Prayag Narula, CEO of LeadGenius, sat down with Demand Gen Report to discuss how B2B sales and marketing organizations are prioritizing personalization with the help of micro-segments, the right data and efficient campaign planning.
Demand Gen Report: Sales (and sales operations) is always trying to identify buyers with a higher propensity to buy. What kind of data is needed for sales to prioritize the most profitable micro-segments of their audience?
Prayag Narula: Marketing and sales teams should think about buying signals in a new way. Buying signals aren’t just about engagement. They can also be specific attributes of micro-segments of your total addressable market.
For example, a publicly traded energy company uses data from LeadGenius to send outbound emails containing personalized messaging with utility rate comparisons. The energy company’s target audience is very specific: commercial building owners in six states with more than 500 square feet of southern-facing roof space. The buildings also need to be located in areas with high enough local utility rates to be ROI positive six months after installing solar panels. Together, all these specific data points combine for a very strong buying signal. If a lead does not fit these exact criteria, it is a waste of resources for the sales team to target them.
DGR: When moving upmarket, how should a B2B company prioritize personalization, volume and efficiency?
PN: Marketing professionals are moving away from mass emails and moving to personalized emails to get better engagement. In more than half of the enterprise companies LeadGenius works with, the sales development function actually rolls up under the marketing department. It is becoming clear that low-quality lead lists and blasting emails based on low-quality data from Marketo or HubSpot just doesn’t work.
DGR: How can machine learning help marketers today with these initiatives?
PN: There is a lot of hype around machine learning at the moment. Machine learning is a key component of LeadGenius’ software but, at the end of the day, human intelligence cannot be removed from the marketing and sales process, even for seemingly simple tasks like finding accurate account information.
Machine learning algorithms (the same technology behind applications like Amazon Alexa or Apple Siri) can consistently get things right around 60% of the time. We want to get closer to 100% using machine learning to go “the last mile” in real-world applications. We found the answer in creating a tight loop between human intelligence and algorithms, and so that our data is close to 100% accurate. Machine learning helps marketers today because it can automate some research and validates the accuracy of data quickly.
DGR: Sales development has rapidly become a mission critical element in just about every high growth sales & marketing organization today. Why is this the case?
PN: Sales development allows companies to specialize within the sales organization. Specialization leads to greater efficiency.
Many companies are also using their sales development teams to validate messaging and targeting in the real world. In this way, the sales/business development team plays a major role in a business’ overall go-to-market strategy.
DGR: What are some of the different challenges B2B companies face when their business sells to SMBs vs. mid-market and enterprise customers?
PN: The challenge for enterprise companies targeting SMBs is reliably sourcing accurate account data at scale. Accurate data for SMBs is notoriously tricky and resource intensive to identify. Valid, usable information is buried underneath layers and layers of noise on the Internet.
There is a massive long tail of potential SMB revenue out there. Enterprise companies that want to effectively target SMBs with outbound tactics must have a reliable way to build new pipeline at scale so their reps can spend more time actively prospecting, demoing or selling, rather than trying to find the right information for every account and entering data.
DGR: Sales velocity is a factor of lead volume, conversion rates, length of sales cycle and deal size. What levers should sales and marketing teams prioritize when seeking to increase lead velocity?
PN: This obviously depends on a variety of situational factors. One of the things that we see being effective for most companies, regardless of whether they have a fully formed account-based strategy, is using a multiple contact-per-account approach, rather than targeting a single decision maker. This has a significant impact on increasing lead to opportunity conversion rates and reducing the time it takes for accounts to move through the buying journey.
DGR: What are a few ways LeadGenius helps solve its customers’ core business objectives?
PN: LeadGenius provides B2B marketing and sales teams with highly accurate lead generation data and go-to-market insights. We do this with a combination of machine learning and human researchers. Most of our customers use LeadGenius as a strategic partner to better inform their go-to-market strategy with either new lead generation or CRM/marketing database enrichment.
Accurate contact and account data is critical throughout the customer lifecycle. Customers often come to LeadGenius with a top-of-the-funnel need, then quickly begin to see the benefits of LeadGenius in other areas, whether it be multi-channel messaging, support of go-to-market analytics, list-based advertising, account-based targeting, etc.
