Understanding Customer behavior through Big data Analytic

The business world has become highly dynamic & competitive. It would be correct to quote,

“Business is not what you want to sell, but what customer want to purchase”

This article focuses on traditional ecosystem based business model as well modern disrupted business model. Customer has become important aspects of business. The traditional business cycle started with shop floor management thus focusing on making efficient & effective operation along with conventional marketing strategies. This has shifted to more agile, highly adaptive & dynamic business operations thus focusing on creating values for customer. One should ask how to create customer value simultaneously delivering products & services? This is stage where Information technology has played greater roles in past few decades. Big data analytic is one of those disruptions which has made major turbulence in business ecosystem. Big data enables the firm to better understand about customer thus rendering service they want. This provides additional competitive edge taking them beyond deductive based methodology to Inductive based methodology. All the business experts think that they know something & using those understanding formulates hypothesis & models while Inductive based methodology states that without preconception or knowledge, you observe condition & analyses to get insight about those conditions. Big data works on Inductive based methodology. Any firm using big data analytic requires three important resources needed for efficient handling of big data are – physical capital, human capital & organizational capital. Some of the firm failed badly due to lack of capital resources while questioning the ability of bid data to predict correct information about customer behavior. We will additionally discuss how beneficial is big data analytic in aligning organizational goal with customer satisfaction goal.

Big data is new avenues for business world to get better insights about buying pattern, purchasing preferences, product image & many more behavioral hidden pattern. Traditionally company used various quantitative & qualitative market studies to predict customer demand & test market feasibility. The major disadvantage of such method is high cost, long time & inflexible product entered market. We have seen many business failure due improper marketing strategies, mainly those which ends up creating zero customer values. This raises questions on marketing principles but reality is wrong target & improper implementation of these strategies are major cause of failure. We see current example of amazon using big data analytic to better understand customer needs thus reorienting marketing strategies accordingly. Amazons based on complex algorithm & data analytic can predict that what his customer wants before they place order. This reduces the shipping time & lead time drastically without increasing overall cost. In modern marketing context, company usually look for economical route to reach maximum customer. Digital marketing & big data helps to achieve this endeavor very easily. Using data analytic, one can know how many time same customer visited my website, which product he liked most. Based on above conditions, company can offer special discounts product which his customer wants. The thing that only requires is

  1. How the company can handle such massive data & explore hidden pattern of customer need?
  2. How company can apply sudden change of customer buying pattern into business framework?

Big data: New era of marketing 

World is progressing at faster pace & so is the business need to adopt in growing world. Marketing plays a major role in presenting product/services at right time-frame & in correct quantity to targeted customer. Traditionally, marketing strategies were limited to promotion tools & products pitching. Now a day, it is very crucial to go beyond this through better understanding of customer wishes & focusing on customer relationship management. If the customer taste changes or competitor came up with innovation, then big data enables firm to take corrective actions to avoid product failure & at same time gives huge opportunities for deep market penetration. However, data collection has become so complex task due to volume, velocity & variety of data at every instant. This complexity is very straight forward reason for failure of traditional database system, thus opening new gateways for BIG DATA analytic techniques. Big data analytic involves data collection, data retrieval & information extraction. This helps to identify the hidden pattern in database. Netflix is good example applying knowledge of big data analytic to select & purchase rights of film which has greater chance of customer acceptance. Using customer demographic data & actor past data, Netflix can decide whether to go for movie or not. Similarly, Starbucks a major tea seller, amazes with grand success while opening multiple stores outlet much closer or in fact on same streets. Starbucks analyses information collected about location, public traffic, residential status, local demographic data & purchasing behavior to determine new store success.

3 V’s ( volume, velocity,variety)  depicts necessity of Big data analytic for success of firm

Worldwide acceptance of data analytic is due to volume of increased data. Data are measured in zettabytes, petabytes & exabytes. The Walmart generates enormous data with size of 2.5 petabytes every hour (one petabytes is equal to 20 million text file cabinets). Such huge data can only be handled by sophisticated technology like data analytics. The velocity i.e. rate of data generation is also important characteristics of big data. Instant decision making by analysing the real-time data gives upper hand over competitors. For example, a wholesale retailer of apparel has information about buying pattern & current market trend. He knows quite well which inventory is moving faster while one which is stacked. Although, manufacturer has annual report of customer buying pattern in previous year but cannot predict probability of pattern remaining unchanged. This is a case where size of data is same although frequency or velocity of data arrival is much faster in case of retailer comparative to manufacturer. Thus, manufacturer can reduce loss if he uses big data technique to evaluate & analyse customer requirement & taste on real time basis.

 Primary marketing data has changed from structured data to unstructured & semi-structured data. The structure data were collected via sensors, scanners & manual reports & database files. Due to increased digitization, company is pressing to collect data via user status, blogs & texts Such type of unstructured data is very complex to analyse but provided deeper individual insight & better success result. The variety of data to under customer is obtained from myriad channels like YouTube, twitter & Facebook. The shared video of person on YouTube using company products gives better understanding of customer. The video shared by users is greater prediction of his/her likes or dislikes.

Big data helps to create value for the customer by evaluating customer preferences, physiological data, geographical data, temporal data & other varieties of data. Geographical & spatial data helps to determine accurate time for promotion & sales activity. Geo spatial data captures mobility of customer thus targeting at right place. Airline company uses big data to estimate the fare of tickets. The traffic on website at few hours enables to surge the charge because of increased demand while similarly keeping low price for unusual time or low traffic duration. Previously, sports organising communities took only rate of booking & timing of ticket sales into account to decide price. Now a day, additional inputs like nearby locations, weekends, playing team ratings & many more factor are incorporated while analysing inputs to determine effective cost which customer will prefer.

 Big data can be of paramount importance for better innovation & product development. Ford uses sensors & remote app management software to capture primary customer data from 4 million of vehicle. Using big data, ford realised that external noise interferes with command given by driver to software. Thus, making immediate upgradation by introducing noise reducing software to enable better interface between vehicle software & driver.

New Paradigm in marketing

Any company wants to enjoy full benefit of big data need to acquire 3 important resources i.e. physical, human & organisational resources. Physical resources include software which collects huge volume of data & process it, human resource included skilled scientist who identifies hidden pattern in data & retrieves information & finally organizational resources which converts idea of customer insight into action. Any of these missing links can cause failure in big data techniques. Suppose, company got good physical & human resource thus, collects new information that customer did not like current packaging pattern but if organizational resource is not sufficient to change packaging pattern then market share of product will fall. This is also called as organizational decisions where company looks for adaptive & dynamic changes. Dynamic changes firm to rapidly upgrade resources per market demand thus growing customer base. Similarly, adaptive change helps organization to take marketing decision to remain in business for prolong time. Once the big data collected from customer is analyzed by using human, physical & organizational resource then firm takes strategic marketing decision about best of 5 P’s i.e. product, price, place, promotion. In this way, big data analytic has changed current marketing from waiting for customer reaction to pre-event positive action through customer behavior prediction.

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