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Closing Deals with Analytics

Targeted analytics, when shared with sales, can close new deals

Throughout my career, I have always made it a priority to stay close to the “front line.” There are many reasons to do this in any role – uncovering new or unmet customer needs, understanding how products and services are performing, and learning about new competitive threats. However, when you are leading a team that is responsible for decision support via analytics and insights throughout Dun & Bradstreet, that relationship and ongoing sharing and feedback loop with sales teams becomes downright vital.

What I’ve also found is that most salespeople are truly unique in the way they approach their portfolios, partner throughout the company, and work with their customers. Oftentimes that is linked to the types of customers they work with, but sometimes not. Although samples are never perfect, I’ve found it important to stay informally in touch with a small group of salespeople that have a mix of personalities and portfolios. Ultimately, this ensures I have a general pulse on both sales and customer needs, and they have an opportunity to tell me what analytics are working and what they need.

With a big data analytics market that will reach $210 billion in revenue by 2020, growing at 11.9% annually (CAGR)*, it is critical to be able to show measurable impact in using analytics to achieve your corporate strategy and revenue goals.
Rachel Richter

To that end, I feel extremely lucky to be part of a data and analytics company and a marketing organization with a truly data-inspired approach. We have invested heavily in “drinking our own champagne” by using data and analytics to target our marketing programs and leveraging behavioral signals to enable website personalization and best-in-class conversion rates. But the more we began using digital behavioral data, the more it became apparent that there was an expanded use case to share this information directly with our sales team, particularly for companies visiting our website that did not convert to a lead.


Enter Wayne Silverman. Wayne is a Sales Vice President in our Hi-Tech Strategic vertical. He is forward-thinking, collaborative, ambitious, and committed. He values and uses data and analytics regularly, and coaches his team that “knowledge is power.” As you might guess, he is part of my informal feedback group and has nicknamed himself our “educated guinea pig.” He also has a deep digital background and has been at the forefront of converging D&B’s offline and online information to drive impact.

As many of you may have experienced, larger companies have very complex family trees, as we call them at Dun & Bradstreet, with many branches and locations. Oftentimes, companies are only doing business with a few locations, while there is whitespace elsewhere. So how do you increase your penetration of these larger companies by precisely identifying the locations with buying power and need for your products/solutions? And how do you know when is the right time to target them? That is a beautiful problem to solve via both predictive analytics and behavioral data, fueled by Dun & Bradstreet’s commercial database.

After a brief discussion with Wayne, we decided to take a deeper look at the whitespace in his portfolio, using both propensity (look-alike) models and digital behavior. My team began dissecting our D&B Visitor Intelligence results from our website, dnb.com, to see if any of Wayne’s customers have visited the site, and if so, what they were consuming. Lo and behold, we quickly discovered several of his customers consuming website content about products or new lines of business that not only were they not buying, but also had propensity to buy based on our analytics models.

One technology customer popped up with a location in Boston that was recently and frequently consuming information digitally about D&B Credit, a new Trade Credit product. Wayne and his team had been focusing on other lines of business and did not realize there was interest in this product. As a result, they immediately took the initiative to schedule a meeting with their contact at that location. Within two months, Wayne and his team advance renewed this customer, avoiding retention risk/pressure by upgrading them to a more modern platform.

Here’s why I think we were successful, and how you can replicate the process for your own business:

  1. Partnership – A deep and open partnership between sales and analytics teams ensures that there is ongoing communication to identify needs and opportunities, test new analytics, and maximize results.
  2. Belief – Trusting analytics is not in every salesperson’s nature. However, believing in its value even if not always 100% perfect is critical to unlocking the potential.
  3. Consistency – Leveraging all sources of data and analytics enables a consistent “conversation” with customers and prospects across all channels and a best-in-class experience.
  4. Timing – Being able to make predictive analytics timely and relevant through behavioral data ensures you are talking to the prospect when they are most interested.
  5. Relationships – Excellent salespeople constantly cultivate and build new relationships, with both current and prospective accounts.

And here is the equation that sums it all up: Targeted Analytics + Omni-channel experience + Best-in-class selling skills = Closing new deals.

From Wayne’s perspective, he attributes the success to “seeing the signal and acting on it.” He advises that “sales and marketing professionals should all be using modern signal data to identify new opportunities. Sales leadership, as well as front line sales, needs to push to have access to this type of information. Your competitors do!”

Now that we’ve unlocked the potential of predictive and behavioral analytics to prioritize sales activities, it’s all about testing with specific sales teams and ultimately scaling it. I can’t wait to see the results!

*Source: IDC “Worldwide Semiannual Big Data and Analytics Spending Guide”, March 2017

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