sales growth: find big growth in big data - lattice engines & mckinsey

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CEO Shashi Upadhyay is interviewed in a new book titled SALES GROWTH: Five Proven Strategies From The World’s Sales Leaders (Wiley; May 2012). Written by Thomas Baumgartner, Homayoun Hatami and Jon Vander Ark at McKinsey & Company, Upadhyay discusses the impact that the big data revolution is having on some of the world’s leading sales companies.

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  • CHAPTER 3 Find Big Growth in Big Data Comparing the capacity of computers to the capacity of the human brain, Ive often wondered, where does our success come from? The answer is synthesis, the ability to combine cre- ativity and calculation, art and science.... Garry Kasparov I f you are still wondering exactly what big data is and whether its right for your sales organization, you have some catching up to do. Big data refers to the use of large data sets and powerful analysis software to uncover hidden patterns or to get real-time information. Big data techniques are used for everything from reading spy satellite photos to tracking the whereabouts of cell phone customers; from mining insights from unstructured data, such as comments about particular products and brands on the Internet, to assessing regional weather patterns to predict beer consumption. With the falling cost of data storage and computing power, big data is being applied in new ways every day. What does big data mean for sales management? It is a way to move beyond the traditional customer relationship man- agement (CRM) tools and can help with micro-segmentation, sentiment analysis, customizing cross-selling, andwith the rise of smartphones and other mobile data deviceslocation-based selling (for example, pushing real-time offers based on the con- sumers whereabouts). The example of a European retail bank helps explain how big data can boost sales effectiveness. It began with the appointment 31c03.indd 31 19/03/12 7:59 AM
  • 32 Find Growth before Your Competitors Do of a new head of sales, who was recruited from a bank that had already discovered how to use ne-grained data and rigorous anal- ysis to build competitive advantage. In her new role, she saw that nancial advisors in the banks branches did not have the right infor- mation at their ngertips to sell new products and services effectively to customers. Most branches preferred local data to identify leads, placing little value in reports from the head Leaders in big data ofce. Worse, nobody in sales had access have so much insight to the broader transaction, socioeconomic, and such powerful or behavioral data that would improve tools at hand, there success rates. The bank had only basic is no reason to rely propensity models for up selling and cross- on intuition alone selling campaigns. Having led the investment in big data in her previous job, she wasted no time lobbying for a major commitment to the new technology. In two years, the number of qualied referrals and leads fed to branches doubled; account balances rose almost 50 percent, and customer satisfac- tion scores were up. In total, the bank saw a 5 percent lift in revenue beyond growth rates in its market. To get to those results, the head of sales doubled down on big data. She asked her team to develop a database that pro- vided a complete picture of the customer, using both bank data, such as transaction history, and external information, such as credit scoring. Using advanced modeling, some competing ana- lytical models, and even experimentation, the bank created a clear set of guidelines and leads for the front line. The analy- sis provided insights such as that customers are more likely to buy a specic product if they have just received a bonus or are more likely to buy product B after buying product A. The bank also modied frontline tools, so that, for example, a nancial advisor now seesa specic offer on his screen for the customer sitting across the desk. The front line also received new scripts that were rened through big data analysis. Rather than using brainstorming or guesswork, the bank conducted extensive test- ing of different scripts and analyzed the results to determine the most effective ones. As we will see below, the leaders in the use of big data leave little to chancewith so much data and such powerful tools at hand, there is no reason to rely on intuition alone.c03.indd 32 19/03/12 7:59 AM
  • Find Big Growth in Big Data 33 This data-driven transformation helped turn the bank into a much nimbler business. The head of sales made enormous invest- ments in collecting, integrating, and analyzing data, and committed to costly real-world experiments. She hired a chief data scientist from an online marketing company to lead data and analytics and created a small team that became a company-wide resource. Then the head of sales and the senior data leader worked with the CIOto make targeted technology investments that allowed the bank to handle data from multiple sources and in various forms, both structured (for example, transactional data) and unstructured (for example, text from news stories or blog posts). What Do We Mean by Big Data? When we talk about big data, we are not talking about reg- ular data sets that companies have in their CRM databases. Those databases are already large (the average U.S. com- pany with more than 1,000 employees stores more data than is contained in the U.S. Library of Congress);1 we are talk- ing about data sets that combine data from the company, its channel partners and suppliers, its customers (for example click stream patterns, searches, social network conversations, location data), and even from external data suppliers (for example, weather forecasts, demographic data). The size and complexity of these data sets is beyond the ability of typical database software tools to capture, store, manage, and ana- lyze. There is no standard denitionbig data could range from a few dozen terabytes to multiple petabytes (thousands of terabytes). To give a sense of scale, it would take all the paper made from 50,000 trees to print a terabyte of text. Nor is big data all about size; other characteristics include that itis collected from multiple sources, in multiple forms (num- bers, text in dened elds, but also free-form text, images, videos, etc.), and that it is increasingly real-time. 1 Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute (May 2011).c03.indd 33 19/03/12 7:59 AM
  • 34 Find Growth before Your Competitors Do By constantly testing, bundling, synthesizing, and making information instantly available across the organizationfrom branches to the head of salesthe bank managed to outsell com- petitors. Just as importantly, the head of sales established a new culture. The sales team started to think more analytically; they understood that if they had a hunch about what would work, they could test it rst and make sure. Our interviews with leading sales executives show that busi- nesses have a major opportunity to turn data into revenue. They also reveal that companies need to overcome considerable hur- dles if they are to capture big datas Mastering data is mission full potential. The rst of these is the critical. scarcity of the necessary analytical and managerial talent. Although big data is a new topic in sales, many leading-edge companies already use big data as an engine of growth. Powerful data strengthens their ability to outperform their peers; simply put, mastering data is mission critical. In looking for growth from big data, three ideas are particularly important: 1. Harvest every source of big data. Create opportunities for customers to provide more data, partner with external pro- viders, and generate insights through advanced analytics and

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