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Predictive analytics is way ahead of even lead scoring in helping companies close new business, and new SaaS vendors make it easier for companies to adopt it.

In this post I'll look at how B2B marketers can make use of predictive analytics to provide double digit increases in leads, opportunities and sales.

But first, here are a few examples of well-known B2C brands achieving big wins with predictive analytics and marketing automation.

Imagine that you have a friend who’s a woman in her thirties. She’s married with one child around two years old. And on Pinterest you suddenly see her pinning things like this:

You might deduce that she is pregnant again. Given her demographic profile and social media behavior, it’s a reasonable guess. And if you did guess that, you’d be engaging in an informal form of predictive analytics.

Now imagine that you’re a retailer and a female who’s aged around 16 suddenly starts looking at items on your ecommerce site that are typically bought by women who are pregnant. And she buys some of them on your site and in your stores.

So, to encourage her to buy more, you send her a few coupons for similar items and before you know it, her angry father is in your store accusing you of encouraging his teenage daughter to get pregnant.

This happened to Target. But what the father soon found out was that his daughter actually was pregnant. Target knew it before the father did.

Target realized that pregnant women in the second trimester often bought unscented lotions as well as supplements like calcium, magnesium, zinc, and other items.  That behavior identified likely customers, but then Target had to be careful about how they marketed to them because a pregnant woman doesn’t want to get a flyer with the headline “Congratulations!” if she hasn’t registered on a Target baby wish list. The incident with the teenage girl and her father was very instructional.

Finally, when Netflix presents you with these suggestions? Yup, predictive analytics:

A spokesperson for Netflix described the breadth of the company's data and signals: "We monitor what you watch, how often you watch things. Does a movie have a happy ending, what’s the level of romance, what's the level of violence, is it a cerebral kind of movie or is it light and funny?"

30% of Amazon’s revenue is produced by its recommendation engine. In 2012, the Obama campaign hired more than 50 analytics experts to prioritize voters and determine which messages and outreach techniques were likely to be most effective with each individual voter.

Predictive analytics in B2B

Large B2B companies have been using predictive analytics for years, too, to better prioritize sales leads, determine which products a prospect would be most likely to buy, nurture contacts who aren’t yet ready to buy, and develop more reliable sales forecasting. It used to be that only the largest companies, like IBM, SAP, Target and Amazon, had the data and data scientists to do it, but that’s now changing.

Predictive analytics is being democratized. Lattice and Mintigo are two of the companies that are providing cloud-based B2B predictive analytics services that eliminate the need to hire increasingly-pricey data scientists internally. Their SaaS services start with the company’s internal CRM and marketing automation data, and then they add in data from thousands of public sources such as company revenue and income, number of employees, number and location of offices, executive management changes, credit history, social media activity, press releases, news articles, job openings, patents, etc. 

From this they use data science to identify common characteristics of the accounts that were won by sales, and predict the likelihood of closing each prospect. For example, a good signal for an office supply company to contact a prospect may be when they sign a lease for a new building, or put out a press release about expanding to more cities or hiring many new people.

Sometimes the signals are far more obscure than that, though; for a company selling CAD-CAM software a key signal was the number of design engineers prospects were hiring and the number of workstations in use.

Sales has prioritized leads and sales people have important new information about the accounts, which cuts down on their research time. Marketing has segments of lower-priority prospects to nurture. And predictive analytics can be equally useful in growing existing accounts and closing new ones.

This goes way beyond the lead scoring of a marketing automation system, as valuable as that is. Marketing automation typically just uses the information from the CRM and the 'digital body language' of a prospect’s online interactions with the company’s website, emails, and other digital communications. Predictive analytics companies are adding a huge amount of data to that, and then sifting through all of it to find the most useful buying signals.

Actually, this image doesn't fully capture the advantage of predictive analytics, which can be basing its recommendations on 50 to 100 times more data than lead scoring that's based on internal data.

With new sales and marketing technologies like marketing automation and predictive analytics there’s a huge advantage to early adopters who get it right. Marketing automation is only being used by about 12% of tech companies, and 5% of all companies, and many of them are not using it fully, or well.

B2B predictive analytics is providing double digit increases in leads, opportunities and sales - sometimes high double digits. Early adopters of sales and marketing technologies can reap huge sales increases while their competitors are wondering what hit them. But several years from now predictive analytics is likely to be table stakes – everyone will be doing it, or being left behind. To the early adopters go the spoils!

Louis Gudema

Published 19 February, 2014 by Louis Gudema

Louis Gudema is the president of revenue + associates and a contributor to Econsultancy. Louis blogs here and can be reached via TwitterGoogle Plus and LinkedIn.

12 more posts from this author

Comments (8)

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B2B companies can definitely learn a trick or two from B2C businesses. However large B2B organisations may benefit more with more data points.

over 2 years ago


Anglena Smith

why are a lot of girls burning all those fats and substituting them with muscles when it is fats that makes them feminine and hot

over 2 years ago


Simon Taylor

A very insightful post Louis. Forward looking predictive capability is key to any personalisation strategy too, applicable in B2B is the notion of product replenishment for example - i.e. a business that replenishes its stocks of toner every 5 weeks doesn't want to see toner pushed to them the day after they just bought some, whereas in 4 and half week's time that's exactly what they'd want to see. A different company may buy coffee every 3 weeks, so the same principal applies, their window being shorter - and personal to them. CERTONA (http://www.certona.com/technology ) have built a platform based on a combination of historic looking behavioural/sales data and forward looking predictive analytics, married with any external CRM or social data. That combination is increasingly being adopted in B2C and more broadly as you say in B2B. If you're interested in this space, check out the recent patent CERTONA was granted and the others pending. This technology is deployed in Staples' properties globally and across multiple channels at some of the UK's largest direct and online suppliers of tools, accessories and hardware products. It's an interesting space to be in, that's for sure! Happy to discuss if you're interested, cheers.

over 2 years ago



Thans Louis, very interesting post. We at BrightInfo definitely believe Recommendations can turn B2B closer to B2C in terms of online performance and uplift in conversions. In fact we just recently also released a case study which showed the same 30% uplift Amazon sees from Recommendations in lead generation. See www.brightinfo.com for more

over 2 years ago


Vinay Bhagat

Louis - thanks for highlighting this important trend. This is clearly an area where B2B can learn from B2C.

It is however my understanding that Lattice Engines and Mintigo serve different but related purposes.

Lattice salesPRISM enables sales forces to better prioritize leads based on their likelihood to buy. It assembles a complete 360 degree view of prospects with rich information from the Lattice Data Cloud. It then delivers intelligent, dynamic sales plays to reps that explain what products a lead is likely to buy and what talking points to use to get the deal done. In-depth end-user reviews of salesPRISM are available here:

Mintigo is a Marketing Intelligence Platform that monitors the total universe of prospects, looking for the messages that will resonate. By targeting business needs & interests, marketers break through the noise to generate more leads. An end-user review of Mintigo is available here:

Vinay Bhagat
CEO, TrustRadius

over 2 years ago


Tarun Gulati, Associate Director at KPMG

An extremely insightful blog Louis and I specifically liked the Target example. This takes me back 5 years when companies with Online reputation management(ORM) tools were trying to penetrate into large brands(both B2B & B2C). These organizations were careful whether to engage or not engage with the end customers and most of them were satisfied with Social Media Analytics only. But look where we have reached today, ORM is an integral part of the marketing plan for all B2B & B2C businesses.

Coming back to leveraging Predictive Analytics in areas like Marketing Automation, Sales forecasting & revenue management. I see most of the large companies in B2B business(even large technology companies), either beginning to realize the importance or in very nascent stages of execution of these projects. I believe there are three roadblocks:-
1. Massive Legacy Data
2. Change Management
3. Lack of a 360 degree strategy - Analytics to Real-time Prescriptions

@Simon/Boaz/Vinay - Great to know about the fantastic work being done. Do share your experience of working with B2B companies in this area, the common objections, challenges.

Tarun Gulati

about 2 years ago


Amanda Maksymiw, Content Marketing Manager at Lattice Engines

Thanks for including us, I wanted to chime in to add to Vinay's description of Lattice. In addition to our sales enablement app, we also have apps to help optimize the rest of the funnel, specifically with scoring and segmenting leads along with identifying opportunities to retain and cross-sell within a customer base.

almost 2 years ago

Rory Prendergast

Rory Prendergast, Owner at Conginuity.com

Love that phrase 'digital body language' Louis! I didn't realize that so much of Amazons revenue was derived from suggested products. 30% is enormous - Rory http://conginuity.com

almost 2 years ago

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