Introduction
One of the primary aims of a brand-protection programme is typically the ability to determine the extent of brand infringements on e-commerce marketplaces - and ideally, to be able to benchmark this metric against comparable competitor brands. In this article, I discuss a simple initial possible methodology for quantifying this characteristic, based on the price point of the items in the listings returned in response to a brand-specific search (with a low price point typically indicating that a listing may be of interest).
The methodology considers the first page of results returned on any given marketplace, in response to a relevant search, and attempts to quantify the proportion of infringing listings within this dataset - a concept which is familiar from other areas in which metrics for measuring infringements or brand-protection effectiveness are required (e.g. where one aim of a brand-protection programme might be to 'clean up' the first page of results, so that only legitimate products or sellers are returned, and no infringing products are present).
On marketplaces, listings of potential interest can typically fall into a range of categories, including counterfeit goods, trademark infringements, compatible items, 'grey-market' trade (i.e. legitimate goods sold outside approved channels), legitimate second-hand goods, and so on. Attempting to quantify the overall level of infringements based purely on price point will always therefore have shortcomings, and it may also be necessary to apply some degree of 'filtering' in order to obtain meaningful results and compare like with like. Of course, in practice, any definitive determination of infringement type will always require more detailed manual analysis for each listing (potentially also combined with other factors such as test purchases). However, in this article, I consider a high-level approach which may be at least partially automatable.
Since a simple search for just a brand name will be likely to return a mix of product types (with an associated range of prices, even for the legitimate items), I take the approach of considering one or more specific products for each brand, each of which will have a single, well-defined price for the legitimate item.
Exploring a test case
In this investigation, I consider the iPhone 14 Pro - an example of a relatively new, high-desirability product of a type which is typically prone to counterfeits and other infringement issues. I consider the listings returned on the first page of results of a specific marketplace on 01-Mar-2023, approximately six months after the initial official release of the product[1].
Since one of the aims of the analysis is to be able to benchmark against other brands and products, it will be beneficial to compare the marketplace listing prices against the actual price of the genuine product, rather than just considering the absolute numbers. This can be achieved by expressing the price per item in the marketplace listing as a proportion of the genuine item list price ($999 in the case of the iPhone 14 Pro[2]) - a measure I refer to as 'relative price'.
Where appropriate, it may also be necessary to apply a product-type filter to the results provided by the marketplace - for example, a simple search for 'iPhone 14 Pro' may return a mix of product types (including phones, accessories, and so on); however, setting the product type filter to 'mobile phones' specifically will (in theory) only return listings for phones themselves, so the spread of prices across the listings will be more reflective of the types of infringements present across the dataset of mobile-phone results.
Even then, a low price point (say) is, in itself, not necessarily indicative that a listing represents a counterfeit product. Other types of listings (such as legitimate second hand-products and trademark infringements - e.g. where a brand name is used in the listing title so as to attract search traffic to the listing, but the listing itself is for a third-party branded product) may also be associated with low prices. The first possibility can be mediated to some degree by considering only specific marketplaces where the extent of second-hand trade is limited (e.g. B2B marketplaces)[3]; conversely, separating out (say) counterfeits from other infringement types is much more difficult using only a largely-automated, price-based approach. However, the argument can be made that all such listings (with low price points) are likely to be infringing in some way - all we are therefore looking to do is quantify the overall size of this general infringement landscape.
As an illustration of the results, shown below (Table 1) is an overview of the top ten results returned in response to a listing for 'iPhone 14 Pro' on the marketplace in question, with the product filter set to 'mobile phones'.
| Title |
Price per item (min. listed) ($) |
Min. order quantity | Quantity (max. listed) |
Brand name |
Relative price |
|---|---|---|---|---|---|
| Wholesale mobile phone Original Smart 5G Mobile Cell Phones for iphone 11 128GB |
50.00 | 2 | 300 | for Apple | 0.050 |
| New Arrival Original Brand Phone 11pro max 12 mini Waterproof Face Recognition 256gb 512gb 1TB Game Mobile Phone for iPhone 13 |
399.00 | 2 | 50 | original | 0.399 |
| Smartphone mobile iphone 11ProMax 256gb 5g usa spec original no scratches body low price for wholesale 6.5inch screen game phone |
497.00 | 1 | 1 | Other | 0.497 |
| Hot Selling PHONE 14 PRO MAX 12GB+ 512GB 6.7 Inch full Display Android 10.0 Mobile Phone I13 PRO MAX Cell Phone Smartphone |
27.72 | 1 | 99,999 | Android Smartphone |
0.028 |
| Low price wholesale smartphone 14 Pro Max 8GB+256GB 7.3in 8core 4G LET global Edition smartphone |
72.00 | 1 | 1,000 | W&O | 0.072 |
| New Global I 14 Pro Max Cell Phone 7.3 Inch Big Screen 5G Smartphone 16GB + 1TB Global Unlock Dual SIM Android Mobile Phone |
95.00 | 1 | 10,000 | Other | 0.095 |
| Free shipping phone I13 pro max 8GB+ 256GB 6.7 Inch full Display Android 10.0 Mobile Phone PHONE13 PRO MAX Cell Phone Smartphone |
66.00 | 1 | 99,999 | Smartphone S22 |
0.066 |
| i13 Pro cash on delivery mobile phone 8+ 16MP New Original Unlocked Smartphone 6.8" Display OEM |
66.00 | 1 | 99,999 | Android Smartphone |
0.066 |
| High Quality i 14 Pro Max 5G 6.8 Inch Original Mobile Phone 16GB+1TB Large Memory Smart Phone Beauty Camera Gaming Cellphone |
32.00 | 1 | 20,000 | Other | 0.032 |
| High Quality i 14 Pro Max 5G 6.8 Inch Original Mobile Phone 16GB+1TB Large Memory Smart Phone Beauty Camera Gaming Cellphone |
55.10 | 1 | 20,000 | Other | 0.055 |
Table 1: Details of top ten listings returned in response to a search for 'iPhone 14 Pro', with the product filter set to 'mobile phones'
Notes:
- The 'price per item' is given as the lowest price referenced in the listing, in cases where the unit price may vary dependent on the quantity offered.
- The 'quantity (max. listed)' value is given as either the maximum quantity stated as being available, or the maximum quantity for which a unit price is specified in the listing (whichever is greater).
From the total set of (48) listings returned on the first page of results, a number of other observations are particularly noteworthy:
- None of the listings contains what would normally be described as a counterfeit product; none shows Apple branding in the product image, and none cites the product brand name as Apple (with the exception of the first listing, in which the product is stated as 'for Apple'; this is a technique commonly used by sellers to describe compatible products - although this would be largely non-sensical for a smartphone listing - or as a means of circumventing enforcement efforts), though some listings do give the brand as 'original' or 'OEM'. However, many of the listings in the dataset would constitute trademark infringements, with brand names given in several cases as 'other', 'Android smartphone', or a brand name referring specifically to the seller in question.
- Several of the returned listings appear not to be infringing the iPhone 14 Pro product in any way, as the marketplace seems to also return a number of listings referring only to one or more of the individual keywords in the search phrase. Only a subset of the results (those referring explicitly to '14pro' or 'phone14' (both with or without spaces) - shown in bold text in Table 1) are likely to be directly infringing. Accordingly, when carrying out the price-point analysis, it will be beneficial to apply some filtering in order to exclude all except these listings.
- It is also informative that a number of the relevant listings do make reference to 'i 14 pro' or 'I14 pro' - presumably as a way of avoiding directly infringing the iPhone brand name, and potentially also aiming to circumvent detection. Use of brand variations of this type is popular with infringers.
- Amongst the listings, a range of maximum quantities (per listing) was observed, from 1 to 10,000,000.
- All except one of the listings are for sellers based in China, with a significant number operating out of the manufacturing centres of Shenzhen, a trend which has frequently been observed for sellers of infringing products.
For the 48 listings, the distribution of relative price per item is as shown in Figure 1.
Figure 1: Distribution of relative price per item, for the full set of 48 listings returned on the first page of results in response to a search for 'iPhone 14 Pro', with the product filter set to 'mobile phones'
The results show strikingly that the listings are dominated by products at a very low price point, with the vast majority of items at 10% of list price or lower (i.e. ≤ 0.10 relative price).
It is instructive to consider some examples of the listings with the lowest price point (excluding the non-'14pro' and non-'phone14' results, as discussed in point (2) above), to analyse the types of infringement present. Of the five listings with the lowest relative prices, for example, all are offering high quantities of items (up to between 10,000 and 99,999), and all appear to represent trademark infringements (or potentially to be involved in the supply chain for counterfeit products) (Figure 2).
Figure 2: Examples of listings with very low price points, both offering 'customized logo' and 'customized packaging' for bulk orders
For developing this methodology further, it is advantageous to express the number of listings in each relative price 'bin' as a proportion of the total, rather than as an absolute number. This has a couple of advantages, specifically:
- It allows for easier comparison across different marketplaces, where the number of results returned by page may differ.
- It allows filtering of results to remove any 'false positives' (as discussed in point (2) above).
It also simplifies the calculations if the bins are a consistent width throughout (in this case, 0.02).
This therefore gives the results for the iPhone 14 Pro search for the marketplace in question in the format shown in Figure 3 below (in which the 20 non-relevant listings have been excluded).
Figure 3: Distribution of relative price per item, for the set of listings returned on the first page of results in response to a search for 'iPhone 14 Pro', with the product filter set to 'mobile phones', and with non-relevant / non-infringing listings excluded
In order to carry out the benchmarking across different brands or marketplaces, it is also useful to construct a single metric (or value) which provides a measure of the distribution of relative price points across the set of listings. Essentially, we would like this number to represent the proportion of the 'area under the graph' at the low-price-point end of the relative price distribution chart.
This can be achieved by summing up the heights of the individual columns, but weighting more heavily (i.e. applying a larger multiplying factor(s) to the heights of) the columns at the low-price end. The weightings can be selected in a number of different ways; one possible methodology is to calculate a weighting which is inversely proportional to the relative price value at the mid-point of the bin in question (such that, for example, the height of the column for the bin associated with a price-point range of 0.00 to 0.02 - i.e. with a mid-point of 0.01 - is weighted by a factor of (1/0.01), or 100).
This methodology allows us to calculate a single price-point metric (P)[4], whose value increases according to the proportion of the listings in the sample associated with lower price points (and therefore provides a measure of the potential scale of the infringement landscape). In the case where all listings have a relative price of 1.00 (i.e. potentially just a set of legitimate product listings), the value of P will be 1[5]. In this case, for the distribution of iPhone 14 Pro listing price-points shown in Figure 3, the value of P is 19.180.
The approach thereby allows us to benchmark the product against other products, brands, or marketplaces. For example, considering the same marketplace, but looking instead at the comparable Galaxy S23 Ultra product (RRP = $1199.99)[6], and similarly applying filtering to remove non-relevant listings, we obtain the price distribution as shown in Figure 4.
Figure 4: Distribution of relative price per item, for the set of listings returned on the first page of results in response to a search for 'Galaxy S23 Ultra', with the product filter set to 'smart phones', and with non-relevant / non-infringing listings excluded
In this case, the price-point metric value (P) is 21.643, indicating a greater proportion of listings at the lowest price points than for the iPhone product on the same marketplace, and potentially therefore a larger infringement landscape. This is consistent with what we can subjectively see in Figure 4, with a greater peak in the lowest occupied relative-price bin (between values of 0.02 and 0.04).
Conclusion
Whilst taking a very simple-minded approach, the methodology discussed above does provide a basic measure of the proportion of listings in a set of marketplace results which show low price points and, by extension, a measure of the potential scale of the infringement landscape. Obviously this assertion is only valid if we accept low price point as a proxy for a listing to be of interest, but previous analysis has certainly shown that it is at least one such valid indicator (and as also borne out by the examples presented in this article).
In practice, calculation of the price-point metric could be automatable, based on collection of marketplace data using monitoring tools, combined with (a) scraping technology to automatically extract the price information and (b) filtering technology to remove false positives in the results.
By applying and expanding these ideas, it would be possible to carry out cross-brand and cross-marketplace benchmarking, and potentially to track trends in the infringement landscape over time (e.g. in conjunction with an active brand-protection programme of monitoring and enforcement).
Acknowledgements
Thanks must go to Angharad Baber, Irene Oh, Agnes Czolnowska and David Franklin for their feedback and input into this article.
References
[1] https://www.apple.com/newsroom/2022/09/apple-introduces-iphone-14-and-iphone-14-plus/
[2] https://www.apple.com/iphone/
[3] Similarly, data will be less likely to be meaningful on (for example) auction-based marketplaces, particularly in the early stages of auctions when the price point is likely to be low by definition.
[4] Formally, P = Si [ (1/mi) × ℓi ], where mi is the relative price at the mid-point of the ith bin, and ℓi is the proportion of listings in that bin.
[5] Approximately(!), depending on where the price-bin boundaries are selected to be.
This article was first published on 3 March 2023 at:
https://www.linkedin.com/pulse/developing-methodology-benchmarking-marketplace-brand-david-barnett/
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