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.
According to World Bank, FMCG (Fast Moving Consumer Goods) market in India is expected to grow at a CAGR of 20.6% and is expected to reach US$ 103.7 billion by 2020 from US$ 49 billion in 2016. Some of the key changes that are fueling this growth are:
- Industry expansion – ITC Ltd has forayed into the frozen market with plans to launch frozen vegetables and fruits, aiming US$ 15.5 billion in revenues by 2030. Similarly, Patanjali Ayurveda is targeting a 10x growth by 2020, riding on the ‘ethnic’ recipes and winning consumer share of wallet
- Rural and semi-urban segments are growing at a rapid pace with FMCG accounting for 50% of total rural spending. There is an increasing demand for branded products in rural India. Rural FMCG market in India is expected to grow at a CAGR of 14.6%, and reach US$ 220 billion by 2025 from US$ 29.4 billion in 2016
- Logistics sector will see operational efficiencies with GST reforms. Historically, firms had installed hubs and transit points in multiple states to evade state value added tax (VAT). This is because the hub-to-hub transfer is treated as a stock transfer, and does not attract VAT. Firms can now focus on centralized hub operations, thus gaining efficiencies
- The rising share of the organized market in FMCG sector, coupled with the slow adoption of GST by wholesalers has led many FMCGs to explore alternative distribution channels such as direct distribution and cash-and-carry. Dabur, Marico, Britannia, and Godrej have already started making structural shifts in this direction
- Many leading FMCGs have started selling their brands through online grocery portals such as Grofers, Big Basket, and AaramShop. The trend is expected to increase with a strive towards cashless economy, and evolving payment mechanisms
- Traditional advertising mediums have seen a dip with the advent of YouTube, Netflix, and Hotstar. Digital medium is being used more and more for branding and customer connect
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.
Our focus for this article are those decisions that are made on a recurring basis, based on processes that can be repetitive in nature. Specifically, processes that can be automated, and re-run with minimal manual intervention. Typically, data-driven automated decision making or insight generating engines, are referred to as ‘models’, and they can be statistical or heuristic in nature. We will talk about the differences in later sections.
Internet of Things (IoT) is a network of connected physical objects embedded with sensors. IoT allows these devices to communicate, analyze and share data about the physical world around us via networks and cloud-based software platforms.
In the current scenario, IoT is one of the most important phenomena revolutionizing the technological and business spheres. Several industries such as Agriculture, Healthcare, Retail, Transportation, Energy, and manufacturing are leveraging IoT to solve long-standing industry-wide challenges and thus transforming the way they function. For example, in Manufacturing, sensors placed in the various equipment collecting data about their performance are enabling pre-emptive maintenance and providing insights to improve overall efficiency. In Retail, “things” such as RFID inventory tracking chips, in-store infrared foot-traffic counters, digital signage, a kiosk, or even a customer’s mobile device is enabling retailers to provide location-specific customer engagement, in-store energy optimization, real-time inventory replenishment, etc.
The Insurance industry, on the other hand, has been rather sluggish by virtue of its size and inherent traditional nature. They cannot, however, afford to continue a wait-and-watch attitude towards IoT. Insurance is, interestingly, one of the industries that are bound to be most impacted by various technological leaps that are being made. IoT, blockchain, Big Data are all expected to push Insurance to evolve into a different beast altogether, including a shift from restitution to prevention.
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.
Analytics have been growing at a rapid pace across the world. The well-established companies have realized the importance of analytics in their business where crucial decisions are taken that drives their revenue. But why do just the well-established corporates need to leverage this statistical and computational modus operandi when it can be implemented in a much-needed arena also?
The idea is to get to use the analytics for non-profit social organizations and provide a breakthrough. These are the organizations which strive to look for the upliftment of society by identifying the social responsibilities. The organizations cover a wide variety of aspects that helps to promote education, health, food, shelter etc
Affine recently completed 6 years, I have been a part of it for about 3 of those years. As an analytics firm, the most common business problem that we have come across is that of forecasting consumer demand. This is particularly true for Retail and CPG clients.
Over the last few years have dealt with simple forecasting problems for which we can use very simple time-series forecasting techniques like ARIMA and ARIMAX or even linear regression these are forecasts which are more at an organization or for specific business divisions. But over the years we have seen a distinct shift in focus of all our clients to get forecasts at a more granular level, sometimes for even specific items. These forecasts are difficult to attain using simple techniques. This is where more sophisticated techniques come into play. These techniques are the more complex machine learning techniques which include RF, XG Boost etc.