The average customer lifetime value for a technology today is less than 2 years. If you are a sales or marketing executive it becomes important to track technology movement for two reasons - a) you want to catch the movement early so you can time your reach outs better b) write more targeted emails so the buyers sit up and take notice. Imagine reaching out to a buyer who just purchased a tool that competes with your product - What a waste of time it would be on your end to reach out to him ! Plus you would have just contributed one more mail to buyer spam. On the other hand, if you knew who would be buying / evaluating a tool like yours today, wouldn’t that be great information to have?
Buyers turn immune to cold emails very quickly, so it is extremely important to reduce buyer spam and focus on improving your conversion rates. In the long run a healthy conversion rate is what helps a company truly scale up.
At Slintel, we track technology movements very closely. We started with a focus on the HR segment and capture details on tool adoption, movement, trends and predicted contract renewals dates.
For instance, when you look at applicant tracking systems, we capture technology movements for over 30 ATSs today. If you have a product service that integrates with these applicant tracking systems, this would be a good way to pick the right companies to reach out to and time the reach outs better. If you compete with these applicant tracking systems, then you can look at their prior contract renewal dates and historical ATS movement for these companies to predict when they are likely to purchase again and what tool they may end up purchasing.
Here are some insights on how we are able to capture this information:
To capture ATS adoption, we first start by tracking the digital footprint of these tools. We scan publicly available information on websites, job postings, and social handles to identify what ATS a company uses. For quite a few companies ATS information is available on the career page of websites. In some cases it may not be available on the careers page, but can be found in job postings. For instance, a company hiring a talent acquisition professional may mention on the job posting that they are looking for someone who worked on Greenhouse ( customer data sheet). By crawling job postings and deriving tool inferences from their descriptions, we are able to identify a few more ATSs . Lastly we look at social references. If HR folks have a great experience with the tools they use, they often blog/ tweet about these.
But there are couple challenges when you capture data online. One challenge is that not everything online can be crawled easily, and the second is that there is quite a lot of junk when you crawl. For the sites that cannot be crawled easily, we have folks analyzing and curating insights from the data they are able to gather. As for the junk data, there is a lot of filtering and cleaning to be done. A number of urls may redirect to other sites, and one needs to pick the final redirected url. Also as you crawl, you’ll encounter job boards/ social sites that don’t actually use these tools. And lastly there may be duplicate links or sites with references to multiple ATS. All of this information needs to be filtered before the clean list can be made available.
Once we capture adoption for each ATS ( ATS data sheets) and aggregate this across ATSs, we have a good view of company - ATS mapping. Then begins the next step, where we start tracking how the information changes over time. Basically this entails a regular refresh of the entire database, and comparing the data with that of the previous month.
By capturing movement over time, we are able to look at customers who just started using a tool. And also look at the customers who just stopped using the tool. We are also able to capture what tool they moved from/ where they are moving to.
Once you capture movements, it is easy to predict when the contract renewal date would be. If you start analyzing purchase history, you can see how often that company churns their products and can predict when they’ll be looking to buy again.
Also by aggregating movements, you can derive insights on which technologies companies are moving to, from their current ATS. So no only can you predict when the company will buy again, but also predict what tools they are going to buy based on what similar companies have done in the past.
From what we’ve seen so far, here are the most common technology movements in the applicant tracking space :
This is very powerful data for sales and marketing folks. It helps them prioritize which accounts to sell to, and when to reach out to them. We’ve seen sales folks factoring these movements having a lot more success in their campaigns. Also the entire community would benefit as we would help reduce buyer spam, and also save a lot of time and effort at the sales guys end.