We are excited to welcome Ran Gishri as today’s guest blogger. Ran is the VP of marketing for Leadspace, a pioneer in social lead targeting. Ran has over 20 years of experience working for technology companies of all sizes and domains.
An increasing number of CMOs view lead scoring, the process of ranking leads based on their sales-readiness, as a key component in their B2B marketing strategy. And for a good reason. Lead scoring done right can lift conversion rates and remove some of the all-too-common friction between Marketing and Sales.
Most lead scoring techniques are based on a mix of demographic, firmographic and behavioral data. For example, if a site visitor reads your pricing page, signs up for a trial, and states she is an executive at a sizable company, the score would probably be high and she’d be fast-tracked to sales.
Here’s the problem, and it’s twofold:
- Scoring “blind spot”: we’re really only looking at what people do while they are in our “store” (i.e. when they engage with our site or emails), and know very little about what they’re up to when they’re out there on the street (i.e. anywhere that is not our site)
- Lead data quality: we trust the information shared with us through web forms, however we know most people can’t or won’t provide accurate information; and, people are constantly on the move, so data tends to be outdated
Enter Big Data.
If you’re like me, you must be getting tired of all the hype surrounding Big Data. However, lead scoring is one of the areas that stand to get most value from the new wave of Big Data analytics.
Think about it – chances are that most of your leads leave “digital footprints” online – in social networks, Twitter, blogs, press releases and corporate websites. There’s an abundance of information about your leads out there–about their true roles, what’s on top of their mind, even who they are connected to. And, the information is always fresh and typically accurate.
The problem is this data is unstructured. It does not reside in database tables, which means you can’t just import and push it into your marketing automation database. This is where Big Data analytic tools come into play, as they structure the unstructured. Armed with these nifty new technologies you will be able take lead scoring to the next level by fusing demographic, firmographic and behavioral scores with a social score.
A social score is based on all the information you can gather about your leads – individuals and businesses – by looking at the professional information that is publicly available online. A social score helps at a couple of levels:
- Purification – verification of demographic and firmographic data you have in your marketing automation tool. For example, you can check if a person (still) holds a certain position, or if a company uses a certain technology.
- Enhancement – adding a social data layer that helps score leads based on how closely they match your ideal buyer profile. For example, if a lead is a member in forums about cloud computing and follows cloud management experts, she might be a great target for your cloud management solution.
Figure out the social score, add it to the mix, and you have taken your scoring program to the next level, making it far more accurate and significantly more effective.
80% of the information generated daily is unstructured. Making sense of some of this data enables marketers to take a gigantic step forward. Not only in how they score leads, but also in understanding their customers, taking information-based decisions and making predictions. Inch by inch, marketing is shifting from art to science with the help of Big Data.