Posts Tagged ‘Search Ads’
The Mind of the Searcher
Intention – before a person searches, she has an intent: intent to buy a pair of shoes; intent to learn about zanzibar; intent to determine the health benefits of a Blooming Onion; and so on. Some sort of information need driven by intention motivates her to search. When we start a search marketing campaign for a business, we begin by defining personae we intend to target with the campaign. The personae represent categories of intent. For example, shoe-shopping personae might include fashionistas and pragmatists. Fashionistas want to know what’s hot, pragmatists want a trustworthy vendor with a good return policy. We tend to organize our campaigns around the personae we define.
Search – once the person has translated intent into an information need, e.g. “I want a new pair of shoes” -> “where should I buy a pair of shoes?”, she turns to a search engine. While this step in the process garners a lot of attention among marketers, it is largely procedural in nature. Search listings do not address the searcher’s intent (you can’t wear them, for example), nor do they meet her information need in any real sense. They are merely pointers to information. The searcher quickly scans search results, looking for cues that indicate her need will be met by clicking on a listing. The right cues to include are a natural consequence of the personae we’ve defined. We also have to be mindful that there is a lot of information on a search results page. We can provide the right cues, but if our listing is boring or far down the results, it may never get evaluated.
Consideration – after clicking on a listing, the searcher evaluates the content on the landing page. Does it address her information need? She has criteria, conscious or unconscious, with which she will make a quick decision and either move forward or back up. The important thing here is to make sure that the landing page aligns with her original intent and is easy to digest. Too much information and she is likely to back up and look for a more suitable source. Too little information and her criteria can’t possibly be met.
Action – lastly, as marketers, we want the searcher to take some form of action, whether it be to call a number, watch a video or buy a product. Again, it is not a good idea to overwhelm the searcher with too many options, nor is it a good idea to present too few. Some people may be uncomfortable calling and prefer to communicate via email. Some people may want to read technical specifications before buying a product. The main thing is to decide what actions you want to emphasize, and make them as frictionless as possible.
This model bears a lot of similarity to the classic purchase funnel found in many marketing textbooks. It can be viewed as a specific instance of the funnel, applied to search marketing. I find it helpful, but the main point is that different types of keywords and search phrases belie different kinds of intent. When focusing on one stage of the process independently, it can be easy to lose track of that fact.
Nico Brooks is a data geek who struggles to get his head around marketing problems, but he always enjoys the struggle. Two Octobers is an internet marketing company that provides marketing services and strategic consulting to businesses selling to local markets.
Paid Search: Bidding with Confidence
In the previous article, Paid Search: Bidding Based on ROI, we showed how you can use ROI data to determine optimal bids. The methods we described assume that you know the conversion rate of a keyword, ad group or campaign. But in fact you can’t ever really know a conversion rate, the best you can do is to estimate using historical data. This article explains how to use confidence intervals to ensure that your estimates are reasonably accurate.
For example, let’s say that a keyword has had 100 clicks and 2 conversions. Do you know what the conversion rate is for that keyword? The obvious answer is 2%, but the correct answer is “no”. From experience, you probably know that you could get fewer or more conversions in the next 100 clicks. Given a set of sample data, the best you can say is that you expect that the conversion rate falls between X% and Y%. In statistics, this is called a “confidence interval”. A confidence interval also has a “confidence level”. The confidence level describes how sure you are that the conversion rate falls between X% and Y%. For example, an 80% confidence level means you can be 80% sure that the actual value falls between the lower and upper bound of the interval. This means that there is a 10% chance that the actual value falls below x% and a 10% chance that it falls above Y%.
Below is a table of confidence intervals, with the column on the left indicating the number of conversions observed, and the row across the top indicating the number of clicks observed. The confidence level for these intervals has been set at 80%. The cells with white backgrounds show the confidence interval for each combination of conversions and clicks. The confidence intervals are expressed as X% – Y%, with X% being the lower bound, and Y% being the upper bound.
| 100 | 200 | 300 | 400 | 500 | |
| 0 | 0 % – 2.3% | 0% – 1.1% | 0% – 0.7% | 0% – 0.6% | 0% – 0.5% |
| 1 | 0.1% – 3.8% | 0.1% – 1.9% | 0% – 1.3% | 0% – 1% | 0% – 0.8% |
| 2 | 0.5% – 5.2% | 0.3% – 2.6% | 0.2% – 1.8% | 0.1% – 1.3% | 0.1% – 1.1% |
| 3 | 1.1% – 6.6% | 0.6% – 3.3% | 0.4% – 2.2% | 0.3% – 1.7% | 0.2% – 1.3% |
| 4 | 1.8% – 7.8% | 0.9% – 4% | 0.6% – 2.7% | 0.4% – 2% | 0.4% – 1.6% |
| 5 | 2.5% – 9.1% | 1.2% – 4.6% | 0.8% – 3.1% | 0.6% – 2.3% | 0.5% – 1.9% |
| 6 | 3.2% – 10.3% | 1.6% – 5.2% | 1.1% – 3.5% | 0.8% – 2.6% | 0.6% – 2.1% |
For example, if we observe 3 conversions over 400 clicks, the confidence interval ranges from 0.3% to 1.7% – I have shaded that cell pink in the table. Therefore, we are 80% sure that the actual conversion rate falls between these two values. Compare the ranges in the “500” column to the “100” column and you will notice an important fact about confidence intervals: the more data we have, the smaller the confidence interval. And the smaller the confidence interval, the better an idea we have of the actual value. But also note that there is always an interval – we never truly know the actual value from observed data. Here are a few scenarios demonstrating how this data might be applied:
- Jane buys 200 clicks and gets no conversions and decides that paid search is a waste of money. Lisa points out that there’s a good chance that Jane’s conversion rate is as much as 1% and that she should wait it out a bit.
- Lisa wants to bid based on a target CPA. After buying 300 clicks, she gets 5 leads. He assumes a conversion rate of 0.8%. Given that the confidence interval is 0.8%-3.1%, Lisa is being conservative by taking the lower bound of the range.
- Jane thinks her conversion rate is 10%, but she measures 4 conversions over 200 clicks for her AdWords campaign. She realizes the data is not supporting her assumption: based on the data she can be 90% confident that her conversion rate is not more than 4%.
The main takeaway from this table is that even 500 clicks is not enough to have a very accurate idea of conversion rate, which is why I say you shouldn’t be bidding on most individual keywords based on ROI goals–there generally isn’t enough keyword-level data to make good decisions.
If you want to test out some confidence intervals of your own, this site has an interactive calculator. Use the binomial confidence interval and change the confidence level at the bottom of the page if you want it to be different than 95%. The higher the confidence level, the larger the intervals you will get. The numerator (x) is the number of conversions you observe, and the denominator (N) is the number of clicks you bought.
The approach I like to take when bidding is to start with an idea of the conversion rate for the campaign as a whole, and set bids based on that, since I have the most data at the campaign level. Then I look at individual ad groups and change bids if the confidence interval tells me that the ad group conversion rate is likely more or less than the campaign conversion rate. Then I look at individual keywords, but only those that have enough data to produce a useful confidence interval.
At Two Octobers we use automated tools to manage bid setting according to these principals, but it’s important to understand the underlying concepts. And bids are just one lever in a campaign. Ad copy, keyword selection and the user experience are at least as important as tweaking bids. More on that to come.
This article gives a high-level overview of a very complex topic. I hope it is useful to you, but please contact us if you would like help optimizing your paid search campaigns.
Paid Search: Bidding Based on ROI
This article explains some basic concepts related to bidding on paid search keywords based on return on investment (ROI). Using ROI to make bidding decisions helps ensure that you are spending your advertising dollars in the most effective ways possible.
To explain these concepts, we will use paid search marketers Jane and Lisa as examples.
- Jane’s goal is to generate as much revenue as possible, but she doesn’t want to spend more than a dollar in advertising for every $5 in sales.
- Lisa’s goal is to drive as many leads possible at a cost per acquisition (CPA) of $10.
A few definitions to start things off:
- Click – a paid search click bought on Google, Yahoo or Bing
- Conversion – a click-to-sale conversion. A conversion is when someone clicks through to your site, then completes a purchase. This could also be a click-to-lead conversion if your goal is to drive leads
- Conversion Rate – the percentage of time clicks convert to sales. For example, a conversion rate of 5% means that 5 out of every 100 clicks result in sales.
- Average Sale – the average value of a sale
- ROAS – return on ad spend: the return in sales you are getting on your paid search investment. I will calculate this as $X in sales for every dollar spent
- CPC – cost per click: what you are paying per click in Google, Yahoo or Bing
- Target CPC – the cost per click you should be paying to achieve your target ROAS
Here’s an example scenario: Jane is paying $0.10 per click, 10% of clicks are converting to sales, and her average sale is $5. This means that on average she generates $5 in revenue for every $1 she spends to buy clicks. Therefore, her ROAS is $5.
If Jane is ok with a lower ROAS, she can generate more sales. Spending more per click will lower her ROAS, but the higher CPC will also drive more traffic. For example, if she can achieve her profit goals at a $2.50 ROAS, she can afford to pay $0.20 per click. Here is the math:
ROAS = Sales / Ad Spend
= (Average Sale X Conversion Rate X Clicks) / (Clicks X CPC)
= (Average Sale X Conversion Rate) / CPC
Solving for CPC, we get:
Target CPC = (Average Sale X Conversion Rate) / Target ROAS
Substituting Jane’s goal for ROAS, we get:
Target CPC = ($5 X 10% / $2.50) = $0.20
The process is similar for Lisa. Cost per acquisition is total cost divided by the number of acquisitions, or:
CPA = Ad Spend / Conversions
= (Clicks X CPC) / (Clicks X Conversion Rate)
= CPC / Conversion Rate
Solving for CPC, we get:
Target CPC = Target CPA X Conversion Rate
Substituting Lisa’s goal for CPA and assuming a conversion rate of 10%, we get:
Target CPC = $10 X 10% = $1.00
These calculations are pretty straightforward, and many paid search marketers use some variation of these methods. There are also a number of automated bidding systems that have formulas like these somewhere under the hood.
One assumption we’ve made is that we know Jane and Lisa’s conversion rates, but that is not necessarily a safe assumption. To learn more about the challenges of estimating conversion rates, have a look at this article: Paid Search: Bidding with Confidence.
And if you do not currently have the ability to track leads or sales to a keyword ad, Google Analytics is free and will do the job. If you would like help tracking performance or optimizing your paid search campaigns, have a look at our services, or contact us.
