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Sales Intelligence: How Reps Find Insight in Customer Data (Part 4)

July 14, 2011 4 Comments


What is Sales Intelligence?

So far in this blog series, we reviewed various data sources that sales reps can access to research their customers and prospects. Part 2 highlighted internal data sources that sales professionals could harvest for customer insight and meaning. We made the distinction that ‘internal’ data refers to data generated by a company’s systems, employees and partners about its customers and prospects. In Part 3, we turned to external data, which emanates from outside the company (e.g., 3rd party databases, company websites, social media, etc.)

Photo credit: Juan Jose Velasquez, November 2009

By now, I hope you’re getting a feeling for how much data is out already out there. There’s certainly no shortage of ‘homework’ that sales reps could be doing to shine in their next customer interactions. And the magnitude of data will continue to grow, in terms of new sources and better coverage of companies and decision-makers within each source (e.g., just imagine when LinkedIn really goes mainstream with millions of SMBs and their employees actively participating in the network).

So, let’s be clear. Sales Intelligence does NOT mean providing even more raw data to reps, not even if it’s of the much-hyped social variety.

Sales Intelligence enables sales professionals to connect individual data dots into a cohesive picture about underlying customer needs. It weaves the challenges and opportunities facing your customers into a broader story about their business journey. Take, for example, the story of a precision-tool manufacturer that has enjoyed an annual growth rate of 17% over the past three years. A significant driver of this growth has been exports to European markets. The company secured a round of growth capital and is now about to open its first international office. The journey is making the transition from a domestic winner to an international competitor.

If you’re a bank, Sales Intelligence would advise you to avoid a generic pitch on credit, and instead, focus on describing how your trade finance and international payment solutions will accelerate the customer’s expansion. If you’re providing business services, you would want to engage in a discussion on how the customer plans to staff and support their international employees (e.g., recruiting needs, office needs, payroll solutions).

Sales Intelligence not only signals when to engage with each account and but also guides your sales team to articulate how and why your company is best positioned to help each of your customers reach their destinations.

Beyond connecting the data dots into an overall customer narrative, Sales Intelligence needs to be relevant for the day-to-day activities of sales professionals. It must fulfill at least the following operational requirements:

  • Customer-specific: Suggests sales approaches to specific accounts and contacts. In the context of Large Enterprise sales, it identifies buying centers for different types of products and services
  • Actionable: Makes specific recommendations on when, how and with whom to engage. As opposed to just providing a lead, the recommendations provide context and guidance on approaching customers and decision-makers with timely, relevant and compelling messaging. The secret sauce is the ability to digest massive amounts of data and transform it into something intuitive that a sales rep can execute.
  • Comprehensive: Integrates the reams of internal and external data about customers and prospects. For example, it’s great to know that someone downloaded three whitepapers from your website, but it’s much better to know who that person is and how this information will help their company succeed with an important business decision.
  • Prioritizing: Makes calculated trade-offs (i.e., incremental sales X likelihood of close) on which accounts/contacts to engage now and which ones to leave for another day. Selling time is a precious resource which must be aligned to the best account opportunities.
  • Justified: Provides data-driven justifications as to why a sales rep should pick up the phone and call a high-likelihood account. One of the biggest advantages that Sales Intelligence provides is the context behind each customer’s unique story and underlying needs. Sales reps are far more likely to engage on data-driven recommendations if they know the ‘why’ and ‘how’, not just the ‘what’ and ‘when.’
  • Social: Connects people and to help sales reps engage with new contacts (i.e., through warm referrals across social networks), reduce meeting prep time (i.e., by sharing knowledge and sales collateral/presentations), and maximize the chances of closing the deal (i.e., by referencing the most relevant and comparable similar selling situations).
  • Mobile: Delivers intelligence within the evolving mobile workflow of field sales. It almost seems like companies are leap-frogging handheld devices and migrating straight to iPads. Mobile delivery is an essential ingredient for Sales Intelligence.

Sales Intelligence is fast becoming a ‘must-have’ for B2B sales organizations, and has enormous potential to foster data-driven decision making at the front lines.

Is your team benefiting from Sales Intelligence? We look forward to hearing your story.

<< Part 1 Part 2 Part 3 Part 4 >>

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Betting Big on Data – Lattice Engines CEO Shashi Upadhyay Interviewed by Knowledge@Wharton

January 14, 2011 0 Comments

The following are excerpts from the interview.  The read the interview in its entirety, please visit: lattice-engines.com/news.htm or knowledge@wharton

When Shashi Upadhyay, Kent McCormick and Andrew Schwartz co-founded Lattice Engines in 2006, their plan was to offer data integration solutions. But based on market feedback, they soon changed their business to offer sales intelligence software. Upadhyay, who holds a doctorate in physics from Cornell University and worked with management consulting firm McKinsey for eight years before becoming an entrepreneur, says that the Lattice Engines software integrates internal and external data and then uses predictive analysis to present the most relevant information to its customers’ sales teams. Companies, he notes, are now increasingly realizing that better data and better information leads to better insights into what consumers want; this in turn enables the businesses to use their sales and marketing investments more effectively. In a discussion with India Knowledge@Wharton Upadhyay talks about how the growth of the Internet has transformed the way data can be used, future trends in data and what Lattice Engine hopes to achieve.

“The term ‘big data’ is an umbrella word that describes a lot of different things that are going on in the world today. It started with the advent of the Internet where we had this platform that could track user activity in great detail. You could track every click, every site that people went to. When that became possible, a lot of things that people used to dream about, like targeted marketing promotions, measuring marketing ROIs on the fly — all these things that you really couldn’t do [using] television or other forms of media — became possible. As mobile devices are coming on, there’s all kinds of new data that you can capture.

But there’s another story which has not got as much attention — that is big data and enterprises, which is where we’re playing. And that itself has two parts to it. One, over time, companies have been implementing systems that capture data. So they put in a CRM system, then they put in a marketing automation system, a service order system and so on. It was very clear even 10 years ago that people who were doing a good job of putting in systems and capturing the data and analyzing it and forecasting correctly were more productive than others. So these systems have become much more mature inside the companies.

At the same time, you have, outside the firewall, massive amounts of data being generated about the consumers and the users of [different] products — [including] product reviews, analyst reports and surveys. All kinds of information that you really couldn’t know earlier about your customers and about your users have now become widely available. As that’s become available, there’s much more of an appetite to bring it all together. You have more data, you have more information. You have more information, you have better insights into what people want, which means you’re less likely to waste your sales and marketing and other kinds of investments.”

“Our sales intelligence software integrates both internal and external data about customers and prospects, finds patterns and triggers events, and uses predictive analytics to present the most relevant information to sales reps at the time of interaction. The software works by integrating existing CRM, marketing automation and transactional systems to deliver B2B sales intelligence directly to sales reps.”

“We observed that B2B has very specific problems. It needs very specific types of data. You need to take all that domain knowledge about B2B and make sure that your software is aware of the domain knowledge. That’s what we have done differently.”

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From the Earth to the Moon: Data Volumes are Truly Astronomical

November 20, 2010 1 Comment

Companies have been harnessing business intelligence for years. So what’s the new news? Byron Acohido points out in a recent USA Today article that “the sheer volume of data amassed over the past decade is not just unprecedented, it’s mind-boggling.” Acohido cites IDC research which estimates that the volume of digital information created and replicated this year will reach 1.2 zettabytes.  That equals 1 trillion gigabytes or enough data to fill a stack of DVDs reaching from the Earth to the moon and back.  What’s more, the amount of digital information will reach 35 zettabytes by 2020.

At the same time, however, large companies are struggling to keep up. Acohido points out the challenges in integrating, updating and maintaining all these disparate data sources, and then layering analytics on top of the data to identify patterns and extract actionable insight.  This is why sales and marketing leaders at Fortune 5000 B2B companies rely on Lattice Engines software. Our sales intelligence software transforms the reams of internal and external data about customers and prospects into account-specific actions for sales teams.

So, we’re quite excited about data reaching astronomical proportions. More data means more insight and more intelligence for businesses.

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Predictive Analytics Must Be Tied to Action

October 27, 2010 0 Comments

Jeff Kelly posted a timely and insightful article on SearchBusinessAnalytics.com following the recent Predictive Analytics World conference. Jeff’s thesis is plain and simple. Even the most innovative, most data-driven, and most predictive technology will fail if it does not lead to action and changes in behavior.

In our domain, B2B sales productivity, predictive analytics can drive the following change in behavior:

  • Sales rep calls on a different set of accounts (i.e., ones with greater sales opportunity and likelihood of purchase) 
  • Sales reps engage with a different set of contacts at each account (e.g., CFO vs. the CEO)
  • Sales reps engage decision-makers more contextually (e.g., messaging that is most relevant to a buyer’s context and needs)

These changes in behavior lead to more pipeline opportunity, higher close rates and ultimately more closed deals. But what does it take to ensure that these changes in behavior happen?

Keep the output of predictive analytics simple and intuitive. Sales reps care less about mathematical complexity, and more about ease of engagement with the output. Who should they contact? Why should they contact that person? It’s not enough to tell a sales rep that a math model suggests calling on Account X. They need context and justification as to why that particular account is being prioritized. Having that context gives reps the confidence to make the call and generate a new sales opportunity.

What else drives changes in behavior? Sharing and celebrating success stories goes a long way. Those ‘early adopter’ reps who close huge deals by following analytic recommendations serve to galvanize an entire sales force around the power of data and predictive analytics to drive sales productivity.

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