Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare Marketing Strategy Analysis & Solution

Marketing & Sales Case Study Analysis and Solution

At Fern Fort University, we use Harvard Business Review (HBR) marketing principles and framework to analyze Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare case study. Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare is a Harvard Business Review case study written by Kiran R, Arunabha Mukhopadhyay, Dinesh Kumar Unnikrishnanfor the students of Sales & Marketing. The case study also include other relevant topics and learning material on – Customers

Strategic Marketing Analysis of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare case study written by Kiran R, Arunabha Mukhopadhyay, Dinesh Kumar Unnikrishnan will comprise following sections –

• Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare Case Description
• Marketing Definition
• Market Potential Analysis of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare
• Market Share Potential Analysis
• Segmentation and Segment Attractiveness Analysis
• Competition and Competitiveness Analysis of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare
• Customer Value Analysis of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare case study

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Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare Marketing Case Description

Sales & Marketing Case Study | Authors :: Kiran R, Arunabha Mukhopadhyay, Dinesh Kumar Unnikrishnan

VMW is a leader in software virtualization with approximately USD 6.5 billion annual revenue. VMW sells Workstation that can be bought online (store.vmware.com) and is used for running Mac on Windows. Workstation forms a significant portion of store revenues and most of it is bought online. There is rich digital/clickstream data for the visitors which can be combined with their past purchase history and other offline features as well. The business would like to increase sales of the product by targeting the right customers and needs a propensity model to be built using machine learning that can target the right set of customers. Michael Butler, the WW head of the store wants to leverage Parag's data sciences team to help him target the right workstation prospects that visit the store. A business conversation between Michael and Parag is followed by a technical discussion between Ravi, the data scientist and Parag. The following are the key questions that Ravi seeks to answer: -Cross-validation and evaluation in the context of huge imbalance in the data -Feature selection techniques -Communicating internal results such as lift curves back to the business -Different modeling approaches that can be followed -Interpreting the results for business decision making

Customers

Marketing Definition

According to American Marketing Association – Marketing is a set of activities that a firm undertakes for creating, communicating, delivering, & exchanging offerings that have value for customers, clients, partners, and society at large.

Kotler explains - Marketing is a process by which organizations can create value for its potential and current customers and build strong customer relationships in order to capture value in return.

Market Potential Analysis of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare

Market potential analysis comprises evaluating the overall market size of the related product that the firm is planning to launch. This will involve defining – Why the target market segment needs the product and how it will provide a solution to full its consumers’ needs. Market potential of Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare products various on factors such as –

• Maturity of the market. In mature markets the profitability is often stable but the market potential is less as most of the players have already taken market share based on the segment they are serving. New players have to go for market share strategies in marketing.
• Technological competence of the existing players and culture of innovation and development in the industry.
• Untapped market sizes and barriers to both enter the market and serving the customers. Often companies can easily see the unfulfilled needs in the markets but they are difficult to serve as there are costly barriers.
• Define the core need that your product is serving and list out all the direct and indirect competitors in the market place. This will help not only in positioning of the product but also in defining or creating a segment better.
• Uncovering the current and untapped market sizes and barriers to serving the larger market. Analyze the areas that you need to sort out while launching the products to wider market and what are the challenges the firm will face in market place.
• Estimate the current stage in product life cycle and its implications for marketing decisions for the product.

Market Share Potential Analysis

• Understanding the buyer behavior model for Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWareindustry.
• Identifying the market share drivers relevant to Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare market.
• Segment Attractiveness Analysis – Our analysis will work out which are the most attractive segments and which are the one the firm should go ahead and target. We point out in great detail which segments will be most lucrative for the company to enter.
• Understanding the different needs and relative value of your offering by segment.
• Developing segment priorities and positioning the product based on the product need fit developed by the firm.

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Competition & Competitive Position Analysis

• Uncovering customer-based competitive positions for key rivals and firm’s offering. This will not only help in assessing the strengths and weaknesses of the competitors but also help in defining and positioning of the product.
• Developing a positioning and launching strategy. It will require not only distribution channel analysis but also promotion mix for the product.
• Strategic Marketing Planning — the process of developing and maintaining a strategic fit between the organization’s objectives and capabilities and the ever evolving marketing opportunities for its products.

Machine Learning Algorithms to Drive CRM in the Online E-Commerce Site at VMWare - Customer Value Analysis

Capturing customer value is essential to marketing efforts as it results in higher return in the form of both current & future sales, greater market share, and higher profits. By creating superior customer value, the organization can create highly satisfied customers who stay loyal and buy more. This, in turn, means greater long-run returns for the firm.

• The crucial role of customer perceived value in acquiring and retaining profitable customers. Product differentiation is often based on building on a value niche that a firm believes that is very important to the customer. This niche contributes to perceived value. If the perceived value is high then customer stay loyal to the product if not then she can switch to the competitor’s product.
• Graphically displaying value differences for deeper understanding and better internal communication. This helps is building a narrative that a customer can identify with. The better the insight more are the chances of connecting with the potential customers.
• Identifying and selecting actionable value creation options. This can help in increasing the customer lifetime value. Customer lifetime value is the value of the entire stream of purchases that the customer would make over a lifetime of patronage.

NOTE: Every marketing case study solution varies based on the details and data provided in the case. We write unique marketing strategy case solution for each HBR case study with no plagiarism. The specific case dictate the exact format for the case study analysis.

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