Updated on September 29, 2022 | 4 minute read
In the ecommerce world, data like click-through rate (CTR), CPC, and CPA are crucial to campaign optimisation. Loyalty and lifetime value (LTV), generated after purchase, can also be valuable in improving performance.
Repeat purchases, return rates, and refer-a-friend schemes can all have an impact on the value of a customer. Using LTV data to adjust your Google Shopping bid strategies can help you capture and retain the most relevant shoppers, which will, over time, help generate the most profit.
At Bidnamic, we work with our clients to identify post-purchase data that can be used to improve targeting and bid optimisation. We’ve compiled some of the best tactics we’ve used to utilise LTV data.
In Google Ads, the Customer Match feature allows you to use your online and offline data to re-engage with your customers across Search and Shopping ads. It also allows you to upload a list of customers to use in retargeting campaigns, as well as to target prospective customers.
This is a great tool for making sure you feature in the Shopping carousel for pre-existing customers. In Customer Match, you can filter a list of customers with the highest LTV. This will show you the shoppers who know and trust your brand the most, and who should therefore have a higher CTR. Overall, Customer Match is helpful when bidding more intelligently than your competitors to retain your customers’ loyalty.
Google Ads allows retailers to create campaigns that target shoppers with a similar profile to their highest LTV customers. With such a huge amount of data at Google’s disposal, the algorithm is able to detect the likes and dislikes of your high LTV customers, which facilitates the creation of audiences with similar preferences who, statistically, are more likely to convert.
There’s more information about Customer Match available here.
Within Google Analytics, there’s a Custom Audience Segments feature that offers options to build audiences with a high potential LTV and export them to Google Ads. This can be used for remarketing purposes, and for targeting prospective customers.
Shoppers who have already made multiple purchases over a specific time period are likely to have a higher LTV. You can create a segment based on customers who have made two or more purchases over a time period through this screen:
Since not all purchases are high value, you can also add a modifier to specify a minimum revenue value:
At Bidnamic, we found a trend in conversion data amongst the customers of one of our clients, an online sports retailer. Shoppers who made an initial (low-value) purchase of golf balls then went on to purchase other, high-value items. Knowing this, we set up a segment for customers whose first purchase was for golf balls using this field:
In Google Analytics, an ‘affinity’ is an area of interest determined by a user’s search history. This is another opportunity to optimise your Shopping campaigns to target high LTV shoppers.
At Bidnamic, we analysed the affinity data for customers of a client that sells shirts. We identified that customers with a historical interest in education were more than twice as likely to convert after clicking on an ad, so we built a custom audience targeting this group.
This also gave our client the ability to focus their loyalty campaigns on customers who they knew worked in education, and to time their campaigns to run just before the academic year begins.
You can read more about Custom Audience Segments here.
In many sectors, the cost of customer acquisition has been gradually increasing. Online competition has increased, making it harder to capture the right click at the right cost. Since the reopening of the high street, the volume of online shopping searches has decreased, and CPC has increased.
Building LTV is a sure-fire way to offset the rising cost of acquisition while building profits. The value of LTV goes beyond repeat purchases, however: LTV data can be used to improve targeting, minimise wasted clicks, and improve the ROAS of search marketing and advertising campaigns.
It’s important to remember that a customer’s ‘lifetime’ may be short or long depending on your vertical. For example, seasonal purchases could have a lifetime of a few weeks, whereas higher-value purchases could be three or more years.
Many of the tactics mentioned can be automated through Bidnamic’s technology platform, which uses advanced machine learning techniques to automatically place the right bid at the right time, based on the potential value of each click.
If you’re interested in learning more about our technology and how we can help you maximise your Shopping channel, book a call with one of our Google Shopping specialists today.
Tom is a Content Marketing Executive, producing content and case studies to simplify the Google Shopping experience, and help our clients discover if Google Shopping is the right channel for them. With an MA in English Literature, Tom has a passion for writing and sharing information with the masses.