you find the optimal pricing parameters that maximize profits, margins and revenue?
Blindly chasing revenue on marketplaces is one of the most dangerous things any ecommerce business can do. Amazon is one of the most efficient markets in the world and a seller with even the slightest miscalculation in pricing will be exposed by the 100+ million Prime members hungry for deals.
Research shows there isn’t much room for error. According to the 2016 marketplace seller survey, about 45% of sellers operate on margins of less than 25%, and about 80% of all sellers don’t use reporting and analytics software to understand and analyze their sales data.
For most sellers it could be days or even weeks until a pricing error is noticed, potentially costing hundreds or thousands of dollars every day it goes unchecked.
Ultimately there is no one size fits all when it comes to determining pricing parameters. Competition, market demand, and inventory turns among countless other factors, all play an important role in this decision-making process.
But one guarantee stands true for sellers of all sizes, if you’re not analyzing you’re not optimizing.
Data is king
In order to fully understand and analyze the effectiveness of any pricing strategy, you must first have access to complete and timely financial analysis of your sales and gross profit data for every order.
What exactly does this mean? Simply put, it’s having one source of truth where all revenue and cost data points underlying each transaction are collected, calculated and displayed at the order level in real time.
Accounting for inputs like product and shipping revenue, product and shipping costs, category and referral fees, credit card fees, currency conversion fees, the litany of FBA fees, storage costs, returns, discounts, promotions and more is an absolute must.
All these inputs generate the outputs of gross profit and gross margin for every single transaction. These calculations are the foundation of what recently launched software company MarginDriver is built on, and they’re done automatically in real-time, for every order on every sales channel.
In today’s lightning-fast world of ecommerce, the only solution to accessing complete and timely sales reports on a consistent daily and hourly basis is through the collection and automation of financial data. This is especially true for operators selling at scale. The larger the inventory and sales volume, the more complex, fragmented and siloed the systems become and the more difficult it becomes to manage.
While reviewing sales data at the end of every month was enough 20 years ago, for ecommerce merchants today it needs to be monitored in real-time because of the pace at which this industry moves. Waiting for a settlement report and payout every two weeks to find out nothing more than sales volume and related fees is exactly what your competitors are hoping you do because it allows them to stay ahead. And management needs this kind of business intelligence fed to them regularly in order to make more informed decisions and to know where to focus their efforts.
Accessing this information daily is critically important to the health of your business because cost data points at the transaction level are the foundation upon which any pricing (revenue) strategy needs to be based. Without knowing the cost breakdown of each order, down to the penny, it’s impossible to formulate optimal pricing parameters.
Analyze to optimize using the Decision Model
Chasing revenue growth must be carefully measured and constantly monitored, so MarginDriver developed a solution called the Decision Model to do just that. The Decision Model takes all the data points mentioned above and puts that information at your fingertips, making your pricing analysis so simple it can be done in seconds.
Much like A/B tests in marketing, the Decision Model allows for inputs of two different custom time periods to compare how each one performed against the other. This allows sellers to alter their pricing parameters over different time periods to test and compare the impact the changes had on gross profit, the decisive indicator to measure pricing optimization.
MarginDriver users are constantly testing and tweaking pricing strategies by vendor, brand, product type, weight, sales channel, fulfillment method, etc.; all to determine the optimal sales price for each set of defined criteria that will maximize profits. Because, at the end of the day it’s not about how many orders you have, it’s about how many orders are contributing margin to the bottom line.
At MarginDriver we believe data-driven thinking drives experimentation, so we empower our customers by providing them not only with the data but also a platform on which to view their experiment results quickly and easily.
Within a day, and sometimes even within a few hours, sellers can evaluate what impact a change to either the minimum or maximum price parameter had on profits and margins. Reducing prices will almost certainly generate more orders and revenue, but now you’ll know almost immediately if it generates more or less profit. That is almost an impossible task without MarginDriver, especially given the speed at which it delivers this feedback.
To put it all in context, let’s look at a case study from an actual customer that very recently used MarginDriver to successfully optimize their marketplace pricing.
For the sake of protecting the user’s anonymity, we’ll call this customer “Z”. With a catalog of nearly 30,000 products, a few hundred daily orders and siloed data across several source systems, Z onboarded with MarginDriver to better understand its financial data.
Within a few weeks of looking at and interpreting its merchant fulfilled Amazon sales, Z came to the realization that its repricing parameters weren’t optimal, and the minimum price point was the primary culprit.
Z was pricing its products at a fair markup, but it didn’t have a good grasp on the impact shipping costs had on each order’s gross profit, which was pulling down its margins pretty significantly and causing it to fulfill some orders at a loss. Z could export and total their shipping spend at the end of the day/week/month but couldn’t tie each dollar spent to a specific order to see if it was making or losing money at the transaction level.
Now with all this data in one place and access to the Decision Model, Z began tweaking, testing, and analyzing various minimum prices within its algorithmic repricer to see how it affected profits. The results were staggering.
In less than four months Z identified its problem, developed a solution and successfully optimized its Amazon repricing and maximized its profits. Below is a closer look at screenshots from Z’s Decision Model and the underlying data between the two time periods of ‘chasing revenue’ in August and ‘optimal pricing parameters’ in November.
Despite shipping 1,590 fewer orders, Z made $16,143 more in gross profit during these two time periods (only 3 weeks). Slightly raising its minimum prices effectively increased its product markup from 1.56 to 1.67 which increased margins by 7.9%, resulting in a 58% margin increase over the base.
If Z had made these changes without the guidance of a tool like MarginDriver, it might have seen orders dip 33% and total revenue fall 24% and probably would have thought its business was suffering as a result.
Again, with gross profit being the ultimate indicator of pricing optimization Z would have been wrong. A closer look at its ‘per day’ averages and ‘per order’ averages are quite impressive.
Per order averages
With profit up 35% and orders down 33%, it’s easy to see that Z is now working smarter not harder. It’s on pace to make $20,000 more in profit every month as a result of these changes just on merchant fulfilled Amazon orders alone. It goes to show the power and importance of finding the optimal pricing parameters and not just trusting that the repricer is maximizing profit.
Bookkeeping and accounting
In addition to the order analytics highlighted in this article, MarginDriver developed a one-of-a-kind solution to tackle the most complex part of selling online; bookkeeping and accounting.
The application transforms the ecommerce segment of your accounting into a completely automated and paperless process. It automatically generates GAAP-compliant gross profit reports by day and month, and summarizes them into journal entries for one-click posting to your general ledger.
This isn’t your standard “accounting connector” software that focuses on pushing your order-related data into your accounting software. It serves as an entire standalone sub-accounting system for ecommerce merchants that handles all transactional and order-related data, and only pushes summary reports to your general ledger.
Nothing like it currently exists.
MarginDriver is offering a free 60-day, no-risk trial to all customers. Learn more at MarginDriver.com to see how it can benefit your ecommerce business.
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