Unfortunately, Google has attempted to make them ubiquitous.
Google Ads has been rapidly expanding their use of auto-applied recommendations recently, to the point where it briefly became my least favorite thing until I turned almost all auto-apply recommendations off for all the Google Ads accounts which we manage.
Google Ads has a long history of thinking it’s smarter than you and failing. Left unchecked, its “optimization” strategies have the potential to drain your advertising budgets and destroy your advertising ROI. Many users of Google Ads’ product ads should be familiar with this. Product ads don’t allow you to set targeting, and instead Google chooses the targeting based on the content on the product page. That, by itself, is fine. The problem is when Google tries to maximize its ROI and looks to expand the targeting contextually. To give a practical example of this, we were managing an account advertising rotary evaporators. Rotary evaporators are very commonly used in the cannabis industry, so sometimes people would search for rotary evaporator related terms along with cannabis terms. Google “learned” that cannabis-related terms were relevant to rotary evaporators: a downward spiral which eventually led to Google showing this account’s product ads for searches such as “expensive bongs.” Most people looking for expensive bongs probably saw a rotary evaporator, didn’t know what it was but did see it was expensive, and clicked on it out of curiosity. Google took that cue as rotary evaporators being relevant for searches for “expensive bongs” and then continued to expand outwards from there. The end result was us having to continuously play negative keyword whack-a-mole to try to exclude all the increasingly irrelevant terms that Google thought were relevant to rotary evaporators because the ads were still getting clicks. Over time, this devolved into Google expanding the rotary evaporator product ads to searches for – and this is not a joke – “crack pipes”.
The moral of that story, which is not about auto-applied recommendations, is that Google does not understand complex products and services such as those in the life sciences. It likewise does not understand the complexities and nuances of individual life science businesses. It paints in broad strokes, because broad strokes are easier to code, the managers don’t care because their changes make Google money, and considering Google has something of a monopoly it has very little incentive to improve its services because almost no one is going to pull their advertising dollars from the company which has about 90% of search volume excluding China. Having had some time to see the changes which Google’s auto-apply recommendations make, you can see the implicit assumptions which got built in. Google either thinks you are selling something like pizza or legal services and largely have no clue what you’re doing, or that you have a highly developed marketing program with holistic, integrated analytics.
As an example of the damage that Google’s auto-applied recommendations can do, take a CRO we are working with. Like many CROs, they offer services across a number of different indications. They have different ad groups for different indications. After Google had auto-applied some recommendations, some of which were bidding-related, we ended up with ad groups which had over 100x difference in cost per click. In an ad group with highly specific and targeted keywords, there is no reasonable argument for how Google could possibly optimize in a way which, in the process of optimizing for conversions, it decided one ad group should have a CPC more than 100x that of another. The optimizations did not lead to more conversions, either.
Google’s “AI” ad account optimizer further decided to optimize a display ad campaign for the same client by changing bidding from manual CPC to optimizing for conversions. The campaign went from getting about 1800 clicks / week at a cost of about $30, to getting 96 clicks per week at a cost of $46. CPC went from $0.02 to $0.48! No wonder they wanted to change the bidding; they showed the ads 70x less (CTR was not materially different before / after Google’s auto-applied recommendations) and charged 24x more. Note that the targeting did not change. What Google was optimizing for was their own revenue per impression! It’s the same thing they’re doing when they decide to show rotary evaporator product ads on searches for crack pipes.

Furthermore, Google’s optimizations to the ads themselves amount to horribly generic guesswork. A common optimization is to simply include the name of the ad group or terms from pieces of the destination URL in ad copy. GPT-3 would be horrified at the illiteracy of Google Ads’ optimization “AI”.
A Select Few Auto-Apply Recommendations Are Worth Leaving On
Google has a total of 23 recommendation types. Of those, I always leave on:
- Use optimized ad rotation. There is very little opportunity for this to cause harm, and it addresses a point difficult to determine on your own: what ads will work best at what time. Just let Google figure this out. There isn’t any potential for misaligned incentives here.
- Expand your reach with Google search partners. I always have this on anyway. It’s just more traffic. Unless you’re particularly concerned about the quality of traffic from sites which aren’t google.com, there’s no reason to turn this off.
- Upgrade your conversion tracking. This allows for more nuanced conversion attribution, and is generally a good idea.
A whole 3/24. Some others are situationally useful, however:
- Add responsive search ads can be useful if you’re having problems with quality score and your ad relevance is stated as being “below average”. This will, generally, allow Google to generate new ad copy that it thinks is relevant. Be warned, Google is very bad at generating ad copy. It will frequently keyword spam without regard to context, but at least you’ll see what it wants to you to do to generate more “relevant” ads. Note that I suggest this over “improve your responsive search ads” such that Google doesn’t destroy the existing ad copy which you may have spent time and effort creating.
- Remove redundant keywords / remove non-serving keywords. Google says that these options will make your account easier to manage, and that is generally true. I usually have these off because if I have a redundant keyword it is usually for a good reason and non-serving keywords may become serving keywords occasionally if volume improves for a period of time, but if your goal is simplicity over deeper data and capturing every possible impression, then leave these on.
That’s all. I would recommend leaving the other 18 off at all times. Unless you are truly desperate and at a complete loss for ways to grow your traffic, you should never allow Google to expand your targeting. That lesson has been repeatedly learned with Product Ads over the past decade plus. Furthermore, do not let Google change your bidding. Your bidding methodology is likely a very intentional decision based on the nature of your sales cycle and your marketing and analytics infrastructure. This is not a situation where best practices are broadly applicable, but best practices are exactly what Google will try to enforce.
If you really don’t want to be bothered at all, just turn them all off. You won’t be missing much, and you’re probably saving yourself some headaches down the line. From our experience thus far, it seems that the ability of Google Ads’ optimization AI to help optimize Google Ads campaigns for life sciences companies is far lesser than its ability to create mayhem.
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I was reading the MarketingCharts newsletter today and saw a headline: “What Brings Website Visitors Back for More?” The data was based on a survey of 1000 people, and they found the top 4 reasons were, in order:
1) They find it valuable
2) It’s easy to use
3) There is no better alternative for the function it serves
4) They like it’s mission / vision
I thought about it for a second and had a realization – this is why people are loyal to ANYTHING! And achieving these 4 things should be precisely our goal as marketers:
1) Clearly demonstrate value
2) Make your offerings – and your marketing – accessible
3) Show why your particular thing is the best. (Hint: If it’s not the best you probably need to refine your positioning to find the market segment that it is the best for.)
4) Tell your audiences WHY. Get them to buy into it. Don’t just drone on about the what, but sell them on an idea. Captivate them with a belief!
Do those 4 things well, you win.
BTW, the MarketingCharts newsletter is a really good, easy to digest newsletter – mostly B2C focused but there’s some great stuff in there even for a B2B audience and you can get most of the key points in each day’s newsletter under a minute.
Principal Consultant Carlton Hoyt recently sat down with Chris Conner for the Life Science Marketing Radio podcast to talk about decision engines, how they are transforming purchasing decisions, and what the implications are for life science marketers. The recording and transcript are below.
Transcript
CHRIS: Hello and welcome back. Thank you so much for joining us again today. Today we’re going to talk about decision engines. These are a way to help ease your customer’s buying process when there are multiple options to consider. So we’re going to talk about why that’s important and the considerations around deploying them. So if you offer lots and lots of products and customers have choices to make about the right ones, you don’t want to miss this episode.
(more…)
Marketers are used to seeing a lot of data showing that improving personalization leads to improved demand generation. The more you tailor your message to the customer, the more relevant that message will be and the more likely the customer will choose your solution. Sounds reasonable, right?
In most cases personalization is great, but what those aforementioned studies and all the “10,000-foot view” data misses is that there are a subset of customers for whom personalization doesn’t help. There are times when personalization can actually hurt you.
When Personalization Backfires
Stressing the points which are most important to an individual works great … when that individual has sole responsibility for the purchasing decision. For large or complex purchases, however, that is often not the case. When different individuals involved in a purchasing decision have different priorities and are receiving different messages tailored to their individual needs, personalization can act as a catalyst for divergence within the group, leading different members to reinforce their own needs and prevent consensus-building.
Marketers are poor at addressing the problems in group purchasing. A CEB study of 5000 B2B purchasers found that the likelihood of any purchase being made decreases dramatically as the size of the group making the decision increases; from an 81% likelihood of purchase for an individual, to just 31% for a group of six.
For group purchases, marketers need to focus less on personalization and more on creating consensus.
Building Consensus for Group Purchases
Personalization reinforces each individual’s perspective. In order to more effectively sell to groups, marketers need to reinforce shared perspectives of the problem and the solution. Highlight areas of common agreement. Use common language. Develop learning experiences which are relevant to the entire group and can be shared among them.
Personalization focuses on convincing individuals that your solution is the best. In order to better build consensus, equip individuals with the tools and information they need to provide perspective about the problem to their group. While most marketers spend their time pushing their solution, the CEB found that the sticking point in most groups is agreeing upon the nature of the solution that should be sought. By providing individuals within the groups who may favor your solution with the ability to frame the nature of the problem to others in their group, you’ll help those who have a nascent desire to advocate for you advocates get past this sticking point and guide the group to be receptive of your type of solution. Having helped them clear that critical barrier, you’ll be better positioned for the fight against solely your direct competitors.
Winning a sale requires more than just understanding the individual. We’ve been trained to believe that personalization is universally good, but that doesn’t align with reality. For group decisions, ensure your marketing isn’t reinforcing the individual, but rather building consensus within the group. Only then can you be reliably successful at not only overcoming competing companies, but overcoming the greatest alternative of all: a decision not to purchase anything.
We recently cited some newly released findings from the Boston Consulting Group (BCG) stating that “display retargeting from paid search ads can deliver a 40 percent reduction in CPA.” It was met with some hesitation from Mariano Guzmán of Laboratorios Conda, who stated:
“[…] when I have clicked on a [life science website] what I have experienced is a tremendous amount of retargeting for 1 month that I have not liked at all as an internet user, and I do not feel my clients would as well”
Being me, I like to answer questions with facts as much as possible, so I dug some up. This one’s for you, Mariano!
To directly address Mariano’s concern, I found some studies on people’s opinions on retargeting. A 2012 Pew Research Study found that 68% of people are “not okay with it” due to behavior tracking while 28% are “okay with it” because of more relevant ads and information (4% had no opinion). I’m a little skeptical of the Pew study because they were priming the audience with reasons to “be okay” or “not be okay” with remarketing. In a sense, these people are choosing between behavior tracking + more relevant ads vs. no behavior tracking + less relevant ads. However, when users actually see the ads the ads don’t say to the viewer “by the way, we’re tracking your behavior.” Are some users aware of this? Certainly. Might some think it consciously? On occasion, sure, but nowhere near 100% of the time. However, 100% of the Pew study respondents were aware of it.
A slightly more recent 2013 study commissioned by Androit Digital and performed by Toluna asked the qusestion in a much more neutral manner (see page three of the linked-to study). They found that 30% have a positive impression about a brand for which they see retargeting ads, only 11% have a negative impression, and 59% have a neutral impression.
The Pew study and the Androit Digital study did agree on one thing – remarketing ads get noticed. In both, almost 60% of respondents noticed ads that were related to previous sites visited or products viewed.
Now to the undeniably positive side… The gains a company stands to make from remarketing.
In addition to the 40% reduction in cost per action cited in the aforementioned BCG study, a 2014 report from BCG entitled “Adding Data, Boosting Impact: Improving Engagement and Performance in Digital Advertising” found that retargeting improves overall CPC by 10%.
A 2010 comScore study evaluated the change in branded search queries for different types of digital advertising and found retargeting had provided the largest increase: 1046%.
In a 2011 Wall Street Journal article, Sucharita Mulpuru, an analyst at Forrester Research, stated that retail conversion rates are 3% on PCs and 4% to 5% on tablets. According to the National Retail Federation, 8% of customers will return to make a purchase on their own. Retargeting increases that number more than three-fold, to 26%.
There are many more studies that sing the praises of remarketing, however I wanted to stay away from case studies that investigate only single companies as well as data collected and presented by advertising service providers.
Here are my thoughts on the matter: Do some customers view retargeting unfavorably? Certainly, but that’s the nature of advertising. No matter what form it takes, some people will object to it. Considering that there is nothing ethically wrong with retargeting, we can’t give up on something that is proven to be a highly effective tactic because some people have an objection to it. In the end, it’s our job as marketers to help create success for the organizations we serve.
As we discussed last week, there are only two fundamental reasons why someone won’t buy from you. Either you are talking to the wrong person, or the prospective customer doesn’t trust you. Unfortunately for commercial professionals, the reasons why someone could lack sufficient trust in you to purchase are myriad. Doubly unfortunately, those reasons often go undiscovered. Many organizations performing little analysis of why any given sale is won or lost, others do so superficially in ways that don’t provide meaningful information. Even more confusingly, many companies think they are performing win / loss analysis when really they aren’t! They are instead utilizing other tools and methods, often in an ad hoc and undocumented manner, which provide biased or misleading information!
Performing win / loss analysis correctly is not a trivial endeavor and requires a good deal of planning, but there are many benefits to doing so. These include:
- Clearer understanding of the customer buying journey
- Better understanding of the competition’s offerings (including pricing, positioning, etc.)
- Early identification of market trends
- Better understanding customer preferences
- Understanding how you and your competition are perceived
- A built-in “warning system” which informs you if your messaging is missing the mark
- Feedback on performance of the sales team and effectiveness of sales processes
- Market feedback to help guide product development
Planning for Win / Loss Analysis
Remember that win / loss analysis is a form of market research. It requires proper planning – and adherence to the plans – to ensure that the execution yields the answers you’re looking for.
The first question that needs to be answered is: who will implement the program? This should not be your sales organization! Ideally, the people running the program and performing the interviews will be far removed from the sales process. An external agency who is familiar with your market and experienced in performing win / loss analysis would be ideal, however other internal departments or functions can be used (usually a market research or CI person / team, if you have one, otherwise the applicable product manager or another relevant marketing person would be a good choice to head the effort).
Next, decide what specific objectives you hope to achieve from the win / loss analysis. There are basics that are central to the reasons a sale is won or lost and will therefore almost always be included, such as understanding the customers’ decision criteria and knowing how you measured up against the competition across a number of key factors, but you will also have the opportunity to obtain a plethora of other information. For the sake of customer participation and limiting the cost and / or effort, you will be limited to how much additional information you’ll be able to collect. You will therefore need to determine what non-core information is the most important. Are you interested in learning more about your competitors’ offerings? Do you want to know more specifics about the customers’ buying journeys? Are you interested in the finer details of how your brand is perceived compared to the competition? For long, consultative sales a customer may be more willing to engage with you in a lengthy interview. For short, low-value purchases where sales interaction was limited or non-existent, you probably won’t find customers willing to sit through a long interview. Know what can be realistically expected from your audience and plan accordingly.
The next question you need to answer is: what opportunities will be analyzed? Given the time and / or cost required to perform win / loss analysis, it is often only applied to major product lines or service areas and / or large accounts. (We do not recommend only analyzing large accounts unless your focus is improving win rate solely to large accounts; if you want to improve the win rate for all customer classes, you need to analyze them all.) You can define which opportunities will be analyzed more narrowly to cut down on the number of interviews and amount of analysis necessary, or you can be more broad to collect information about more opportunities and then perform post-hoc analyses of specific products, markets, etc. You also need to determine the frequency at which opportunities that meet the defined criteria will be analyzed. If the nature of your business is such that you have a low number of high-value opportunities, you may want to analyze them all. If you have a high number of low-value opportunities you may want to analyze only some of them. If you will be analyzing only some, you should select them either at random or at regular intervals (for example, at the conclusion of every fourth opportunity, chronologically) to prevent bias. Furthermore, ensure your criteria don’t exclude wins! It’s just as important to understand why you win as why you lose, and understanding your wins can be even more informative.
From the defined objectives, plan your questionnaire. There are a massive number of potential questions, and if you’ve clearly laid out your objectives the questions you need to ask should become somewhat obvious, but here are a few common ones to get you started:
- What caused you to initially consider a purchase of this type?
- Which other companies / products / solutions were being considered? Which one was ultimately chosen?
- What actions on the part of our team made notable positive or negative impressions?
- What selection criteria was used to make the ultimate decision?
- What interaction influenced you most during your decision-making process?
- How did our pricing compare to the competition?
- Why did / didn’t we win your business?
- Who was involved in the purchase decision?
- Were you comfortable with the product features / company’s capabilities? Which were most / least important?
- How do you perceive our company? How do you perceive our competitors?
- Would you be likely to recommend our solution to others?
A common issue with win / loss analysis questionnaires is the tendency to focus almost exclusively on the latter stages of the buying journey. Remember that the early stages of the buying journey are often more influential. Ensure you ask questions that will inform you how well you are setting the stage for a win, as many lost opportunities aren’t simply failures to close.
If you end up wanting to ask more questions than they reasonably can, remember that not every interview needs to ask the same questions. If you feel that a question has been sufficiently answered, change it out and ask another which would provide more new knowledge. You can also have multiple sets of questions and rotate through them to collect input, albeit less of it, on a larger number of some ancillary questions of lesser importance. (We strongly recommend always asking a set of “core” questions which directly address the most influential reasons for winning or losing.) If you ultimately want to ask more questions than would be feasible in an interview, you can create an accompanying questionnaire to collect additional data. This can be particularly useful if you wish to collect sizeable amounts of quantitative data which can be easily collected via an online survey or similar tool. Just remember that everything you ask a customer to do effectively has a conversion rate. Asking your customer to do two things will invariably lead to an increased number of incomplete data sets from respondents who either did not take / complete the interview but completed the questionnaire or vice versa.
Preparing for the Interview
Determine who will conduct the interview. Similarly to choosing the person or team to run the program, it’s best if the interviewer is not on the sales team. The interviewer should never be someone who was involved in the sales process for that particular customer. That consideration aside, the interviewer should be someone who is familiar with the product or service being sold, familiar with the market, understands the sales process without being too intimate with the sales team, and will make the respondent feel comfortable with the interview process.
Interviews should be scheduled with the customer or prospect very soon after the opportunity has ended. A good rule of thumb is that if more than a month has passed since the opportunity was closed or lost, don’t conduct an interview. Details of their decision journey and interactions with various companies need to be fresh on their minds in order to obtain accurate information, and collecting inaccurate information is often worse than collecting no information at all. When scheduling the interview, let them know exactly what to expect and what topics you are going to discuss. If there were multiple people involved in the prospect’s decision, they should be interviewed separately as they may have differing opinions and these differences can be stark at times. If you interview them collectively, you run the risk of those differing opinions not being expressed or falling victim to groupthink.
Before the interview, the interviewer should sit down with the sales team / person who was handling the opportunity and document some facts and perspective regarding the opportunity. How did the opportunity arise? Was there any previous relationship with the prospect? What tactics and sales tools were they using and why? Were there any noteworthy challenges during the process? What was the result and was it anticipated?
Performing the Interview
Interviews are generally performed by phone, although analogous communication tools such as teleconference can be used. In-person interviews can be performed as well so long as the customer is local and the interview can be performed without becoming cost-prohibitive. Being able to see the interviewee an be helpful, as gestures and body language can convey feelings which can in turn be used to help guide the conversation. (The interviewer’s impressions obtained from body language should not be documented as it could introduce a large degree of subjectivity. Additionally, when performing win / loss analysis across cultural borders, body language could be misread due to cultural differences.)
Any expectations of confidentiality should be discussed up front. As some purchasing processes involve sensitive information, ensure the interviewee(s) feel comfortable using any information necessary to fully explain themselves while knowing that any confidential information will not be recorded or shared.
The interview should have a “script” to ensure the interviewer asks all the questions, although some of which will likely vary slightly interview-to-interview (in phrasing or approach, not in intent) based on the nature of the opportunity and how the interview progresses. However, the questions on the script should be taken to be a minimum of the questions that need to be answered. A good interviewer will probe the interviewee to uncover the underlying reasons behind their answers. Simply surveying the interviewee by asking a set list of questions in sequence is a waste of a live interview and a good way to end up with incomplete information that is difficult to understand and / or leaves a lot of opportunity for guesswork. The ability to be meaningfully spontaneous is dependent on the interviewer’s knowledge of the market, the product bring sold, and the details of the opportunity and sales process for that specific prospect.
Post-Interview Analysis and Assimilation of Knowledge
Soon after each interview, send the customer a message to thank them. As with any customer interaction, a win / loss analysis is a branded experience and you want to ensure the customer experience is a good one in order to earn future business and cultivate brand advocates.
There is no single, correct way to analyze the information from a win / loss analysis because the information, and the kind of information collected, will vary based on the questions you are trying to answer and potentially other factors as well (as discussed earlier). However, data analysis provides ample opportunity to derail your win / loss analysis. It’s likely that most of your data is qualitative. If your organization has a tendency to be political, various groups may try to influence how the data is analyzed or presented in order to make themselves look better or further their own ends. It’s the job of the person managing the program to ensure this does not happen. Any quantitative data should be handled using proper statistics, and qualitative data should be analyzed in a way that is logical, defensible, and allows you to extract the necessary insight. Applying semi-quantitative methods to the analysis of qualitative data may help, but you shouldn’t limit yourself to them. Whatever methods you use to analyze the data, you need to ensure that they are consistent!
Once the data is distilled into knowledge, you need to ensure that it is utilized! When there is enough analyzed information to answer at least some of the questions that you defined in your objectives, a report should be drawn up and a meeting called with people from all departments who would stand to benefit from the resulting knowledge. (Depending on your company policies and culture, the reports and analysis may also be made available to anyone in the company who cares to learn from it, or restricted on a need-to-know basis.) At this meeting, the data and analysis are discussed, lessons learned are shared, and ideas can be generated for ways to improve – these ideas are the foundation for change. The results should inform your sales processes, market segmentation, product development, messaging, marketing communications, sales collateral, and other areas.
If you’ve obtained answers to some secondary objectives, you can remove the associated questions from the interview script. These may be replaced with questions to fulfill other knowledge objectives. Remember, however, that the primary purpose of win / loss analysis is to understand why you win or lose business! The core questions facilitating the answer to that question should, under most circumstances, not be removed or replaced. If you find yourself desiring the answer to other questions more than the answer to why you are winning / losing business, then you should use a different tool or approach which is more suited for the information you seek to gain. You may, however, rotate through other product lines or service categories in order to obtain information specific to other areas.
Closing Remarks
A recent Gartner study (“Tech Go-to-Market: Three Ways Marketers Can Use Data From Win/Loss Analysis to Increase Win Rates and Revenue“) found that less than one third of organizations conduct win / loss analysis properly. The same study found that win / loss analysis can increase win rates by as much as 50%! That should be no surprise. Understanding is the foundation upon which improvement must be built. Sure, win / loss analyses require a good deal of rigor and effort, but that 50% should be well worth it.