3.9.4 Assessing greater use of digital technology

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    3.9.4 Assessing greater use of digital technology

    The pressures to adopt digital technology

    Digital technology:

    • E-commerce & M-commerce
    • Big data
    • Data mining
    • Enterprise resource planning

     

    E-Commerce = buying and selling online when business transactions are conducted electronically on the internet

    M-Commerce = business transactions are conducted electronically by mobile phone (this is a subset of e-commerce)

     

    Steps to adopting e-commerce/m-commerce:

    • Designing and posting a website – many sites such as WordPress and SquareSpace offer e-commerce templates that help get stores up and running quickly
    • Collecting payments from customers – owners need a way to collect credit card payments from consumers online using ways through PayPal and Apple Pay
    • Delivering products to the consumer – when selling physical goods, firms need to consider how to ship them using companies such as DPD and DHL
    • Gaining reviews from customers – social media sites such as Facebook and Twitter need monitoring to ensure that positive comments are emphasised and complaints are quickly dealt with
    • Example = Amazon is their biggest e-commerce site which has a focus on long term strategy and not their profit – they want to sell the most

     

    Concerns of e-commerce:

    • Lack of handling the product by the consumer before purchase
    • Growth in transport – higher pollution and more vehicles on the road
    • Not adopted equally by all age segments
    • Lacks the personal contact between the consumer and business
    • The decline of high street shops leading to growing local unemployment
    • Lack of local council business rates leading to less local public services
    • Social problems of a lack of used shops

     

    Big Data = this is the process of collecting and analysing large sets from traditional and digital sources to identify trends and patterns that can be used in decision making

    • Example = supermarkets use loyalty cards to look at trends and popular products and times for shopping which improves their business performance because they can tailor offers and products to their customers
    • These large data sets are both structured (e.g. sales transaction from an online store) and unstructured (e.g. posts on social media)

     

    How big data is generated:

    • Retail e-commerce databases
    • User-interactions with websites and mobile apps
    • Usage of logistics, transportation systems, finance, and health care
    • Social media data
    • Location data (e.g. GPS generated)
    • Internet of things (IoT) data generated (e.g. google search)
    • New forms of scientific data

     

    Data Mining = this is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records, or dependencies

    • Example = McDonalds identify trends of customers and promotes the foods that are most popular

     

    Enterprise Resource Planning (ERP) = a software system that helps businesses integrate and manage their complex financial, supply chains, manufacturing, operations, reporting, and human resource systems

    • Although the introduction and management of ERP systems is both complex and costly, there are some significant business benefits if ERP is implemented successfully
    • Example = UPS has a system that means that customers can change where their parcel is being delivered whilst it is already on route OR multi car dealerships would use ERP system whereby they wouldn’t hold all of their stock but still offer the option of buying their product – stock control

     

    The value of digital technology

    Key uses of big data:

    • Tracking and monitoring the performance, safety and reliability of operational equipment (e.g. data generated by sensors)
    • Generating marketing insights into the needs and wants of customers, based on the transactions, feedback, comments (e.g. from e-commerce analytics, social media posts) – it is revolutionising traditional market research such as questionnaires and surveys
    • Improved decision-making – for example analysing the real-time impact of pricing changes or other elements of the marketing mix (the use of big data to drive dynamic pricing is an example of this)
    • Better security of business systems – big data can be analysed to identify unusual activity, for example on secure-access systems
    • More efficient management of capacity – the increasing use of big data to inform decision-making about capacity management (e.g. in transportation and logistics systems) helps firms to run more efficiently

     

    Advantages of data mining:

    • Identify previously unseen relationships between business data sets
    • Better prediction of future trends and behaviours
    • Extract commercial (e.g. performance insights) from big data sets
    • Generate actionable strategies built on data insights (e.g. positioning and targeting for market segments)

     

    How data mining supports marketing competitiveness:

    • Sales forecasting – analysing when customers bought to predict when they will buy again
    • Database marketing – examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles (how customers respond)
    • Market segmentation – a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation, or gender
    • E-commerce basket analysis – using mined data to predict future customer behaviour by past performance, including purchases and preferences

     

    Advantages of enterprise recourse planning:

    • Financial management – better control over assets, cash flow, and accounting
    • Supply chain and operations management – streamlined purchasing, manufacturing, inventory, and sales order processing
    • Customer relationship management – improved customer service, and opportunities to cross-sell
    • Project management – complex projects are better managed and costs are lowered
    • Human resource management – may help attract and retain good employees
    • Business intelligence – improved management reporting, analysis, and business analytics
    • International business – helps coordinate multi-location business management

     

    Disadvantages of enterprise resource planning:

    • Expense of implementation (high fixed cost)
    • Resistance to change from the employees leading to low user adoption
    • Poor training during implementation means that it is too complex for the staff to use
    • Departments become possessive over the data (SILOS)
    • Ongoing upgrading and management costs

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