A wish list for the shopping engines.
For anyone running or working with an e-commerce site, you know what I mean by the title. While shopping engines are a boon to certain consumer segments, they are both a boon and a bane to marketers trying to optimize programs or forecast channel results.
The concept is pretty straightforward and plays to the Internet's strength: third-party sites aggregate content from multiple retailers, allowing consumers to search across retailers in one place for goods and pricing, rate sellers, and complete a transaction. Audiences on shopping engines are at the end of the online shopping experience where they're coming down to a critical pricing decision, making this channel very sensitive to price and seasonality. Any marketer with multiple products to sell needs to strongly consider this channel -- despite the limitations, nuisances, and nuances.
Straightforward But Limited Process
The process is also straightforward. Retailers contract directly with shopping engine properties like SiteMatch, Nextag, Yahoo Shopping, Shopzilla, or Shop.com (among many others) or with shopping engine managers like Quigo (recently acquired by AOL) or Channel Advisor (among others), which may or may not manage the process of collecting product feeds or crawling sites to create feeds and then managing and optimizing the campaigns. Retailers pay a flat CPC (define) negotiated with the shopping engine, usually tied to the category and season.
Also available are some limited premium placement opportunities that float marketers to the top of the listing, where they profit from the increased visibility and enhanced credibility. These premium placements are often awarded to marketers who are willing to place surveys to get their sites ranked by consumers as trustworthy.
How do you decide which shopping engines to use? The site selection process doesn't differ much from any other online media planning. In this case, buys depend on traffic and past experience, if available.
There is a top tier of shopping engines against which you try to forecast budget and then move to next tiers as budget allows. Long-running programs benefit from year-over-year analysis, but like search engines the environmental factors rule all and they change every year, often making those comparisons moot. Monitoring performance is an ongoing struggle and optimization tactics are limited.
From a practical standpoint, consumer shopping engines operate in almost a binary mode. Either they're on or off. Either you include products or categories in the feed or you don't.
The feed is usually drawn from the site content, so manipulating or testing product descriptive copy (the same as ad copy in this context) is difficult due to the dynamic nature of keeping product feeds current. Consumer demand dictates the number of relevant searches and the pricing is fixed, which leaves you with few levers to pull to optimize results. Results can vary widely and unexpectedly from month to month and season to season.
Shopping engines aggregate both products and audiences, providing a useful service to marketers. Like paid search programs (SEM), the shopping engines earn revenue by the click, while the marketer earns revenue by the conversion. Both search engines and shopping engines capitalize on consumer intent and promise scale.
The big difference between the shopping engines and search engines is that search engine programs are tagged at a level of minute detail and advertisers or their agencies have control over multiple variables on which to optimize the campaign to maximize the conversions. They have levers.
To monitor shopping engine programs, we can only log into a reporting interface, either at each engine or at the aggregated level, but there is usually no tagging and no third-party verification. You could conceivably run the shopping engines as individual programs, place their tags, and use their reporting, and then use an analytics program like Google Analytics to obtain clicks and costs from each.
But that is an extremely inefficient process that leads back to the same lack of options. All you can realistically do is take the products or categories out of the feed to manage a portfolio cost of marketing or other ROI (define) metric.
Shopping engine feeds tend to be unpredictable. They scale up or down as the engines perceive opportunity for their own revenue enhancement, such as when they see a lot of activity in search or display that will drive clicks.
Some shopping engines have contextual ad opportunities with "powered by" ads within product reviews to help drive quality traffic. Many of the shopping engines do a superb job of injecting their listing in organic and paid search results.
Other frustrations of working with shopping engines include:
Some solutions for the shopping engines:
If we had a wish list for shopping engines, stronger optimization opportunities would be right at the top. That would make this logical, effective consumer touch point more a part of an integrated plan to maximize revenue and less of a crapshoot.
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Robin is the CEO and cofounder of NetPlus Marketing Inc., a top 50 interactive agency established in 1996 to focus exclusively on online marketing and advertising best practices. Robin brings innovative strategy and a depth and breadth of marketing experience to the agency's practice and management. As one of the industry's pioneers, she is a driving force behind NetPlus Marketing's ongoing success with a diverse and discerning client base that considers online results critical to their business success.
Robin is a frequent speaker at national industry events, including ClickZ, internet.com, OMMA, Ad:Tech, SES, Online Marketing Summit, and Thunder Lizard conferences and is a sought-after resource for industry and business publications for her insight and advice on such topics as digital strategy, social media marketing, and behavioral targeting.
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