With all of the noise in the predictive lead scoring space, it is easy to get lost in the clutter.
Here's a quick look at the questions we hear most often about all things predictive lead scoring: best practices, comparing vendors, measuring impact and more.
Q. How does predictive lead scoring work?
Predictive lead scoring works by blending the account, contact profile and behavioral information from your internal systems like marketing automation system with thousands of additional attributes that could contain hidden buying signals from external sources. Whether it’s the Web, internal data, or third party sources, predictive lead scoring can find patterns in the data that rules-based scoring or gut instinct would simply miss.
Q. Is your company’s solution based on data science or marketing expertise?
Many companies offering predictive lead scoring software apply data science to sales and marketing without understanding the nuances of the lead-generation and nurturing process. The key is to partner with a company that truly understands B2B demand generation, including relevant processes and best practices. Such a vendor can advise and guide you on developing the most fitting model and making the right lead-scoring decisions. After all, marketing isn't just about predicting the probability that the prospect will buy. It's about influencing that probability.
Q. Do you have experience with my funnel dynamics?
Find out if the vendor has developed a lead-scoring model for a company of your size and in your vertical, with an approach that accommodates your unique funnel stages and lead flow. Also ask if it has experience working with companies using your revenue model, whether that’s traditional software sales, or a trial- or freemium-based software or service.
Q. What is your source of buying signals? How do you integrate internal data with external data?
Predictive models are only as good as the data fed into them. That’s why it’s critical to augment buying signals already in your internal systems with external data. Look for a vendor that handles all the collection, cleansing and normalizing of the thousands of attributes that could indicate your prospects are likely to buy. In addition, ensure the company seamlessly connects to your internal systems such as CRM and marketing automation.
Q. What’s your coverage rate for my universe of leads?
A predictive model isn’t very valuable unless it delivers good match rates to the majority of your leads. Ask the vendor what percentage of your leads can be matched to its external data, and to share specific examples of the external data sources and attributes it provides.
Q. Can you supply best practices?
A proven vendor should provide guidance on where to use predictive technology throughout your integrated sales and marketing funnel, and how to succeed with any process changes. If the vendor only supplies a point solution, it is likely unable to address this requirement or will simply offer a single one-size-fits-all approach.
Q. Does your model do anything besides predict likely events?
Every predictive lead scoring model is designed to predict certain outcomes, such as which leads are most likely to buy, or which campaigns are most likely to work. But these insights are useless unless you act upon them. Find out if the vendor’s tool provides recommendations that help you make business decisions based on the insights. For example, can it help you project the change in lead volume and conversion rate based on the percentage of leads you pass to sales? Can it help you determine which product to pitch to an existing customer in order to achieve a specific revenue goal? Or which marketing channels are driving the highest quality leads?
Q. How do you drive user acceptance of your model(s)?
Ask about methods and tools for encouraging sales and marketing to buy into predictive analytics. Find out how many sales and marketers make resource-allocation decisions based on the vendor’s predictive lead scoring model.
Q. How many leads can you score and at what rate?
As your business grows – or as you pump more leads into the funnel – you want to make sure your predictive lead scoring model can keep pace. The average B2B enterprise maintains 250,000 to 2,000,000 leads. Find out the largest database size the vendor has worked with, how quickly it can score various numbers of leads, and how many leads it can score in one hour.
Q. Where in my funnel can I apply your predictive lead scoring model?
Once you have determined whether you want to focus on leads in the early, mid or late part of your funnel, ask whether the vendor’s solution can score those. More importantly, confirm the model can score along many – and any – conversion points within the funnel, to ensure it can address your company’s unique requirements.
Q. Do you maintain enterprise-level security?
The vendor will replicate a certain amount of your data in its environment as it trains the predictive model. Find out whether or not its systems are housed in a secure environment backed by enterprise-grade security practices. Also ask whether its customers include organizations such as banks that are serious about security.
Q. How long does it take to deploy and realize ROI from your solution?
Find out the steps to implement the solution, how long the process takes from end to end, and the resources and time commitment required of your organization. Also get a sense of when scores will be delivered, and when you can expect to see the first lift in conversion rates. Even a sophisticated model can be developed within 3-5 days, and should deliver demonstrable lift within 8-12 weeks.
Q. What impact can we expect to see?
You should expect a minimum of 10-15 percent improvement in revenue realization by optimizing any part of the funnel. By optimizing lead scoring simultaneously across the funnel, you can boost revenue attainment by 20-25 percent.
Predictive lead scoring is quickly becoming the edge to gain an advantage over your competitors. If you are looking to understand more about predictive lead scoring, download our free ebook, The Buyer's Guide to Predictive Lead Scoring.
(This was originally posted to the Lattice blog.)