so this would be sent as marketing emails - even if we send the email from a rep (or 'send as' email, I believe we would still need to add a subscription type to that email... we wouldn't be using sequences for this right now
Hey everyone, for those using HubSpot with data privacy turned on, how do you manage subscription types? Here’s my conundrum: let’s say someone submits a form but doesn’t tick the box to accept marketing comms. Even if they don’t tick it, I’d still want to mark them as a marketing contact and send them the auto-responder email (and maybe 1–2 marketing email follow-ups related to their enquiry) That means I’d need to add them to an email subscription type (because of 'data privacy' being on). Does this mean I need to create a dedicated subscription type just for auto-responders and enquiry-related marketing emails, and then a separate one for broader marketing comms (if they tick the box)? I’d prefer not to create a whole new subscription type just for form follow-ups, but maybe there’s no way around it. How have you handled this? Of course, I want to keep this fully GDPR compliant.
thanks for your inputs, it all makes sense - a few more options to consider 👍
Hey all, I was wondering how you approach the infamous issue of “we’re under our pipeline coverage target, we need to plug the gap quickly, and demand gen has to 'step in'.” I get that there are many ways to handle this, but in my experience, short-term fixes usually derail existing priorities, burn teams out faster (in a global company there’ll always be a region underperforming), and rarely deliver meaningful results. More often than not, it feels like stakeholder management - keeping sales/revenue leaders happy - rather than something that really adds much pipeline. This feels especially true in established companies, where anything that works in the short term is already part of the regular playbook. There isn’t much new we can just suddenly add on top. At the same time, simply saying “we’re already focused on things that build pipeline over time” and ignoring requests from stressed sales leaders doesn’t work either. One idea I’ve had is to plan a little extra bandwidth each month for new tests or small adjustments that could support a region if needed. For context, we’re still working out some basics, like even setting up weekly pipeline meetings. And if it helps, this isn’t a VC-backed startup running at a loss, there’s no 'actual' pressure to keep chasing short-term wins over longer-term growth. It’s more about internal performance and whether people meet their goals. Anyway, I don’t want to make this too long. Curious to hear how others handle this. 👂
hey everyone, do you know of any good resources on building an email nurturing program in 2025? Someone on my team is starting to build one and I'd like to give them guidance. Email nurturing can quickly become 'busy work' with no meaningful impact, so I want to avoid that. I define it broadly - anything from mass newsletters to segmented flows for specific personas at particular stages.
I apologise for taking so long to reply Dan Rényi, Tyson P. I still need to work on this idea, but the way I used predictive lead scores before, it's all done for me. We would assign a likelihood rate of conversion to any free trial we received and prioritise accordingly. It was based on a few properties, and the model determined the weights. We weren't the ones saying that 'country' property is worth 20%, employee size is 10%, etc. The model determined that by itself. So I didn't have to worry about how the model was built. All I had to do was choose which properties to include in the model, and it would do the rest. So, all I'm thinking about is how I can apply that simple model (how likely is it that this free trial user will convert to a paid user within the next 30 days based on these five properties?) to a more complex model in which we say (how likely is it that this company will become a customer within the next 90 days based on all of the properties we discover in the CRM?). The complex model would "run" the analysis and update the likelihood conversion rate whenever a key event occurred, which would have to be specified. Maybe I need to be less ambitious and do like Tyson P. suggests. P.S. All this was done on HubSpot, so I don't know how this would work on Salesforce. I'm also using HubSpot the moment. To be honest, I was even thinking about developing this with a developer friend of mine and releasing it as a HubSpot marketplace plugin.
Pat H. what do you mean by creating the scoring model? So, the idea here would be to avoid that entirely. The predictive lead score does all the calculations for you, etc. Chandana P. I think it's a different context. 🙂 Jean T. based on everything 🙂 not just intent. The idea is that anything that happens in the CRM feeds that specific lead score which is automatically calculated for you.
hey everyone, has anyone developed a predictive lead score based on their CRM data? For context, a few years ago I worked at a SaaS with hundreds of inbound trial sign ups, and we developed a lead score - both co-founders were developers, which helped - that would tell us the likelihood of someone converting to a paid user based on historical data and a few key variables - country, company size, whether they use their work address, company type, and so on. I'm wondering if anyone has ever created a lead score based on the entire CRM database and all of the various datapoints captured across all objects to determine the likelihood of any given contact or company converting to SQL or opening a new deal within the next 30 days, or something similar. The lead score would be updated when a key property is changed, a new object is created, a form is submitted, and so on. This should be technically feasible, and given the well-known frustrations of building manual lead scores with manual inputs, I'd be surprised if no one has ever tried to develop something similar before. I'm asking this now because I have access to a large database (hundreds of thousands of records) and I'm currently using Clay for data enrichment, so there's a lot of potential for developing such a predictive lead score.
