This session offered research and methodology for ecommerce and merchant sites “can and should” be included in shopping search engines. The Moderator was Kevin Heisler, Executive Editor, Search Engine Watch.
Speakers: Heather Dougherty, Analyst, Hitwise The holiday season started well ahead of the traditional trends this year. The patterns have changed in that growth this year has been far more significant than the last few years with traffic steadily increasing since May-which is really early. The growth will continue for the next couple of weeks.
The surge is sending traffic to a wide array of retailers and product categories including house and garden, appliances and electronics, apparel, computers, sports and fitness, toys and hobbies, video and games, and health and beauty.
For shopping comparison engines, the audience is aging with significant growth in users 55, an increase of 56% over last year. Use of comparison engines for the age groups 18-24, 25-34, and 35-44 have decreased as customers migrate to search.
The breadth of search terms driving traffic to the engines is declining. That’s the weekly number of unique search terms driving traffic to the comparison shopping category over last year. More visitors are conducting exploratory product-specific research. 27% for branded products, 24% were for generic products, and 23% were looking for shopping tools. 11% were looking for a specific retailer.
Comparison shopping engine brands themselves and generic products are the most common search terms. Top search terms by volume are Shopzilla, Pricegrabber, and Bizrate. Ninendo Wii was the only branded product in the top 20 search terms. Target and JC Penny were the only retailers in the top 20 search terms.
The holiday season continues to start earlier each year so it is critical for retailers to make sure that their feeds to comparison shopping engines are accurate and dated well ahead of time. Searches are becoming more specific and that must be taken into account with feed detail.
Brian A. Smith, Analyst, ComparisonEngines DFO (Data feed optimization) = intelligently manipulating your data feed to achieve a desired marketing goal. You’re in control. Shopping feeds are incredibly dynamic, help with SEO/PPC, and the algorithms are simpler to figure out than SEO.
Engine setup means getting your products listed on the shopping engines and is the first step in data feed optimization. Without a proper feed, many products or an entire feed may be rejected. Remember that each shopping engine has a unique data feed specification.
Are all your SKUs listed? Did you include all the optional info? Did you send the right information? Did you send a unique data feed to each shopping engine? Uniqueness, attribute headers, HML/JS, image links/product links, no commas in the URL string for images, no “$” sign, correct FTP info, mapping, etc…are important attributes.
Qualitative: Once a data feed is up and running you’ll immediately look at the numbers and want to cut your listings. Before you do that you need to understand why your listings are not performing well. Run tests to see if the quality of the data is good enough to achieve desired results.
Watch categorization, titles/descriptions, and product attribute comprehensiveness. Add as much information as possible, use the optional attribute fields, do not spam or make things up, always include whatever unique IDs you have. If manufacture is not listed on shopping engine, tell them to add it. Google base custom attributes for specialized products can make a huge difference.
Quantitative: Determine profitability of the channel of individual engines and individual SKUs. Do not just cut SKUs from your product fee “MasterCard”, not “master care” - be careful.
Get to work! If you invest time and resources into the channel it can work.
Scot Wingo, President and CEO, Channel Advisor Corporation spelled out a list of serious conversion analytic formulas.
How do you impact CSE ROI? At the data feed level, measure each product’s ROI, nuke those that don’t convert, keep those that do. SKU level bidding which is not available on all engines is a useful tool. ETR = sales/cost of sales and sales = clicks * CR*AOV.
ROAS = (AOV*CR / CPC CPC-“floored”, but you can bid up AOV – use price filtering to impact CR – the biggest variable you can impact CSE level – make sure the data feed is optimized Site level – entire industries available to help with CR
Price, website optimization, on site search, up-sell, cross-sell, rich images, reviews, etc…are all helpful.
***Note this is “live” unedited blog coverage of SES Chicago 2007. Some typos, grammatical errors, or incomplete thoughts may exist.
Marty Weintraub writes for aimClearBlog and is President of aimClear, a Duluth advertising agency specializing on organic / paid search along with social media marketing.