1.1 Data driven Marketing on Amazon (Amazon PPC): Product Level Profitability and Hero Products Identification

1.1 Data driven Marketing on Amazon (Amazon PPC): Product Level Profitability and Hero Products Identification

Identifying hero products to advertise is the second step (first being understanding the customer acquisition funnel) in crafting a Amazon PPC strategy but how do you identify hero products from your product portfolio? Are they the one liked most by the brand owner? ones with the most sales? Ones with highest RoAS or Ones with the lowest return ratios? This is a question that every brand needs to answer to build an efficient Amazon PPC campaign strategy and achieve growth and profitability objectives. Precisely this is the question that I try to answer in this article.

This is the 2nd part of the series, Data Driven Marketing on Amazon (Amazon PPC). The objective of this series is to provide a framework, for D2C and digital B2C brands, to build, and implement a data-driven Amazon PPC strategy on Amazon.com. There are 4 parts to this series as listed below:

We will try to address this question using a case study. A brand has 5 products in it's portfolio and is selling on Amazon. The brand owner wants to identify 2 products as hero products to focus the Amazon PPC spend on these 2 products. The brand owner can access the past 3 months PPC campaigns data from Seller central to do this exercise. Let's evaluate the commonly followed approaches and in the process try to build an optimum approach to identifying hero products.

Approach 1: Based on the subjective assessment of the brand owner

Subjective Assessment of Brand Owner

According to this approach product A and product B would be hero products. The subjective assessment of brand owners might be formed by their personal preferences towards products, performance of products in a specific period or on a specific channel. While a good input to have to identify hero products using this alone would lead to inefficiencies. Let's try combining sales data in our next approach.

Please note all data that is represented in % is percentage of gross sales.

Approach 2: Based on the Gross Sales Data

Last Quarter Sales Data for the Products

According to this approach also product A and Product B qualify to be the hero products and may be the subjective assessment of brand owner is aligned with data. But wait what about ROAS for this products? ROAS is reciprocal of Average cost of Sales (ACOS). ACOS is the marketing spend that you need to incur to generate INR 1 in sale. Well let's examine the ACOS data in our next approach.

Approach 3: Based on the Average Cost of Sales (ACOS)

Quarterly ACOS Data for the Products

As per the ACOS data it would seem that it is cheaper to sell product A and product E and hence these should be the hero products. This seems to be in conflict with the outcome of earlier approaches. But this approach also doesn't consider the return data of products. Let us take a look at that in the next approach.

Approach 4: Based on the Return Data

Quarterly Return Data for the Products

Product D and Product E clearly have the lowest returns and hence should be the hero products if we are to rely on this approach. Also we can clearly see that product subjectively identified as very good has the worst returns. In fact, more returns than the sales in the last quarter. But this approach also does not consider the Amazon Charges (FBA Fee and Selling Commission). Let's include these in our next approach.

Approach 5: Based on the Amazon Charges (FBA Fee and Selling Commission)

Quarterly Amazon Fee Data for the Products

Just by looking at this data, product B and product C should be hero products as these have least amount of Amazon fee (FBA fee and selling commission) as a percentage of gross sales.

In sum under each approach we are looking at a specific metric, to decide hero products, and do not have a holistic view of the product performance. The approach that includes all these metrics while identifying hero products is product level profitability. Let us look at it next.

Approach 6: Based on Product Level Profitability

Quarterly Profitability Data for the Products

Based on this the product D and product E should be hero products. This approach consolidates all the relevant data to identify hero products and hence is most optimum one amongst all the approaches discussed. That being said there are a few more metrics that you should include in identifying hero products.

Other Important Metrics

  1. Percentage contribution to total profit: This metric is important to understand the scale of product being evaluated. Say a product has 80% profit margin but the total contribution to overall profit is less than .1%, it might indicate that the market for that specific product itself is small. Other way to evaluate this can be to keep a minimum threshold for product sales while identifying hero products.
  2. Customer Feedback: It is also critical to evaluate the feedback score, CX score and reviews of customers while identifying hero products.

Well but how do you get product level profitability?

Calculating Product Level Profitability

Amazon doesn't give out product level profitability directly but this can be calculated if you have above average expertise in Microsoft excel. Following reports from Amazon would be required to do this:

  1. Transaction level payment report - To get the sale, return, selling commission, and FBA fee.
  2. Product Level PPC campaign data from campaign dashboard to get ACOS data
  3. COGS data is not available in Amazon and brands need to manually include that in the analysis

Please write to me on [email protected] or LinkedIn for a discussion around computing product level profitability.

Conclusion

Brands often do not have complete visibility of their product level profitability and this hurts their decision making related to not only Amazon PPC campaigns but with production planning, inventory planning, and organic marketing also (store design and posts etc.). Therefore having complete view of product level profitability is non-negotiable for achieving growth with profitability.

Now that we have a clear understanding of customer acquisition funnel and identification of hero products, in the next part of this series we will understand how to craft and implement a Amazon PPC campaign strategy, allocate budget and create a PPC campaign structure, so stay tuned. Till then wish you all the best with your marketing efforts!

Follow me on LinkedIn for more such content on role of data in marketing and how to grow D2C brands through digital channels.



Irina Poddubnaia

Results-Focused Investor | Strategic Advisor. I turn big ideas into unstoppable ventures that scale fast. I talk about AI, Robotics and Growth

1 年

Your series is a goldmine for marketers! Wondering how emerging AI tools might further refine these strategies in the future?

Manish K M

Amazon Advertising & Brand Growth Strategist | Performance Marketer | PPC Expert | Scaling E-commerce Brands on Marketplaces (Flipkart, Myntra, Etsy)

1 年

Much needed information ??

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Piyush Makhija

CTO & Founder @ Moneyflo.ai | Ex-vahan.ai (YC S19) | GATech | IITR

1 年

Very well put Sachin Kumar Tyagi !! Amazon also has a lot of indirect costs associated with orders or products e.g. storage and warehouse related costs, product removal fees, etc. There are also adjustment charges one needs to be careful of such as commission correction, FBA inventory reimbursements,, etc. All in all there are 150+ charges and corrections which Amazon puts in its disbursement report. The time and effort required even for an excel expert to sift through this data is huge. At moneyflo.ai, we provide an automated solution for order and product level profitability which completely takes away this headache.

Ravdeep Singh

Data Analytics & Product Management | 8+ Years Experience | Expert in SQL, Python, AWS | Driving Scalable Data Solutions & Business Performance

1 年

Kudos for sharing such a valuable post on data-driven marketing! The metrics you've mentioned are critical for success.

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Ashish Jain

FIG, DBS Bank || FMS (Dean's Roll)

1 年

Good read ??

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