"First, I ping Customer Success to see if they know any happy customers in [Region / Vertical / Business Segment]. Then, we ask the customer to do a case study interview."
"Once the interview is over, we transcribe it. Then, we read the full transcript and pick out the best quotes before we send it to our Content team. They review our outline and draft a story, which comes back to us. We review the draft, and then we send the customer story to the customer for review and approval."
Me: "How long does this take?"
"It takes us about 6-8 weeks to get a draft to our customer. But it's not over yet, because once a customer approves, we send the story to Design so they can create slides for Sales."
Me: "Do you know which customer stories are popular with Sales?"
"No, but Sales tells us every quarter that they need more stories. Do you want to see the Asana project for the last case study we did?"
Why does it take months to produce a single customer story?
I've heard some version of the above story from too many companies. To get one case study into production takes 2+ months and involves 6+ people across several teams.
This approach does not scale. It never did.
But for the longest time, there was no other way. Marketers needed Customer Success to tell them which customers might be good candidates. They needed customer marketers, product marketers, content marketers, and designers to produce a single customer story.
Today, after rounds of layoffs, these functions are lean. But the workload remains. And Sales still needs more customer proof. At least in B2B software, every category has become more competitive. Sharing the stories of happy customers achieving their dreams is the difference between marketing a prospect as 'Closed-Won' vs. 'Closed-Lost'.
Why hasn't AI solved this problem yet?
CEOs expect AI to fill the gap. Predictably (albeit disappointingly), most software vendors have slapped on the phrase "AI-powered" to their pitch decks, instead of building new products that re-imagine entire workflows end-to-end.
Marketers today are stuck in a tough spot. The workload continues to pile up. The emergence of AI has created unreasonable output expectations. Finally, grandiose perceptions of AI's capabilities outweigh reality.
Much of the creating-from-scratch work that marketers did two years ago is no longer rewarded by the market. The bar has shifted, and modern marketers must meet the challenge.
What if you could tell the story of every happy customer?
If your company has 1,000 customers, are 50 customer stories enough? Why not 200? Why not 1,000?
Does your sales collateral highlight your happy customers? Does Sales use all the stories you already have? Or are they telling the same story they memorized at bootcamp on every call?
It's time for marketers to think much bigger. We'd like to help.
FAQ
Why does creating a case study take so long? The traditional case study process involves 6+ people across multiple teams and takes 2+ months from interview to finished asset. Marketers have to coordinate with Customer Success, Content, and Design before a single story reaches Sales.
How many customer stories does a B2B company need? Most companies have far fewer customer stories than they need. If you have 1,000 customers, 50 stories is not enough. Sales teams need relevant proof for different verticals, regions, use cases, and business segments.
Why are marketers struggling with increasing workloads? After rounds of layoffs, marketing teams are leaner than ever, but the workload has not decreased. AI has created new output expectations from leadership, while the reality of most "AI-powered" tools has not matched the hype.
Why doesn't Sales use the customer stories marketing creates? Often it's a discovery problem. The stories exist, but reps can't find the right one at the right time. They default to the same story they memorized at bootcamp because it's easier than searching through a repository.
What does a scalable customer story process look like? A scalable process removes the manual bottlenecks. Instead of pinging Customer Success, transcribing interviews by hand, and routing drafts through multiple teams, AI can surface advocates, generate drafts, and deliver finished proof to Sales automatically.
