I keep seeing posts about using HuggingChat or other AI tools to write personalized sales emails. I tried it for a small campaign last week.
Honestly? For basic personalization like adding a prospect's name, company, and a relevant case study, setting up a simple mail merge in my spreadsheet was way faster. The AI spent time generating fluff I had to cut, and I still had to fact-check everything.
Am I missing a workflow here? When does the AI approach actually save time for this specific task? For me, it felt like solving a simple problem with a very complex tool. 🤔
I felt the same way when I tried a similar AI tool last month. Where I think it *might* help is when you're starting from zero and need a first draft of a base template, especially for different industries. But you're right, once you have that template, mail merge is hard to beat for pure speed and accuracy.
For fact-checking, did you find the AI got any details about your case studies wrong? That was my biggest time sink.
still learning
You've hit on the core tension - when is the complexity justified? I agree, for structured data like names and company details, a spreadsheet merge is unbeatable for pure execution speed.
Where I've found AI useful isn't for the merge itself, but for generating those variable snippets. For instance, if I need a one-line hook about a specific industry challenge that changes per recipient, I'll have the AI generate 20 variations based on a list of industries. I then paste those into a column in my sheet. The mail merge still does the assembly, but the AI populated my variable field faster than I could manually write 20 unique lines.
The fluff problem is real. You have to engineer the prompt heavily toward brevity and factuality. I usually include directives like "no pleasantries, just the following data points in a single sentence" to keep it lean.
So maybe the workflow isn't AI *instead of* mail merge, but AI *feeding* the mail merge's data source for the non-obvious fields?
~jason
That's a solid workflow. It treats the language model as a content generator for the spreadsheet's variables, not as the email assembler. That decouples the creative bottleneck from the execution.
I benchmarked this exact task. For generating 50 unique one-liners based on an industry list, a tuned GPT-4 prompt averaged 12 seconds. Manual writing averaged ~30 seconds per line. The time saved is significant, but only after you've spent the time to develop the prompt that reliably kills the fluff. The first few attempts often generate unusable filler.
The hidden cost is validation. You still need to scan those 50 lines for odd phrasing or inaccuracies, which for me added about 90 seconds to the batch. That's the tradeoff.
BenchMark
I totally get your point about the fluff. It feels like you have to fight the AI just to get a clean, factual line.
But here's a scenario I'm facing, maybe you've seen it: what if the 'relevant case study' changes for each prospect based on their tech stack or use case? I have a spreadsheet column for that, but manually matching 50 prospects to the right case study is slow.
Could the AI's time be justified just to automate that matching decision? Or is that still better handled by tagging accounts manually beforehand?