How businesses are leveraging data to take better decisions
With the increasing complexity of today’s marketplaces, businesses consume millions of data points to make over a hundred decisions daily. Today, technology is playing a huge role in driving the decision-making process in a variety of ways. It enhances and optimises the processes resulting in speedy actions, in turn, increasing their competitiveness and market shares. In the new age of big data, below are some common areas that have demonstrated successful implementation.
1.Ad tech – Customer acquisition and Targeting
One of the biggest conundrums for marketing departments across companies is finding the right message for the potential consumer in their customer journey. Digital technologies can provide the required information through collated customer data. User interactions, transactions, and historical data provide in-depth insights into customer behaviour, paving the way for effective customer targeting. Further on, social media channels augment to create 360-degree user profiling. Aggregated user information can be used for segmentation and targeting.
For example, Tribal Fusion (now Exponential Inc), was looking for a solution that enabled a more personalised audience targeting mechanism. They found their answer in an ad technology platform that worked at the webpage level to understand the context of the page and user inclinations to serve ads that maximised conversion.
2.Analytics – for actionable insights
From recording customer activity to financial transactions, data can now be stored, accessed, and analysed by businesses in real time. Visualisation tools analyse these data points and present the data in easy-to-read-and-absorb format – mostly to identify patterns, a key for actionable insight. It can be used for more precise forecasting, planning, and monitoring of business.
Snapdeal, with its 12 million products and 800+ categories from over 100,000 brands is an excellent example. Being big data and analytics enthusiasts, they deployed real-time analysis of data across their platforms to distil key patterns. They were able to identify the implications of inventory and product distribution during the festive season – extremely critical for faster deliveries. The insights influenced, critical decisions like product placement on the home page, targeted promotions, assisted buying for older age groups etc. Based on the demand forecast, Snapdeal provided its sellers and brands with data points on the expected volumes, helping maintain requisite stock levels. This happened to be the core reason for customer delight.
3.Machine learning – for a more personalised user experience
Every business interacts with its customers in one or more ways – mobile, web, chat, e-mail, phone, feet-on-street sales etc. Each of these channels collects data that helps to create a detailed profile of the customer, allowing businesses to personalise their user experience. The browsing data of users is used by e-commerce sites to drive profits and sales, as the product recommendations and customised promotions during the purchase journey.
Integrating recommendations into each step of purchase process – from product browsing to check out – not only improves the user experience and customer loyalty but also helps e-commerce portals drive further sales by cross-selling other products to increase their bottom line. Natural Language Processing and machine learning techniques can be leveraged to conduct sentiment analysis of the reviews and ratings provided by the users, giving the objective user feedback about the products and service experienced by them. This helps businesses understand customer expectations and efficiently solve the problems customers are facing while using their product and services.
Personalised services enhance the user experience across markets and industries. For instance, Wynk crunched millions of data points to analyse songs and user information to find India’s favourite artists, peak times, and mass user preferences. This was used to curate playlists and promote specific ones to the user at precisely the right times, based on user data. It is no coincidence that many of their promotions are advertised to users in the evening, as per the data suggestions.
Natural Language Processing is a great potential use-case for machine learning.BidAssistis using this technology to read over5,000 documentsevery day to extract tender details with much greater efficiency, and accuracy. This helps in matching and recommending tenders to organisations, a function that machine learning can be taught to perform. Further, data crunching analytics atBidAssistare being developed to provide insights through awarded tenders.
4.Chat – for collaboration and commerce
As AI and ML are becoming more common, there is an increase in the adoption of these technologies into the industry, and they are being used in applications such as chatbots.
Chatbots are revolutionising the way businesses interact with their customers. Conversation commerce, where businesses engage with their customers using AI-powered chatbots have started to pick up. Companies have also begun to use chatbots to automate and complete tasks across business workflows.
With the advances inMLandNLP, leading B2B companies are now working towards using chatbots to provide a more “natural” conversational commerce experience and deliver information on demand, and can help optimize the process of posting RFQs, and reviewing old orders and past invoices. With intelligent mapping of user profiles, a chatbot can provide a user nearly instant access to required data by fetching it from their servers.
As smartphones and other internet-enabled devices have become ubiquitous, organisations have started using user-accessible digital channels to augment communication, which has led to high engagement and collaboration with the target audience and business partners. This provides high-quality customer-centric inputs that can enhance the product or service delivery of companies. Successful businesses have repeatedly demonstrated that the right decisions made on data-driven insights have helped optimise and improve business performance on relevant metrics.