Tearing The Pricing Band-Aid Off In One Yank

by:

Joe Patti

Very interesting post from Colleen Dilenschneider and IMPACTS Experience about research on gradually increasing pricing over time versus making one big increase.

According to them people have an internal meter that evaluates whether a price increase is too much to continue making a purchase. By having multiple incremental increases it forces people to continually re-evaluate whether the experience is now too expensive versus instituting one big increase and leaving the price there for the same amount of time.

In a study of price changes published in the Journal of Consumer Research, the authors found that multiple price increases were evaluated more unfavorably than a single price increase (Mazumdar & Jun). In general, consumers react more strongly to price increases than decreases because increases over an internal reference price are generally viewed as a loss by the consumer. While small increases may all still land within that band of elasticity identified as optimal by a pricing study, the repeated reassessments of their internal reference point can lead to and over time exacerbate frustration and dissatisfaction among audiences.

Essentially, rather than easing audiences through the “pain” of a price increase, stairstepping a price increase actually serves to prolong the discomfort.

The other thing they point out is that you lose revenue by gradually increasing a price versus making one big increase. They note that if you have 100,000 visitors a year and make one big $5 increase that you hold for five years, you making $1 million more than if you increased the price $1 each year for five years.

They also note holding a price for a number of years results in a perception of stability versus bumping on a yearly basis. There is a sense that you planned well and haven’t needed to make changes rather than always playing catch up with the pricing.

Now in terms of dynamic pricing, the outcomes are a little less clear:

Since dynamic pricing offers a different price dependent on market conditions, potential attendees’ internal reference points may prove less of a factor in evaluating their admission options, and thus attenuate their reaction to a price increase.

On the other hand, however, dynamic pricing can also increase distrust among consumers (Vomberg et al). Different prices for different potential visitors may be seen as unfair, and the short-term gain in revenue could be offset over time by the loss of trust in the institution

Personally, I am left wondering about the nuances that may be present in all this. Just thinking about grocery shopping, if I see prices varying in small increments, say between .59 and .89, I mostly suck it up. However, if something is now $1 more than it was last week, I hesitate to make a purchase. I have stopped buying goods where the price has shot up $1 in one week and not dropped again.

I am pretty sure there are things I have continued to purchase whose purchases are currently more than $1 more than they were a year ago, but have been increased more incrementally and haven’t tripped my internal calculus.

So I wonder if Dilenschneider’s research is based on intermittent purchases rather than weekly purchases or purchases of experiences versus purchases of goods and materials.

It may be if you are purchasing every week, you notice the big increases but aren’t alarmed by smaller increases because it part of a blur of 20 different items you are purchasing at a time.

Then if you are purchasing a handful of tickets once a month or maybe once a quarter or more, you may be more aware of what the price was last time because you are only purchasing a few items with less frequency.

If anyone has any insight into consumer behavior in these situations, I would be interested to know more. I know I am not the only person paying attention to the price of their groceries these days.

AI Can’t Practice Law

by:

Joe Patti

Entertainment lawyer Gordon Firemark made a blog post recently warning people not to ask AI for legal advice. As you might imagine, he cited some examples of how AI had made mistakes, gotten the law wrong, and generated language that put all the risk on the client rather than the supplier of the service.

He also noted that AI tends to validate the perspective of the person asking the question. He related the story of a client who wrote a strongly worded letter based on AI advice which created an antagonistic relationship with another party which resulted in a prolonged conflict between them.

He said the whole issue could have been solved with a minor revision in contract language and a conversation.

But even more importantly, Firemark says, when you ask AI for advice or to help you produce something, you are making assumptions about what the solution is. One example he gives is asking AI to generate an independent contractor document when the legally the relationship is employer-employee.

Likewise if you aren’t familiar with the difference between copyrights and trademarks, you may end up insufficiently protecting your corporate identity. You may incorporate your business using the wrong/disadvantageous structure.

As much as you may wish to avoid the cost of working with a lawyer, they are still able to evaluate your goals and the context of your requests better than AI. They can also help you recognize that what you think is important isn’t really relevant and that you have overlooked some crucial arrangements.

If you’re facing a legal issue, resist the temptation to ask AI what you should do.

Don’t ask it what agreement you need.

Don’t ask it what legal arguments to make.

Don’t ask it to draft the first version of an important legal document.

Bring your problem to your lawyer instead.

Bring the facts.

Bring your goals.

Bring your concerns.

[…]

Because the value isn’t in the document.

The value is in the thinking that produces the document.

Not As Many Agree With You As You Think

by:

Joe Patti

Last month Seth Godin linked to a seven question quiz based on data from the General Social Survey.

Apparently, the most your responses can align with those of others in the United States is 7%.

Godin scored 4%. My score was around 1%.

So when you believe that millions of people think like you do, you are technically correct in that at most ~24 million people out of 342.6 million in the US hold the exact same set of beliefs as you.

But also there are even more millions who don’t.

I have been writing a bit about marketing to different audiences the past few weeks. In the context of this data it makes me wonder how valid the exercise of creating hypothetical profiles of audiences actually is.

If at most 7% of the community you are trying to reach is going to perfectly share the beliefs of others in the community, you are really angling for a small segment of the community.

The percentage is guaranteed to be smaller than 7% because at least a few percent will be knocked off when people who don’t like or feel comfortable with whatever you are offering self-select out.

Obviously the remaining 2%-4% (which may still be an overestimate) don’t have to be perfectly in-synch with one another to enjoy experiences your organization offers.

Ultimately, participation in arts and culture may indeed have niche appeal because that is the best you can hope for.

Though again, 2% of the US population is about 7 million people which isn’t a small number, except in relation to the whole nation.

Tips For Generating AI Messaging For Different Audiences

by:

Joe Patti

Ceci Dadisman posted some guidance on LinkedIn for arts marketers using Ai to write promotional copy. She talks about creating different messaging for each segment of an audience. In this case, Insiders, Recent Buyers, Lapsed Buyers, and Cold Prospects.

The basic approach is the AI agent is writing the four different sets of copy you would write if you didn’t have the time. This is different from having AI writing copy for you because you are taking responsibility to review the copy and revise parameters so that there is basically no commonalities between the message for Insiders and Cold Prospects.

Ceci walks through the steps of writing a comprehensive prompt that ensures that messaging is crafted specifically for each audience. This requires providing details about the audience, what they know, what they don’t know, the voice to use, and most importantly; what concepts, words and jargony terms not to use.

For example from her prompt instructions:

When rewriting, do:

-Lead with what the reader will experience, not what we are offering
-Use “you” more than “we”
-Define any word a smart friend outside our industry wouldn’t recognize
-Keep sentences short. Sixth-grade reading level or below.

When rewriting, never:

-Use the words: [list of terms — e.g. “transcendent,” “iconic,” “immortal,” “unforgettable”]
-Assume the reader knows any staff member’s name
-Reference internal season structure or programming language without translation
-Add urgency the original draft didn’t have

She includes tons more insight and instructions, including how to test and evaluate what AI produces against itself and content a human has written.