Data has changed dramatically over the years in terms of volume, variety, velocity, and veracity. We are living in the age of data. Data is being generated in all possible ways by people, machines and physical assets. At the same time technology advancements in the areas of Machine learning and Artificial intelligence is creating huge opportunity. Benefits to the business are only limited by how you connect the dots with data.
How Innovyt is helping its customers to stay ahead:
- Better visibility oftheir businesses and inmaking better predictions
- In manufacturing, remote monitoring is used to understand the performance and condition of the equipment at customer sites or at remote locations
- In retail, data is used foroptimization of demand forecasting, to better predict what customer demand will look like in next seasonIn financial services or other areas where risk-mitigation is needed, itin identification of the fraud signalsmore effectively
- Advanced analytics to ensure materials, parts and products are optimally distributed to the right places at the right times
- In manufacturing, data analysis using historical data helps in optimizing the supply chain,ensuring that the parts are on hand in the right warehouses and at the right suppliers
- In retail, this involves managing inventory and ensuringdelivery of the products without any fault, which prevents any losses inmargins by eliminating the need to ship inventory elsewhere. Getting insights of customerswho surf web for a product online, thenmake offline purchase, the retailer can be better prepared to address the customer demand and be readywith the item in stock (or know where it is in a nearby store)
- These are both examples of better optimizing the marketing mix – using advanced analytics and intelligence to ensure the right product is available atthe right place, at the right price
- Across industries, getting the right items to the right places contributes to operational efficiency objectives
- Offering customers what they want, when they want it:using advanced analytics to develop deep, 360-degree view of customers and deliver personalized experiences
- In manufacturing, this involve tracking a customer’s usage pattern of a productand extending theoffering on how to increase the product’s durability, and developing promotion strategies on replacement parts. Using this, manufacturers anticipate what customers need before they even ask.
- In retail, this involve tracking every customer interaction and purchase, whether in-store, online, via social media, via a mobile app or any other channels. Organizing the customer demands in a picture this way helps retailers better predict what customers are looking for, and can offer targeted promotions and offers.
- In financial services, making personalized product recommendations based on customer information from across channels – from detailed demographics to social media interactions.
- Across industries, using data, advanced analytics and intelligence capabilities leads to a successful introduction of a new product. Such insights help in mitigating the possibilities of failure of new product launch.
Fixing problems proactively, before products and equipment ever break down
- Across many industries, starting predictive maintenance programs – whether that’s for your own equipment, assets or facilities, or for products installed at customer sites
- Across many industries, getting problems proactively handled can support operational efficiency gains – it’s usually cheaper, faster and easier to prevent a problem than to fix it after the fact. Whether you’re talking about chronic disease care or expensive equipment at a manufacturing site, the principle remains the same.
- Across many industries, customer service improvement because of advanced analytics and big data is an example of fixing problems proactively before they start. For example, if you’re a cable provider, and you know there have been service quality problems for a certain customer, you can proactively, automatically offer them a discount, and notify them that you are working on the problem, and offer a target resolution time. This could prevent them from calling into your call center multiple times, and may even prevent them from switching to another firm.
Building data-driven service offerings, such as delivering products-as-services or packaging data for commercial sale
- In industries offering physical products, this might involve offering products as services. Instead of a single transaction where a product is exchanged, the product is automatically serviced and updated, maintaining the relationship with the customer and creating opportunities for cross-sell and upsell.
- In manufacturing, using advanced analytics and big data to identify typical usage patterns and next-logical-purchases, firms can offer targeted promotions on replacement parts as well as complementary products
Apart from the above listed solutions, other examples include real-time recommendations, better customer acquisition, optimized loyalty programs and lifetime customer value, customer churn forecasting, fraud detection, pay for performance, more accurate product segmentation, better pricing strategies and many more.