Perfect Your Prospecting: Four Data Factors That Matter For Effective Targeting
06 July 2015
→ Brad Young, Global Content Marketing Strategy Leader, Dun & Bradstreet
Cartoon credit: Tom Fishburne, The Marketoonist
My sister-in-law teaches advanced placement statistics at a good high school in the Philly suburbs, which means her students are probably smarter than most of us. Well, smarter than me, anyway. The only math class I ever liked was geometry because there were pictures.
This was a bit of an intimidating factor when I visited her class as a guest speaker a couple Fridays ago. I was nervous – like, nervous – as I talked with them about the whole Mad Men Meets Math Men thing that will continue to dominate marketing. Stat and math majors don’t need to grow up to be accountants or astronauts, I told them. While there were already many noble professional options for the numbers-minded, I wanted to make sure they knew modern marketing was starving for them.
The theme of my talk? I wanted to meet the people who were going to take my job one day. And when their prepared questions included stumpers like Since compiling and analyzing large sets of data can be overwhelming, what is the first step in your process for analyzing such large amounts of data? and What are your standards or requirements to see if a data source is reliable and unbiased?, it’s no wonder I think they will be the ones kicking my tired old bones out to the curb in a few years.
Spending time with them was a ton of fun, and the kids were as impressive as expected – other than the fact that not a single one of them could recognize a picture of Muhammad Ali. (“O.J. Simpson?” one of them sheepishly guessed.) They redeemed themselves when I tried to test them with a question of my own.
My five-year-old daughter is, in fact, a girl (50% of the population), but she is also left-handed (10% of the population), has green eyes (2% of the population) and is a redhead (1% of the population). My question for the students, then: How many left-handed, green-eyed, redheaded girls are there in the world? The answer they gave me: About 1 in every 100,000 people would share this combination of traits. So only about 70,000 people in the entire world. I trust them that that is correct, because I have no way of knowing otherwise.
A few minutes later, I brought this back around for them. As I walked them through the core dynamics of data science-inspired marketing – the ways intelligence like theirs will be applied by companies like yours and mine across the buying cycle – we started with targeting. While my daughter’s example might be a somewhat extreme overlaying of rare characteristics, I told them, B2B marketers (like Dun & Bradstreet and our customers) often feel like their most valuable prospects – the ones that can truly drive the big deal – are just as niche and therefore brutally hard to find. Much less find, then convince, then convert.
Indeed, successfully targeting and engaging your company’s equivalent of redheaded, green-eyed lefties – say, perhaps, procurement VPs and above at growing mid-market firms with suppliers in China – without also bringing in a lot of blue-eyed, righty blondes you don’t care about can seem as knee-knocking as being the only thing between a bunch of wicked smart 12th-graders and the cafeteria. But like seemingly all things data, you don’t need to get caught up in the complexity. In our Quest for Data Clarity, lets boil effective targeting down to its lowest common denominator. (Yeah, math term!)
To do so, I talked to someone else a lot smarter than me – Dun & Bradstreet Chief Analytics Officer Nipa Basu…
Slight in frame and quiet in voice, Nipa Basu might not seem on the surface like the heavy hitter she is. But when a large-enterprise executive wants to find growth and is asking how data can tell him where it is, she is the one you want in the room.
“At the high end,” Basu says, “everybody knows who to target. The issue is your competition is all targeting the same prospect. It’s about how and very often when should I target, not who should I target.”
Understanding the how does begin with the who – the basic firmographic data of industry and company size. “All B2B marketing analytic solutions start with those two,” Basu says. But where should you go from there in your effort to separate the signals from the noise in your targeting and segmentation data? Here are the four essentials Basu recommends you focus on.
1.) Make the choice between volume and quality of potential customers
“I have had customers say to me, ‘I don’t care how much potential revenue they can generate, I have been asked to grow the customer base,’ ” Basu says. “I have also had customers the other way around, saying, ‘We have a bunch of low-margin, low-profit customers. We are now trying to attract the cream of the crop.’ “
This is a Sophie’s choice for many execs, who tell Basu they want both “tons and tons of customers but also those with the highest revenue potential.” But that will mean either one approach settling for an intersection where you sacrifice a little of both on either side or, as Basu often recommends, creating separate strategies. You develop one that targets the high-value potential customer that is hard to convert because undoubtedly all your competition has found him, too, and another that expands your customer base with those that are low-value at the outset but have the potential for growth.
2.) Know who will grow
After choosing the right industries and rank ordering the companies within it by size, a simple demand estimation filter lets you see that “the companies that are likely to buy more computers are not going to be the same companies that are buying hospital beds,” Basu says.
And after sorting through all that, wouldn’t you really want to know one very key factor about the remaining companies: Which ones are ready to grow? Your key to effective segmentation and targeting could very well boil down to attracting a list of prospects before their size and success made them a target for all of your competitors.
Yes, this can be somewhat evident in publicly available signals like social media chatter, number of patents being filed and jobs being posted. In addition to those, Basu and her team rely more heavily on proprietary signals about companies that they draw from the Dun & Bradstreet database. These include one company inquiring about another, a company updating its own information, a company that starts monitoring other companies, one company paying another, and so. Taken individually, these indicators might not tell you much. But taken together and analyzed for their changes in volume over time, you see the combinations that point to a company on the rise. “It’s a matter of synthesizing the information to draw the right insight,” Basu says.
3.) Don’t go it alone
This one is pretty straightforward: You very likely don’t already own the data and analytics you need for effective prospecting. You have to supplement with third-party insight.
“When you are talking about existing customer management, there’s still a lot of value from external data, but you already know so much about the customer that the relative value goes down,” Basu says. “In the case of prospecting, you should always go outside for the data and often go outside for the analytics.” Even if you have a large analytics team of your own, you want to tap into the insight and experience of external partners that have processed a lot of data on your segment of interest rather than building that all yourself.
4.) Focus on the right questions
Partners can also help make sure you are focused on the right things. Basu will often hear from companies that their need is simple – they just want some data, a straightforward calculation, and away they go. “It’s almost never the case,” Basu says.
She and her team more regularly engage companies in deep, detailed “problem formulation” sessions where what a company truly needs is drawn out from what they originally said they wanted. Far from a scripted list of questions, the conversations focus on not only the goals of the company but also the prospecting behaviors prevalent in their industry. It involves probing whether they want more customers like those in their existing customer base or if they want to branch out. The conversation also addresses the success of previous efforts, brand perception of the company and the products at the heart of the campaign. Is the product they are marketing similar to what they have sold before or is it new? Is it the product appropriate for the target market or should they rethink who they are going after?
“Hiring a bunch of statisticians is not that difficult,” Basu says. “Our customers expect us to refine their ask before we actually do something.”
Basu has seen the dynamics of prospecting change a lot in her career, and the biggest factor now is immediacy. How can people, processes and technology combine to give you real-time updates of the data and real-time tweaking of analytics models – all providing an in-the-moment perspective of your company’s most important relationships? “I don’t think anyone has cracked the code of actually putting high-end, sophisticated analytics into automated delivery engines – but there’s a lot of investment going into it,” Basu says.
Maybe one of the kids in my sister-in-law’s class will be the ones to do the cracking.
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Brad Young is the Global Content Marketing Strategy Leader at Dun & Bradstreet. You can follow him on Twitter @byoung07016 and email him your Connectors content ideas at firstname.lastname@example.org. This article was originally published in Dun & Bradstreet’s Connectors, which can be found here.