Maximizing Profit in Shopping Campaigns

Search marketers have long been a quantitative bunch, leveraging a range of sophisticated techniques to try to squeeze more performance out of their campaigns. This is especially true in the retail space, where margins are very thin and competition for top positions is fierce.

However, when I speak with many of these same search marketers, they are often setting overall performance goals for their campaigns sub-optimally.

Nowhere is this truer than in Google Shopping campaigns, where bids can be set and data can be analyzed at the individual SKU level. Even with these levels of transparency and control, most optimization strategies do not focus on maximizing profit for the organization – here are a few examples.

CPA

Not every sale and not every customer is created equal, so why would you value them all equally? A $25 accessory purchase should not be as exciting as a $250 prom dress purchase. However rules are often applied to value those equally at the expense of the overall profitability of the program.

Return on Advertising Spend (ROAS)/Return on Investment (ROI)

These are the most common measures of performance that I see in the industry. While these metrics do take average order value (AOV) into account, they can be managed by cutting bids and costs from the campaign. From afar it can look like things are going well because ROAS is steady or increasing. In actuality the campaign is bleeding opportunity, because marginal traffic is always being cut from the campaign.

Revenue Maximization

While the ultimate responsibility of the search team is to generate new customers and sales for the organization, this should not be done at an unreasonable expense. Once costs of media, service, and technology are taken into account, it usually becomes clear that these types of programs are not long-term sustainable.

If So Many Are Doing It Wrong, What Does Doing It Right Look Like?

Basic economics would tell us that an organization looking to maximize profitability should operate – or in this case bid – until the point which marginal revenue is equal to marginal cost. This is also known as marginal profit optimization.

Because Google Shopping allows us to both set bids and see results at the SKU level, bidding for traffic to equate the marginal revenue and the marginal cost of every click is actually not overly complicated.

Marginal revenue is a pretty straightforward concept – it is strictly the additional revenue generated by purchasing another click. For Google Shopping campaigns, marginal revenue can be calculated by multiplying the probability of conversion by the average order value associated with SKU A.

MRA = P(CRA) * AOVA

Marginal cost is slightly more complicated as it needs to account for conversion probability, the expected cost of the media at a given bid, any variable technology or service costs, and finally the cost of goods sold for SKU A. All retailers should have this data readily available, however:

MCA = [eCPCA * (1+T/S % Media Fees)] + [P(CRA)*COGSA]

Optimizing these campaigns to a marginal profit is the most accurate method of campaign optimization, and tends to bring much better results than target cost per conversion or target ROI. The presented formulas should make adopting this methodology relatively easy – the bids in the campaign should be increased or decreased to equate marginal revenue to marginal cost at the SKU level.

Obviously there are variables such as multiple publishers, promotions, inventory levels, and so forth that could make these calculations more challenging. But all things being equal, this methodology should drive significantly more profit from Google Shopping.

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