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PPC Bid Management Tips For The Holiday Season
The fourth quarter is “the most wonderful time of the year” for many retailers, and managing paid search programs well through this period requires particular attention to detail.
Let’s take a look at some of the common trends we see in Q4 data and discuss how to, and how not to address these effects through anticipatory bid management.
We’ll use actual data from one of our client’s first holiday season with RKG to highlight some of the common phenomena. The first holiday season is always challenging, because we don’t have data to use to help us anticipate the timing. As such, this data reveals some missed opportunities that we can model the next time around.
On each chart 100% means the average week between mid-September and mid-October. Lifts above or below reflect the week’s changes from that norm. We’re using “sales dollars” as a proxy for value because it is the most widely used. A better metric is margin dollars with frauds and cancels knocked out.
Newsflash: Sales go up at holiday!
This happens for two reasons:
- Traffic volume increases. More people search, more people buy => higher PPC sales and advertising costs.
- Shopping behavior changes. Not only are there more consumers in the market, their propensity to buy and how much they buy fluctuates throughout the period.
It is this second factor that allows us to bid differently, and makes anticipatory bidding imperative. An increase in the volume of traffic by itself does not change the value of the traffic or what we can afford to pay for each click; it is the changes in the traffic’s value that allow for bidding adjustments.
So, yes traffic volumes spike:
But it’s the change in conversion rates and average order sizes that is interesting and actionable:
(There is undoubtedly social commentary to be made over the fact that AOV dips at gift giving season—we spend more on ourselves than on our loved ones?—but it’s also important to recognize that discounting leads to thinner margins which may in turn demand different efficiency targets.)
When we roll these phenomena together we get the true picture of changes to the traffic value over time.
If the goal is to maximize sales during the period at the same efficiency as normal then we should push/pull bids proportionally to the changes in traffic value. That way we maintain constant efficiency, and generate the most sales possible. (Folks who budget search instead look to spend the budget at the lowest cost to sales ratio possible. This is a totally different problem, and, we think, the wrong approach to paid search.)
So, how did we do?
Not bad, given that we didn’t have good data to use to help us anticipate the changes. From this perspective it looks like we may have overspent just a touch the first two weeks in November, and may have underspent a bit the week of the Christmas holiday and the week after.
But hold on! This view ties the clicks and costs on a given day to the sales that happen that day. We know because of order latency that many of the orders placed today came from clicks on ads long before. This suggests we might have actually underspent during the ramp up and left opportunity on the table. So, let’s instead tie the orders to the time of the clicks that drove them and see what that reveals.
Well, it’s not an entirely different picture, but it is slightly different. By this view we were pretty much on target for those first two weeks in November, and it was the two weeks after that where we may have left a bit of opportunity on the table. Good to know! We’ll use these insights this year to do our jobs that much better.
Some folks out there will suggest that you should push the gas much harder early on to catch consumers in the “early phase of the consideration cycle.” I’ve heard folks say they’ll increase their cost to sales threshold by 50% or even 100% prior to the real increase in conversion rates, arguing that those “inefficient” weeks will appear very efficient when viewed by the orders those clicks seeded. I say: if the data from previous year supports that, go for it! But we haven’t seen shifts that dramatic, or anywhere close.
One other pitfall to avoid: Black Box bidding systems that don’t allow for anticipatory bidding. We see data that looks like what’s below every year from agencies that allow the algorithms to do it all, and every year the algorithm reacts too late during the ramp up and overspends greatly after the holiday ends.
These folks didn’t fish enough when the fish were biting, and fished too much when they weren’t. Overall, they may hit their efficiency targets, but they won’t end up with as many fish as they would have had with a smart analyst at the controls.
No two retailers will show the same trends, so let your data be your guide.
Here’s to a profitable Q4!
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