ChatBots have been evolving for almost 3 years now. However, by 2018, ChatBots stand at a cusp, with the technology and usage, ready to take off in an exponential way; powered by AI, NLP, and Analytics. ChatBots are intended to provide end users with a Cognitive, Conversational and Customized Interface to get their job done.
Many companies, especially those in BFSI and Legal sectors, deal with a large volume of handwritten and scanned documents. It is difficult to easily use the granular information in these documents to perform an analysis or even browse through the documents in a convenient manner. A simple classification of the documents into meaningful bins or folders would make it a lot easier to leverage the information within the documents.
Banking is re-inventing itself as it always has
“Help us build the kind of bank you want to use” – That’s what Monzo says to its customers. Monzo is a UK based banking app with no usage fees, no branches, no mortgages and zero charges for spending abroad. While few of the banks in the UK might have a similar offer, but where Monzo has gained grounds is how it emotionally connects to its users. It auto-budgets your expenses, highlights where you are spending more, helps in finding the best deal e.g. it puts the money in the best savings accounts. They have formed a partnership with Google and Amazon to ensure real-time banking. For generations of users who spend a considerable amount of their time on their smartphones, it’s more than a conventional bank. Not surprising, they are termed as the new “wonder kid” in the banking industry and are aiming to have a billion-user base in next 5 years (ref: www.thegaurdian.com).
Forecasting demand for new product launches has been a major challenge for industries and cost of error has been high. Under predict demand and you lose on potential sales, overpredict them and there is excess inventory to take care of. Multiple research suggests that new product contributes to one-third of the organization sales across the various industry. Industries like Apparel Retailer or Gaming thrive on new launches and innovation, and this number can easily inflate to as high as 70%. Hence accuracy of demand forecasts has been a top priority for marketers and inventory planning teams.
Product Life Cycle Estimation
“Watch the product life cycle; but more important, watch the market life cycle” – Philip Kotler
The product life cycle describes the period over which an item is developed, brought to market and eventually removed from the market.
This paper describes a simple method to estimate Life Cycle stages – Growth, Maturity, and Decline (as seen in the traditional definitions) of products that have historical data of at least one complete life cycle.
Here, two different calculations have been done which helps the business to identify the number of weeks after which a product moves to a different stage and apply the PLC for improving demand forecasting.
A log-growth model is fit using Cumulative Sell through and Product Age which helps to identify the various stages of the product. A Log-Linear model is fit to determine the rate of change of product sales due to a shift in its stage, cet. par.
The life span of a product and how fast it goes through the entire cycle depends on market demand and how marketing instruments are used and vary for different products. Products of fashion, by definition, have a shorter life cycle, and they thus have a much shorter time in which to reap their reward.
The Need For Processes
Across industries, an increasing number of organizations are moving to data-driven decision making. Some of these decisions are contingent on historical data, and trends, whereas some are made on the fly, usually the more critical ones, based on the point in time data. Some of these judgments must be made with the ever-metamorphosing nature of data, whereas some are repetitive, based on data, that does not change all that often.
Organizations have realized quantum jumps in business outcomes through the institutionalization of data-driven decision making. Predictive Analytics, powered by the robustness of statistical techniques, is one of the key tools leveraged by data scientists to gain insight into probabilistic future trends. Various mathematical models form the DNA of Predictive Analytics.
A typical model development process includes identifying factors/drivers, data hunting, cleaning and transformation, development, validation – business & statistical and finally productionalization. In the production phase, as actual data is included in the model environment, true accuracy of the model is measured. Quite often there are gaps(error) between predicted and actual numbers. Business teams have their own heuristic definitions and benchmark for this gap and any deviation leads to forage for additional features/variables, data sources and finally resulting in rebuilding the model.