Big Data for Big Sales: How Data-Driven Selling is Revolutionizing Sales & Marketing [Part 1]


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Big Data is widely acknowledged as the next $100B opportunity. As several observers have pointed out (including McKinsey and the Economist) this wave promises to transform entire industries, enabling a quantum jump in performance and productivity.

What is not so well understood are the many slices of the big data opportunity – specifically how these productivity gains will be realized in individual functions like sales, marketing, HR or supply chain operations.

In this three-part blog series, I will talk about how Big Data is transforming the world of sales into an information science, driving real impact while touching every aspect of the customer experience.

Leading thinkers in the sales & marketing space have realized the importance of this trend:

  • Paul Greenberg believes that insight solutions, based on Big Data, will unleash the era of customer engagement
  • Esteban Kolsky posits that Big Data will only have an impact if practitioners focus on insight generation by filtering the signal from the noise
  • Christine Crandell points out that Big Data is starting to change even simple spreadsheet-based activities like sales forecasting
  • Baumgartner, Hatami and Vander Ark’s excellent book on sales growth, highlights the importance of Big Data as one of the most important weapons in a sales leader’s arsenal

But I am getting ahead of myself. Let me first start by recapping what Big Data is, and why it is important for sales professionals.

Big Data is Not About Data

Companies have seen data explode on two fronts. Internally, customer systems like CRM, marketing automation, quoting systems, transaction systems and so on, have become more ubiquitous, more standardized, and more mature. Externally, current and potential customers are spewing massive amounts of data in social networks, blogs, user forums, product review sites, etc. In both cases – internal and external – the data is often unstructured, disorganized and growing at an exponential rate.

Unfortunately, less than 0.01% of this information is useful for discovering buyer’s intent. For a given company, it is possible to find over 1000 attributes for a customer across all the data sources that are available. If you pass all this information to a sales rep, it is entirely overwhelming and completely useless, since the rep would not know what to focus on.

So Big Data is not about data at all – it is about how effective you are at actually gaining insight from data. If you are not generating or receiving insights, you are barking up the wrong tree.

Big Data Democratizes Excellence

From a rep’s perspective, Big Data answers a major question – how do I find the customers who are most receptive to my product or service at a given time? The best reps already have a talent for this. Big Data democratizes this excellence by automating the three habits and skills of excellent reps:

1) A sixth sense when it comes to identifying opportunity: Things change – companies open new offices, win new government contracts, or increase hiring. Some of these changes may be internal – a new financial management system, changes in the network, or the opening of a new data-center. Every one of these changes is an engagement opportunity. Big Data is very effective at identifying these engagement opportunities for the rep. In the era of Customer Engagement, Big Data is the enabler.

2) Saying the right thing: You have to say the right things. A rep doesn’t have time to research and learn what it takes to be effective in every single account. Effective conversations are driven by a deep understanding of the customer’s need and an ability to identify situations where your solution can help solve their problem. Most customers are eager to be educated but they need help bridging the gap between their problem (felt or unfelt) and the solution you offer. Full contextual awareness is the first step towards gaining an understanding of their need. And Big Data can automate this process.

3) Testing and learning: Understanding how each rep is doing against different types of engagement opportunities is key to improving their performance. Leading sales teams will often organize their sales activities around the concept of plays – established targeting and engagement motions that can be measured down to the activity level. Measuring these plays across each stage of the funnel – and adjusting the approach based on results – is key to a successful Big Data program for sales.

It’s All About Generating Insight

There are five steps to generating insights for your sales team:

1) Integrate – Integrate all the (legally) knowable internal and external information about customers and prospects in one place.

2) Identify patterns – Find patterns using machine learning techniques. Patterns for prospecting are often different than those for renewals or cross-selling, so the flexibility to create and manage a large number of predictive models is key.

3) Test & Learn – Where there is no historical reference, point to learn patterns (e.g., new products) and create hypotheses based on your beliefs about markets and customers. These hypotheses can be tested by setting up structured A/B tests in order to learn what is working and what is not

4) Deliver – Integrate systems through CRM so that the prioritized tasks can be delivered to reps and their final action tracked through completion

5) Measure – Set up a system to measure effectiveness. The measurement should include both the soft metrics of engagement (e.g., did the rep act on an engagement opportunity) as well as the hard metrics of performance and productivity (e.g., what was the conversion rate?)

Exhibit 1: Small Data vs. Big Data

In my next post, I will discuss how innovative companies are already using these techniques to derive excessive returns from their investments in sales and marketing efforts.

Meanwhile, if your organization is embarking on a journey to sales excellence using Big Data, we would love to hear of your experiences.

Republished with author's permission from original post.

Shashi Upadhyay
Shashi Upadhyay graduated from the Indian Institute of Technology at Kanpur and gained a Ph.D. in physics from Cornell University. After eight years as a partner with McKinsey, in 2006 he co-founded Lattice Engines, a company specializing in B2B sales intelligence software. Now CEO, he is also an advisor to Amar Chitra Katha, India's major publisher of children's books.


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