Technological advancements are advancing at a very rapid rate. Technology has as a result become the main feature of the contemporary business environment. Businesses normally adopt technologies so that they can be able to accrue competitive advantages and in recent times, businesses that do not adopt the use of the technology commonly end up becoming redundant in the long run. The main impact of growing technological development is that the world as a whole has become increasingly digitized. This means that there are massive tracts of data that are available in digital platforms. Businesses commonly use such information to develop effective strategies of enhancing their efficiency and increasing their service delivery so that they can achieve business bottom line of maximized profit and minimized costs. The latest technologies with regards to business analytics for data based decision making, is within the sphere of artificial intelligence. Specifically, the latest technology is the use of Chatbots for data analytics purposes.
It is important to note that Chatbots are not a new artificial intelligence technology. The technology can trace its roots back to 1966 when ELIZA was published. The contemporary Chatbots technology continues to use ELIZA’s pattern-matching foundation with regards to communicating with humans by responding to their questions in a human like manner. However, unlike in the past, businesses have found ways of integrating the technology into their data analytics processes. Chatbots over the recent past 4 years have been increasingly adopted for companies, across messenger-based platforms. The technology is continuing to revolutionize the communication industry as well as the manner in which businesses collect, analyze and use data for their decision making processes.
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The new features of Chatbots include that they have installed learning codes and they also have Natural Language Processing (NLP) capabilities. They are now able to effectively and independently collect data from active and potential consumers, and respond to the queries and concerns of the consumers. They have also become very intelligent, learning from every interaction that they have with consumers in the market. Evidence of the growing levels of importance of Chatbots with regards to the performance of data analytics functions can be seen in the fact that all of the world’s major technology companies, either have a Chatbot, or are developing their Chatbot. Some of these companies include Google, Facebook, Mattel, Apple, and Samsung among others. Most of the businesses use the technology as a cutting-edge technology that functions with as conversation-based interfaces, as well as automated data collectors. For the mentioned companies, the Chatbot is commonly used to enhance interactions with other businesses by offering increased access to extensive data, so that they can effectively leverage their brands.
From a general overview, Chabots may appear as not being very progressive. However, in the backlines, the technology continues to evolve and offer increased data based services to companies. Through having Chabots interacting with consumers, businesses, especially the marketers, get to gain valuable information and lessons regarding the real world environment. Some of the notable yet considerable benefits of Chabots include that they are devoid of human bias; hence, the outcome of the massive data that they analyze is usually very accurate and effective. They also evolve steadily, learning from their experiences, making them ever effective and relevant. Additionally, the Chabots can log and store key data points regarding consumers.
Such information can be used by companies to enhance quality of product and service delivery, and in turn consumer experience. They facilitate the optimization of product development. They also enhance the interactions and relationships between consumers and brands, hence they increase consumer loyalty. Most importantly, they automate the data collection and data analytics processes of organizations. The Chabots can be programmed to learn from phenomena such as consumer reactions and consumption behaviors. Such information when used effectively can result in businesses knowing what consumers want before they even order for it. This is a perspective that was argued out when amazon.com was prevented from making deliveries to consumers before the consumers made orders for the products. Through its Chatbot, Amazon.com was able to know what consumers want before they made purchases.
Within the organizational setting the three main technological components that are required for data-driven decision making include data collection, data management (storage and documentation) and data analysis tools. Most companies usually adopt Information Technology infrastructure that facilitates the effective collection of data. The companies usually collect data from various different sources including sales records, profitability, volume of consumers and consumer responses among others. Under some circumstances they also collect information from social media interactions with the consumers and from research survey. Data collection components are important because the type of information and how the information is collected directly impacts on the decisions that the business makes. With regards to data management, many business organizations usually implement data management software within their systems. The organizations usually adopt the data collection approaches and tools that they feel are best suited for their needs. Different organizations usually have different operational networks which they use within the work setting to facilitate their activities. These software are interlinked allowing the organization to effectively manage its data. When data resources are effectively managed, the organization will be capable of making accurate decisions. With regards to analysis, different businesses usually implement different data analytics tools depending on their specific data needs. The more effective an analytic tool is, the more effective the decisions it influences.
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