The globalization of the world’s economy, coupled with the ever-increasing competition for global talent and an increase in remote workers has led to one notable shift amongst companies — expanding recruitment efforts beyond the location of their physical headquarters. Companies are turning to global talent for a host of reasons, most notably as a catalyst to expanding into new markets and remaining competitive in recruiting talent.
Even before our “new normal”, flexible work arrangements were already in high demand among the workforce and new applicants. Now in the post-COVID era, many companies are operating, out of necessity, in a remote work environment. While some have returned to the office, many companies, including major multinationals like Mondelez, Morgan Stanley and Google, are embracing a digital model that allows their teams to work from home across the world. For many companies, working remotely is now the new normal, as they don’t plan to return to a physical office or are significantly reducing office space. Continue reading
With the rise of big data, more companies are collecting and storing vast amounts of information about their business, revenue, and customers. However, while the adoption of big data has accelerated significantly in recent years, many companies are struggling to extract meaningful information from the abundance of data at their fingertips. Others are unable to take full advantage of their data due to cumbersome dashboards that are difficult to use and laborious, manual data retrieval methods.
In today’s digital economy, data is a company’s biggest asset. Though data comes in many forms, identifying whether structured or unstructured data will meet your business’s needs is of the utmost importance, and ultimately determines which method of analysis to use.
Sentiment analysis is a machine learning technique that employs text analysis algorithms, natural language processing (NLP), and statistics to analyze customer sentiment — classifying opinions into positive, negative, or neutral categories. Understanding a client’s reactions on an emotional level is vital for unearthing the deepest insights required to perfect the customer experience. For this reason, companies are in a race to understand their customers — what they’re saying, how they’re saying it, and what they mean.
An estimated 80% of the world’s data is unstructured, the majority of which is unstructured text such as customer reviews, feedback forms, surveys, social media data, and the like. This data is hard to analyze, understand, and sort through, making the process time-consuming and expensive. Sentiment analysis, also known as opinion mining or emotion AI, uses NLP to understand the context of data, and instill structure into it via tagging and categorizing.
By analyzing unstructured customer feedback at scale, such as customer reviews and social media conversations, businesses are better able to listen to their customers and make informed decisions relating to their products and services.
While there are various types of sentiment analysis approaches, those with the highest level of adoption include fine-grained sentiment analysis, emotion detection, aspect-based sentiment analysis, and intent analysis.
Embracing digital transformation within your organization will be vital to your business’s success in the near to long-term future. Whether you’re already immersed in the digital economy, or transitioning towards fully embracing what it has to offer, there’s no question that it will continue to change rapidly. Digital marketing is evolving at a similar pace, and trends suggest a drastically different landscape in the coming years. Fortunately, businesses that embrace digital transformation will be able to harness the power of data and in turn improve customer experience and product innovation, while adding more tools to their marketing toolbelt.
Below we discuss what we can expect from digital marketing in the future — these trends will drastically change the industry while allowing businesses to make the most of their data on the path to digital transformation.
Data analytics is the science of analyzing raw data using various techniques and processes to make conclusions about that data, and eventually package it so that it can provide insights. These techniques can reveal trends, patterns and metrics that would otherwise be lost in the mass of information that is generated by a business in their daily operations. Companies can use these valuable findings to inform and improve how they do business, engage with customers, and make decisions related to their products and services.
Analytics is the process of consuming and transforming data so that it answers important questions about your business. Data analytics companies follow a clear process when conducting data analysis and rely on the analytics maturity model (AMM) to identify and present valuable information, helping organizations to improve their internal and external processes. The analytics maturity process involves an initial stage of data evangelism, followed by putting together the infrastructure, and eventually conducting various levels of analytics.
The process isn’t necessarily linear and when it comes to conducting analytics, very often multiple iterations are required to ensure that the questions being asked have been answered. Below we discuss the analytics maturity process in greater detail to provide greater insight into what each of its stages entails.
According to The Economist, data is now the most valuable resource in the world, ahead of oil. The shift comes as no surprise — 97% of businesses use data to power their business opportunities, and for 76% of businesses, it serves as an integral part of forming a business strategy. But, what makes data so valuable?
Put simply, data is customer information that, when analyzed and monitored correctly, is able to provide insights into your existing and potential customers. Clean, organized and up-to-date data can help your company meaningfully engage with customers, make business decisions with confidence, add value to the organization, and better inform product and service development.