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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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How Sales Reps Can Do More To Differentiate Their Product Offerings

March 22, 2011 0 Comments

In a recent posting, technocrat blogger Geoffrey James has taken on the topic of differentiating roughly commoditized offerings. He summarizes the winning options facing sellers of identical products as thus:

“There are five other differentiators that you can put into play which will keep the customer buying from you at a higher price.  They are:

  1. Convenience. If your product is easier to purchase than the competition’s, the customer may pay more for it.
  2. Tradition. If purchasing your product is a well-established habit, the customer may pay more for it, at least for a while.
  3. Perceived Quality. If your product is perceived to be better made, the customer may pay more for it (even if in fact it is identical).
  4. Your Personality. If the customer personally likes you, the customer may be willing to pay more to keep you “on retainer.”
  5. Mutuality. If you are involved in a partnership with the customer that’s crucial to his business, he may pay more for your product.”

All of the options on this list are viable ways to get your product (or keep your product) in the customer’s hands. Certainly, brand-driven values, such as “Tradition” and “Perceived Quality”, are a very real reason for customers to choose a good. Of his other options, “Your Personality” and “Mutuality” are roughly approximate to “Relationship value”, both leading a buyer to stick with a given seller. And I tend to agree with his closing sentiment that “’Convenience’ is the one that’s the most powerful (and the most commonly neglected by sales professionals).” But again, that is really part of the product offering (meaning they weren’t truly identical in the first place).

Unfortunately, if you don’t already have a relationship with a buyer, most of these options (other than “Convenience”) won’t be as available to you. Moreover, you will likely be on the other side of some of these options, looking in.

One differentiation that he may have left out is the ability to build expectations in your customers without an existing relationship. While charm is one (possibly antiquated) way to do this, an easy way to elevate your pitch is to avoid the product offering completely; instead, tell the buyer what you know about them. With more information available every day, a fast way to breed confidence in your customers and elevate your brand is to show what you know about their environment, challenges and goals, and implicitly their needs. When you prove you know what a client needs, you become trusted, and can help them make a product decision, rather than keeping them from going with the known quantity.

A simple analogy exists in Internet radio. There is a glut of online music options, but with a growing number of what I’ll call ‘intelligent stations’ (such as Pandora and Grooveshark). These are ones in which your past listening preferences determine your future playlists, based on a combination of data being gathered on your listening patterns and explicit preferences. While they are playing ‘exactly what you want to hear’, all they are really doing is telling you that they know what you like. In particular, you will end up choosing the one that “knows you the best”.

In the same way, if you, as a seller, can read back all the things you know about what your buyer is seeing day-to-day, your recommendations will have much more weight; even if you are proposing the same good as your competitors.

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Predicting Customer Behavior in B2B

December 22, 2010 2 Comments

As the CEO of Lattice Engines, I frequently visit our customers and prospects to talk about how they can use predictive analytics to improve the performance of their sales teams. Some of my most interesting conversations have been with analysts inside these organizations, usually very smart Ph.D’s in Operations Research, Economics or the hard sciences, often with a background in B2C direct marketing.  A common lament I have heard in these meetings is that while they have tried to build recommendation models for their sales teams, gaining adoption (and respect) has not come easily. As one analyst eloquently put it, “In my B2C job, a lift from 1% to 4% would have made me a hero, whereas to a B2B sales rep – there is no difference between 1% and 10%.”  So what makes predictive analytics for B2B different from B2C?

In our experience, the difference lies along five dimensions.

  1. Customer hierarchy: Target entities, i.e., the businesses that you are trying to sell to, have internal structure. Just saying that “Wells Fargo” is a good customer for a particular product is not helpful to sales reps; they also need to know which “Buying Location” is likely to be a good candidate, and within that which contacts are likely to be good candidates.
  2. Product structure: Products are often configurable, complex and have internal structure. Unlike a consumer product, say a book or a piece of clothing, a business product can often be sold in multiple configurations or as solutions comprising of multiple SKUs. A model built at the SKU level would be useless to a rep because that’s not the unit in which they make their sale.
  3. Sparse data-sets: The time-scale of commerce for most B2B businesses is months to quarters. Trying to find patterns at a level below this does not make sense, as it would for a B2C ecommerce site. However, aggregating data to this level creates its own problems. Even if you have 4 years of data, and the customer transacted half that time, you only have 8 data points to train on. How do you “learn” from a sparse data-set using statistical techniques that assume a dense data-set?
  4. Multidimensional data:  One of the positives of the B2B world is that there is a massive amount of data available about customers and prospects including attributes (e.g., Employee count, Revenues), triggers (e.g., recent CIO transitions), product usage (e.g., call-backs, logs) and customer interaction history (e.g., CRM, Transactional systems). The combination of sparse data with multiple dimensions creates the curse of dimensionality, and modeling approaches need to take this into account.
  5. Incomplete, Inconsistent & Dirty Data:  Different data-sets used as training parameters have different levels of cleanliness. For example, CRM data is notoriously dirty because it is entered manually by sales reps who would rather be out selling. Similarly, data from Marketing Automation systems is often inconsistent and unreliable because it does not respect customer hierarchy (see point 1), and is often built off purchased email lists. While these data-sources are very useful, any predictive model must recognize their limitations.

Since the ultimate consumer of this information is a very motivated but skeptical human-being – a Sales Rep – the bar for clarity and correctness is much higher in the B2B world. Simply porting standard statistical models from the B2C world tends not to work so well.

At Lattice Engines, we are investing heavily in making B2B sales reps more effective through the use of data and analytics, and have built our products (salesPRISM sales intelligence software and playMAKER marketing intelligence software) to overcome these challenges of providing accurate predictions for sales teams. Our models are cognizant of the unique characteristics of different types of B2B products (e.g, configurable, services, transactional etc.) and selling models.

Are you part of a B2B analytics effort? What has your experience been?

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