One of the most important pieces of the sales cycle, especially when dealing with new and immature products, is deal sourcing. When launching a brand new product, leads from inbound and marketing pipelines are initially scarce, as there is little information on who the right buyer is and how to approach them. Part of effectively introducing a new product is helping the rest of the company understand those pieces.
Let’s assume a new product was built to solve a specific, niche problem — and that it is very good at it too. So while nobody knows about your “new new thing” yet, there is pent up demand. All that’s left now is to find a list of every company that has this specific problem and pitch them your solution. Somebody will bite eventually.
By virtue of being “a new problem to solve” for your company, you are likely talking to an entirely new set of customers. Consequently, the leads won’t yet exist in your CRM. For many teams, this means logging a ticket somewhere and hoping someone will build them the list they need. But if you want to be effective at releasing new products, you need to be nimble and iterate fast. In my experience, getting your hands dirty and compiling your own data is what pays off. Speed is essential, so the solution does not need to be pretty, reproducible, or easy to integrate with the rest of the system. But it has to be accurate, and ready as soon as possible.
Step 1: Gather your data
The first step is to figure out your source. This is where you have to get a bit creative. Sometimes it’s obvious, sometimes not, typically it will involve some form of scraping. In my most recent project there happened to be paid API services that fetch the sort of data I was interested in. That data also included an entry for the company website of each potential target. As my internal CRM also captures the same field, I knew I could easily cross-reference the two datasets and find all companies in our CRM that are present in the external dataset (effectively a LEFT JOIN).
Step 2: Transform your data
Once you know where to get your data from, you will need a way to turn it into something usable, something you can distribute among your salesforce, like a list of target companies, or even associated contacts if you want to go a step further. There are many tools to help you with that. A popular tool to collect and modify data, without the need for complex development environments (remember, speed is one of our main goals), are data science notebooks. They help you to quickly sketch out ideas and easily play around with data. My favourite tool to use here is Deepnote.
In addition to being an incredibly easy to get started on, cloud-based data science notebook, Deepnote allows for real-time collaboration with other team members, similar to a Google Doc. If, like in my case, your engineering skills are rather limited, being able to quickly ask colleagues from the product, sales operations or business intelligence teams for help with your code is extremely valuable. You can simply share the notebook URL with others, edit it with your colleagues in real time, or get their feedback through comments. All without having to send code back and forth, or having to deal with version control systems (the last time somebody thought I could be trusted with Git access I accidentally pushed my dev branch to production…). Given your primary objective of getting this done fast, all of the above are incredibly valuable.
Step 3: Final touches, execution and distribution
Finally, you can run your little notebook and it spits out a CSV with high quality prospects. Mission accomplished. For me, this approach is the perfect mix of being fast and independent, without sacrificing transparency and collaborative input. Even better, if later on somebody in the Sales team wants to refresh the data, all they need to do is open the Deepnote notebook, run it again, and they get a new and up to date version of the data. Now anyone is empowered to create lists, share them with team members who are interested in selling this new product, and hopefully make it all worth it by finding (and closing) a few high quality deals. Good luck!