Every day we walk into the store, we pause a bit and take a look at the items displayed. Our choice would be a perfect combination of a price, quality and most importantly the right product. The shopping experience has radically changed in the 21st century. The Consumer has evolved, and so did the product. With the influence of social media and the digital space, brand manufacturers are shifting to a focused personalised customer experience driven by Big Data.
In a highly competitive market price, the consumer is king. Brand manufacturers and retailers are in a race to win the highest market share. In an attempt to predict the choices of consumers, they will make use of all available technologies in order to win the customer’s loyalty and achieve the optimal shopping experience.
The goal of marketing is to reach to the consumer at a moment of doubt in order to influence the decision of customers. Most companies try to dominate the touch points through a funneling process. A customer would start with a couple of potential brands in mind, and then marketing would step in, directing him to the best choice. Leaving the customer with just one product in mind, which could then result to a company gaining brand loyalty. However, the availability of various product choices, intense digital marketing and the rise of a well-informed discerned customer have led to the failure of the funnel metaphor. Over the past decade, digital technologies have contributed to the empowerment of customers. Nowadays, customers are able to do their own research on the product and get it delivered to their doorstep for the nominal market price. Accordingly, retailers have worked on developing data analysis tools that help in predicting the choices of the consumers in advance, and guide them through the shopping journey.
In an attempt to optimise this trend, marketers have been dealing with this journey as a product. Starting from the point of purchase through the after purchase, they ensure that consumers are heavily engaged with the brand. This in return, would lead to an immense exposure for the product with consumers will share their experiences publicly, promoting the product and cooperating in the brand’s development and market presence. Optimal results for this shopping journey can be achieved through Big Data Analytics.
“Customer Analytics” is applied to the Big Data that companies gain from customers. Through the utilisation of this data, companies are able to gain insights on customer’s buying patterns and habits. Companies would be able to target consumers more precisely and provide them with an optimal shopping experience. This would also lead to securing the returning customers that are considered to be the most valuable ones, reducing turnover, increasing revenues per customer, and driving brand loyalty.
Through this data, companies will be able to track and analyse customer events. These event include, but not limited to, flights, calls, and other day to day activities. Companies will be able to focus their effort on successful strategies, identifying the gaps in marketing schemes.
Contextual marketing is a type of marketing that we come across daily when using our computers. This type of marketing integrates various real time data that is collected from searching platforms and geo data in order to present a focused advertising that hits the customer at the right time and place. An example of that would be an Emirati resident spending his vacation in the Switzerland. In this case the advertising would be shifted to ski gear instead of beachwear. On the other hand, omni-channel marketing provides a personalised buying experience that is consistent across all the customer’s devices. This type of marketing offers one on one experience that attains full engagement with the customer. The personalisation of the advertising comes from analysis of data from different sources including social media and online activities.
All of what has been presented is just a portion of the many faces of Big Data. The ability to reach out to customers might be one of the most important aspect of this technology. Being able to predict a certain fault in a product, before even getting any complaints about the matter is considered to be a huge leap for customer service.
Through predictive analytics, Big Data can also predict the number of consumers that might be experiencing issues with a product or service, the company can contact them before they have to call a customer service line. This provides several advantages to the company as the service representative will have a good idea of what the issue is before contacting the customer. The response would be much faster. It also prevents customer annoyance with long waits on the customer service line. Finally, customers who may have switched products due to a problem may be inclined to stay thanks to the organisation being proactive.
Another aspect would be identifying and acquiring new customers. This would be more relevant in the services sector. For example, insurance companies would be able to analyse previous claims and incidents in order to come up with a perfect package that would attract new customers. Thus, instead of losing time calling the customer and trying to convince him with the service, a ready-made package could be the magical formula for his needs.
The modern consumer is seeking a custom built product. People want to feel special, they are always searching for a product that represents them and reflects their unique character. Generic promotional offers are no longer effective. Through Big Data, marketers are able to send precise promotions that suits the gender, location and social data of the customer.
However, collecting data in not enough to form a proper shopping journey. The data must not be used in confinement, instead it must be integrated with feedback from customers. Moreover, given that customer behavior is never fixed and always in change, the data must be updated as much as possible in order to stay in line with current trends. What’s important to note as well is that collecting this data must be governed by policies that maintain the customer’s privacy and anonymity.
