Navigating the idea stage 🔭

So what does Winrate do? It was almost a few things… but most of those ideas are now in the trash bin. 

The first idea we shot down was a “Buy now pay later” SaaS financing app.

The concept: Enable sales teams to say “yes” to requests for monthly or quarterly pay terms, while collecting the full multi-year term of the contract paid up front. This gives monthly payments to the end customer (they would pay Winrate each month) while the vendors finance team collects full multi-year contract value up front from Winrate (less interest). Essentially Winrate would sit in the middle like a B2C financing app, but for B2B SaaS.

Why we shot it down: 2 core reasons. 

1. Founder market fit - I found myself speaking with leading capital markets experts constantly and I figured out a path forward. It wasn’t rocket science but it was fintech. As Kyle at York.ie asked me “Why is a SaaS GTM expert trying to build a Fintech Company?”. I loved the idea, but he was right, it was outside of my strike zone.    

2. Product market fit - I interviewed a ton of people, probably too many, including the major players in the space, and their employees. Everyone was struggling. The reason I was given was simple… CRO’s loved the idea, CFO’s hated it. I then interviewed dozens of CRO’s and CFO’s, it was the same reaction. CRO’s were all-in, CFO’s were out. 

Once the fintech play was off the table it was a great sigh of relief. I took that as a good sign. My gut knew something was off about it but I had to get it out of my system.

I went for a run. I needed think about what I was really trying to do. Sales is really hard right now, harder than it’s been in a long, long, time. I want to help sellers improve their win rates. That is my true north.

In sales when someone has a low win rate, you always go back to first principles, back to the basics.

It starts with targeting the right people at the right companies. This is often referred to as working the “ideal customer profile” or ICP. I actually believe most companies do not truly understand their ICP. It’s usually something vague like “100-1000 employees, B2B, Technology companies, with Series C or $50mm in revenue, CRO’s or VP’s of Sales” etc. Thats not an ICP, it’s a loose map for new biz prospecting at best, but what’s it even based on?

This brings us to the second idea: AI Powered ICP analysis for “True ICP” a combo of historical New Biz Win Rates, Renewal rates, sales cycle and deal size. 

The concept: Enable companies to define an intelligent ICP based on historical win rates, for not only New Biz, but also for renewal! Sales targeting is one dimensional. Every CRO worth their salt can tell you what the ICP is for new biz but I would argue less than 10% can tell you what the ICP is for the highest renewals. I would go even further to say that customer success teams don’t know either. Of course they have a loose idea, but it’s not obsessed over and tracked by rev ops like it is for new biz, but it should be. They should be intertwined.

From there, show your reps which accounts are in the True ICP and which are not. Make it easy for them to grab “green accounts” and stay away from “red accounts”, saving time prospecting, increasing new biz win rates, while decreasing bad fits.

Why we shot it down: 3 main reasons.

1. Data integrity & onboarding: CRM’s are full of incomplete and inaccurate data. To pull this off, you would really need to be in the data enrichment game. CRM’s are also heavily customized which Iwould lead to many one off projects and unique onboardings. Getting people to bite would be easy, delivering consistent value would be challenging.

2. Product market fit challenges: Earlier stage companies have less data, with these companies “ideal customer profile” is largely a guessing game based on “ideal accounts we would like” vs. using historical data on wins/losses to optimize for efficiency. This approach is no better than applying your own filters within Zoominfo, Apollo, or Linkedin Sales Nav. Later stage companies have more data and would be a great fit for analysis, but would have more IT security concerns. In the early days as a small startup it would be tough to get approved to ingest a mature companies CRM data. It’s something that we could figure out, but those same customers are also the most complex and customized within CRM, leading back to point one. Onboardings would be one offs and not scaleable.

3. Competition and AI for this use case: There are some great companies doing work on ICP today. My favorite being Gradient Works. Their angle is more on territory creation, and a bit different from the angle I would take so it wouldn’t be 100% competitive. What they have done really well is using AI to scrape the internet to avoid some of the data issues above. TLDR: I don’t think anyone has it nailed quite yet, but the big data players and companies like Gradient Works are headed in the right direction. The AI that I have witnessed in this space is off to a good start, but it’s far from bullet proof. Which leads to my biggest turn off for the idea which is sales rep buy-in. As a CRO my team would bail on a tool the second they felt they couldn’t trust it. Reps don’t have a ton of patience for AI tools that don’t get it right the first time.

So let’s assume that the industry is moving quick to help sellers prospect the right accounts. It will only get smoother as AI gets better.

Once teams are working the right accounts, its all about working them the right way.

So how are the best reps closing more business?

What are they doing in their deals that others are not?

How do we attack that? 

That’s what we are doing with Winrate. More to come next Week!

Tech I am using and loving

PhantomBuster I am not a huge fan of automation, so “love” might be a strong word, but hey sometimes you gotta’ go to the dark side. I received 900+ likes on my Winrate announcement on Linkedin. But this newsletter only got 100 signups. You may have received a message to your inbox on Linkedin saying “Hey Jeremiah, or Hey Stephanie, thanks for the support on the post, you should signup for the newsletter” when you know ol' Steve only calls you Jerry or Steph. Yes. I cheated... but I got 500 more signups! (and I responded to every person who replied! so that has to count for something!)

So what is phantom buster… it’s basically a way to perform tedious bulk tasks on various platforms including linkedin (which do not make it easy to do so).

I ran two “phantoms” which is a cool way of saying “automations”. One did an export of the 900 or so likes from the post. You then get a .csv export, that you dump into google sheets.

The second phantom needs a link to the google sheet, and then it sends out the canned message to everyone who engaged with the post.

Tasteful & relevant automation can work, and did in this scenario! Over half of the people who received the message signed up!

🔊 Spotify Playlist of the Week