Traditionally, marketers used to make their best guesses about the demographics of their ideal segment but it not the case anymore. Using big data analytics, the companies can easily determine who is buying, how much and what is being bought. More information about the prospective customers like the websites they visit frequently, social media channels used by them and even the sections of a webpage they are interested in can help a marketer make the right decision.
Identifying and targeting the right segment not only enhances the reach and efficiency of the marketing efforts but also helps the companies in cutting their marketing costs by helping them achieve more with less.
2. Customer Engagement Optimisation
Big Data proves crucial in providing customer level information about where they live, how often they buy and what they want. Analysing how customers interact with the company’s online portals and even offline stores turns out to be beneficial in improving the user experience.
The ability to know when and how the customers would prefer to be contacted is imperative in a world where consumers are getting more and more impatient and easily irritable due to excessive marketing efforts being put in by different players in the market.
3. Optimising Performance
Tracking impact and return on investment used to be difficult in traditional advertising. But continuously monitoring and optimising their marketing efforts through measurement, analysis and testing through big data analytics can help companies in making the optimal marketing purchases across different advertising channels.
The level of granularity that can be achieved through big data is what makes it the next big thing in marketing. For example, now we can track the performance of different marketing channels for different segments in different geographies for different age groups and gender and so on.
4. Enhanced Customer Retention
It’s very common to see companies deploying loyalty cards, known by many names underlying concept remaining the same, to track a customer’s purchases. Loyalty card systems also track which promotions and incentives have been the most effective in encouraging a customer, individual customers or group of customers, in making another purchase.
The system can also help the marketer in identifying which segment of customers is the most influenced due to the deployment of loyalty cards. Data Analytics can also help in figuring out the particular complaints regarding the products which have caused the most dissatisfaction in the consumers and accordingly remedial steps can be taken before the situation gets out of control.
5. Real-Time Personalisation
Big Data Analytics when combined with machine learning algorithms, enables marketers to personalise their offers to customers in real time. A perfect example of this would be the ‘customers also bought’ section on online shopping websites like Amazon and Flipkart and ‘recommended’ videos section on Netflix and YouTube. Marketers can now personalise the products and promotions that a particular customer has access to right down to sending personalised offers and coupons to the customer’s phone when he enters an offline store. It can thus help in strengthening the marketing strategy of the firm and in integrating the various tactics that the company employs to create an interest for the product in its target segment.
6. Budget Management
By enabling companies to monitor, assess and optimize their marketing campaign for highest performance, big data analytics helps them in better allocation of marketing budgets to get the highest ROI. It also helps the companies understand how their budget allocation among the different marketing strategies deployed in the past panned out to make any changes if necessary. Companies can strategize about the amount of expenditure that needs to be incurred in order to achieve the set goals by analysing the data across the industry and the strategy of the competitors. Data analytics can also help in understanding the changing trend in the consumer behaviour and these future predictions can serve as the yardstick to finalise the budget that needs to be allocated.
7. Predictive Lead Scoring
Lead scoring has long been used by sales teams to rate their hottest leads but predictive analytics on big data can now be used to generate a model to successfully predict consumer behaviour and sales.
Predictive lead scoring takes into account property information of leads, behavioural data, social information and demographics to figure out which properties should be included and how much to weigh each property. The algorithm finds the information customers have in common and the information that unclosed leads have in common to come up with a formula to identify the most qualified leads.
8. Improved content marketing
Historically, it used to be extremely difficult to measure the ROI from a blog post but with the help of big data analytics, marketers can analyse which pieces of content are the most influential at moving leads through a sales and marketing funnel. Analysing content is highly affordable making it easier even for small businesses to implement it. This would prove helpful in highlighting the pieces of content responsible for closing sales.
The use of content marketing is on the rise. As per the Content Marketing Institute’s 2017 report, 87% of the companies in U.K. have adopted the practice. But even among those who do, companies analyse only 12% of the data they have available as per a Forrester report. This clearly highlights the need to develop big data analytics capabilities to make the most of data available to them.
9. Market Research
Data is the biggest asset in today’s world. It can be used to gather the kind of information which was impossible to collect just a few years back. Marketers can get deeper insights into the minds of the consumers and customize their products accordingly. They can now conduct both qualitative and quantitative market research in a more efficient, quick and inexpensive manner than ever before. Having the capability to process a bulk of data obtained from online and offline survey tools helps in easily implementing customer and focus group feedback.
10. Market Analysis
Access to a bulk of data about competitors and their marketing efforts through new social monitoring tools and third-party vendors enables the companies to have a distinct competitive advantage over the market. This could form a key in devising a short-term and long-term strategy for the growth of the company.
The accessibility of product performance data broken down into various parameters for the company’s own product and its competitors helps in analysing and benchmarking where the product lacks and make targeted efforts towards growth.
11. Brand Image Management
By leveraging big data analytics, companies can now easily monitor any reference to their brand across many different online portals, websites and social channels to know about unfiltered reviews, opinions and testimonials about their products and organisation as a whole. The marketers can also find the channels most influencing the brand image of their product and take necessary actions to change it.
Plotting the brand image among different sections and subsections of the customer segments on the perceptual map helps in identifying which points lie away from the desired region and putting in efforts to bring them in.
The applications of big data analytics in the field of marketing listed above, of course, just scratch the surface of what is possible today and what could be in the near future when big data analytics would become accessible and affordable even to the smallest businesses and novice users. It can only be a boon by helping companies in improving their marketing efforts and reaching their customer in much more innovative ways.
Big data analytics has revolutionised the way marketing decisions are made by providing the marketer useful insights about the performance of his/her product. It is now possible to dig deep into how successful the past marketing efforts have been to retain the best ones and replace the lower ones with the help of additional insights from the data. The early movers are going to enjoy the biggest advantage. So, what are we waiting for?
Disclaimer: This article was originally published in the November 2017 edition of Markathon, the marketing magazine of IIM Shillong.
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