Some people talk about Big Data. Jason Garoutte lives and breathes it. As chief marketing officer for Mintigo, he has valuable insights into how data is revolutionizing sales and marketing.
Garoutte covered this timely topic during his presentation at the Inside Sales Virtual Summit. You can view his presentation in the YouTube video below.
4 Crucial Questions
1. What data is available?
About 2.5 quintillion bytes of data are created on the Web each day, according to an IBM study. And 90 percent of online data was created in just the last two years.
Common sources of data include:
When people share information on the open Web, they are revealing their interests. This is really rich information for a sales team.
For example, LinkedIn is amazing. Most sales reps already use it to figure out what they’re going to say to a prospect on a call. But a lot more data is available than what you can sort through on your own.
2. How can you identify your prospects’ interests?
Garoutte demonstrated how you can use Facebook’s Graph Search to figure out what your prospects care about. It’s a relatively new tool. So, it should become more useful over time as Facebook upgrades it.
Here are the steps:
1. Go to www.facebook.com/about/graphsearch
2. Enter a search, such as “People who like Microsoft and work at salesforce.com.”
Facebook will show you people who work at Salesforce and who have liked Microsoft. That’s information that might be useful in a sales conversation.
Facebook’s graph search is a great place to start. But the truth is, you need to create a complete picture of the person you’re trying to sell to. You probably can’t find all of this information in one place. Seek to identify patterns across multiple pieces of data.
Here are a couple of places to find useful data:
Social profiles: People reveal things about what’s interesting to them and who’s influential to them. If you look at LinkedIn, Jigsaw and other sources, you can infer things about a company’s org chart. Do they have a small IT department? Do they have a large IT department? Have they been doing a lot of hiring lately?
If you sniff out the interests that are relevant to what you sell, you can boost the performance of your inside sales team.
Garoutte offered up a strong example of this magic in action. Orange, a telecom company, started using big data to find signals about who was ready to switch phone companies. Their inside sales teams saw their conversion rates skyrocket from about 4 percent to just over 9 percent. That’s proof that you can take Big Data to the bank.
3. Are you smarter than a machine?
The good news is that you probably are smarter than a machine. But machines can still help you sort through data more efficiently.
Garoutte says everyone he talks to in marketing and sales says, “Sure, we know our buyer.” But a lot of them still rely on dinosaur data, like SIC codes. This data often lacks the precision you need to succeed.
Garoutte recalls one client that was targeting software companies. His team analyzed who was buying this company’s product and identified a common interest. That interest centered on technical sales. Being a software company was a good indicator, but it wasn’t the only indicator.
If a company had a large part of its org chart working in sales, that was helpful. If it was hiring a lot of sales engineers, that was a good sign. If the company’s website included words like “request a demo” or “free trial,” that was a plus. But if the website said, “Pay with your credit card,” that was bad.
This analysis revealed a ton about this company’s ideal prospects. The target account list for its sales reps is not software companies. It’s companies with a complex technical sale. Machines can help you establish more precise criteria.
People are not that good at guessing what things matter in a sea of Big Data. Machines learn by example. You ask, “What are the characteristics of people who have bought versus those who have not?”
Machines find valuable patterns, some of which you guessed on your own and some of which you didn’t. The result of all this synthesis of data is that you can identify the perfect prospect for your product. You also can identify the best way to talk to that prospect when you call them.
Golden Factoid: VPs are 80 percent less likely to register for a webinar than managers are. So, if you’re pitching a webinar, it might not be the best time to email executives on your house list. However, you might want to send them an infographic because executives do like to look at pretty infographics.
Machines aren’t smarter than people. They’re just a lot faster. They can scour the Internet a lot faster than your sales reps can.
4. What are the essential tools for sales productivity?
You start with a list, right? You go to Data.com or NetProspex. Maybe you search for names on LinkedIn. You attend trade shows.
You need a machine that takes those names, enriches them with data, and figures out who’s interested in what.
Golden Factoid: As a general rule, 90 percent of your opportunities come from just 10 percent of the names on your list, Garoutte says. Are you wasting time with prospects who aren’t a good fit?
You have to know your personas. Figure out what your ideal prospects are interested in. You can find out who influences them by looking at Twitter. You can pinpoint interests on Facebook. LinkedIn shows you what groups they’ve joined. If you don’t do this with machines, hire some interns to do it.
Use a scoring system. If a lead meets the right criteria and is ready for sales, send it to sales. If it’s not, nurture it.
Share all of this rich data with your sales reps. Your reps check their lead records. They click through. They get the data. It helps them figure out: Does this company use a certain technology? Is this company actively growing or hiring? Which of my products is the best fit for this prospect?
Make the most of Big Data. It can help you identify your ideal prospects. It can reduce the time your sales reps waste with the wrong prospects. And it can give your closers the information they need to win more deals.
Remember, machines can analyze vast amounts of data a lot faster than humans can.
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