In my last post, I talked about how the quality of leads is more important than the number of leads in your sales pipeline. Here, I’ll talk about how data science can help improve the quality of your leads.
Thomas Edison once said, “The value of an idea lies in the using of it.”
The idea that we are talking about is that big data will transform sales and marketing. Of this there seems to be little disagreement. It’s easy to find articles that proclaim that big data can be used to make marketing decisions and target prospects, while marketing strategists point out that marketers should become “data capable” themselves.
It’s good that we all agree. But how can marketers use big data meaningfully without requiring a team of data scientists?
Before we dive into the numbers, let’s define some terms: Big data is the phenomenon of vast and growing quantities of structured and unstructured data on the Internet. But it is also generally taken to mean the capability to manage such data, “at the right speed, and within the right time frame to allow real-time analysis and reaction.” The science that focuses on assessing such large amounts of data is, not surprisingly, called data science.
So how does marketing factor in all of this?
Marketing has seen a lot of innovation in the past few years. Sales and marketing automation platforms have incorporated sophisticated tools that analyze large data sets to glean insights from blobs of bits. The analyses can be descriptive, summarizing what happened, or predictive, make assumptions on what is likely to happen. In fact, tools that analyze customer interactions, either on the inbound side or in the CRM database, are now widely deployed in most companies. From that standpoint, marketing has done well to incorporate data-driven insights into the age-old funnel.
However it’s a bit paradoxical that even as the entire funnel has become data savvy, the input into the funnel – leads – relies on limited information. Inbound leads, whether sourced from events, landing pages or search engines, rely on user activity as an indicator of interest. For instance, a prospect downloading a white paper from a company’s website or attending a webinar is assumed to be an interested buyer. But that’s only part of the story. It’s almost as if Alice is looking through a keyhole and trying to get a clear picture of what lies on the other side of the door. Like Alice, most marketers view their prospects through the tiny lens of inbound activity.
Data science can help give you the full picture. It can tell you exactly who your buyers are, when they’ll be in the market and what their preferred products are.
Looked at this way, the approach of traditional lead generation in casting a wide net and discarding the “bad fish” seems inefficient, to say the least. What data science does is apply a sieve at the top of the funnel to separate generic prospects from the ones that are relevant to your company.
What does the whole picture reveal? Data points, spread across the Internet, that, when collated and linked, indicate a pattern. For instance, aside from the initial nod of interest from a prospect, other bits of information that may confirm buying intent are: How much does the company invest in the kind of product that we sell? What is the current product that they use? When did they last renew or update it?
Consider the following example. Two prospects, one a director of IT at Bank A and the other the chief information officer at Bank B, attend a webinar hosted by an IT storage company. For the storage vendor, both these prospects are hot leads. Now let’s consider additional pieces of data. Bank A plans to open new retail branches this year. Additionally, Bank A’s storage network is more than three years old and uses a vendor that has had recent performance issues. Given the additional data points, which do you think is the right prospect to pursue?
The answer seems obvious once you have the full picture; of course Bank A is a better target. But getting to that clarity requires sifting through large data sets and normalizing signals across multiple dimensions. This level of sophisticated correlation was not possible before, but the technology exists today.
Why not use it to deliver better leads to your sales funnel?
My next post will discuss how marketers can actually use these data-science driven leads. In other words, if you know the defined universe of your prospects, how do you win them over?
By leveraging data-science and machine learning techniques, demand generation teams can now automate the opportunity identification process, completely reshaping how public sector marketing is driven.