Why Shoppers Quit Mid Checkout

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In Partnership with Share Local Media 

Direct Mail Didn’t Stop Working. Brands Stopped Testing It.

Direct mail still earns attention in a way digital rarely does anymore. The issue is not performance; it is commitment. 

Most programs require long timelines, high minimums, and upfront decisions before brands ever see meaningful results. That hesitation keeps direct mail on the sidelines while acquisition costs rise everywhere else.

Shared Mail from Share Local Media offers a simpler entry. Your brand appears inside a premium shared envelope alongside other well-known names, putting you in front of millions at a fraction of the usual spend.

No long setup, no operational drag, and no big upfront commitment required. That is why Shared Mail has become an excellent entry point for brands curious about direct mail.Here’s what brands are actually seeing:

• Knix achieved sub-$20 CPAs and 7-9x ROAS on new customer acquisition with shared mail.
• Primary hit a $45 blended CPA and strong response rates using SLM’s geo-targeted shared mail.

With nearly a billion mailings delivered over the past decade, SLM’s Shared Mail Program is a proven, measurable channel at an all-in cost as low as $0.06 per piece.

April Shared Mailings close on 3/2. Secure your spot now!

📝 Reduce Checkout Abandonment With Psychological Momentum

Checkout drop-off is rarely a pricing problem. It is usually a momentum problem.

Inside your company, checkout feels like a logical sequence: shipping details, payment, confirmation. Every step has a reason. But for your customer, checkout feels like a gauntlet of micro-decisions. Each new screen is another moment where their brain asks: “Do I keep going?”

That is why a checkout can make perfect sense internally and still lose 60% of buyers externally. Customers are not evaluating checkout like an analyst. They are experiencing it like a driver: fast, instinctive, and low patience.

The key is designing for psychological momentum, not logical progression.

Steps to Build a Checkout That More People Finish

1️⃣ Make Trust Obvious Instantly
Use familiar layouts, payment logos, security cues, and simple trust signals. Checkout should feel boring in the best way.

2️⃣ Show the Journey Upfront
Add a clear progress indicator like Step 1 of 3. Unknown length creates anxiety.

3️⃣ Remove Surprise Costs
If shipping appears late, you break their mental budget. Show it early or make it free.

4️⃣ Kill Forced Account Creation
Guest checkout should be the default. Ask for an account after purchase, not before.

5️⃣ Reduce Decision Points
Every dropdown is friction. Remove anything not essential to delivery or payment.

6️⃣ Make Forms Feel Effortless
Use address autocomplete, smart defaults, and inline validation. Never clear fields after errors.

7️⃣ Offer Low-Friction Payments
Include Apple Pay, PayPal, Google Pay, and BNPL where relevant. More options means fewer exits.

8️⃣ End With Reassurance
Confirmation should feel like relief. Tell them what happens next, when it ships, and how to track.

The Takeaway
Checkout is not a form. It is a momentum test. The brands that win are the ones that make buying feel safe, fast, and effortless, from the first field to the final confirmation.

In Partnership with MnH Advisory

You Earn Well. But Is It Organised?

You generate consistent income. Clients pay. Work moves fast.

But bookkeeping usually sits in the background. Transactions wait to be reconciled. Accounts are updated only when necessary. Everything feels manageable until it suddenly isn’t.

When records fall behind, clarity fades, and tax planning shifts from proactive to reactive, slowing decisions and weakening control over time.

High earners do not struggle because they lack income. They struggle because they lack structured financial visibility.

At MNH Advisory, they handle your monthly bookkeeping runs on a fixed cadence, so your numbers are reconciled to the bank, and your reporting is grounded in actual cash movement.

That gives you financial control without committing to another full-time hire.

If you operate at a serious level, your books should reflect it.

👉 Book your free strategy call today!

📝 Get Better AI Outputs Using Multiple Choice

Most people assume better AI outputs come from longer prompts. More detail. More background. More context. But in practice, that often backfires. The more text you add, the more noise you feed the model. And once the model is confused, it starts guessing.

A smarter approach is to reduce miscommunication before it happens. Instead of dumping paragraphs into the chat, you guide the AI to build context the way a great strategist would: by asking the right questions first.

The easiest universal method is multiple choice. It forces clarity, removes ambiguity, and saves tokens. Even better, it works across any AI model, whether you are generating logos, writing ads, building landing pages, or designing product concepts.

Steps to Get Better Outputs Using Multiple Choice

1️⃣ Break Your Prompt Into Three Parts
Start with a clean structure: Goal, Task, and Next Steps. This keeps the AI focused and prevents it from over-interpreting your message.

2️⃣ Ask the AI to Interview You
Instead of explaining everything, prompt the AI to ask you 5 to 10 multiple-choice questions. This lets the model gather only the context it actually needs.

3️⃣ Respond With Simple Choices
Answer with letters like A, B, C. This reduces misinterpretation and forces the model to build a clean mental picture of what you want.

4️⃣ Generate Concepts in a Token-Efficient Way
Once context is clear, ask for outputs in batches. For example, request a 4x4 grid of concepts instead of 16 separate long explanations.

5️⃣ Expand Only What Works
After seeing the grid, pick the best options and ask the AI to generate standalone versions. This keeps iteration focused and cheap.

6️⃣ Reuse the Best Context Forever
If you get great results, ask the AI to extract the final context as system instructions in Markdown. Save it, and reuse it for future projects.

The Takeaway
Better AI outputs do not come from writing more. They come from guiding the model with structure, reducing ambiguity, and using multiple choice to build clean context fast.

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