Mining Big Data to Find New Markets

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1 Mining Big Data to Find New Markets featuring Manish Goyal and Homayoun Hatami Sponsored by

2 WEBINARS Mining Big Data to Find New Markets OVERVIEW Even though companies today have limited resources, they still desire significant growth. Companies also are able to access more types of data and a greater volume of data than ever before, including real-time data from the Internet and social media. By mining this Big Data, companies can develop insights and identify micromarkets that represent opportunities for growth. When these data-driven insights are translated into specific plans and cascaded to the front lines, companies can produce exceptional results. Tools and technology are important, but even more important are committed leadership, having the right analytical talent, and focusing on specific areas that can create significant value. CONTEXT McKinsey consultants Manish Goyal and Homayoun Hatami co-authors along with Maryanne Hancock of the Harvard Business Review article Selling into Micromarkets described how companies can use Big Data to find new markets and drive growth. They provided firsthand examples and responded to numerous questions. contributors Manish Goyal Partner, Marketing and Sales McKinsey & Company Homayoun Hatami Director, Marketing and Sales McKinsey & Company Angelia Herrin (Moderator) Editor for Research and Special Projects, Harvard Business Review Key learnings Big Data can produce insights that drive business growth. For years businesses have had a great deal of data, including CRM data and transactional data. But Big Data is different because of its: Sources. Big Data involves pulling together data from multiple internal and external sources. This includes data from customers, channel partners, suppliers, web searches, social media, location data, and even external data such as weather and demographic information. Scale. Because so much data is brought together, the amount of data is now far greater than has been the case in the past. Timeliness. Data is now available in real time to show what customers are talking about in social media (via sentiment analysis) and what they are buying. The value of pulling this data together and analyzing it is to glean new and valuable insights. Just a few examples: retailers can use purchase data to estimate a pregnant woman s due date and can target relevant offers; and Google used insights from data to modify the font color in its ads, boosting its rate of click-throughs and increasing revenue by $200 million. 2

3 By mining Big Data, companies can identify micromarkets that represent opportunity for increased revenue. As many companies deal with the reality of constrained resources, they see analyzing and extracting insights from Big Data as a key way to improve the efficiency and effectiveness of their sales and marketing organizations. By putting Big Data at the heart of sales and marketing, insights can be leveraged that improve decision making and innovate a company s sales model, which can involve using data to drive real-time actions. Some of the ways that Big Data can be used to drive improved sales and marketing performance include: Identifying micromarkets. Big Data which pulls in external data can be used to identify new markets, which might be specific customer segments or geographies. Focusing on opportunities. Many companies are largely focused on selling to and serving their existing customers. They create goals, budgets, and plans based on looking at past results from current customers. But by using Big Data, organizations can think differently; they can focus not on past results and existing customers, but on the opportunities that existing in a market. Big Data will help identify new markets and opportunities that they might not know even existed. Maximizing sales effectiveness. Using Big Data an organization can assess the targets it is pursuing, the way that sales time and resources are being allocated, and what selling propositions and promotions are working best with different customer segments. Prioritizing opportunities as opposed to existing customers can entail risk. It should be supported by strong analytics. The insights on which decisions are based must be disseminated to all levels of sales and marketing; particularly the front lines. But companies that have used analytics have shown that they can use their finite resources more effectively to pursue the best opportunities with the right propositions and promotions in the most efficient way. In using Big Data to identify new opportunities, practical prioritization steps include: 1. Define the right granularity for your micromarkets. This might be a specific customer segment, or a geography like a county or a geographic radius such as 25 miles surrounding a sales rep. 2. Determine the growth potential. By using external data, the total opportunity for a micromarket can be estimated with high precision. For example, a maker of plumbing products might look at data such as the number of construction starts in a market and the age of the housing stock. 3. Determine your share of each micromarket. With the definition of a micromarket and knowledge of the opportunity, it is possible to determine your company s existing share of a micromarket. Data from large data sets can take the guesswork out of selling. People have always talked about the art of sales. But with Big Data, art is being replaced by scientific analysis. Homayoun Hatami 3

4 4. Understand why there is variance in market share in different micromarkets. Inevitably, when different micromarkets are identified, a company s market share within them will differ. It is important to understand the underlying reasons for this variance. 5. Prioritize growth pockets. Having used external sources to determine the opportunities that exist, the opportunities can be prioritized and resources allocated to support these opportunities. Case studies showed that organizations generated growth by identifying opportunities and reallocating resources, without adding resources. The Big Data toolkit changes the way that data has traditionally been used. Manish Goyal A key to growth is using Big Data to unlock deeper and deeper insights. An example was shared of a faucet manufacturer analyzing its business, digging ever deeper to glean insights, and using these insights to decide upon actions to grow the business. Insights from this example included finding that: The company s overall market share in California looked strong, which might cause a company to conclude there were no opportunities for growth, but the market share by county varied by 4X. One area with low market share had no sales coverage, and another had no channel partners indicating obvious next steps in both situations. For the geography with no channel partner, analysis of social media identified the most influential partner in that area. In one geography the company faced a strong local competitor. To compete more effectively, the company s analysis led it to conclude that it needed to better understand customer needs and strengthen its value proposition. It also needed to spend more on marketing to counter competition. Cluster analysis found another US geography with similar characteristics, leading the company to share best practices. The example showed that a granular analysis yielded insights, and each insight led to further analysis and an even deeper insight. As a result of such insights, a company can develop specific actions to drive growth. It is essential to get meaningful insights to the front line. Just developing insights at a headquarters location is not adequate. The speakers recommended building a Big Data toolkit to bring micromarket insights to the front line. It is essential that sales people understand the insights that have been gained and believe in the validity of the data. This toolkit: Gets data to the front lines so it can be used and acted upon, instead of holding data in a central location or in silos. 4

5 Is predictive and forward looking, as opposed to looking backward at performance and problems. Is consistent, standard, and repeatable, as opposed to time-consuming and one-off. An example was shared where data was used to predict the win rate of different value propositions with different potential customers. Sales reps, who were furnished with this data, could use this information to tailor their approach and win more business. Ideally, data and insights are used not to tell sales reps what they should do, but to enable managers to engage in conversations and coaching that helps produce better results. The key to using Big Data is getting started by focusing on a limited set of outcomes that data can impact. It is important for an organization to realize that using Big Data is a journey. Businesses of any size can embark on and realize value from this journey. A good starting point is to determine a few outcomes that could be improved through analysis, and to determine what data is needed to support this analysis. No company will have all of the data it wants, but that shouldn t preclude the need to get started. Other Important Points B2B. When analytics in business is discussed it is often in the context of B2C companies. But this session and the examples discussed showed that analyzing Big Data also has tremendous value for B2B companies in identifying micromarkets and improving sales effectiveness. Leadership. Having leadership that understands and supports the use of Big Data and analytics is an important element of success. Google and Procter & Gamble were mentioned as organizations whose leaders are strong supporters of using data to make better decisions. Organizational structures. Some organizations have a centralized analytics team in strategy, marketing, finance, or another function. With this structure the focus on sales opportunities often begins when a creative sales person speaks with a data analyst, who becomes interested in analyzing a business problem/opportunity. In other organizations, analytical capabilities reside in small teams that are distributed throughout the company to support different businesses and functions. They are often closer to the action and might reside in the marketing department or in pre-sales. A key to success is that business people work closely with analysts as opposed to handing off a project to them. Emerging markets. There is interest in using analytics to identify and prioritize opportunities in emerging markets. This makes sense because many companies see significant growth opportunities in emerging markets and are resource constrained. A challenge can be a lack of good data from these markets. Learn More For more details about customer intelligence solutions: sas.com/software/customerintelligence To read more thought leader views on marketing, visit the SAS Customer Intelligence Knowledge Exchange: sas. com/knowledge-exchange/ customer-intelligence To get fresh perspectives on customer analytics from marketing practitioners writing on the SAS Customer Analytics blog: blogs.sas.com/content/ customeranalytics 5

6 BIOGRAPHIES Manish Goyal Partner, Marketing and Sales, McKinsey & Company Manish is a partner in McKinsey s Marketing & Sales Practice. Based in Dallas, he helps clients focus find growth in micromarkets. Homayoun Hatami Director, Marketing and Sales, McKinsey & Company Homayoun Hatami co-leads McKinsey s Sales & Channel service line and the firm s work in sales growth. He has a broad range of experience working with clients in Europe, the United States, and Asia to power growth through excellence in sales and channels. He is also a leader in knowledge and insight development. Homayoun co-authored Sales Growth: Five Proven Strategies from the World s Sales Leaders. In his recent client work, Homayoun crafted a channel growth strategy for a high-tech player, addressing channel partners territory, indirect coverage, rules of engagement, and partner enablement; developed strategies for a consumer electronics player to grow sales through retailers in France and the UK; and put in place an overlay sales organization to help a telecom operator grow in new services segments. Homayoun was a member of the MIT Corporation (the board of trustees of the Massachusetts Institute of Technology) from 2001 to He has worked in McKinsey s Boston, Silicon Valley, Seoul, and Paris offices. He was born in Iran and raised in Paris. Angelia Herrin (Moderator) Editor for Research and Special Projects, Harvard Business Review Angelia Herrin is Editor for Research and Special Projects at Harvard Business Review. At Harvard Business Review, Herrin oversaw the re-launch of the management newsletter line and established the conference and virtual seminar division for Harvard Business Review. More recently, she created a new series to deliver customized programs and products to organizations and associations. Prior to coming to Harvard Business Review, Herrin was the vice president for content at womenconnect.com, a website focused on women business owners and executives. Herrin s journalism experience spans twenty years, primarily with Knight- Ridder newspapers and USA Today. At Knight- Ridder, she covered Congress, as well as the 1988 presidential elections. At USA Today, she worked as Washington editor, heading the 1996 election coverage. She won the John S. Knight Fellowship in Professional Journalism at Stanford University in The information contained in this summary reflects BullsEye Resources, Inc. s subjective condensed summarization of the applicable conference session. There may be material errors, omissions, or inaccuracies in the reporting of the substance of the session. In no way does BullsEye Resources or Harvard Business Review assume any responsibility for any information provided or any decisions made based upon the information provided in this document. 6 SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies _S

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