March 29, 2017
This is a follow-up post on my earlier post about “fake” conversions — the post is in Finnish but, briefly, it’s about the problem of irreversibility of conversions in the ad platforms’ reporting. In reality, some conversions are cancelled (e.g., product returns), but the current platforms don’t track that.
So, my point was to include a ‘churn coefficient’ which would correct for the CPA calculation. In other words, it adjusts the CPA reported by the ad platform (e.g., AdWords) in regards to churn from “conversion” to conversion (as per the previous explanation).
The churn coefficient can be calculated like this:
in which churn is the churn from the reported conversion to the lasting, real conversion.
However, I got to think about this and concluded this — since we consider the churn taking place due to real world circumstances as a lift to the reported CPA, we should also consider the mitigating factor of customer-to-customer references (i.e., word-of-mouth).
Consider it like this – on average, converted customers recommend your company to their friends, out of which some convert. that effect would not be correctly attributed to the referring customers under normal circumstances, but by attributing it uniformly to the average CPAs we can at least consider it in aggregate.
So, hence the ‘wom coefficient’:
1-(Cn / Cm), in which
Cn: conversions from new customers non-affiliated with any marketing channel
Cm: conversions from all marketing channels
The idea is that the new visitors who convert can be attributed to wom while conversions from marketing channels create the base of customers who are producing the recommendations. Both pieces of information can be retrieved in GA (for Cn, use an advanced segment).
So, the more accurate formula for “true” CPA calculation would be:
1-(Cn / Cm) * 1/(1-churn) * CPA
In reality, you could of course track at least a part of the recommendations through referral codes (cf. Dropbox). In this case you could have a more accurate wom coefficient.
Consider that in period t, not all Cn are created by Cm. Hence, it would be more realistic to assume a delay, e.g. compare to period t-1 (reference effect does not show instantly).
The formula does not consider cases where the referred customers come through existing marketing channels (this effect could be eased by not including branded search campaigns in Cm which is a good idea anyway if you want to find out the true performance of the channel in new customer acquisition).
Finally, not all customers from non-marketing channels may not originate from wom (especially if the company is using a lot of non-traceable offline marketing). Thus, the wom efficient could have a parameter that would consider this effect.
Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.
Contact email: [email protected]
March 29, 2017
Recently I had an email correspondence with one my brightest digital marketing students. He asked for advice on creating an AdWords campaign plan.
I told him the plan should include certain elements, and only them (it’s easy to make a long and useless plan, and difficult to do it short and useful).
Anyway, in the process I also told him how to make sure he gets the necessary information from the client. These four things I’d like to share with everyone looking for a crystal-clear marketing brief.
1. campaign goal
2. target group
First, you want to know the client’s goal. In general, it can direct response (sales) or indirect response (awareness). This affects two things:
The channel selection is the first thing to include into your campaign plan.
Second, you want the client’s understanding of the target group. This affects targeting – in search-engine advertising it’s the keywords you choose; in social media advertising it’s the demographic targeting; in display it’s the managed placements.
Based on this information, you want to make a list (of keywords / placements / demographic types). These targeting elements are the second thing to include into your campaign plan.
Third, the budget matters a great deal. It affects two things:
The bigger the budget is, the more channels can be included in the campaign plan. It’s not always linear, however; e.g. when search volumes are high and the goal is direct response, it makes most sense to spend all on search. But generally, it’s possible to target several stages in customers’ purchase funnel (i.e., stages they go through prior to conversion).
Hence, the budget spend is the third thing to include into your campaign plan.
The daily budget you calculate by dividing the total budget with the number of channels and the duration (in days) of the campaign. At this point, you can allocate the budget in different ways, e.g. search = 2xsocial. It’s important to notice that in social and display you can usually spend as much money as you want, because the available ad inventory is in effect unlimited. But in search the spend is curbed by natural search volumes.
I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: http://goo.gl/QRc11f
March 29, 2017
Startups, and why not bigger companies, too, often test marketing channels by allocating a small budget to each channel, and then analyzing the results (e.g. CPA, cost per action) per channel.
This is done to determine a) business potential and b) channel potential. The former refers to how lucrative it is to acquire customers given their lifetime value, and the latter to how well each channel performs.
However, there is one major issue: scaling. It means that when we pour x dollars into the marketing channel in the test phase and get CPA of y dollars, will the CPA remain the same when we increase the budget to x+z dollars (say hundred times more)?
This issue can be tackled by acquiring enough data for statistical significance. This gives us confidence that the results will be similar once the budget is increased.
In AdWords, however, the scaling problem takes another form: the natural limitation of search volumes. By this I mean that at any given time, only a select number of customers are looking for a specific topic. Contrary to Facebook which has de facto an unlimited ad inventory (billions of ad impressions), Google only has a limited (although very large) ad inventory.
Here’s how to assess the scalability of AdWords campaigns:
1. Go to campaign view
2. Enable column called “Search impression share” (Modify columns –> Competitive metrics)
This will tell you how many searchers saw your ad out of all who could have seen it (this is influenced by your daily budget and bid).
In general, you want impression share to be as high as possible, given that the campaign ROI is positive. So, in general >80% is good, <10% is bad. (The exception is when running a long-tail strategy aiming for low-cost clicks, in which case <10% is okay.)
3. Calculate the scalability as follows:
scalability = clicks / impression share
For example, if you have an impression share of 40 % with which you’ve accumulated 500 clicks, by increasing your budget and bids so that you are able to capture 100% impression share, you will accumulate 1250 clicks (=500/0,40) which is the full potential of this campaign.
Note that the formula assumes the CTR remains constant. Additionally, increasing bids may increase your CPA, so improving quality score through better ads and relevance is important to offset this effect.
March 29, 2017
Update [24th March, 2017]: In addition to the formula explained in the post, I would add the following general criteria for a good AdWords case: 1) Low-Medium competition (high CPCs force to look for alternative channels), 2) Good website/landing pages (i.e., load fast, easy to navigate, have text information relevant to the keywords.
Google AdWords is a form of on-demand marketing which matches demand (keywords) with supply (ads). Because it provides good relevance between demand and supply, it efficiently fulfills the core purpose of marketing which is, again, to match supply and demand. However, while this property of AdWords makes it generally much more effective than other forms of online marketing, it also leads to a major limitation: the campaigns cannot scale beyond natural search volumes.
I often tell this to my students participating in the Google Online Marketing Challenge (GOMC), but a few of them always fall into the “trap of low search volume”. I will explain this in the following.
First, the relevant dimensions for assessing the potential in AdWords are:
These can vary from low to high so that
Low geographic range x Low product range = Trap of low search volume
Low geographic range x High product range = Potential risk of low search volume
High geographic range x Low product range = Potential risk of low search volume
High geographic range x High product range = High search volume (Best case for AdWords)
In other words, this formula favors companies with nationwide distribution and large product range. These campaigns tend to scale the best and offer the best ratio between cost and value of optimization. In contrast, local business with one or two products or services are the least feasible candidates.
Well, first of all it means the spend will be low. In GOMC, this means some teams struggle to spend the required $250 during the three-week campaign window.
Second, and more importantly, it means these cases are less interesting for marketers. They offer little room for optimization (because spend is low and there is very little data to work with).
Also for this reason the management cost of running these campaigns (=the amount a marketer can charge for his/her services) can become unbalanced: for example, if the yearly spend of a low-volume campaign is, say $400 and the marketers charges $100 per hour for his/her work, there is no point for client to pay for many working hours, as their cost quickly exceeds that of the media budget.
As a marketer, you always want to select the best case to amplify with your skills. You can think of it through two dimensions:
By multiplying them, we get the following.
Bad marketing x Bad product = Bad results
Bad marketing x Good product = Okay results
Good marketing x Bad product = Bad results
Good marketing x Good product = Good results
The same in numbers:
0 x 0 = 0
0 x 1 = 0
1 x 0 = 0
1 x 1 = 1
In other words, it makes sense to choose a case which is good for you as a marketer. A good case will work decently with bad marketing, but not vice versa. And only coupled with good marketing will the maximum potential of a good product be achieved.
March 29, 2017
I teach this very simple formula to my students when they are required to write a pre-campaign report for the Google Online Marketing Challenge (GOMC).
You want to report metrics in a table like this:
budget ctr cpc clicks impressions
250 0,05 0,2 1250 25000
(The numbers are examples.)
To calculate estimates for a campaign plan, you only need to know three figures:
In the case of GOMC, the budget is set to $250. In other marketing cases, it is based on your marketing plan.
Goal CTR is what you want to accomplish with your ads. I usually say a CTR of 5% is a good target. Based on bidding strategy and competition, however, it can range between 3 and 10%. Less than 3% is not desirable, as it indicates poor relevance between keywords and ads.
Goal CPC is what you want to pay for clicks. Ideally, you want the CTR to be as high as possible and CPC as low as possible to maximize traffic (website visitors). The actual figure will be based on competition as well as your quality score (to which CTR contributes, among other factors of relevance).
Quality score can be enabled by customizing columns in keyword view; the bid estimates for your keywords can be retrieved via Keyword planner, as well as by looking at bid estimates (first-page and top-of-page) in the keyword view. In Finland, I usually say €0.2 is a good target for average CPC. In other markets, the CPC tends to be higher.
Out of the previous figures, you can calculate other metrics:
The calculation assumes full usage of budget, which is not always possible when organic search volumes limit the growth (this is just a general limitation of search advertising).