VertifyData’s Post

Transcript

Broken into four different quadrants, we typically group can group our organizations into these four different areas, whether they're operational, exploration, innovation and transformation. And that's really kind of speaks more to how they use data more than anything else. You know, at the bottom of the the model we have operations and this is where we find organizations that really have disconnected data. Courses so. Uh, looking at organizations that may have CRM, but they may also be having data may also live in various Excel spreadsheets that may live with sales or customer service or even with. With our renewals group itself, and there's not a lot of forecasting that's being done with this data. It's pretty much straight up the data is there. There's no real experimentation, any type of renewals program. They're just really kind of taking that data and then looking to see, OK, when's the date hitting a send to do some kind of renewal notification. Usually these organizations typically have just one kind of single data source. As we go up the maturity model, you'll see that. Revenue increases as we start doing more with data. Once we get to the exploration model, these are companies that are starting to have real simple integrations, maybe pulling in together, pulling data, different data sources together and combining that data, trying to find a single source of truth or record. Starting to use forecasting and starting to do some kind of prioritization as far as either the contacts that they want to reach out to or maybe even the systems. Or the type of renewal that is that they're wanting to go after. If you're talking about hardware, maybe this particular particular model or a particular piece of hardware and software based particular service line within the software or, or or a certain term or link for the for the renewal. And with that, that's when we start talking about some of that experimentation, looking to see what the data is starting to tell them how they can combine data. Have better outcomes and how they can build and we're forecasting as far as what's going to result when they're combining the various points, various pieces of data. It's interesting. We started working with some of our clients. This is kind of where we a lot of times come in with this the exploration area and then we grow into the innovation and transformation. So when we first started working with some clients on renewals programs, we weren't really weren't doing propensity modeling. It was really kind of basic starting at this kind of operation exploration starting with maybe one data source and then growing into two data sources and growing it out from there when we got to the innovation stage with clients. This is where we're starting to really kind of have some fun with the data, so to speak. We're really starting to pull in a lot of those different data sources, combining them, looking for an overall, which may call like a rose record, Roseville record or gold record or platinum record of who's going to be the most likely person to renew. The renew the service warranty or renew the warranty forecasting starts to play a huge role in this area. Now we started getting a better idea of the different cost associated with the renewals and revenue associated with renewals, start looking at what's the likelihood that they're going to renew. We start doing law of revenue forecasting as well as have a better idea of what particular wines may have a better publicity to renew and then you get to transformation. And this is kind of the ultimate goal. All the data sources are unified across multiple systems. You really start getting into account intelligence and really doing in the predictive modeling and this is where we start pulling out a lot of like kind of AI and machine learning into the into the system. So some of the programs we have now leverage a lot of that machine learning of taking all the records and identifying like, OK, who from this company or who's in this organization is going to have the most likely to renew. A lot of times when you're first starting on the renewal's journey that information's. Back to the person who purchased it, once we kind of travel down this journey of data, we started realizing, well, maybe it's not the person could purchase it. It's more likely the person who is using it on a day-to-day basis. And you start taking into account information from accounts and information from like any kind of break fix calls, customer support that's calling in and customer service to really consider doing predictive modeling and see who has maybe engaged with previous conversation communications before who's called in. On systems and using that kind of information to really predict and learn as far as like who's going to have the most likely to respond to something that's being sent out materials for sale.