Cloud platforms and SaaS technologies have boosted the adoption of Big Data in business. A recent report from Forbes found that in 2017, 53% of organizations were using Big Data analytics, up from 17% in 2015. That represents a 212% increase over a two-year period! Cloud storage providers such as Amazon Web Services, Google Cloud or Microsoft Azure, or SaaS technologies like Salesforce, Oracle or SAP, have removed the barriers to entry related to expensive servers, storage, and computing capabilities, allowing companies of all sizes to take advantage of Big Data to grow and differentiate themselves. As providers continue to expand access, organizations are mining strategic value and critical business intelligence from their Big Data efforts that in the past were too expensive or difficult to achieve.
A CX Strategy Fit for the Experience Economy
Big Data Powers Recommendation Engines, Predicts Customer Preferences
The digitally-native customer offers loyalty and advocacy in exchange for exceptional brand experiences. They are the tour de force behind the experience economy, reshaping business, and turning the customer experience into, arguably, the main pillar of success and disruption. Customers increasingly opt for products and services served up by data-driven recommendation engines, tailored experiences, and personalized services to escape the ever-increasing noise of the marketplace. Businesses are at an increased advantage if they capture both new customer information and apply existing insights related to customer purchase history and purchase behavior. This data can be used to drive Big Data analytics and predictions that help refine a customer’s experience while purchasing a product or using a service.
For businesses striving to enhance their CX, recommendation engines are a strong first step in putting Big Data analytics and predictions to work. Such engines use data collected from across the organization to predict what might interest the customer. These predictions power and refine various touchpoints including websites, online services, and mobile apps. Chegg, a leading provider of scholastic material and assistance, improved their user experience and increased user interest by powering its online search tool with predictions provided by Big Data tools.
Businesses can also use demographic data, usage history, and purchase history to improve revenue by offering differentiated pricing that is driven by Big Data analytics. Such pricing strategies are very effective at attracting new customers and reducing churn. Similar datasets can be used in solutions that provide customers with more relevant ads, ensuring that any investment in marketing is utilized well. Big Data-driven multi-channel marketing, marketing powered by analytics and predictions, is significantly more effective than generic campaigns. It leads to a personalized experience for customers, powerful insights, a great degree of automation and control, better product development, and greater clarity.
Big Data tools have also proven effective in better managing inventory, making warehouses smarter, and improving operational efficiency. Analytics and predictions powered by data gathered from across organizations and their customers can provide insights and predictions that help manage warehouses and inventory in more informed ways. These predictions include rises and declines in demand for certain products, features, and services. Manufacturers can also deploy a variety of Big Data solutions that rely on data gathered from research, simulations, past products, the market, and the Internet of Things devices found across processes. Such solutions can help organizations design better products, improve resource utilization, reduce maintenance costs, automate maintenance, and tailor the manufacturing process to suit each day’s requirements. While these solutions might prepare Manufacturing for the digital age, they require significant investment and expertise when compared to Big Data solutions such as recommendation engines and smarter marketing tools.
Overcoming Big Data Challenges
Common challenges organizations face when implementing Big Data are
- Making data insights actionable
- Identifying areas where Big Data investments can provide quick returns
- Consolidating and migrating siloed data
Here are ways to meet and overcome these challenges:
Making Data Actionable
Companies hold vast amounts of data about their customers, but many struggle with how to put it to use. What’s more, departments use customer data differently. For example, Marketing uses customer data to not only create excellent data-driven customer experiences but to improve predictions around purchase behavior. Support relies on customer data to get a full context of the customer’s relationship to the company and how they can better serve them to provide optimal customer service. IT will leverage customer data to make informed technology purchase decisions that empower employees to innovate the customer experience.
Quick Returns for Big Data Investments
Identifying areas where investments in Big Data can provide quick returns requires strategic planning and expertise. A viable way to overcome this challenge is by working with technology partners. Technology partners have the experience and expertise to provide strategic direction on how to make Big Data actionable and tips and best practices on where and how to get started. This can reduce risk and lower costs while promoting interaction between the various teams and departments within organizations.
Consolidating Siloed Data
Data silos present a huge problem for organizations striving to become data-driven. However, services like Amazon Web Services offer a “Pay As You Go” model to make data-migration and consolidation relatively painless and affordable. Moreover, these platforms may eliminate the need for proprietary solutions, expensive talent, and rare expertise necessary to consolidate, migrate and analyze the data. Larger organizations often use services from Google or Amazon Web Services to ensure that they are maximizing the insights and predictive capabilities from their data sets.
As Big Data analytic technologies proliferate and become more accessible, organizations are harnessing the power of Big Data to innovate processes and operations, improve the customer experience, and better manage supply chain and inventory. And these are just a few of the ways they are using Big Data. It’s clear the power of Big Data is limitless.