1,209,600 new data producing social media users each day.
More than 4 million hours of content uploaded to Youtube daily.
4.3 billion Facebook messages posted every day!
67,305,600 Instagram posts uploaded each day
Source: IBM Marketing Cloud study
Aren’t these numbers mind-boggling? Such humongous amount of data generated on various digital channels can become great food for analytics. Digital marketers often downplay the impact of Big data or analytics in decision making. While most are familiar with Google analytics, there are many other analytical tools that can used to track the success and generate better results out of Digital Marketing.
Web Analytics has its own limitations. They mainly focus on the traffic on the site, its timing, sources etc. However, data analytics is beyond all this. It plays a major part in digital marketing because of all the granular information it provides on customer behaviour, sentiments, purchase pattern, online experience etc. Data analytics has crucial applications in Digital Marketing in two stages-
(1) Before creating the Ad Campaign (Targeting the prospective buyer)
(2) Measuring the success of the Ad Campaign
Let us look at how it works in each of these stages:
(1) Before creating the ad campaign (Targeting the prospective buyer)
In the modern times, customers prefer personalized ad campaigns and mass mailing rather than generic ad content that garners no eyeballs. This is the age of customer segmentation and offering them bespoke products and services. Ad campaigns need to be targeted very well to reap maximum benefits.
Customized Analytical Tools
The vast amounts of data when analysed properly provides actionable insights and helps to take major business decisions. The key to a successful campaign is proper targeting of customers. Data analytics helps to compartmentalize prospective customers based on demography, gender, likes/ preference, education etc. Today, we have various analytical tools which integrates data from various social media sources, emails etc. and analyses it. These tools are equipped to perform Social Media trend analysis, predictive analysis models, network mining, sentimental analysis, as well as content tagging. Some examples of such tools are RapidMiner, KNIME. Most of the data analytics tools operates in connection with database tools such as MySQL and also integrates seamlessly with languages such as Perl, Python and R.
There are also something known as Call analytics which facilitates capturing the details of every caller on your e-commerce site or business. Through this tool, you can know the exact ad or keyword that led them to this website. The conversation, their position in the sales funnel and the true intention behind the call. Only then can a company gain an insight into their preferences, tailor an ad message targeted at them and work towards converting a call to a buyer.
86% of companies that have been carrying out predictive marketing initiatives for at least 2 years witnessed “increased return on investment as a result of their predictive marketing”.
(Source: Forbes Insights report based on a survey of 306 executives from companies with $20 million or more in annual revenue)
This is evidence enough that Predictive Analysis plays a big role in producing targeted ads and measuring the success of a digital marketing campaign. It is mainly used for optimizing customer intelligence. A usual marketing database will not provide all information for planning the campaign. Combining predictive analytics with customer data helps us understand the exact trends to formulate a customized campaign for the buyers. Predictive analytics is known to look for finer nuances in data and if the correct information is fed, we can expect to detect the right pattern so that we can present the most suitable offer to the customer.
(2) To assess the performance of the ad campaign
Digital Marketing not only means designing ad campaigns but also measuring its performances. The trends and patterns in consumer activity can be studied to assess the return on investment (ROI) of the campaign. This metric can then be used to make necessary alterations and derive optimal results from the campaign.
Data visualization is a powerful and the real time success of an ad can be measured using dashboards and graphs. As the Key Performance Indicators (KPIs) are monitored continuously, necessary changes can be made in the existing campaign to steer it towards desired outcome. This is the uniqueness of visual analytics. The results can be measured by the company while the campaign is live, instead of waiting till the end and making losses out of a failed campaign.
Sentiment analysis is the process of opinion mining through various analytical tools such as Natural Linguistic Processing, Text Analysis, Computational Linguistics, Machine Learning and biometrics. This works by decoding the emotional tone behind the the messages posted on social media like Facebook, Twitter and Instagram. By understanding the deeper sentiment behind the messages, Digital Marketers can assess the success or the feedback behind the campaign and optimize it to derive desired outcome.
Experts explains that globally the budget allocated to Marketing Analytics has gone up by 80% and it is anticipated that India will also follow suite. Deriving optimal results, creating brand awareness and converting that prospective customer into an actual buyer is the aim of every digital ad campaign. But this can only be achieved if the customer is targeted properly, the campaign is personalized and the results are measured on a real-time basis. Data Analytics bridges this gap. So if you are a Digital marketer who wants to achieve maximum results out of your meticulously designed campaign, steer up with Data Analytics and harness its potential.