Abstract
Firms all across the world have been investing substantially in data analytics, and analysts predict a 60% increase in investment in customer analytics by the year 2023. The function of marketing has always grappled with gaining complete attention of customers and keeping away the competition. For this, the firms have been consistently redesigning their strategies and introducing new techniques to enhance customer centricity. Digital era and emergence of artificial intelligence (AI) and machine learning (ML) are enabling marketeers to reach their ultimate goal of generating individualized or personalized recommendations. With unconventional data sources at hand and equally unconventional tools to analyse these streams of data, customer experience enhancement knows no bounds. Therefore, considering the novelty and significance of customer analytics, this article sheds light on sources of customer data, tools for customer analytics, and implications and benefits of customer analytics.
TopIntroduction
Customer analytics is defined as “the use of data to understand the composition, needs and satisfaction of the customer. Also, the enabling technology used to segment buyers into groupings based on behavior, to determine general trends, or to develop targeted marketing and sales activities” (Gartner, 2021). The leading analytics firm across the globe, SAS, defines customer analytics as “the processes and technologies that give organizations the customer insight necessary to deliver offers that are anticipated, relevant and timely” (Gray, 2021). As per the figures of a report by Mordor Intelligence (January, 2021), the customer analytics market, currently valued at USD 3.74 billion, is expected to grow three times in a period of five years (by 2026), to a value of approximately USD 10.2 billion. The compounded annual growth rate will amount to 18.2% over the forecast period 2021 – 2026, as per this report. Proliferation and advancement of cloud-based tools will enable integration of customer related intelligence such as data storage, analytical models, applications, in addition to added computing power. This integration will in turn enable generation of better and unprecedented insights about customers, and that too in real time.
The report also says that currently the leading provider of customer analytics solutions is North America; while the leading players in the field are giants like Adobe Systems Inc., IBM Corporation, Oracle Corporation, and so on, who have made substantial investments in R&D and expanded their innovative capacities through mergers and acquisitions. It is interesting to note that Asia Pacific market is the region identified with fastest growth rate. The scope of customer analytics is huge as it has diverse applications in all industries irrespective of their sizes, or region of geographical operation. The firms which leverage on Big data and customer data are seen to grow massively and sustain their business (Palmatier and Martin, 2019). Further, the companies that have developed business models centralized on customer data have been one of the most successful and promising firms.
Loyalty of customers is highly questionable, and firms are debating globally whether there is a need to purse this aspect anymore (Deloitte, 2020). Irrespective of the industry of operation, dwindling customer loyalty is hurting profits of the companies. Primarily, globalization, more information accessibility, higher interactions among consumers, global reach are a few factors which leave a daunting impact on consumer’s mind and ultimately make them fickle and less loyal. The only alternative available to the firms is to develop ways to understand their customers intimately, evolve their needs, and through this understanding help them grow thereby retaining them for company’s growth. The basic point of emphasis is that for this intimate understanding, companies need to tap into all probable sources of customer data by leveraging on customer analytics.
Key Terms in this Chapter
Facial Analytics: A stream of analytics used to interpret facial expressions of consumers, maybe in real time or using the consumer images stored in firm’s database.
Customer Analytics: Customer analytics drills into the trail of data generated from every interaction with customer to draw a wider and clearer picture of this interaction, enlisting everything from their product choices, preferences, reasons for purchase and need for interaction with the firm.
Social Media Analytics: A stream of analytics involved in interpreting data collected from social media sites such as Facebook, Twitter, and so on. It is useful to segment the consumers by different demographics, identify influencers (who fetch maximum likes and followers) among each segment, understand consumer behavior and perform sentiment analysis.
Voice of Customer (VoC) Analytics: This analytics stream refers to application of text analytics on reviews posted on sites such as Google, Yelp, and so on and gauge customer feedback devices with smiley faces at airport terminals.
Customer Data: Each point of interaction with the customer generates some knowledgeable insight about the customer enhancing the chances of delivering greater customer value and enhancing their experience. These can be generated through Internet, social media, Artificial Intelligence and so on. This may be structured or unstructured.
Internet of Things Analytics: A stream of analytics primarily focusing on analysis of data collected from different sensors placed on consumer devices such as mobiles, cameras, microphones, etc. useful to explain consumer experience.
Recommendation Engines: Refers to a dominant application of customer analytics which allows dynamic real-time display of tailored offers during product searches. These are sort of information filters which can mine user preferences from huge datasets of past transactions of the users, and then recommend them new relevant content.
Speech Analytics: A stream of analytics used to interpret the voice recordings of consumers, for example their call centre phone logs and can be analysed to understand their sentiments, rate of speech, identify solutions from past documentation to resolve customer queries.
Text Analytics: A component of Natural Language Processing (NLP) which encompass set of techniques used to process natural languages to glean meaningful insights from the same such as detect common themes in the text, mine sentiments and categorize texts.
Sentiment Analysis: A subset of NLP techniques which refers to the use of analytics to detect the common sentiment prevalent in each text or speech. This is useful to generate “buzz” value, an indicator of volume and frequency of comments of customers around a product, service or marketing activity; helpful to explain real time customer churn.