Deploying Predictive Models
MODULE 3 LESSON 3 – DEPLOYING PREDICTIVE MODELS
Deployment of your predictive model is the point at which you can start smelling the ROI. In this section, we will share a few tips and best practices for successful deployment and two case studies on how predictive marketing helped leading marketing and sales organizations boost their results.
Your model has been developed–now it’s time to deploy. Here are a few steps that ensure successful deployment of predictive marketing programs:
- Step-by-step process: When deploying your model, apply best practices and expert reviews at any stage of the process to make sure that you are creating the most powerful models to achieve your goals.
- Training: Make sure that you get the proper training and customer support experts on building successful predictive marketing models.
- Sales-Marketing alignment support: Convincing sales to adopt predictive marketing is one of the best investments that you can make.
- Engineering support: If you run into challenges with your models or deployment, get help from data scientists that can resolve custom modeling challenges.
The first case study that we will explore here is from a leading manufacturer of personal computers. The company wanted to use predictive marketing to identify its CustomerDNATM and to discover what separates wins from other opportunities out of their huge database of Milions of contacts.
Analyzing their CustomerDNATM discovered a few interesting insights. The company tends to be very strong among companies with very robust IT infrastructure and emphasis on IT and security. In addition, many of its clients tend to be heavy users of Microsoft products such as SharePoint, BI Tools, SQL Server or a be Microsoft Business Partner.
The second question that they wanted to find is what differentiates buyers from other sales opportunities. Results show that wins tend to be larger, Fortune 1000 companies, using virtualization such as VMWare and have large number of database and engineering titles.
Opportunities on the other hand tend to have CMS like WordPress and have job titles that are more focused on infrastructure and networks. Overall, differences between opportunities and wins were minimal, which shows that reps are doing a great job selling.
The second case study is of a leader in real time analytics. This company used predictive marketing in order to segment leads into two market segments.
First, the analysis discovered that 16% of the leads in the database or house list are responsible to 80% of the opportunities. This is a very important finding as it allows the sales team to focus only on the portion of the database that is likely to convert to maximize the ROI.
Next, the company used predictive marketing in order to segment it’s house list into two segments:
- Marketing service and IT security & data services. The prominent Marketing Indicators for IT security & data services were using: Cisco Certified Employees, technology like Akamai, and using VMware.
- For Marketing services, the leading Indicators were using Optimizely, which is an A/B testing tool, live chat; and having a Blog.
Overall, there are many indicators that differentiate between the two groups, but the important thing is that using data, you can tell which product will fit which prospect.
If you have any questions, feel free to contact me directly at PMU@mintigo.com.
To learn more about Predictive Marketing & Big Data, watch this webinar replay presented by John Bara (Mintigo), Megan Heuer (SiriusDecisions) and Russ Glass (LinkedIn).
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