Not only does product data provide crucial information that consumers consult when making purchase decisions, but it also informs query matching on Google Search and retailer websites. To power and optimise Google Ads shopping campaigns, advertisers feed their product data into Google Merchant Center. Product data within an advertiser's Merchant Center feed includes fields such as product title, description, price, and category. This data informs query matching and influences shopping campaign success metrics such as return on ad spend (ROAS) and impression share.
If the impression share is low, bid optimisation is only part of the solution. Marketers can increase their ad quality by improving their product attributes with rich, up-to-date product data. With more than 40% of online transactions made on mobile devices,[i] it’s imperative for advertisers to win top ad placements for key terms, especially on smaller screens where visibility is more challenged. For example, the left-most ad on mobile shopping results gets up to three times more shopper engagement. Absolute top impression share indicates how often products show up in this top Shopping Ads placement. Marketers should make sure their brand is showing prominently when it counts - whether that’s for their top products, top categories, key shopping days, or for new product launches. Providing accurate, high-quality product data is an excellent way to drive product visibility and help consumers make well-informed purchases.
If marketers only consider cost per acquisition (CPA) to assess the success of a campaign, they mistakenly treat each acquisition with the same weight. CPA ignores the fact that the lifetime value (LTV) of customers will differ based on their propensity to spend, and spend regularly, with each brand. Segmenting campaigns by LTV provides a way for advertisers to adapt CPAs and optimise acquisition efforts. Using website, sales and CRM data, advertisers can implement dynamic remarketing campaigns, personalising ads for people who have visited their website before.
Advertisers can do this in three simple steps. First, they should segment consumers in three LTV buckets - high, medium & low - using data from multichannel reports in Google Analytics, CRM profile and engagement data, as well as transactional data, if available. Next, advertisers should calculate the max CPA for each LTV bucket using Google Analytics audience reports and audience insights within AdWords. Lastly, advertisers should use their LTV segments and bid strategies as inputs to build remarketing lists and similar audiences in order to maximise the effectiveness of their campaigns.
In the past five years, foot traffic to retail stores has declined by 57%[ii] but the value of every visit has nearly tripled. Store visits have become more informed as consumers research products online before they visit a physical store. Additionally, local shopping queries have increased by 45%,[iii] indicating that more consumers are searching for a product to purchase near their physical location. These consumers exhibit a high intent to purchase with three in four people visiting a physical place within 24 hours of their online local search.[iv] As such, store visits are an important metric for advertisers employing an omnichannel strategy on top of other more monitored KPIs such as impressions, ROAS and click through rate.
To capture shoppers during this pivotal moment in their journey, advertisers can draw on local store inventory data, query intent signals, and the user’s distance from a store to drive hyper-relevant campaigns. For example, adidas Japan noticed that more consumers were using local search queries, which indicated a consumer need to move from “now I want to see” to “now I want to buy.” After setting up more than 150 stores within Google My Business, adidas Japan implemented local inventory ads, which improved store visit rate by 42%.
Being found at the right time is more than just optimising for consumer intent. It also means ensuring the most important items are found, and purchased, at the right time. To capture maximal returns from their Shopping campaigns, marketers need to take control of their traffic by implementing multiple, segmented campaigns with separate bids and budgets. This can be achieved by segmenting Shopping campaigns to indicate to Google Ads which products should be shown the most. For example, marketers can indicate a high priority campaign for holiday or high margin products, a medium priority campaign for top sellers, and a low priority campaign covering everything else. Advertisers can evaluate campaign performance based on ROAS and profit. Advertisers with e-commerce tracking should monitor their ROAS and evaluate products based on revenue in relation to cost. Advertisers can also optimise for profit by creating a custom column in their Google Ads reporting and entering their profit margin in the formula: profit = total conversion value * profit margin - total cost.
This article is excerpted from the report Data-Driven Commerce. Download it now for key insights on winning at commerce in the new digital economy.
[i] Google data, June to September 2017
[ii] Google, Ipsos MediaCT and Sterling Brands, Digital Impact on In-Store Shopping, Published on Think with Google, May 2014
[iii] Google/Purchased Digital Diary: How Consumers Solve Their Needs in the Moment, May 2016
[iv] Google/Purchased Digital Diary: How Consumers Solve Their Needs in the Moment, May 2016