Optimize Business using Big Data and Implementing Strategies

FORMING YOUR STRATEGY FOR BIG DATA AND DATA SCIENCE
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Table of Contents TOC \o "1-3" \h \z \u Table of contents PAGEREF _Toc64737422 \h 2Executive Summary PAGEREF _Toc64737423 \h 3Big Data Opportunities PAGEREF _Toc64737424 \h 4Value Creation Using Big Data PAGEREF _Toc64737425 \h 6Strategies for Implementing a Big Data Strategy PAGEREF _Toc64737426 \h 75 V’s of Big Data PAGEREF _Toc64737427 \h 9Conclusion PAGEREF _Toc64737428 \h 10References PAGEREF _Toc64737429 \h 11
Executive Summary
Big Data analytics can help businesses improve their operations. Most companies are investing in mining more data and finding new uses to give them leverage in businesses. Companies that harvest huge amounts of data from consumers, such as Google, are selling that information to businesses to find new ways to optimize operations and increase revenue. Businesses used big data to forecast demand and optimize their operations to meet it. Big data can be used to create new revenue opportunities. New features such as smart baskets that profile customers and help them shop faster and more conveniently are some of the new applications of the technology in increasing revenue. Big data can be used to personalize the experience each customer would get from the business, including what products to be recommended for purchase and offering a competitive but dynamic process to influence the customer to make a purchase. Businesses devise strategies to implement big data, which necessitate them to define their objectives, conduct assessments, develop strategy and determine their big data road map. All these processes are informed by the 5 V’s of data; volume, velocity, variety, veracity, and value.
Big Data Opportunities
How Big Data can be used optimize your business
According to Mckinsey, Big Data Analytics in a retail chain can improve the operating margin by around 60% (Aeologic, 2020). There are many areas big data can improve in retail, including; doing targeted marketing, finding and bringing back sleeping/cold customers, enhancing customer satisfaction, mining and listening to social media, data-driven store layout, and staff management, optimizing the discounting system to target customers to build the strong and healthy customer-retailer relationship, analyzing customer preferences and purchase behaviors, introducing smart pricing for maximum revenue, etc. These are some of the areas retailers can leverage big data to optimize their businesses.
To create new revenue, a business can use big data to do better forecasting. Predictive analytics uses big data to find patterns and forecast customer sales. This helps businesses to make informed decisions on how to serve their customers. One way the business can create new opportunities is by predicting the demand for flash sales and planning promotions. Cyber Monday and Black Friday are some of the busiest days in the retail industry. Leveraging on consumer data, businesses can predict demand and optimize the pricing of their products for maximum profits. Amazon Fulfillment Centers rely heavily on predicting demand. Amazon ships some products before they are purchased from their website (Bensinger, 2014). Using such technologies that can only be built on big data, businesses can optimize their business operations and save on their expenses. Shipping products before they are ordered by consumers can help save on shipping costs.
How to leverage Big Data to create new revenue opportunities
Secondly, a business can ‘help’ the customer with the process of shopping. A smart basket is a shopping aid that relies on the customer’s preferences, shopping patterns, search history, etc., to recommend items for purchase. The smart basket can reduce purchase time from nearly over 30 minutes to less than 5 minutes. The smart basket helps in saving shopping time and improves efficiency in ordering. It could be a handy technology if integrated with the internet of things to ease customer experience through shopping. In the end, this technology which is heavily reliant on big data, helps businesses increase their revenue by recommending and helping customers purchase goods they may need.
Personalization is the new marketing (Aeologic, 2020). By personalizing how businesses treat a particular customer, they can find more ways to influence his/her shopping behavior. By recommending products based on the customer's previous shopping patterns or information collected from other customers, the business can target the customer and influence them to purchase particular products. In a classic case of how Target analyzed the shopping patterns of pregnant women, the company's AI system sends coupons by mail to a teenage girl (Hill, 2012). While the father was infuriated by Target, he soon discovered that his daughter was pregnant and apologized to the Target manager. This shows the power of personalization as an AI system trained on big data could predict the early stages of pregnancy; the customer is likely to purchase a particular item. The combination of machine learning, Artificial Intelligence, and Big Data leads to smarter suggestions and recommendations for shopping based on data values collected from the customer. It is by far more effective than traditional marketing.
It is also possible to use predictive models to identify customers who are willing to buy more. Leveraging on Big Data and artificial intelligence, businesses can predict how best to influence customers to purchase more items. For example, they can be offered coupons and discounts on items they may have researched or marked as favorites. Predictive models can also offer ‘accurate pricing. Big Data Analytics will help achieve real-time pricing to adjust in-store prices for the customers (Altexsoft, 2019). Dynamic pricing can use structured to adjust prices dynamically and keep profit margins at a certain level. The system can also offer competitive pricing relative to competitors or increase or decrease prices based on demand and supply. These big data-powered technologies optimize revenue according to business objectives.
Big Data can be used to develop customer-centered products. For any new product to the market, the target consumer has to be taught about it. Big data can help create a profile of likely consumers and target them to be early adopters (Zhan, Tan, Li, & Tse, 2018). Secondly, the profile can be used to design marketing tools to penetrate the market. Generally, big data can be applied throughout the entire product launch cycle. It can help decision makers connect with the right consumers, offer increased consumer value and avoid the pitfalls of new products’ launch. AI and big data-powered decision support systems can help predict the performance of the market and adjust the approach accordingly. It can also optimize the distribution chains and marketing strategies to reach a large consumer base at th...