Amazon sales velocity is the number and dollar amount of a seller’s transactions per month–in essence your rate of sales. Higher sales velocity will improve your product rankings which in turn will help you increase your Amazon sales again. This relationship is very rewarding for brands on Amazon.
Until it’s not. What happens when sales velocity slows? How do brands make up lost momentum? Brands are always searching for new ways to generate growth and profitability. Yet, several years of supply chain shakeups, material shortages, and labor scarcity have left you run down and still questioning the data. You can’t predict the unpredictable, so how do you optimize product availability and meet consumer demand?
With a heavy focus on profitability, Amazon is doing everything it can to conserve cash, at the expense of both 1P and 3P sellers. On the 1P side, brands are dealing with lower POs and declining support for Vendor Central data availability and inventory metrics. On the 3P side, brands are dealing with FBA limits that are increasing logistics and fulfillment costs. These added expenses combined with product availability issues result in one expensive hybrid channel strategy.
Brands can probably expect more Amazon data surprises this year after the overhaul of Retail Analytics in late 2022. Amazon dropped metrics under Traffic Diagnostics, Forecast and Inventory Planning, and Net PPM. This has made it difficult for brands to validate projections with product availability issues to solve.
Brands with a hybrid 1P and 3P strategy will experience lower POs and compete for FBA storage space. They will need to churn out quicker and smaller shipments to meet demand if sales remain steady. If they sell out too quickly, then lost sales from stockouts become painful missed opportunities.
In 2023, budgets have been scrutinized more astringently so optimizing sales will be critical for team members. Ecommerce operations need to execute root cause analysis quicker than before to maintain sales velocity. Teams often have to scramble to react to daily changes to listings on the marketplace which pulls their attention away from the bigger picture.
In a workforce that’s now dominantly remote, it’s more difficult to connect the dots across all the teams such as channel ops, paid media, customer service, logistics, and content marketing. Teams spend too much time, effort, and sparser budget on tactics that don’t move the needle.
Without good data to identify factors behind your sales velocity, there is high risk of wasted ad spend when sponsored campaigns miss the mark. Ecommerce teams often need to look at performance reporting from several angles including brand, category, or product lifecycle. This type of sales analysis requires spending countless hours combining Amazon reports which may not supplement the metrics and history required to make better ecommerce decisions.
No matter the quantity of your assortment, it’s nearly impossible to manually analyze all the datapoints through siloed spreadsheets. You need to connect the dots across all core KPIs to align your decision making. We look at sales data as a decomposition into the following (3) pillars of performance:
Next, look at variances across periods such as YoY, T4 average, and WoW. Consider changes in the relevant metric under each of the (3) pillars, such as drops Ad Spend under the Traffic Variance.
Looking at ecommerce performance by brand or category is a starting point, but we wholly believe that the key to analyzing Amazon profitability is best approached by considering overall sales as an equation.
The i2o Ecommerce Sales Equation (ESE) Navigator uses an effective and efficient methodology that helps you maximize ordered revenue because it segments sales into the key pillars of performance:
The ESE Navigator groups products by performance to initiate your focus on the biggest contributors to Amazon sales velocity, positive and negative. Calculate gains & losses in actual dollars by product, so you can prioritize what makes the highest impact on ordered revenue.
The ESE Navigator is an automated methodology that works for both Amazon 1P and 3P. It allows you to make better ecommerce decisions using actionable Amazon data. It is flexible enough to combine or separate for 1P vs. 3P, and sort reporting by brand, category, lifecycle stage, or even ASIN level.
The ESE Navigator connects the dots across ecommerce sales metrics with proprietary methods of Amazon data analysis that allow you to:
The ESE Navigator is such a versatile tool and serves the account manager all the way up to VP of Sales. It is powerful enough to take variances and boil them down to single issues which can help groups of products. It begins at the lowest level of ASIN and goes all the way up to category, brand, or the entire business.
How does it work?
The ESE Navigator doesn’t try to boil the ocean and makes it easier for you to act on sales insights. Most BI tools are limited in what they can do and ultimately fracture your attention. They only tell you whether sales have increased or decreased and combine this into one lump, unusable sum. This limits your capability to truly analyze variances across periods, and the variances can point to positive or negative changes to your sales.
By applying automated approaches based on clustering and machine learning, the ESE methodology groups products based on similar factors which account for the biggest week to week or year over year changes.
The ESE Navigator is a dynamic solution with methodology based on Data Science and Machine Learning. Rather than regurgitating a bunch of datapoints or sharing high level sales trends without explanations, the ESE insights are far more usable and help to align teams on the same set of facts.