Most engines require some type of assistance or set format. However both Shopping.com and NexTag.com offer the ability in their administrative panels to remap the data which is coming though. This can be a great benefit if data available changes, or if you just want to make sure that data is coming in properly.

Or maybe you are just looking to see what it would take to get on board with a shopping comparison engine. That is why I created this quick reference guide. Remember, this is just a guide, not an absolute schedule. This is based on an average depending on your experience – could take more or less time than this. It also depends on where you are heading to. If you are trying to get on Amazon Seller Central, this may take a little bit longer. So here is what you are in for on each channel:

1 hour to set up an account per engine: This includes finding the link to enter in billing information, typing it all in, entering any pre-requisites. Once you have done this 10 times, I am sure the average will go down to 10 minutes.

5 hours to format a feed: Even if you are using a third party solution, you can expect to invest some time here to really understand what is being asked for. You or anyone sending a datafeed for the company should know what is needed, and what the consequences are for not having a shipping rate, for example.

3 hours to transmit the feed and confirm proper transmission: This is the most important and sometimes over looked aspect. You want to make sure the feed is properly being updated. This 3 hour estimate is spread over a couple of days time, to ensure updates are being processed on the engines’ side.

3 hours to trouble shoot initial feed: This is an average, but I truly believe that every new feed will need to be tweaked based on the engine’s requirements. This goes back to the previous 2 steps. Chances are you mis-understood or just plain overlooked something. You definitely want to spend some time in reviewing all categories you have assigned, product names, and pricing. And if you have not spent time assigning categories, then you need to go back to the previous steps.

5 hours for observation and further optimization: This is where a good merchant is separated from the average ones. Review the analytics, product by product. What is the competition doing? Which products are selling? Which products are leading to upsells? What are the differences between this engine and other engines? Are there additional things that can be done like signing up for customer feed back tools? Ask questions, and many of them.
17 hours total, for one shopping comparison engine feed: Remember this is just an estimate, but truly anyone who is looking to simply shoot a product feed out there, I would suggest either waiting until Q1 2008, or get yourself a responsible professional to put this together. You don’t want to spend $500 to sell 3 pairs of iPod headphones.

Details are still vague, and Amazon is still not publicizing this, but it looks like Amazon is not too far away from a fully operational system. Currently it appears Amazon is using a variation of the Shopzilla datafeed format to acquire product information for the CSE version. This will be a huge relief for many sellers as Amazon Seller Central holds the title for the toughest datafeed formatting standards.

UPC stands for Universal Product Code. The product code is meant to consist of 12 digits that give a unique identifier for  every individual product. UPC is a push to standardize product information in a way that can be applied to all products, and in theory making life easier. Some engines and marketplaces are turning toward UPC as an answer to placing products in the right place, but there is a problem for vendors…many do not have UPC in their product data.

One of the main issues for standardizing data is not only resolving a set format, but then making sure sellers have the information needed. UPC presents an issue because while it seems to be a great way to integrate products, many distributors and manufacturers do not provide this information to sellers.  The seller is then stuck trying to integrate a UPC code, possibly for all variations of products including products with size, color, or other options. This creates a huge challenge as even a mid-level business can be looking at trying to add UPC values across thousands of options. Amazon is one of the leading proponents of UPC integration based on their own datafeed requirements, and other companies are following on this same path.

This creates a big dilemma for sellers: How to get a UPC value, and then how to add this to the data. The first question is hard, the next is slightly better. Depending on the data structure, the best scenario is to have a way to integrate UPC number with the option value of a product (if needed).  The best way to integrate…well, that all depends on the seller’s products, shopping cart, database, third party marketplaces…etc. Unfortunately, there is not an easy answer here. The creative burst that led to different sites and different ways of selling has led back to next challenge: How to put all this back together?

 1. No, you can’t assign an item to multiple categories

Only a few companies have this principle in place, but most do not. The reason is because a merchant could potentially flood an engine with only a few products spread out in many places. The consumer will quickly tire of seeing the same products over and over, abandoning the engine to shop elsewhere. This same principle means options are not typically allowed to be listed as multiple items, because a single item could then take up an entire page of results. For example, some engines like GoogleBase and NexTag only allow one item per product url or link page.

2. Picking a category

Next idea: there is no perfect category! Yes, an item could conceivably fit into multiple categories so it is up to the merchant many times to pick the best category (A-B testing anyone?). Now, many engines will actually recognize a product by SKU or UPC, and have a predetermined place for that item. So, this issue could be rendered moot. However, most times a merchant is forced to pick a category assignment so it’s best to pick a few items from a given group and search for them. See where those items come up. Then, decide if you want your items directly competing with similar items from other merchants, or if you would prefer your items elsewhere in seclusion. This is a competitive analysis that could help greatly with conversions if not total click count.

3. Try sending your categories first!

Many merchants get caught running in circles and drowning in the possibilities of where to put items. Try sending your categories first. That is, send the category assignments from the home website and see how the CSEs handle those. In the CSE back office, you should then be able to get a listing of products and category assignments. Based on those results, then see what alteration is necessary. First send what you have, and then make adjustments as needed. Don’t get caught up in unnecessary work!

To make this readable, lets take 2 perspectives.

1) The simple perspective: ” I need to make more money. How do I do that?”

Using this outlook, it is best to consider a straight forward profit and loss equation, assuming the best product data in already being used (see Optimization Pt. 1)

2) The more complex, and in some cases, best perspective: “Understanding that bidding may play a significant role in achieving the best results, how do I maximize profits?”

This outlook understands the all important variable of bidding. When items are are listed at a set cost, a proper formula is easier to construct. However, when factoring in bidding as any user of SEM or Google Adwords – Overture/Yahoo based advertising knows, can significantly impact profitability.

So lets take a look at both scenarios:

1. The simple strategy

The most important thing to understand here is the need for product level tracking and reporting. Seeing whether an entire feed or even category is profitable does not provide accurate enough information to truly get good results. The cummulative effect of all products is important, but without being able to take apart the engine that drives profits, there is no good way to fine tune the engine for performance.

Using a simple formula like this should suffice:

X= listing price of the item (cpc)
Y= profit from a sale (selling price – cost of the item to the merchant)
N= number of sales
C= number of clicksN*Y – (C*X) =

So, for a sample product, lets imagine a futon bed:

X= $.35 per click for listing the item online
Y= $90.00 ($199.00 selling price – 109.00 cost of goods)
N= 3 sales through using an engine
C= 125 clicks generated through the engine over a given time range

$90.00*3 sales= $270.00 in profit
275 clicks * $.35 per click= $96.75

Net profit or return on investment (ROI) in advertising the futon= $270.00-$96.75= $173.25

Using this scenario, this is where profit and loss is essential. The conversion rate in this scenario is a low 1.09%, however the profit is significant.

Using this equation, you want to sum all products to see how the engine is doing as a whole, but of course any product which has a negative or unsatisfactory profit level, would be removed from the feed.

2. Look Deeper

Looking a little closer, many engines allow product level bidding. This increases the variables one step further allowing for increasing and decreasing bidding based on a products performance. This requires an analysis of bidding and results over time and with different variations.

Lets assume over a given time range, we have changed the bid on a product and measured the ROI (all other variables kept constant). This change in bid structure has resulted in high visibility in some cases, and low visibility in other cases. So the results are as follows:

In this scenario, more clicks through more traffic but not necessarily more sales. For this product, $.45 would be the optimum scenario. However,continual testing should be done because in a real world environment, other merchants would also adjust their bids resulting in changes in exposure.

ROI is one method for determining profitability. Many retailers use different qualifiers for success. We have already covered ROI, but there is also:

-Cost Per Acquisition (CPA): Total Advertising Cost/Number of Sales
-Return on Ad Spend (ROAS): Revenue Made in Sales/Amount Spent on Advertising
-Conversion Rate: Total sales/Divided by Total Number of Clicks

The following depicts how these products from the first example would show using these indicators:

Across hundreds or thousands of products, an ideal solution would be to continually adjust bid and data values. If a product continually under-performs across a determined time period, then that product should be discontinued or bidding should be set to zero. Using this type of process, return can be gradually increased even if overall traffic and clicks may actually decrease. An increase in profit does not always mean an increase in sales or traffic. Sometimes efficiency is quiet but will keep you in business and lead to success in long term where it matters most.

To properly speak about optimizing, we need to first break this apart into 2 discussions. The first will revolve around data. Data refers to the actual information being sent to the shopping comparison engine or marketplace. This data needs to conform to the destination site and this means the comparison engine data may look very different than the data taken from the origin website or merchant platform. The second portion of optimization revolves around product selection and return. I am not aware of any merchant who sees equal profitability on all products. Many products will loose money no matter what is done to the data, and essentially should be removed. Product selection should ultimately be determined by the often misunderstood Holy Grail of web marketing, ROI (return on investment) or ROAS (return on ad spend).

1. Product Data

Product data is defined for this article as any type of data relating to the listing and display of an item for sale. Product Data is different for each product and includes everything from product name, to product sku, to product description. The biggest misconception about product data is that it if a successful formula is found on a merchant’s website, this formula or structure should be the same across all selling channels.

INCORRECT! Actually, product data should be changed and altered based on the destination site. Every good public speaking or writing class will emphasize either speaking/writing based on the audience. CSE’s and marketplaces are the same. Each is a different audience and the information needs to be changed for that audience.

I am going to skip past basic manipulations such as indicating information such as ‘In Stock’ or what type of shipping is available. This should be an automatic first step, but finer adjustments are best illustrated by the description field and product name.

Product Attributes

A big deal is being made about product attributes as the future of data structure. Attributes are an important piece, and engines like Shopzilla and Google Product Search are turning to attributes as a way to classify products accurately across millions of skus. The best way to show how these fields come together is using Shopzilla. Shopzilla/Bizrate uses the name and description fields to place products using product attributes.

Shopzilla has a pre-defined list of product attributes that look like categories, but are in fact, attribute groups. There is no category id or category indication that can be made to place products in these groupings. The attribute terminology must be present in the name and description fields.

In the example show below, if a merchant sells golf clubs, and has a set irons for sale, the should absolutely make sure the term ‘iron set’ is included in the product name, description, or both. Using this term allows Shopzilla to correctly identify this set of golf clubs as an iron set, and will then place the clubs in the proper group. If a merchant uses a term like ‘set of irons’, this will not place highly and may not even find its way into the ‘Iron Sets’ group.

Using the correct Shopzilla category id when sending these items, cat id#12070300, is advised, but this will only take a merchant so far on this engine. By changing the product names and descriptions to match what Shopzilla is looking for, a merchant will see much more targeted traffic and then more sales.

Creating New Customized Data

Another possible manipulation would be adding a short description specifically geared for Shopzilla. Since in this example, Shopzilla is only displaying the first 50 characters of the description, most of the website description is never even seen by the consumer! As we have already pointed out, the engine in this case is using the description field mostly for placement, so what if for the first product, a description was instead set like this, where bold characters are within the first 50 characters to be displayed, and the rest is for placement:

Free Shipping on Orders over $50. Thousands of clubs available. Callaway Golf 06 Big Bertha Iron Set. S2H2, Tru-Bore and VFT — are part of the DNA of Big Bertha Irons. Add extreme Notch Weighting, a deeper 360-Degree Undercut Channel and a constant-width sole.

These are just a couple examples of data optimization. Many more are possible and advisable. For example, the actual ranking of these items on Shopzilla are determined by many factors including relevance to the group or search term, participation in the customer ratings program, price, and bid amount. Of course, data is just one factor in optimization, the next are is in regards to product selection and return on ad spend.

*In the below illustration, a search was made on the Shopzilla.com home page for ‘golf clubs’. Then when the selection came up, the prospective customer clicked on ‘Iron Sets’. The arrows indicate the ‘Iron Sets’ attribute grouping and then where the term ‘iron set’ is used the product names and descriptions to have products come up in these sections. Actual merchant names have been hidden. Click on image to enlarge.