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Social Media Marketing: Advantages and Challenges

Social media marketing is a marketing strategy that uses social media to build brand awareness and generate leads, customers, or other desired outcomes. Social media allows companies to create and share content quickly, which is often done in collaboration with other companies, brands, or non-profit organizations. This helps increase trust and credibility between the brand and its target audience who are often more receptive to messages because the information comes from friends.

TOP TIPS FOR DEVELOPING AN MARKETING PLAN

Nowadays, with technology creating massive marketing opportunities, there are still some important criterion for developing an effective one to support your business mission. ASHAVI looked at some of the most essential factors that to help you get clear on all things you need in the plan. These are the following.

CRM mobile application for sales team

Research thinking vs Design thinking

Research thinking vs. Design thinking

Retail research methodology

Have you ever walked into a store and wondered why certain products are placed in specific aisles? Or maybe you've questioned why some retailers offer discounts at particular times of the year. Well, wonder no more, because that's where retail research comes into play! Retail research is the process of examining consumer behavior, market trends, and industry standards to help businesses make informed decisions. From surveys to focus groups and everything in between, there are several types of retail research methods available.

Business Intelligence, Retail Industry, and Multi-Market Competition

As a retail business owner, you want to create products that help people and businesses grow. You want to provide products that are innovative and useful, of top quality and affordable so your customers will come back. But the real problem is that there are thousands of online retailers, who all appear to be competing vigorously against your own business. So how do you differentiate yourself from the others and emerge victorious? Uncovering the most important business intelligence opportunities is the focus of this strategy. This involves tracking end-to-end processes, collecting as much data as possible, and then processing it to produce the most insightful information. For example, BI can track how customers interact with your stores, & this information can be used to enhance customers’ shopping experience making them your loyal customer & generating a series of repeat purchases which boost your revenue – the golden bird which we all eye. With the help of Successful BI Strategy, retailers can make smart, efficient decisions allowing businesses to quickly alter prices or change merchandise offerings.

Mobile Business Intelligence Trends

BI technologies allow direct access to data and information. With the help of these technologies, a business can make better decisions upon analyzing big data. They are used widely in data-centered warehouses or data marts and can help to achieve various business outcomes by integrating big data analytics with traditional BI systems. Mobile BI is one of the effective ways to use the massive amount of data. Mobile BI helps businesses deploy data analysis methods to collect, analyze and segment data. It makes business intelligence accessible everywhere and at all times, thereby enabling organizations to make decisions based on analytics-driven insights. According to a report by Datameer, mobile applications will be the second-largest technology integrating Business Intelligence and Big Data. Also, Gartner reports that over 50% of Mobile BI users will depend on mobile devices for insight delivery. Companies such as SAS Business Analytics are already positioning themselves as “mobile-first” companies through a diverse range of services and offerings.

What Are the Latest Trends in Online Marketing?

Decision Trees: Business Intelligence, Data Latency, and Data Mining

The book begins with an overview of decision trees and discusses how decision trees are related to other concepts such as decision theory, optimization, and statistical inference. The author then explores the use of decision trees in data mining tasks and describes how these techniques complement and supplement other business intelligence applications, such as dimensional cubes (also called OLAP cubes) and data mining approaches, such as regression, cluster analysis, and neural networks. The author then demonstrates how decision trees can be used to solve real-business problems by applying them to scenarios in manufacturing, sales forecasting, CRM applications and marketing analytics.