Knowledge is power in business, and data is the fuel that creates this power. Being able to harness the power of data through data science is extremely valuable, and often is the biggest differentiator between success and failure.
Due to insights that data science can offer, more and more businesses are utilizing the same to make evidence-based decisions to maximize their customer interactions, tailor micro-communications and explore the full potential of available resources. However, the vast amount of data available can be overwhelming, and if not interpreted and simplified, is rendered useless. This can be problematic at times, since roughly 80% of all data is unstructured, and needs the right tools and expertise to make it consumable.
Data-first methodology aids agile business strategies besides helping companies with enhanced business decisions. Hence it’s important that companies “test-measure-implement” anything and everything—which means that this data first culture should strongly be embedded in our DNA. Companies can have in-house scientists that help them in optimizing decision making, KPI tracking, marketing, and sales. Data security should be the main priority since it will not only help you to secure data but stack the same in a highly regulated setup that extracts, transforms and loads data seamlessly every single day.
There are several mechanisms in place to improve our product and business metrics to provide high-quality consumer service. Through PowerBI, which is used for dashboarding visualization and reporting, one can track product performance and business health. This enables the senior management and CXOs to be aware of all the key metrics and also gives them a UI through which, based on the filters, they can extract metadata.
Machine learning is a crucial aspect to utilize current market trends for product optimization and devising innovative marketing strategies. It is a robust system to help achieve one’s business goals at a faster rate. A Product Cross-Sell system bases on product usage, location, and line of business can help you identify gaps in consumer needs. PAN Card and OCR system use an extensive layer of Google Vision and machine learning, which allows one to interpret data directly from an image, thereby reducing human errors. It further corroborates the motto to “Automate or Eliminate”.
Another aspect where machine learning is extremely useful is price optimization. As a business, it is critical to build effective pricing automation solutions to achieve sales targets. Factors such as competition, market positioning, production costs, and distribution costs play a key role for retailers in order to create a significant business impact. ML can be of great help in this case and have an enormous impact on KPIs. Its power lies in the fact that the developed algorithms can learn patterns from data, instead of being explicitly programmed. ML models can continuously integrate new information and detect emerging trends or a new demand, and help identify how consumers react to different pricing or how that price impacts business goals. Additionally, Market Estimation using the stacked ML model helps us understand new areas where markets can be created and how sales strategies should be developed to maximize market share.
The use of data science in business is a constantly evolving spectrum, and trends will keep cropping for businesses to leverage. Reaching the right target audience, providing the metrics to measure the impact of marketing campaigns and allowing businesses to emulate the impact of any business decision on growth before actually implementing the strategies are key ways that cement the fact that data science is the future of decision making.
By Geetanjali Prasad, Head Analytics, PayNearby