A Study to analyze the effectiveness of using Big Data Analytics for “On-Demand Marketing”

Philip Kotler has defined marketing as “a social and managerial process by which individuals and groups obtain what they need and want through creating, offering and exchanging products of value with others.” For long, marketing has depended on the four P’s - Product, Price, Promotion and Place - as envisaged by Kotler. Later, proponents of Service Marketing added another three P’s - People, Process and Physical Evidence - to the existing strategies. But, with the advent of technology, the strategy of marketing has also undergone a sea change. Today, products are getting commoditized, thanks to the plethora of choices before the customer. Premium pricing has become tougher with most of the brands resorting to price wars whenever the customers shift their allegiance towards rival brands. Brand promotions are losing their appeal with the same celebrity endorsing a huge number of brands, and the brand message getting lost in the clutter. The advantage of having a huge dealer and distributor network is also not working in the favor of brands with web portals making products available to the customers at the click of a mouse. With the complexities of marketing increasing, the marketers are now resorting to innovative approaches to reach out to customers in more effective manner. One such approach is “On-Demand Marketing” - Marketing, which is not just always “on,” but also always relevant, responsive to the consumer’s desire for marketing that cuts through the noise with pinpoint delivery - according to the definition given by Peter Dahlström and David Edelman of McKinsey. The main driver of “On-Demand Marketing”, in the coming several years, is going to be Big Data. This study shall endeavor to develop a process by which big data can be utilized effectively to implement “On Demand Marketing”. The research work shall employ both primary and secondary data and analyze the same using an empirical framework to come out with strategic insights.


Philip Kotler, in his book “Marketing Management” has defined marketing as “Marketing is a societal process by which individuals and groups obtain what they need and want through creating, offering and freely exchanging products and services of value with others.”

Philip Kotler has also inspired, guided and motivated practitioners of marketing for close to five decades when he put forward the now famous 4Ps Model of Marketing Management in the first edition of his book published in 1967. The 4Ps dwells on four aspects which are Product, Price, Promotion and Physical Distribution. The product should possess an USP (unique selling proposition). The next component was price. The pricing had to be done in such a way that it encompassed value for money proposition - the price should not be so high that consumers start postponing their purchase or look for cheaper alternatives and it should not be so low that it starts hurting the profitability of the company. There were also several strategies of pricing a product - you could go for penetrative pricing, cost based pricing or competitive pricing. The third component was Promotion. The choice of media available now is quite large, varied and humungous - you could go for print media, electronic media, internet pop-ups or outdoor billboards. The fourth component called Place or Distribution Channel ensured that the product reached the end consumers and customers in a seamless and effective manner. There were also various alternatives available out here - one could use C&F (carry and forward) agents, wholesalers, retailers or do direct marketing through a dedicated sales force or use e-marketing channels like Internet.

However, Marketing is now getting complicated because the competition is increasing as customers are spoilt with choices. The USP (unique selling product) factor is getting blurred because too many me-too products and services are getting launched or similar offers are being given to customers at a very short span of time. The aviation companies offer airline tickets at prices which hardly differ from that of the competitor. Many of the highly popular features of the Apple i-Phone, like apps for games, maps, money management, healthcare and voice assistance, are also available on Android devices which are much cheaper. Liril, in an effort to reposition their product offering, had come out with New Liril soap with Aloe Vera, which Hindustan Unilever Limited (HUL) thought would be a killer proposition. Within a month, there were 25 other soap variants launched by competitors that had Aloe Vera as a key ingredient. Liril, till today, is struggling as an underperformer among the power brands of HUL.

Brands are also losing the pricing power because of intense competition. We have seen self-destructing price wars happening in Aviation, Telecom and FMCG sectors. Air Deccan created a stir in the aviation market by introducing air tickets at Re 1/-, Rs 99/-, Rs 599/- and Rs 999/-. This detrimental price war lead to a blood-bath in the aviation sector which saw Air India, Jet Airways, and Kingfisher Airlines end up with steep losses. Tata DoCoMo created a similar turbulence in telecom market when they offered rock bottom call rates at 1 paise per second. The other telecom players were rattled and also had to go for price cuts to match those rates. The detergent war between Surf of Hindustan Unilever and Ariel of Proctor & Gamble is quite well known. Both the brands ended up with huge losses after undercutting each other on price.

Promotion is also losing it’s importance as a strategic marketing tool because of the clutter and the confusion created by too many media channels. With every channel jostling for TRP (television rating point), things are not very clear about which channel can give better visibility. Celebrities are also endorsing so many products that the brand top-of-mind-awareness (TOMA) is coming down. On the other hand, we have brands like Xiaomi, who use unconventional strategies like viral marketing using internet marketing tools to create excitement in the market.

Physical distribution has become much easier with specialized logistics firms providing doorstep delivery. This has become boon for e-commerce companies like Flipkart, Amazon and Snapdeal. This has posed a serious challenge for traditional retailers who are losing out on business as customers prefer to shop from home instead of going to retail chains for shopping. A study done by ASSOCHAM revealed that during this Diwali the sales of physical retailers dropped by 50%, while the sales of e-retailers shot up by 350%.

As the 4Ps model started to lose importance as strategic tool of gaining marketing edge, the new parameter that is gaining in importance is information. Companies who have the right kind of information, know how to process the information and how to use the same are going ahead in the competition. Hindustan Unilever Limited (HUL), who correctly understood that the Indians have a strong penchant for fair complexion came out with a fairness cream called Fair & Lovely that is giving them substantial earnings. Emami, who understood that even Indian men prefer fair complexion, came out with a fairness cream called Fair & Handsome for males, that created a different category and earned them good profits. Paras pharmaceuticals understood the latent need to take care of the soles of the feet and came out with Krack cream, that created a niche category which created fortunes for them.

However, in the current business scenario, getting the information is not tough, but processing the information is a very big challenge. As per information given by IBM, everyday 2.5 quintillions of data are getting generated (1 quintillion = 1018). This data is getting generated from traditional sources like market research, customer feedback and data from retailers as well as non-traditional sources like E-Mail, SMS, Google search, Facebook, Twitter, YouTube, Blog, Instagram and other internet based platforms. This data is collectively known as Big Data and has substantial value for marketers who are looking at getting customer insights to create breakthrough products and successful marketing campaigns.

The use of Big Data Analytics has brought about a new opportunity for marketers in the form of a strategy known as “On Demand Marketing.” According to the definition given by Peter Dahlström and David Edelman of McKinsey, “On-Demand Marketing is marketing, which is not just always “on,” but also always relevant, responsive to the consumer’s desire for marketing that cuts through the noise with pinpoint delivery.”

Literature Review

Figure-1 : Consumer Buying Behavior Model

Consumer Buying Behavior Model

The traditional consumer buying behavior process follows the Awareness-Familiarity-Consideration-Purchase-Loyalty-Advocacy Model as depicted in the diagram above. On-Demand Marketing seeks to cut the cycle time of purchase by converting an exciting experience into a buying decision. For example, a real estate marketer can take the customer through a 3D visualization of the housing project that he is planning to construct. The customer can be taken on a virtual journey through visual graphics through which she can get a feel of the layout, texture, space, aesthetics and various amenities offered inside the apartment as well as the housing complex. If the experience is wholesome and exciting, the customer can be expected to deposit the booking amount with a shorter time lag.

On-Demand Marketing is becoming popular because of the availability of high end technology that helps to reach out to customers in a ubiquitous manner. The use of SEO (Search Engine Optimization) and various platforms of social media through the computers, tablet devices and mobile phones have helped the marketers to reach very close to the consumers like never before. Today, smart marketers are sharing data about what your friends and peers are buying at this moment, thus encouraging you to go for similar purchases. Also, data aggregator sites can very quickly give a comparative analysis of various brands so that customers can either buy the one with the best quality or the one available at the lowest prices, as per their choice and preferences. Companies like Ebay are actually giving the customers the choice of choosing the price they prefer to pay by allowing them to bid for products.

One of the main drivers of “On-Demand Marketing” today is Near-Field-Communication (NFC). Today, most high end mobile phones are embedded with chips that can exchange data when it comes in contact with objects that have NFC tags. The high end mobile phones, or smartphones as they are popularly known, today help the customer to do banking transactions, book airline tickets, pay utility bills, search for information as well as buy products and services from e-commerce portals.

The other main driver of “On-Demand Marketing”, in the coming several years, is going to be Big Data. In the words of McKinsey Director, David Court, “Big Data and Analytics have climbed to the top of the corporate agenda. Together they promise to transform the way many companies do business, delivering performance improvements not seen since the redesign of core processes in the 1990s. As such, these tools and techniques will open new avenues of competitive advantage.”

Gartner defines big data as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Every day, we create 2.5 quintillion bytes of data. Data volume is increasing exponentially from terabytes to petabytes, exabytes and now zettabytes. According to IBM, 80% of data captured today is unstructured, from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. All of this unstructured data is big data. Big Data is a collection of huge and complex data sets that becomes difficult to process using on-hand database management tools or traditional data processing applications.

Figure-2 : Big Data = Transactions + Interactions + Observations

Big Data =  Transactions + Interactions + Observations

Big Data is getting mainly getting generated by:

  • Social Media and Networks
  • Scientific Instruments
  • Mobile Devices
  • Sensor Technology and Networks

The payoff from using big-data for on-demand marketing is enormous. The quantum of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their marketing operations, they can deliver effective sales and higher profit gains. The new marketing strategy of data-driven sales, more in-depth information about consumer behavior, better predictions, and shorter decision making cycles is making companies adopt this model at a faster rate.

Hence, there is a requirement for a strategic plan on how companies can leverage data, analytics, frontline tools, and people to create business value. The effectiveness of the plan shall lie in creating a common language allowing senior executives, technology professionals, data scientists, and marketing managers to discuss where the greatest returns will come from.

Figure – 3 : The Four V’s of Big Data

The Four V’s of Big Data

As per an article by Stefan Biesdorf, David Court and Paul Willmott of Mckinsey titled “Big Data: What’s your plan?”, published in Mckinsey Quarterly March 2013, “The main challenges of effective utilization of big data for on-demand marketing shall lie in the following areas :

Matching investment priorities with business strategy

As companies develop their big-data plans, a common dilemma is how to integrate their accumulated database across transactions, operations, and customer interactions. Integrating all of this information can provide powerful insights, but the cost of a new data architecture and of developing the many possible models and tools can be immense.

Balancing speed, cost, and acceptance

A natural impulse for executives who acquire critical data and adopt analytics strategy is to shift rapidly into action mode. Once some investment priorities are established, it’s not hard to find software and analytics vendors who have developed applications and algorithmic models to address them. These packages (covering pricing, inventory management, labor scheduling, and more) can be cost effective and easier and faster to install than internally built, tailored models. But they often lack the qualities of a killer app—one that’s built on real business cases and can energize managers. That’s why it’s crucial to give planning a second dimension, which seeks to balance the need for affordability and speed with business realities.

Ensuring a focus on frontline engagement and capabilities

The process of utilizing big data for on-demand marketing starts with the creation of analytic models those which frontline managers can understand. The models should be linked to easy-to-use decision support tools and to processes that let managers apply their own experience and judgment to the outputs of models. While a few analytic approaches (such as basic sales forecasting) are automatic and require limited frontline engagement, the major share will fail without strong managerial support.”

Figure-4 : Size of Big Data

Size of Big Data

In the article, “The coming era of On-Demand Marketing” , Peter Dahlstorm and David Edelman states that, “As these digital capabilities multiply, consumer demands will rise in four areas :

Now : Consumers will want to interact anywhere at any time.
Can I : They will want to do truly new things as disparate kind of information (from financial accounts to physical activity) are deployed more effectively in ways that create value for them.
For me : They will expect all data stored about them to be targeted precisely to their needs or used to personalize what they experience.
Simple : They will expect all interactions to be easy”.

To deliver on-demand marketing, each company as a whole must mobilize to deliver high quality experience across sales, service, product use and marketing. As interactions multiply, companies have to use techniques such as design thinking to shape consumer experiences. They also need to be familiar with emerging tools for gathering the right data across the consumer decision journey. The marketing organization’s structure will need to be rethought as collaboration across functions and businesses.

Research Methodology

Research Objective

A Study to analyze the effectiveness of using Big Data Analytics for “On-Demand Marketing”

Research Design

The Research Design for the study is based on descriptive research model in which the analysis has been done on the basis of data collected through primary research and also research of relevant published secondary data.

Data Capturing Instrument

The data was captured using questionnaires. Each questionnaire comprised of 25 questions, out of which 20 were psychographic and 5 were demographic in nature.

The data that was sought pertains to the customer’s shopping experience, the satisfaction garnered and what motivated them to choose online shopping and whether they have higher level motivation to buy because of on-demand marketing strategies adopted by the online marketers.

The questionnaires were personally administered as well as sent and collected through e-mail.


The primary data has been collected through questionnaires administered to the customers who have done some kind of shopping through online portals like Flipkart, Amazon and Snapdeal. I have chosen these customers as my preferred respondents as they have gone through the online shopping experience and will be in a better position to appreciate the effects of on-demand marketing.

The sample size for primary data collection was 150. The respondents were selected through simple random sampling process.

The secondary data has been collected from a diversified pool of resources such as newspapers, journals and research articles published by consulting companies like Mckinsey, KSA Technopak and others, and various report published by industry bodies like CII, FICCI and ASSOCHAM. The secondary data shall help to validate the inferences drawn from the primary research.

Data Analysis

The data has been analyzed using statistical tools like graphical analysis and percentage analysis.


Out of 150 respondents, 131 people said that they do online shopping, while 19 people said that they do not prefer online shopping. This shows that online shopping is becoming very popular nowadays. People have taken up online marketing as a viable alternative to visiting brick and mortar retail chains. Among the people who said they do not prefer online shopping, a vast majority (42%) said that they feared a security breach, while 37% said they had problem in accessing the internet for online shopping.

The most popular online shopping sites are Flipkart (23%) and Amazon (20%). OLX and Snapdeal also rank high on the preference list, with 11% respondents preferring to shop with each of these sites. The other online shopping sites like Indiatimes, Yebhi, Jabong, Koovs, Amazon and Quickr are also growing in popularity.

Flipkart’s ads have been most effective in reaching out to the masses, with an impressive 32% responding that they have noticed the ads in print, electronic and social medium. Amazon comes in second with 18% respondents stating that they have seen the ads in various mediums. This corresponds with the finding that Flipkart and Amazon are also the most preferred online shopping portals. The ads of both these companies have been successful in delivering traction as well as sales from the websites.

Figure-5 Comparative Analysis of different brands on a Semantic Differential Scale

Comparative Analysis of different brands on a Semantic Differential Scale

Pop-up Ads have come a common interface while browsing through the internet and a major group of respondents (85%) have taken cognizance of the same. But most of the respondents do not like the pop-up ads. A major group of 48% respondents feel that pop-up ads are very irritating and another 35% feel that pop-up ads are disturbing.

Most of the online companies are doing data mining and customer research and making customized follow-on offers to customers once they have bought something online. Majority of the respondents (83%) have responded that they have received a follow-on offer after online shopping.

The reaction to follow-on offer is mixed. While 39% of the respondents feel that the follow-on offers are customized, a considerable 21% of the respondents have hardly taken cognizance of the follow on offer. Surprisingly, a major 55% of the respondents are not aware that their activity in internet is being tracked by cookies, net-bots and spyware.

However, an overwhelming 92% of the respondents do not feel comfortable that their activities in internet are being tracked by third party softwares.

Most of the website pages nowadays feature banner ads which have customized and personalized offerings. A major 33% of the respondents have noticed the ads and taken cognizance of the same.

This is an interesting observation which might be important for on-demand marketers. A major 45% of the respondents have reported that the products and services advertised in the banner ads are worth buying. Another 23% have said that the ads were very interesting.

This data will be of much importance to on-demand marketers. A major 31% of the respondents are not swayed just by ads, they would rather do research before buying the market or service. While 19% of the respondents have said they would buy within a few days, another 19% said they would buy instantly if the product or service offering was attractive.

30% of respondents feel that retail chains are more preferable because they offer the touch and experience while 25% feel that online shopping companies engaged in E-Commerce are more exciting.

55% of respondents have said that they decide the monthly shopping budget through advance planning. Quality and Brand Name are the main criteria while making a buying decision as per information given by 23% of the respondents.

While choosing between shopping in retail chains and online shopping, 32% of the respondents prefer retail chains while 30% of the respondents prefer online shopping. Regarding the usage of technology in online marketing, a major 30% respondents highly disagree that technology cannot replicate the touch and feel experience offered by physical format stores. However, a considerable number of respondents (21%) offer a contrasting view that they agree that technology cannot replicate the touch and feel experience offered by physical format stores.
39% of the respondents are highly uncomfortable about sharing personal details online while 23% of the respondents are uncomfortable about sharing personal details online.


To make on-demand marketing successful, companies must excel on the following three levels :

  • Engaging customers in manifold new ways by designing interactions that are grounded in use cases.
  • Assembling data offering new lenses on the behavior of consumers by pulling together and evaluating all their touch points with a brand.
  • Developing new processes and skills across all functions to transform the delivery of brand experiences.

While we are at an early stage in the evolution of on demand marketing, it is imperative to get started with good practices so that companies can leverage what they are learning and the experience that they are gaining. As with every important emerging technology, it is important to understand why marketers need to leverage the technology and have a concrete plan in place. Here, I would like to provide marketers with the best recommendations that can be useful to make On-Demand Marketing successful through Big Data Analytics:

Understand the goals of On-Demand Marketing

The main purpose of On-Demand Marketing is to understand the specific needs of the consumer, customize the marketing process and offer her only the actual products or services she is looking for at the moment. Xiaomi, the Chinese mobile phone company, launched their Mi3 brand of mobile phones in India with breath-taking features like 5.00-inch 1080x1920 display powered by 2.3GHz processor with 2GB RAM and 13-megapixel rear camera at an astonishing price of Rs 13,999/-. On the first day of launch, 5,000 units of the phone got sold through an exclusive tie-up with Flipkart, in a mere 30 minutes. In the very next week, 10,000 units were sold in 5 minutes. The week after, 15,000 units were sold in less than a minute. In the fourth week, they created a record by selling 20,000 units in 5 seconds. The product offering was so much in consonance with the customer needs, that people were crazily buying up whatever quantity the company was offering.

Establish a road map

On-Demand Marketing cannot be achieved in a short span of time. It takes considerable investment in time and resources to acquire the data, process the data and come out with meaningful strategies to connect with the customer in a meaningful manner. Hence, an organization has to develop a clear-cut road map with specific milestones and strategies on how to achieve that.

Discover the data

In the olden days, it was deficiency of correct data that was a big challenge for marketers. Today, most of the companies are swimming in a deluge of data and not having the faintest idea of what to do with the Big Data. Google processes more than 24 Petabytes of data every day. Facebook puts up 10 million photographs every year. About an hour of video is being uploaded on YouTube every second. In total, about 2.5 Exabytes of data is getting generated every day. The goal of on demand marketing strategy and plan should be to find a way to leverage data for more predictable business outcomes. The company needs to get a handle on what data it already has, where it is, who owns and controls it, and how it is currently used.

Understand the technology options

At this point, the company has to know what technologies are available and how they might be able to assist the marketer to produce better outcomes. The marketer has to begin by understanding the value of technologies, streaming data offerings, and complex event-processing products. The marketer should look at different types of databases such as in-memory databases, spatial databases, and so on. They should get familiar with the tools and techniques that are emerging as part of the on demand marketing ecosystem. It is important that the marketing team has enough of an understanding of the technology available to make well-informed choices.

Study best practices and leverage patterns

As the on demand marketing processes matures, companies will gain more experience with best practices or techniques that are successful in getting the right results. One can access best practices in several different ways. They can meet with peers who are investigating the ways to leverage on demand marketing to gain business results. They can also look to vendors and systems integrators who have codified best practices into patterns that are available to customers. It is always better to find ways to learn from others rather than to repeat a mistake that someone else made and learned from. As the on demand processes begin to mature, the marketer will be able to leverage many more codified best practices to make the strategy and execution plan more successful.


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