I am a big fan of walking into a retail store versus buying things online. This is specifically true for clothes from my favorite retail chain since it gives me access to physically see a wide variety of options combined with great ambiance, music, and sales assistants who go the extra mile to help me make the right choice. I fear I end up buying more items than I planned, but the store-buying experience makes me feel valued as a customer. However, before I head to the store, I check out the store’s website to see what’s new, find different colors and styles, and sometimes add things I like to the online cart.
I am also a huge admirer of social media. I often use Twitter as a way to communicate with my retailer and check out their Facebook page to see what’s new. When I am in the shop, I check-in to the store using Swarm (Foursquare). When you look from the retailer’s point of view, I am giving them several ways in which they can communicate with and understand me. By connecting my interactions via these different channels with my location data and my preferences, the retailer has a great opportunity to give me a consistent and quality experience, leading to a win-win situation for both me and the retailer.
So, what is stopping these organizations from achieving great customer service? It is a no-brainer that if they make their customers happy, they not only beat the competition, but they also achieve their business goals and become more profitable.
In reality, there are many road blocks to their success. The trouble with insight is it doesn’t just pop out of data. It takes intelligence and an understanding of the business, combined with the ability to identify useful relationships between customers, their relationships, locations, products and social interactions. Once you are able to connect these dots, you need a comprehensive understanding of the content, context and correlations within. The most important aspect of this exercise is the foundation of quality data about your customers and products.
This is why Master Data Management (MDM) has become such a crucial layer in the enterprise analytics stack; it prepares your customer, product and other master data. MDM identifies critical relationships among the data and combines interactional and transactional data associated with your customers. As a result of this great technology, organizations are now becoming more customer and decision ready.
As companies are trying to master digital transformation fueled by social, mobile, cloud and big data, they are finding it hard to connect data flowing to their organization in huge volumes and varying formats. It’s becoming extremely difficult for these organizations to put data to work to identify different relationships that exist between their customers, prospects, their households, the products they have bought, the products they wish to buy and locations they are in, etc.
In last few years, we have seen the advent of emerging technologies developed to tackle big data. Hadoop, which quickly gained popularity, has dramatically lowered the threshold of viability for big data analytics. Designed to run natively on Hadoop, Informatica Big Data Relationship Management helps organizations handle master data in large scale. It identifies customers, their household and social relationships that exist across billions of records representing millions of people. Combining this information with customer interactions, transaction data and social media data helps organizations create a “Social 360” view that helps them understand their customers more intimately.
And, understanding customers is key to every organization’s success today. What’s even better for companies is to have the ability to anticipate what their customers’ needs and wants are and deliver the right offers at the right time so they can not only meet, but exceed customer expectations and win them for life. So, the next time I walk into my favorite retail store, it would be wonderful if they could walk me straight to the changing room based on what I put in my online cart.