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These days we hear a lot about the notion of Account-Based Marketing (ABM). But the idea is not that new. We always had key account managers who drove the sales and marketing activities within a large target account. However, recently there has been a surge of excitement in the marketing community about this concept with the intention to “flip the funnel.” Many new ABM products have emerged, and the existing marketing automation systems have extended their capabilities to make ABM more programmatic. Wikipedia states , “Account-Based Marketing (ABM), also known as key account marketing, is a strategic approach to business marketing based on account awareness in which an organization considers and communicates with individual prospect or customer accounts as markets of one. Account-based marketing is typically employed in enterprise level sales organizations. " ABM has been contrasted with traditional the B2B marketing funnel to be more personalized and more effective. Here are some typical steps in Account-Based Marketing: Identify key accounts Create a list of buyers and influencers in each account Understand stakeholder relationships, affiliations, and sphere-of-influence Identify teams and committees that make the buying decision Understand the needs and preferences of key contacts Collaborate with sales to develop multichannel engagement strategy Create personalized content for key contacts Send relevant content and offers Fine-tune the campaigns for better engagement There are a multitude of resources on the web to learn more about ABM and best practices. But even with a compelling value proposition, many companies find it hard to execute on the ABM strategy. A key issue that often comes up is data reliability. Since the whole premise of ABM is deep customer understanding and providing highly tailored information to the customer at the most appropriate time, the account data needs to be impeccable. For ABM strategies to be successful, companies must ensure that they have a reliable customer data foundation – one that enables comprehensive understanding of the account, understanding of relationships in the account, sales and marketing collaboration, relevant engagement and offers, and closed-loop analytics and data quality. How to make data the heart of ABM: 1. Establish a comprehensive understanding of the account In a B2B scenario, customer understanding means creating a 360-degree account view that brings together information from CRM systems, marketing automation, support and supply-chain systems and enriching it with information from third-party sources like Dun & Bradstreet to create comprehensive account profiles. The profile should include account hierarchy information, not just legal, but hierarchy with product penetration information or risk rollup across the account hierarchy. The profile should also include information about various contacts, teams and committees that influence the buying decision. 2. Understand relationships in the account Next is to understand the contact, account and product relationships. Who are the key people in the purchase committee? Are there people within or outside the target account that can introduce you to the decision makers? Have contacts used your product in the past or are they connected to a person in another business unit who uses your product? Understanding such relationships in an organization helps identify key people that sales and marketing should engage. Today, graph technology can help companies understand and visualize such relationships across people, products, accounts and locations in target accounts. The technology also allows companies to understand the influence of stakeholders in the account and reveal product whitespaces across large accounts. Understanding relationships and whitespaces help uncover cross-sell and upsell opportunities and fill product gaps in a large account. 3. Cultivate sales and marketing collaboration Account-based marketing and selling efforts need close coordination between the two teams. They should be able to work off the same account information and collaborate with each other to enhance the account data. If marketing finds an incorrect email address or phone number, they can flag that information to be corrected after verification. If a new contact is identified in the account, sales can use voting or discussion threads to verify the importance of that contact in the account. Even though the account information is presented in the context of sales and marketing roles, all views are provisioned from the same single source of data. Sales and marketing teams are always in sync and have access to current and complete account data. 4. Implement relevant engagement and offers Once reliable account information is established, including a full understanding of relationships between people, products and locations for the account, now we can start engaging with the contacts using the right information and offers. B2B companies can now send personalized messages to targeted contacts that discuss their particular issues and interests. Many systems also use machine learning and predictive analytics to provide guided intelligent recommendations. Based on past multichannel interactions, systems can suggest the right time to connect with prospects, using the right channel and with the right offer. 5. Drive closed-loop analytics and data quality ABM techniques need continuous fine-tuning of campaigns. Over time, companies learn more about contacts including their relationships and content and channel preferences. Capture all this information, analyze it, and make it a part of the contact and account profiles. Use this information to make your next campaign more personalized and relevant. This iterative process will help you deliver more relevant information to the key stakeholders progressively and move the prospect account closer to opportunity and closing. ABM is an effective way to market to key new accounts or expand within a large account. Even though there are many applications and tools available to design and execute multichannel ABM campaigns, the data quality issues can be a roadblock. As you embark on your ABM journey, take a look at all your data sources and understand how you are going to bring all the data together from internal, external and third-party sources. Then determine what you can glean from this information to turn account-based strategies into tactics and execution. Article written by Ajay Khanna Image credit by Getty Images, DigitalVision Vectors, filo Want more? For Job Seekers | For Employers | For Influencers
A n artificial intelligence (AI) tool—trained on roughly a million screening mammography images—identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists." In 2014, more than 39 million mammography exams were performed in the United States to screen women (without symptoms) for breast cancer and determine those in need of closer follow-up. Women whose test results yield abnormal mammography findings are referred for biopsy, a procedure that removes a small sample of breast tissue for laboratory testing. A new tool to identify breast cancer In the new study, the research team designed statistical techniques that let their program "learn" how to get better at a task without being told exactly how. Such programs build mathematical models that enable decision-making based on data examples fed into them, with the program getting "smarter" as it reviews more and more data. Modern AI approaches, inspired by the human brain, use complex circuits to process information in layers, with each step feeding information into the next, and assigning more or less importance to each piece of information along the way. Published online recently by the journal "IEEE Transactions on Medical Imaging", the current study authors trained their AI tool on many images matched with the results of biopsies performed in the past. Their goal was to enable the tool to help radiologists reduce the number biopsies needed moving forward. This can only be achieved, says Dr. Geras, by increasing the confidence that physicians have in the accuracy of assessments made for screening exams (for example, reducing false-positive and false-negative results). For the current study, the research team analyzed images that had been collected as part of routine clinical care at NYU Langone Health over seven years, sifting through the collected data and connecting the images with biopsy results. This effort created an extraordinarily large dataset for their AI tool to train on, say the authors, consisting of 229,426 digital screening mammography exams and 1,001,093 images. Most databases used in studies to date have been limited to 10,000 images or fewer. Thus, the researchers trained their neural network by programming it to analyze images from the database for which cancer diagnoses had already been determined. This meant that researchers knew the "truth" for each mammography image (cancer or not) as they tested the tool's accuracy, while the tool had to guess. Accuracy was measured in the frequency of correct predictions. In addition, the researchers designed the study AI model to first consider very small patches of the full resolution image separately to create a heat map, a statistical picture of disease likelihood. Then the program considers the entire breast for structural features linked to cancer, paying closer attention to the areas flagged in the pixel-level heat map. Rather than have the researchers identify image features for their AI to search for, the tool is discovering on its own which image features increase prediction accuracy. Moving forward The team plans to further increase this accuracy by training the AI program on more data, perhaps even identifying changes in breast tissue that are not yet cancerous but have the potential to be. "The transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars—slowly and carefully, building trust, and improving systems along the way with a focus on safety," says first author Nan Wu, a doctoral candidate at the NYU Center for Data Science. Along with Geras, study authors from the Department of Radiology at NYU School of Medicine were Eric Kim, Stacey Wolfson, Ujas Parikh, Sushma Gaddam, Leng, Young Lin, Joshua Weinstein, Krystal Airola, Eralda Mema, Stephanie Chung, Esther Hwang, Naziya Samreen, Beatriu Reig, Yiming Gao, Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, S. Gene Kim , Laura Heacock, and Linda Moy. Authors from the Center for Data Science at New York University were Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Thibault Févry, and Kyunghyun Cho, who is also on the faculty of NYU's Courant Institute of Mathematical Sciences. Also authors were Kara Ho at SUNY Downstate College of Medicine; Masha Zorin in the Department of Computer Science and Technology at the University of Cambridge in the United Kingdom; and Stanisław Jastrzębski from Jagiellonian University in Poland, and Joe Katsnelson in the Department of Information Technology, NYU Langone Health. This work was supported in part by National Institutes of Health grants R21CA225175 and P41EB017183. The model used in this study has been made available to the field to drive innovation at this site. Article published by icrunchdata Image credit by NYU Langone Health Want more? For Job Seekers | For Employers | For Influencers
When developing your organization’s data monetization strategies, the first ideas that come to mind are probably not the best. For example, you might assume that all you need to do before you can start selling your customer data to external organizations is to obfuscate or remove a few sensitive fields – think again. Doing only that could potentially risk your organization’s very existence. The best data monetization strategies begin by applying an organization’s intellectual capital to raw data elements to create truly new and unique insights. That is the fastest path to creating ‘data products’ that a broader market will be motivated to purchase. Finding the right people Naturally, data monetization strategies are most effective when an organization has the right people analyzing the data. Your leadership team might reasonably think that the best person to tap for this role would be the Chief Information Officer, or possibly the Chief Marketing Officer. Such individuals might indeed be fine for the task, but in my opinion, you should be looking beyond titles to find individuals with the innate sensibilities and personal preferences that make them suited to the practice. To be specific, I recommend finding those rare individuals with analytic skills in both qualitative and quantitative areas. In a sense, they need to be ‘hardwired’ for this type of insight and practice. In my experience, it’s much more important to find analysts with such insights instead of requiring that they had this or that previous job title, or even a particular degree. As a matter of fact, when I’m trying to fill data scientist positions, I’m far more interested in seeing how the individual rates on the Keirsey Temperament Sorter , a personality instrument similar to the Myers-Briggs, in conjunction with their quantitative reasoning. It’s not as well known as the Myers-Briggs, but I find it gives me more useful insights into the preferences, abilities and ways in which a candidate prefers to think that I value most in a data scientist. To drill down a little more, I tend to look for rational, curious and analytical people. They might not even need to be familiar with my particular industry. My reasoning is that by the time they’ve sliced and diced my data to derive truly new inferences from it, it’s likely that it will be relevant to (and valued by) organizations in other industries. To go even further on this point, it might make strategic sense to focus on developing data products with the specific aim of selling them to companies with whom you could never directly compete. By the same token, your new data should feature insights that are based on your customer information, but bear little or no resemblance to the original data. Ideally, your data analytics team members should be able to learn rapidly and also be familiar with the latest data analytics products. A few years ago, that would have probably been Hadoop, but in today’s data analytics world, technologies like Spark and Storm have begun to attract more market attention. The stakes are high – and your standards should be, as well The better decisions you make about monetizing data, the greater your likelihood of maximizing the revenue it will generate. In addition, by making sure that you produce and sell only data that is truly yours, you can minimize the risk of angering or alienating customers, shareholders, regulators and other interested parties. The world of data analytics is changing – not only in the types of data monetization strategies that organizations are pursuing but also in the types of technology, processes and people involved. If you think that data monetization is worth doing (and there are plenty of reasons to think so), it’s also worth doing right. For those and other reasons, it pays to start by hiring the right people for the task. Article written by Reuben Vandeventer Image credit by Getty Images, DigitalVision, Thomas Barwick Want more? For Job Seekers | For Employers | For Influencers
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