Suppose you had to quickly reduce your PPC spend. Where would you cut?
One very helpful analysis is rank your campaigns (or better yet AdGroups) by ROI. This tells you where you’re getting more return-per-dollar, and where you’re getting less.
To simplify this analysis we built a ClickEquations Analyst Report, which runs in Microsoft Excel. To use it you define three thresholds – a high ROI that you desire, a low ROI that you detest, and a medium ROI that is minimally acceptable.
With a single mouse click you can then find out what number of your campaigns and what percentage of your spend and revenue fall into each of these bands. On the next tab in the worksheet you can find out exactly which campaigns fell into each group.
In the example above, we look at how ROI falls into four clusters and two charts visualize the spend and difference between spend and revenue.
Armed with this visual, most people are encouraged to look inside the campaigns – probably first by running the Ad-Group version of this report – and find ways to fix the losers or kill the problematic groups or keywords.
But you can’t drive to those actions and decisions without information. This report is a good example of how ClickEquations Analyst can help you to understand the performance of your accounts, and make good decisions about how to prioritize your PPC efforts.
You can begin your use of ClickEquations with a 30-day free trial. Sign up here.
Yesterday I noted that paid search managers face three challenges in trying to effectively manage paid search campaigns:
- A lack of clarity (reporting problems)
- Difficulty defining priorities (strategic and planning problems)
- Horrible inefficiencies (mechanical and processes problems)
I believe that these problems need to be solved in order to improve paid search management, both the profession and the results.
First you need to see what’s happening, then you’ll want to decide what needs to be done, and then you can hopefully get it done with a reasonable amount of effort.
That doesn’t sound like too much to ask.
But 4-5-6 years into explosive growth in paid search and we’re hardly out of the starting gate. Today I’ll expand on the issues regarding reporting and clarity, and in future posts dive more deeply into the problems of setting priorities and executing paid search tasks.
What Paid Search Reports Don’t Tell You
Paid search is about answering questions. People type queries and search engines return results, which are lists of possible answers to the questions they believe are being posed. I want to structure my campaigns as tightly as possible around those search queries.
Every search engine tells you how many impressions your ads had, and how many clicks you got. They have to I suppose, since the CPC is what drives your billing. What I really want to know is what did I miss? And why? Then I can set goals and define strategies or tactics (or at least design tests) to do better.
Each conversion hopefully generates more revenue than it cost to cause that conversion, which is reflected in the rather innane ROAS metric. Being impressed with a good ROAS seems akin to believing you’ve saved money by buying something you didn’t want when it was on sale. Goods or services have costs (COGS) and the only metric that matters is ROI taking account (at least) both direct-marketing and goods/services expenses.
When my clicks do generate revenues, I’d like to know which ones. Then I can make wise decisions about future investment and effort around certain keywords and queries.
So I’d like to know which search queries generated which results, how many clicks I didn’t get and why, the actual amount of profit made on each transaction (and from each keyword, query, and click).
Do any of these sound unreasonable? Far-fetched? Demanding?
Yet these desires are not generally or specifically fulfilled through the paid search reporting capabilities provided by the search engines, popular web analytics software, or even specialized PPC management tools.
Surprised? The devil is certainly in the details, and some of the information defined is available in some packages/places, but generally with huge compromises and limitations that disqualifies or invalidates them as actual or sufficient information.
Really? Yes to the best of my knowledge, as the next post will review in somewhat excruciating detail. I’m happy to learn new facts or discuss this further in the comments – significant corrections will be appended to that post.
User search queries, accurate revenue & expense allocation and matching, and ROI reporting are just three of the ways that the current generation of PPC reporting generally fail paid search advertisers and managers.
The fact that these problems/limitations are seemingly not well known, frequently discussed, and therefore clammored for as improvements is one of the things that has to change to move the business/market forward.
NOTE: This is part of a post series. It’s available as a single post for easier reading: The Match Type Series.
The previous post introduced the idea of building a Match Type Keyword Trap. This layering of keyword & match type combinations provides control over which, where, and how queries are attracted, and therefore their cost-per-click.
In the simplest case, you’d buy one keyword (say ‘Whaazooh’) three times in one campaign – once on Exact Match, once on Phrase Match, and once on Broad Match.
The goal is to catch all queries which are literally ‘Whaazooh’ with the Exact Match keyword, all queries which are ‘Whaazooh’ plus some word(s) before or after it with the Phrase Match, and all other related queries with the Broad Match.
Because in almost every case where many different queries exist for a single word or topic, some of those queries are very valuable, some are mildly valuable, and many are not valuable (or at least not valuable enough). We want to segregate these queries by their value to us so we can pay highly for the high value ones and less so for those less valuable.
In the simple cases (I have to keep saying that because not all cases are simple, there are many complex variants of this) we’ll do better by trapping the best ones with the most specific Match Types (Exact if possible or Phrase if not) and using Broad Match to harvest winners and losers which are acted upon accordingly.
Winners are promoted (to Phrase Match or Exact Match). Losers are demoted via lower bids or even made into negative keywords.
We do better not because of the place they’re trapped, but because by segregating them we control the bid (as well as the text-ad, landing page, etc.)
Forcing The Stack
Buying the same keyword three times at different match types does not itself bait the trap. If the same word is purchased at both Exact and Broad, and has the same bid and earns the same quality score, chances are good a related query with be matched sometimes to one and other times to the other.
To force the trap to work you have to stack the bids – higher for the Exact Match versions and sequentially lower for the Phrase and Broad Match versions. This gives the Exact Match keyword multiple reasons to attract and win the Exact Match queries; it is a better match and it is bid higher (which is good in itself and factors into quality score).
When you do this, leave enough room between the various bids. The Average CPC the engines report are averages, so expect a range of bids in each and leave enough room so the ranges don’t overlap.
In this example, we bid higher for several terms that have proven great performers, setting them on Exact Match and bidding $1.25. Several others that are good performers and perhaps come in some variations are set at Phrase Match for $0.65. A larger collection of phrases and concepts are bid Broad Match at $0.15. Over time we shift, add, put in more negatives, and generally take control over how we pay for and catch queries.
How do you know if it’s working?
In theory you’ll normalize the ROI (or ROAS if you must) for your Exact, Phrase, and Broad Match keywords. In other words, you’ll raise bids for your Exact Match keywords to maximize profits. You’ll set accordingly lower bids on Phrase and Broad Match keywords until they produce the same return as the Exact Match does – so their lower conversion rates and ROI are compensated for with proportionally lower bids.
They get the bid they deserve.
To Be Continued
Again there are many exceptions and details left out of the above descriptions for the sake of time and length, but I’ll move into examples in future posts which should illuminate the concept. In the meantime, if you have any questions about this leave a comment and I’ll elaborate.