In order to stay competitive in the dynamic business world, startups need to have an unconventional wisdom of marketing. Already struggling with a limited budget for marketing, startups have to achieve a high consumer conversion rate.
The key differentiator between two startups is pace. Things need to be done at a fast pace for startups to be competitive against large companies. And, in order to react to market conditions and changing consumer trends, startups today rely heavily on big data analytics.
Big data can provide numbers backed by insights on consumer behaviour and can play a critical role in helping startups in areas like marketing, advertising, operations, inventory management, customer service, etc.
For example, for a startup organisation, product marketing act as a growth catalyst in establishing brand value in the market, which is very costly and usually eats up a huge part of the budget.
Big data can help startups in identifying and reaching out the right target market for launching product(s) and providing better return on the marketing investments. Moreover, it can also help in understanding the customer needs and leveraging their requirements for designing or updating offerings.
“Big data has been instrumental in identifying and predicting behavioural patterns to a great deal. Given its popularity and growth in recent times, I would say the predictions have been quite accurate,” says Mangesh Panditrao, CEO, Shoptimize.
Shoptimize Inc is a startup providing end-to-end e-commerce solutions that includes creation of online store, integration with payment gateways and logistics, generating traffic to the online store and driving conversion to actual orders. Shoptimize enables Indian merchants to realise and fully leverage the potential of e-commerce.
“Advertising and marketing without data based insight are akin to trying to hit a target in an unfamiliar dark room with only 2 to 3 bullets in your gun. While Big Data science is evolving and is not fully precise, it does tell you the direction in which to shoot, so that your probability of hitting the target is maximum,” explains Vishal Soni, Big Data and Analytics consultant, Advaiya Solutions.
Founded in 2005 in Udaipur, Advaiya Solutions helps companies build a vision and craft their story with services like strategic consulting, technical and marketing content, training and evangelism, software development, creative expertise and staffing. Working with over 80 professionals worldwide and 50 plus valued customers like Microsoft, Google, VMware, Citrix, AT&T etc, Advaiya has completed over 1,200 projects averaging more than 5,000 deliverables per year.
“With almost everyone who is a significant buyer expressing their true sentiments through likes, tweets and pins, there are big data tools which indicates – how strong the ‘Buy’ sentiment is,” tells Supam Maheshwari, Founder & CEO, FirstCry.com.
FirstCry.com is a brand owned and operated by Mumbai-based Brainbees, which started its operations in late 2010.
Big data technology can prove to be a costly affair, if not executed properly. The real cost lies in hiring skilled big data professionals i.e., People.
“Engineers with advanced skills in big data are rare and expensive. It’s a niche skill set and less than 1 percent of IT professionals can configure the system and code business logic to get it working. This involves routine mapping of servers, load balancing and aggregation of result sets from a multitude of servers. Skilled big data professionals may be one of the biggest cost and deterrent in implementing it,” reveals Maheshwari.
The next challenge could be the servers. Higher is the need of computing power, when organisation’s process more and more data at a faster pace. A typical Linux server may cost around $300 per month and when there are hundreds of servers, the monthly bills can hit the roof.
Cost Effective Solutions
Big Data technology is usually considered to be expensive, and therefore, it is targeted at the large enterprises. But with gradual evolution and maturity of the concept, this outlook is soon going to change.
Many cloud-based and SaaS-modelled platforms are already available, which enable organisations to harness the potential benefits of big data without making any upfront investments.
Using such low-cost options, startups can implement various big data solutions across multiple areas without making any much investment in the infrastructure setup.
“These cloud-based solutions come with additional benefits like high availability and scalability, which makes these big data solutions more dynamic and agile for startups. Therefore, in the coming years, we can expect many big data success stories from startups and small businesses,” asserts Soni.
Moreover, frameworks like Hadoop, MonogDB and Cassandra etc., are fully open source available in market and they are free to download and use.
“Some eCommerce companies are coming with big data as a service. Companies can use big data power without investing into servers or people. In a perfect world, the smaller companies will be able to plugin APIs of BWS (Big Data web services) and just pay as they go like their mobile bills,” points Maheshwari.
Big data can be leveraged by startups and small businesses alike with usage of cloud computing reducing the cost of storage and computing both.
“It is important to know the possibilities and go after very small tangible benefits. For example, when we pick Facebook users to target for a certain campaign, we pull up data related to similar campaigns we have run in the past one year and come up with correlations that help us improve click-through percentages of our ads. We would have needed a super computer to crunch that kind of data in the past,” explains Panditrao.
Tracking Potential Customers
Given the popularity and growth of big data analytics, it has been helpful in predicting behavioural patterns of consumers. For instance, if an organisation is using big data to find target customers for its new product range, it would do all the market analysis like sales report, social feedback, competitor analysis etc.
However, the same data can also be leveraged to identify and understand the most common needs of the targeted audience. Organisations can use this information to update their product range and add specific features that could meet the requirements of the target audience.
“In the process of finding the right customers for the existing product range, organisations can potentially use the same method to find the right product for its existing customers. I believe that big data can help organisations in either ways, but its real worth is realized only when you use it to explore the unexplored aspects of data,” asserts Soni.
Buying pattern of customers is based on their behaviour and preferences, which gets affected by multiple factors. For example, the probability of any customer buying an apparel would depend on various visible factors (price, offers/discounts, brand popularity, competitors etc.), as well as some hidden factors (customers’ past experience, his need for the product, etc.)
With big data, organisations have become capable of performing such complex analysis, taking into consideration a large number of input datasets from a variety of sources like organisation’s internal Line of Business (LOB) applications, current market trends, analyst reports and customer sentiments from social networks. In fact, many organisations (including large number of startups) using this kind of predictive analytics for developing wide range of offerings, including fashion trends prediction, real-estate predictions, movie or event response prediction, match-making services etc.
Most startups run their businesses with limited budget, and are very keen to gain maximum value out of it. Big data lets users to run the ‘Experiment-Fail-Learn-Repeat’ cycle pretty economically. It not only helps startups in reducing their marketing or advertising expenses, but also provides an additional enterprise-grade experiences like anywhere accessibility and unlimited scalability backed by highly reliable infrastructure.
This makes organisations more agile and enables them to understand and react to the changing market requirements more dynamically. For instance, while conducting any product launch, they can setup cloud-based infrastructure to drive the entire campaign, add big data capabilities to churn out the data generated from social media and other sources in real-time, and use it to do more targeted marketing.
Bootstrapped entrepreneurs have limited margin of error and the only way to sustain, survive and grow is by increasing the market share faster than competition.