Comparison Shopping Engine Strategies

Talking $17.5mm with Become.com's Jon Glick

Become.com just announced that it raised $17.5mm in a Series C round from Texas Pacific Group Growth  (TPG), a large private equity / VC firm that's previously done deals with Petco and Travelocity.

The last time I wrote about Become was back when they launched a complete site overhaul, integrating social aspects like shopping lists, putting more focus on users, product information / education, and reviews. Here's an excerpt from Jon Glick, VP of Product Search when this was released:

From the outset Become.com blended product-focused web search with comparison shopping.  What we started to see was a third information source, UGC (user generated content), becoming increasingly important. The new site design seeks to tightly integrate all three information sources into a single experience.  Now users can compare products and prices, research products using our 5.6B webpage index, and view/create UGC all on the same page.  The goal is to make the site increasingly comprehensive and engaging for shoppers and a more frequent destination for them.  Also, we see a bright future for social shopping on the web.  I don’t think any site has nailed the right online user experience that taps the innately social nature of offline shopping. This launch lets us offer features to users, get their feedback, and move toward being the site that is truly able to bring social to shopping.

I spoke with Jon last night to follow up on the success of these changes and, of course, how they plan on using all that cash!

What new or continuing initiatives are going to be made possible with the investment – improving search technology, expanding into other verticals, more partnerships like the one with the Washington Post, more aggressive SEM...?
This investment allows us to continue to grow the site aggressively and invest in core areas like user experience, search relevance and SEM.  We also now have the financial backing to expand into new verticals (sorry that I can’t pre-announce which ones) this year, whereas without additional funding we would have had to hold off for a while on some of our expansion plans.  For Become.com this is “step on the gas” money that will help us grow even faster.

Did recent changes to the site make Become a stronger candidate for such a large Series C (noticing how both Become has benefited strongly from increased traffic after UGC additions)?
I think it was a combination of factors.  Both organic and SEM growth have been strong; organic was the fastest growing segment of our traffic in 2007, and the site improvements were a big part of that.  The site “stickiness” more than doubled and we’re continuing to add features (ex. price drop notifications are coming out this week) to keep users engaged.  SEM has also really gotten rolling; we have five PhDs working on it and that’s really starting to pay dividends.  It’s amazing how advanced web marketing has gotten in just the last few years!  Having these diverse traffic and revenue streams really helped attract investment, along with a great team, and being in our 2nd quarter of profitability also made us attractive to investors outside the VC community.

Who are your key competitive targets? The obvious competitors (Shopping, Shop, NexTag), search engines (Google…well, and Google) or CSE 2.0 entrants (Pronto, TheFind, etc.)?
We don’t really focus on specific competitors.  There are a lot of players in this space and the cream will rise to the top, so we focus on how to make our site creamier.  When the team here discusses tactics we don’t say “how can we beat so and so?”, we say “how can we grow our traffic 30%+ next month and continue improve merchant ROI?”.  More and more users are discovering and using comparison shopping sites, and with $300MM of growth in the CSE space projected in 2008, there are enough new users to go after without targeting anyone’s existing base.

Michael Yang, CEO, also posted this on his blog:

We have been profitable for 2 consecutive quarters since Q4, 2007 and our business is still going through a very fast growth. We now have over 10 million unique visits to our site per month which is over 300% growth from the same period last year. Everyone at the company and all the investors are very happy with this investment. With the additional funding we are going to invest in key areas of the business to accelerate the growth with an eye toward IPO by the end of 2010. Our goal is to become a top comparison shopping engine company in the world.

written by Scott Hurff -- scott.hurff at channeladvisor dot com

Managing the long tail

My team and I deal with inventory sets in the hundreds of thousands.  Such large product catalogs cause logistical pains, but in many cases, the larger issue is getting to profitability when the aggregate cost of that "long tail" of low click, non-revene generating products outweighs the revenue generated by the head of the distribution. Sometimes, this is the case whith feeds that are smaller in size.

Below is an example that shows the order distribution by sku of one of our merchants over a 30 day period. This data is for a single CSE, but the total distribution looks very similar.  The x axis is the number of products in the feed, and the y axis is the number of orders each of those products generated. The most important thing to note here is that though the x axis ends at 4000, the total number of offers is actually around 20,000, which means the long tail goes well off your monitor.

Take a close look at the percentages in each of those areas (click to enlarge).  Over half the cost lives in that long tail of products, the majority of which have incurred just a few clicks.  This means the individual product cost is almost invisible, but in aggregate, this poses a serious threat to profitability.  The gut reaction for many is pretty simple. Chop off the long tail and leave in the feed only the products that have generated revenuve (ROAS will double!).  This is where that middle yellow range comes in to the conversation.  That range represents products that generated exactly one order over this time frame.  If no action is taken, the next 30 day distribution will likely look similar to this, but the products that appear in that middle area will not be the same products next time around. So if you blindly chop off the entire long tail, you're likely cutting off a big portion of your future revenue. You then wind up with a similar distribution that does have a shorter tail, but also a much smaller head.

The ideal solution is to remove only the correct products from that tail.  The question is how do you define correct?

There is no way to get it right every time. As soon as you remove a product, you risk losing revenue that could have come on the next click. But most retailers will find there are products that just aren't worth including. Here are a few ideas on how to identify those. Please note that all of these can be loosened or tightened based on your business's tolerance for risk.

• Reverse-engineer your target conversion rate: Look back at the equation in my last post. Drop in the product cost, the CPC you are paying and your target ROAS, then solve for conversion rate. Divide 1 by the result and you have your click bogey. If the product gets that many clicks and no sales, it is officially in a hole. Unfortunately, the above distribution is the result AFTER applying this rule regularly. It also is only addressing the head of the cost tail, not the really long part of the tail. This approach can help cut an unusually large portion of your cost that comes from a relatively small number of products. You could also occasionally widen the time frame on the data set used in this analysis to catch the second tier of products that are not meeting that target conversion rate, but taking longer to reach the click tolerance level.
• Reverse-engineer a price filter:  Try the same exercise as above but solve for AOV. Use your current conversion rate on the CSE in question. The result is the theoretical inflection point of product level profitability.  Products under that price are less likely to work in the long, assuming they convert at or below the standard rate used in the equation.  If you have a lot of products at a low price point, you may wind up with a much smaller feed, but hopefully a higher ROAS.
• Use data from outside your CSE campaign:  If you have 100,000 products, odds are some subset of those products (possibly larger than you'd care to admit) have never sold on your website.  Well, if that long sought after first sale does come some day, it is as likely to come via a CSE as it is to come from any other marketing initiative.  But if you are fighting this problem, it may not make sense to include that initial marketing cost here. Since CSE CPCs are pretty much flat, advertising these self ascribed long tail products is a risk. These products are probably either incredibly niche, or there is a problem with the offer itself (probably the price).  If they do generate traffic, it will probably only be a few clicks, but that is exactly what we're targeting here.

written by Mark Vandegrift -- markv at channeladvisor

The Importance of Conversion Rate

"How can I improve the return I get from Comparison Shopping Engines?"

This is by far the question I hear most from merchants using CSEs. The answer is pretty simple: Improve your conversion rate. Making that happen, however, isn't quite as easy.

First, let's look at why conversion rate is so critical.

Unless you can drastically increase your average order value or miraculously pay less than the minimum CPC, increasing conversion rate is the only way to impact performance since it is the only remaining piece of the equation. The main reason for this is that CSEs basically charge you the same price for every click. Whether that user searched on your brand or your exact product title, or if that user stumbled across your product through a browse mechanism, the cost to you is the same. Qualified traffic and non-qualified traffic look identical from the merchant's perspective.

So now on to the hard part...how do you increase it? While there is no silver bullet, there are some best practices you can follow to maximize this metric.

• Categorize products appropriately
• Ensure titles and descriptions are accurate
• Populate as many feed fields as possible
• Submit clear and accurate images
• Ensure your action URLs work, take the user to a page where the product in question can be easily located and that the price matches the price on the CSE
• Complete the Merchant Information section in the account login area of all CSEs
• Actively remove products that do not convert
• Unless you have already done so, analyze and improve the landing pages on your site

Reviews/ratings on CSEs can impact conversion as well, but only if those reviews are positive, so be sure to provide great service to keep those ratings high.

Ideally, comparison shopping engines will one day expose some level of information as to how a user found the product listing, suggesting some indication of how likely that user is to purchase after clicking, and charge the merchant appropriately. In the mean time, doing everything possible to maximize your conversion rate without the benefit of that insight is your best bet.

"What if my conversion rate is nowhere close to my goal?"

If you use the equation above and enter in your actual average CPC and order value, plus your ROAS goal, you can solve for your target conversion rate. If your current conversion rate from your CSE initiative is significantly different, you may need to adjust your ROAS goal. You can also work to increase your average order value by removing low priced products from your feed or via promotions such as $10 off orders of $100 or more. However, if your target conversion rate is 2% and you are sitting at 0.5% (difference of 4X), it's unlikely that attempts to quadruple average order value will be successful.

written by Mark Vandegrift -- markv at channeladvisor

iStorez -- the CSE of deals

One of the biggest problems I've faced when reaching the checkout page of a merchant is knowing if I'm overlooking any potential deals -- a promo code, or if I'm missing the threshold for free shipping, or if I could have purchased one small item to get it free.  Yes, these are the woes of online shopping.

Newly-launched iStorez, now in beta, seeks to change that and more.  It aggregates thousands of current online retail deals with the intent of driving you to make a purchase decision.

This naturally seeks to capture users who are driven mostly by what deals are being offered (and, subsequently, often by price)  as opposed to finding a product you like and happening to have it be on sale, subject to free shipping, etc. etc.

In other words, iStorez flips the funnel.

The source of these promotions is the stores themselves -- iStorez harvests thousands of retailer promo emails and presents them on the site, which is sortable by merchant and category.  And it's all customizable depending on your preferences (i.e. I want jewelry deals but not American Eagle...).

So, merchants, this is another reason to ensure that those emails are accurate and relevant!

Thanks to Anand Jagannathan, CEO of iStorez' parent company Kriyari, for the heads-up on this.

written by Scott Hurff -- scott.hurff at channeladvisor