Finding the Right Shopper at the Right Moment in Retail Paid Search

Throughout 2013 and 2014 Google has made available a wider array of tools for SEM’s. Enhanced Campaigns facilitated more efficient use of geography, device, time of day and day of week as targeting parameters. Through Remarketing for Search (RLSA), marketers can target users based on prior activity on their retail site. In the future, Google may enable demographic and ‘similar user’ targeting for paid search, bringing Search Ad targeting options further in line with Display Ads.

These tools are valuable, but by themselves are not impactful for retailers. Product intelligence and sophisticated analytics must accompany these new targeting features in order to thrive in retail paid search. Retail marketers have the potential to target the right ads to the right users at the right time, but it is essential to first identify distinct audiences and the product catalog segments that best correspond to those audiences. As new targeting strategies are implemented, it will also be crucial to determine how much audiences are worth, and whether or not new targeting parameters are effectively reaching audiences.

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In enterprise retail, product catalogs are rich and varied: thousands of items from many brands and categories, appealing to a wide range of consumers across age groups, income levels, genders and physical locations. Retail marketers now have more power than ever to find these users with the right products and high-impact ad copy at the best moment. The challenge is, who are these groups of users, and what is the best moment?

Begin with a thesis

What are the key segments of your catalog with respect to revenue or bottom line contribution? What does the typical high-intent shopper look like for each of these segments?

Sometimes these connections are fairly obvious; high-intent shoppers for women’s Frye Boots are typically young, urban women with relatively high disposable income. Sometimes high-demand audiences for a given set of products may be more difficult to pinpoint.

One example is multipacks of non-perishable snack foods. Are these products converting best when searched for by parents stacking the pantry, or office managers filling out their snack budget? Perhaps it depends on the time of day or the day of the year. Monitor data by ad group and campaign in order to answer these questions as best you can and identify your target audiences. At the outset, or without a sufficient sample size, leverage site analytics across your retail product pages to identify trends.

Target users with bid modifiers

Once you have identified performance trends that parallel the shopping activity of your audiences, use bid modifiers to direct traffic toward higher-return ad moments. If your ‘snack food’ category performs best early in the work day, only during the work week, and shows much stronger conversion on desktops than mobile devices, set positive bid modifiers for snack food ad groups during those hours, and negative bid modifiers for mobile devices. Start small and get more aggressive in increments, and be sure to assess performance versus historical benchmarks, keeping seasonality in mind.

Track the results

As time goes on, continue to assess the performance of your segmenting strategies. Once segments are identified and modifiers applied accordingly, begin comparing performance of those campaigns and ad groups against historical benchmarks. Consumer behavior evolves over time, be sure that your use of bid modifiers keeps up.

Factor brick-and-mortar presence into your retail paid search strategy: as we’ve heard many times from Google in recent months, the purchase funnel is no longer linear; shoppers’ decision-making processes bounce not only from screen to screen, but also from online to offline. Consumers evaluate price and availability of products online as they stand directly in front of them in physical store locations. Conversely, shoppers use search engines to research price and availability of nearby products. With Google’s new location capabilities, retailers can surface ads for products available in locations close to the shopper, providing a map link and phone number.

As with time of day and device, you can automatically modify bids in accordance with the shopper’s location. As with display, this is done at the state or DMA (designated marketing area) level. For any given geographic area, the presence or absence of brick-and-mortar locations will likely be a huge factor in how online channels perform. For many retailers, search marketing and physical store presence have a symbiotic relationship: brick-and-mortar locations contribute to overall brand presence and demand, thus driving search shopping activity.

Again, analyze conversion data by geographic location to identify high-return and low-return DMA’s, then use geo bid modifiers to capture high-intent traffic and scale back where return is poor. If you identify a pattern with respect to brick-and-mortar influence on your Search program, you can be ready if and when new physical locations open up by using prior learnings from your bid modifiers.

Leverage RLSA to further refine and target your audiences

Remarketing for Search furthers the array of audience-targeting capabilities for retail marketers. By implementing remarketing tags and building remarketing lists, retailers can market to users who have already visited their site or particular pages within their site. Again, it is crucial to identify distinct audiences that can be captured with remarketing lists, and the segments of your product catalog that correspond to these audiences.

Think about how the sales cycle for your products is reflected by user activity on your site. For some products, such as high-price-tag couches, the buying cycle may be long, involving much research and many touch points. For such items, it may be well worth paying a high CPC to remarket an item – for which the user has displayed intent by visiting certain product pages – many times in order to find the user at the moment of decision. For such products it may make sense to evaluate performance on a wider attribution window than other segments of your catalog, as purchase of expensive durable goods merits much consideration.

For less expensive, more routine items like staplers, a shopper will be less likely to deliberate and research, average order value will be lower, and remarketing these products may not be worth the added CPC. However, non-durable or semi-durable products such as guitar strings, tennis balls or snack bars – are likely to be purchased repeatedly. It may be worthwhile to build remarketing lists of existing customers and market to users who have already converted with retention-oriented messaging. In these cases, consider the interval at which shoppers will make repeat buys and be sure to configure cookie duration to allow for remarketing at the proper moment.

Conclusion

As a Retailer and Paid Search Marketer, you now have more tools than ever for finding the right user at the moment of highest intent. In order to capitalize, you will need a solid campaign foundation and the technology to see your strategy through. Make sure that subsets of your product catalog – corresponding with different sets of shopping behavior – are expressed distinctly for bidding and performance evaluation. Utilize a reporting solution that affords device-level performance analysis and allows for ongoing evaluation of your segmenting and bid modification strategies.

CommerceHub offers business intelligence tools, reporting, and expert service to take your retail paid search to the next level.

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