Many times, the strategy or solution was the result of a brainstorming session filled with type A personalities (me included) ready to make things happen.

You may be familiar with the type of session I’m referencing. Usually, there’s a guru consultant leading the charge. He separates the group into teams and gives them Post-It notes and colored sticker dots. “Write down as many ideas as you can in the next 20 minutes. Don’t think too much. Be creative! No idea is dumb. Stick your ideas on the wall. Now go!” After 20 minutes, a leader from each group presents their best ideas to the rest of the room. Then each person in the room is allowed to vote for maybe six of his or her favorite ideas using the colored sticker dots. A few people are assigned the winning ideas and off we go.

Those types of session frustrate me. I’m concerned there’s too much action, too many unspoken assumptions, and not nearly enough serious thinking.

Over the years, I’ve developed a problem solving technique that I’ve found to work a lot better. I call it the Monkey Cage Sessions. The technique is all about thoroughly identifying the problems from all angles before developing carefully considered, thoughtful and collaborative solutions.

It’s got an intentionally silly name because the process should be fun.

Here’s how it works:

Step 1 – Define the problems.

We start by gathering a group of cross-functional people – ideally from different levels of the organization – together in a room to talk about the problem or problems we’re trying to solve. This could be as simple as enhancing a Careers page on the corporate website or as complicated as building a complete company strategic plan. It’s important to define the general scope of the problem, but it should be defined fairly loosely so as not to stifle the discussion.

The rules of the meeting are fairly simple. We only discuss problems. No solutions. This is a license to bitch. Let it be cathartic.

I usually stand at the whiteboard, marker in hand, and write down everything everyone says. There is no need to be overly structured here, and anything anyone says is legitimate. We throw it all at the wall and we’ll sort it out later.

Sometimes people want to debate whether or not something another person says is really a problem. If someone said it, it’s at least a perceived problem. It’s legitimate. Also, there is often an attempt to offer an explanation for why a problem exists. The explanation is covering for another problem, so that problem should be written down.

People are always tempted to offer solutions, even when they think they’re offering problems. For example, someone might say it’s a problem that we don’t have a content management system. Actually, a content management system might be the solution to a problem. What problem might a content management system solve? Beware of any problem statement that starts with “We need…” and be prepared to break down that need into the problems needing the solution.

Sometimes the problems offered up are very broad and vague. In those cases, it’s important to work with the group to dissect that broad problem into its component parts.

This first session generally uncovers a LOT of problems, but the problem is still usually not completely identified yet. Which leads to…

Step 2 – Categorize the problems

While the chaotic approach of the first session works well to get an initial set of problem descriptions, it’s important to create some order in order to prepare for the problem solving stage. So Step 2 involves writing down all of the problems and sorting them into logical categories. I don’t have any pre-determined set of categories. Instead, I prefer to the let the problems listed dictate the categorization.

Step 3 – Widen the circle

We probably have a pretty good description of the problems now, but we’ve also still likely missed some. For Step 3 we send the typed and categorized list of problems to the original group as well as a widened circle of people. The original group will likely have thought of a couple more issues since the day of the meeting, and the new group of people will almost definitely add new problems to the list. Since this is the final stage of problem description, we want to give this step at least a few days to allow the team to think this through as completely as possible.

Step 4 – Develop the solutions

Finally, we can start solving the problems. Woo hoo!

Now it’s time to gather a subset of the original meeting to start working towards solutions. There should be at least a few days between Step 3 and Step 4. We want to give people some time to think over the full problem set. The group should enter the Step 4 meeting with at least some basic solution ideas. There is no need to come into the room with comprehensive solutions that solve every problem on the list, but the solutions considered should certainly attempt to solve as many problems as possible (without causing too many new problems).

I usually find that by this point many of the solutions are fairly obvious. But there should be good discussion about the relative merits of each suggested solution, and the solutions should be measured up against the problem list to determine how comprehensive they are.

I like to end the meeting by assigning people to lead each of the proposed solutions. Obviously, any suggested solution from this session will need to be fleshed out in a lot more detail, and the leader from this meeting is responsible for determining the viability and solution and then potentially leading the development and ultimate execution to completion.

Subsequent progress is then handled via a separate execution process.

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I’ve had very good luck over the years using this technique. Some of the primary benefits I’ve found are:

1. Better understanding of the problems
   As the initial meeting wraps up, most people are inevitably feeling enlightened about the problem. They’ve outwardly expressed their own assumptions (which sometimes even they didn’t know they were making) and they’ve understood the perspectives and assumptions of others. They’ve seen the problem in an entirely new light.
2. More comprehensive solutions
   The heightened understanding of the problem and the critically important time between steps to allow the team to be more thoughtful in their ideas. Those ideas are usually pretty all-encompassing solutions to start with, but the discussions in Step 4 lead the team to collectively choose the best of the best of the solutions offered.
3. Better execution
   Solutions are nothing but fancy ideas until their executed. And poor execution can cause even the best ideas to fail. The process of fully defining the problems and sharing that work with wide circles of people is an incredibly important stage that sets the foundation for success in execution. When the execution team provides input in the process and understands the basis for the solution, they are far more supportive in the effort. They are also far more prepared to make the daily, detailed decisions that are often the difference between success and failure.

So, that’s the Monkey Cage Sessions. I hope you find it helpful. If you try implementing the process in your business, I’d love to hear how it goes.

What do you think? Would this process work in your organization? Have you ever used a similar process?

But the more I thought about it, the more I realized that many of the reasons we aren’t happy with the results of the analyses come down to fundamental disconnects in human relations between all parties involved.

Groups of people with disparate backgrounds, training and experiences gather in a room to “review the numbers.” We each bring our own sets of assumptions, biases and expectations, and we generally fail to establish common sets of understanding before digging in. It’s the type of Communication Illusion I’ve written about previously. And that failure to communicate tends to kill a lot of good analyses.

Establishing common understanding around a few key areas of focus can go a long way towards facilitating better communication around analyses and consequently developing better plans of action to address the findings.

Here’s a list of 11 key ways to stop killing good analyses:

1. Begin in the beginning. Hire analysts not reporters.
   This isn’t a slam on reporters, it’s just recognition that the mindset and skill set needed for gathering and reporting on data is different from the mindset and skill set required for analyzing that data and turning it into valuable business insight. To be sure, there are people who can do both. But it’s a mistake to assume these skill sets can always be found in the same person. Reporters need strong left-brain orientation and analysts need more of a balance between the “just the facts” left brain and the more creative right brain. Reporters ensure the data is complete and of high quality; analysts creatively examine loads of data to extract valuable insight. Finding someone with the right skill sets might cost more in payroll dollars, but my experience says they’re worth every penny in the value they bring to the organization.
2. Don’t turn analysts into reporters.
   This one happens all too often. We hire brilliant analysts and then ask them to spend all of their time pulling and formatting reports so that we can do our own analysis. Everyone’s time is misused at best and wasted at worst. I think this type of thing is a result of the miscommunication as much as a cause of it. When we get an analysis we’re unhappy with, we “solve” the problem by just doing it ourselves rather than use those moments as opportunities to get on the same page with each other. Web Analytics Demystified‘s Eric Peterson is always saying analytics is an art as much as it is a science, and that can mean there are multiple ways to get to findings. Talking about what’s effective and what’s not is critical to our ultimate success. Getting to great analysis is definitely an iterative process.
3. Don’t expect perfection; get comfortable with some ambiguity
   When we decide to be “data-driven,” we seem to assume that the data is going to provide perfect answers to our most difficult problems. But perfect data is about as common as perfect people. And the chances of getting perfect data decrease as the volume of data increases. We remember from our statistics classes that larger sample sizes mean more accurate statistics, but “more accurate” and “perfect” are not the same (and more about statistics later in this list). My friend Tim Wilson recently posted an excellent article on why data doesn’t match and why we shouldn’t be concerned. I highly recommend a quick read. The reality is we don’t need perfect data to produce highly valuable insight, but an expectation of perfection will quickly derail excellent analysis. To be clear, though, this doesn’t mean we shouldn’t try as hard as we can to use great tools, excellent methodologies and proper data cleansing to ensure we are working from high quality data sets. We just shouldn’t blow off an entire analysis because there is some ambiguity in the results. Unrealistic expectations are killers.
4. Be extremely clear about assumptions and objectives. Don’t leave things unspoken.
   Mismatched assumptions are at the heart of most miscommunications regarding just about anything, but they can be a killer in many analyses. Per item #3, we need to start with the assumption that the data won’t be perfect. But then we need to be really clear with all involved what we’re assuming we’re going to learn and what we’re trying to do with those learnings. It’s extremely important that the analysts are well aware of the business goals and objectives, and they need to be very clearly about why they’re being asked for the analysis and what’s going to be done with it. It’s also extremely important that the decision makers are aware of the capabilities of the tools and the quality of the data so they know if their expectations are realistic.
5. Resist numbers for number’s sake
   Man, we love our numbers in retail. If it’s trackable, we want to know about it. And on the web, just about everything is trackable. But I’ll argue that too much data is actually worse than no data at all. We can’t manage what we don’t measure, but we also can’t manage everything that is measurable. We need to determine which metrics are truly making a difference in our businesses (which is no small task) and then focus ourselves and our teams relentlessly on understanding and driving those metrics. Our analyses should always focus around those key measures of our businesses and not simply report hundreds (or thousands) of different numbers in the hopes that somehow they’ll all tie together into some sort of magic bullet.
6. Resist simplicity for simplicity’s sake
   Why do we seem to be on an endless quest to measure our businesses in the simplest possible manner? Don’t get me wrong. I understand the appeal of simplicity, especially when you have to communicate up the corporate ladder. While the allure of a simple metric is strong, I fear overly simplified metrics are not useful. Our businesses are complex. Our websites are complex. Our customers are complex. The combination of the three is incredibly complex. If we create a metric that’s easy to calculate but not reliable, we run the risk of endless amounts of analysis trying to manage to a metric that doesn’t actually have a cause-and-effect relationship with our financial success. Great metrics might require more complicated analyses, but accurate, actionable information is worth a bit of complexity. And quality metrics based on complex analyses can still be expressed simply.
7. Get comfortable with probabilities and ranges
   When we’re dealing with future uncertainties like forecasts or ROI calculations, we are kidding ourselves when we settle on specific numbers. Yet we do it all the time. One of my favorite books last year was called “Why Can’t You Just Give Me the Number?” The author, Patrick Leach, wrote the book specifically for executives who consistently ask that question. I highly recommend a read. Analysts and decision makers alike need to understand the of pros and cons of averages and using them in particular situations, particularly when stacking them on top of each other. Just the first chapter of the book Flaw of Averages does an excellent job explaining the general problems.
8. Be multilingual
   Decision makers should brush up on basic statistics. I don’t think it’s necessary to re-learn all the formulas, but it’s definitely important to remember all the nuances of statistics. As time has passed from our initial statistics classes, we tend to forget about properly selected samples, standard deviations and such, and we just remember that you can believe the numbers. But we can’t just believe any old number. All those intricacies matter. Numbers don’t lie, but people lie, misuse and misread numbers on a regular basis. A basic understanding of statistics can not only help mitigate those concerns, but on a more positive note it can also help decision makers and analysts get to the truth more quickly.

   Analysts should learn the language of the business and work hard to better understand the nuances of the businesses of the decision makers. It’s important to understand the daily pressures decision makers face to ensure the analysis is truly of value. It’s also important to understand the language of each decision maker to shortcut understanding of the analysis by presenting it in terms immediately identifiable to the audience. This sounds obvious, I suppose, but I’ve heard way too many analyses that are presented in “analyst-speak” and go right over the heard of the audience.

9. Faster is not necessarily better
   We have tons of data in real time, so the temptation is to start getting a read almost immediately on any new strategic implementation, promotion, etc. Resist the temptation! I wrote a post a while back comparing this type of real time analysis to some of the silliness that occurs on 24-hour news networks. Getting results back quickly is good, but not at the expense of accuracy. We have to strike the right balance to ensure we don’t spin our wheels in the wrong direction by reacting to very incomplete data.
10. Don’t ignore the gut
    Some people will probably vehemently disagree with me on this one, but when an experienced person says something in his or her gut says something is wrong with the data, we shouldn’t ignore it. As we stated in #3, the data we’re working from is not perfect so “gut checks” are not completely out of order. Our unconscious or hidden brains are more powerful and more correct than we often give them credit for. Many of our past learnings remain lurking in our brains and tend to surface as emotions and gut reactions. They’re not always right, for sure, but that doesn’t mean they should be ignored. If someone’s gut says something is wrong, we should at the very least take another honest look at the results. We might be very happy we did.
11. Presentation matters a lot.
    Last but certainly not least, how the analysis is presented can make or break its success. Everything from how slides are laid out to how we walk through the findings matter. It’s critically important to remember that analysts are WAY closer to the data than everyone else. The audience needs to be carefully walked through the analysis, and analysts should show their work (like math proofs in school). It’s all about persuading the audience and proving a case and every point prior to this one comes into play.

The wealth and complexity of data we have to run our businesses is often a luxury and sometimes a curse. In the end, the data doesn’t make our businesses decisions. People do. And we have to acknowledge and overcome some of our basic human interaction issues in order to fully leverage the value of our masses of data to make the right data-driven decisions for our businesses.

What do you think? Where do you differ? What else can we do?

The most effective analyses I’ve seen have remarkably similar attributes, and they happen to work into a nice, easy-to-remember acronym — F.A.M.E.

Here, in my experience, are the keys to achieving FAME in analysis:

Focused

Any finding should be fact based and clear enough that it can be stated in a succinct format similar to a newspaper headline. It’s OK to augment the main headline with a sub-headline that adds further clarification, but anything more complicated is not nearly focused enough to be an effective finding.

For example, an effective finding might be, “Visitors arriving from Google search terms are converting 23% lower than visitors arriving from email.” An accompanying sub-heading might further clarify the statement with something like, “Unclear value proposition, irrelevant landing pages and high first time visitor counts are contributing factors.”

All subsequent data presented should support these headlines. Any data that is interesting but irrelevant to the finding should be excluded from the analysis. In other words, remove the clutter so the main points are as clear as possible.

Actionable

Effective findings and their accompanying recommendations are specific enough in focus and narrow enough in scope that decision makers can reasonably develop a plan of action to address them. The finding mentioned above regarding Google search visitors fits the bill, and a recommendation that focuses on modifying landing pages to match search terms would be appropriate. Less appropriate would be a vague finding like “customers coming from Google search terms are viewing more pages than customers coming from email campaigns” accompanied by an equally vague recommendation to “consider ways to reduce pages clicked by Google search campaign visitors.” Is viewing more pages good or bad? Why? The recommendation in this case insinuates that it’s bad, but it’s not clear why. What’s the benefit of taking action in quantifiable terms?

Truly actionable analysis doesn’t burden decision makers with connecting the data to executable conclusions. In other words, the thought put into the analysis should make the diagnosis of problems clear so that decision makers can get to work on determining necessary solutions.

Manageable

The number of findings in any set of analyses should be contained enough that the analyst and anyone in the audience can recite the findings and recommendations (but not all the supporting details) in 30 seconds. Sometimes, less is more. This constraint helps ease the subsequent communication that will be necessary to reasonably react to the findings and plan and execute a response. Conversely, information overload obscures key messages and makes it difficult for teams to coalesce around key issues.

Enlightening

Last, but most certainly not least, effective findings are enlightening. Effective analyses should present — and support with clear, credible data — a view of the business that is not widely held. They should, at the very least, elicit a “hmmm…” from the audience and ideally a “whoa!” They should excite decision makers and spur them to action.

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The FAME attributes are not always easy to achieve. They require a lot of hard thought, but the value of clear, data-supported insight to an organization is immense.

The most effective analysts I’ve seen achieve FAME on a regular basis. They have a thorough understanding of the business’ objectives, and they develop their insights to help decision makers truly understand what’s working and what’s not working. And then they lay out clear opportunities for improvement. That’s data-driven business management at its best.

What do you think? What attributes do you find key in effective analyses?

Heck, I’ve also written a white paper and blogged about social media.

Don’t get me wrong. I think social and mobile are important opportunities for us to improve our businesses. I just don’t think we should focus on them to the exclusion of some of the core aspects of our sites and businesses that still need a lot of work.

The level of our success with any of these new technologies is going to be limited by the effectiveness of our core site capabilities and constrained by any internal organizational challenges we might have.

Here are some topics I’d love to see get a little more press and conference content time:

• Usability
  From my vantage point at ForeSee Results, where I can see customer perceptions at many different retailers, it’s clear that our sites have not come close to solving all of our usability issues. In fact, I’ll go as far as saying improving usability is the #1 way to increase conversion. I’m currently reading a book called “The Design of Everyday Things” by Don Norman. The book was written in the ’80s (I think) so there’s no mention of websites. Instead, he talks a lot about the design of doors, faucets and other everyday objects and, most interestingly, the psychology of we humans who interact with these things. The principles he discusses are absolutely relevant to web page design. Other books, such as “Don’t Make Me Think” by Steve Krug and anything by Jakob Nielsen are also great sources of knowledge. I’d sure love to see us cover these types of topics a little more in our conferences and trade mags. Also, how do different retailers approach find and solve usability issues? In the end, if the experiences we create aren’t usable our social and mobile strategies won’t reach their potential.
• Organizational structure
  How often do we come back from a conference with great new ideas about implementing some new strategies (say, a new social media or mobile commerce strategy) only to run into competing agendas, lack of resources or organizational bureaucracies? Discussing and writing about organizational structure doesn’t have the panache of social media or other exciting new frontiers, but there’s little doubt in my mind that the structure of our organizations can make or break the success of our businesses. When we were first setting up the organization for the new Borders.com, we spent a LOT of time studying the structures of other companies learning about the pros and the cons from those who lived through different schemes. It was hugely useful and more interesting than you might think. Mark Fodor, CEO of Cross View, just wrote an excellent piece for Online Strategies magazine that discussed the hurdles involved in going cross-channel and included a very good discussion about the need for mindset shifts. I’d love to see these topics further explored in interactive environments at industry conferences.
• Incentives
  Books like Freakonomics make strong cases for the fact that incentives drive our behaviors. I’d love to hear how other companies set up their internal incentive structures. And there are multiple types of incentives. Certainly, there are financial incentives that come in the form of bonuses. But there are also the sometimes more powerful social incentives. What gets talked about all the time? How do those topics of discussion influence people’s behaviors? How do all those incentives align with the needs generated by new strategies to maximize the power of social media or mobile commerce?
• Data/analytics storytelling
  We have so much data available to us, and we all talk about being data driven. But how do we get the most from that data? How do we use that data to form our strategies, support our strategies and communicate our strategies. John Lovett of Web Analytics Desmystified wrote an excellent piece on telling stories with data recently. There are also several great blogs on analytics like MineThatData, Occam’s Razor, and the aforementioned Web Analytics Demystified. I’d love to see more discussions in trade mags and conferences about how to get the most from our data, both in analyzing it and relating the findings to others.
• International expansion
  We used to talk a lot about international, but it doesn’t seem to be a big topic lately. Yet the opportunities to grow our businesses internationally are immense. So, too, are the challenges. Jim Okamura and Maris Daugherty at the JC Williams Group wrote an absolutely excellent white paper late last year on the prizes and perils of international expansion. Jim did have a breakout session at last year’s Shop.org Annual Summit, but I’d love to see more discussion from retailers who have gone or are going international to learn more. Or it would also be good to hear from those who simply ship internationally or those who have decided to stay domestic to learn more about their decision making processes.
• Leadership
  Leading lots of people and convincing big, disparate groups to do new things is hard. I just read the book Switch: How to Change Things When Change is Hard by Dan and Chip Heath. There are some amazing tips in that book about implementing change in organizations (and in other parts of life, for that matter). I would love to see more discussion of these types of leadership topics that help us all implement the changes we know we need to make to take advantage of new opportunities like social media and mobile commerce.

I know a lot of these topics are more business basics than retail or e-commerce specific. But the reality is we need to be our absolute best at these business basics in order to implement any of our new ideas and strategies. I personally always enjoy talking to other retailers about some of these basics, and I certainly never tire of reading books that expand my horizons. I’d love to see more about these topics in our conferences and trade mags.

But these are just my opinions. I’d really love to know what you think. As a member of the executive content committee for Shop.org, I’m actually in a position to influence some of the excellent content that my good friend Larry Joseloff regularly puts together. But I’d love to know if you agree or not before I start banging the drum. Would you mind dropping me a quick comment or an email letting me know if you agree or disagree. A simple “Right on” if you agree or a “You’re nuts” if you don’t is plenty sufficient; although, I certainly appreciate your expanded thoughts if you’d like to share them.

Please, let me know what you think of my little rant.

The missing links are Satisfaction and Referral.

Before discussing these missing links, let’s take a quick second to define the other stages:

Awareness: This is basic branding and positioning of the business. We certainly can’t progress people through the cycle before they’ve even heard of us.

Acquisition: I’ve always thought of this as getting someone into our doors or onto our site. It’s a major step, but it’s not yet profitable.

Conversion: This one is simply defined as making a sales. Woo hoo! It may or may not be a profitable sales on its own, but it’s still a significant stage in the cycle.

Retention: We get them to shop with us again. Excellent! Repeat sales tend to be more profitable and almost certainly have lower marketing costs than first purchases.

Now, let’s get to those Missing Links

In my experience, the key to a strong and active customer engagement cycle is a very satisfying customer experience. And while the Wikipedia article on Customer Engagement doesn’t mention Satisfaction as often as I would like, it does include this key statement: “Satisfaction is simply the foundation, and the minimum requirement, for a continuing relationship with customers.”

In fact, I think the quality of the customer experience is so important that I would actually inject it multiple times into the cycle: Awareness, Acquisition, Satisfaction, Conversion, Satisfaction, Retention, Satisfaction, Referral.

Of course, it’s possible to get through at least some of the stages of the cycle without an excellent customer experience. People will soldier through a bad experience if they want the product bad enough or if there’s an incredible price. But it’s going to be a lot harder to retain that type of customer and if you get a referral, it might not be the type of referral you want.

I wonder if Satisfaction and Referral are often left out of cycle strategies because they are the stages most out of marketers’ control.

A satisfying customer experience is not completely in the marketer’s control. For sure, marketing plays a role. A customer’s satisfaction can be defined as the degree to which her actual experience measures up to her expectations. Our marketing messages are all about expectations, so it’s important that we are compelling without over-hyping the experience. And certainly marketers can influence policy decisions, website designs, etc. to help drive better customer experiences.

In the end, though, the actual in-store or online experience will determine the strength of the customer engagement.

Everyone plays a part in the satisfaction stages. Merchants must ensure advertised product is in stock and well positioned. Store operators must ensure the stores are clean, the product is available on the sales floor and the staff are friendly, enthusiastic and helpful. The e-commerce team must ensure advertised products can be easily found, the site is performing well, product information in complete and useful,  and the products are shipped on time and in good condition.

We also have to ensure our incentives and metrics are supporting a quality customer experience, because the wrong metrics can incent the wrong behavior. For example, if we measure an online search engine marketing campaign by the number of visitors generated or even the total sales generated, we can absolutely end up going down the wrong path. We can buy tons of search terms that by their sheer volume will generate lots of traffic and some degree of increased sales. But if those search terms link to the home page or some other page that is largely irrelevant to the search term, the experience will be likely disappointing for the customer who clicked through.

In fact, I wrote a white paper a few months ago, Online Customer Acquisition: Quality Trumps Quantity, that delved into customer experience by acquisition source for the Top 100 Internet Retailers. We found that those who came via external search engines were among the least satisfied customers of those sites with the least likelihood to purchase and recommend. Not good. These low ratings could largely be attributed to the irrelevance of the landing pages from those search terms.

Satisfaction breeds Referral

Referrals or Recommendations are truly wonderful. As I wrote previously, the World’s Greatest Marketers are our best and most vocal customers. They are more credible than we’ll ever be, and the cost efficiencies of acquisition through referral are significantly better than our traditional methods of awareness and acquisition marketing. In my previously mentioned post, I discussed some ways to help customers along on the referral path. But, of course, customers can be pretty resourceful on their own.

We’ve all seen blog posts, Facebook posts or tweets about bad customer experiences. But plenty of positive public commentary can also be found.  Target’s and Gap’s Facebook walls have lots of customers expressing their love for those brands. Even more powerful are blog posts some customers write about their experiences.  I came across a post yesterday from entitled Tales of Perfection that related two excellent experiences the blogger had with Guitar Center and a burger joint called Arry’s. Both stories are highly compelling and speak to the excellent quality of the employees at each business. Nice!

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Developing a business strategy, not just a marketing strategy, around the customer engagement cycle can be extremely powerful. It requires the entire company to get on board to understand the value of maximizing the customer experience at every touch point with the customer, and it requires a set of incentives and metrics that fully support strengthening the cycle along the way.

What do you think? How do you think about the customer engagement cycle? How important do feel the customer experience is in strengthening the cycle? Or do you think this is all hogwash?