Article | April 13, 2020
There’s a lot of information out there related to COVID-19. But right now—when it’s more important than ever to quickly access and analyze data —figuring out how to effectively use COVID-19 data to better manage your business can still be a challenge. We can help. Several customers have leveraged their Incorta platforms to instantaneously integrate COVID-19 data into their enterprise data and analytics dashboards.
Article | July 13, 2021
When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take.
Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data.
Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT).
Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results.
With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience.
In this article, we will examine how big data analytics are transforming the marketing industry.
Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing.
When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically.
Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services.
With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly.
Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities.
Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too.
This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products.
Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics.
Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location.
These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns.
The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with:
• Identifying most profitable and least profitable customers
• Building loyal relationships
• Predicting customer patterns
• Pricing products accordingly
• Developing products based on their interests
Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts.
Optimized Ad Campaigns
Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign.
Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website.
Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it.
When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy.
Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly.
The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same.
These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands.
Fortunately, businesses are taking note and making sure that this information remains secure.
The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit.
Frequently Asked Questions
Why is marketing analytics so important these days?
Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success.
What is the use of marketing analytics?
Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels.
Which companies use marketing analytics?
Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well.
"name": "Why is marketing analytics so important these days?",
"text": "Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team and the resources you invest in channels and efforts are critical steps to achieving marketing team success"
"name": "What is the use of marketing analytics?",
"text": "Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels."
"name": "Which companies use marketing analytics?",
"text": "Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well."
Article | February 11, 2020
The latest pace of advancements in technology paves way for businesses to pay attention to digital strategy in order to drive effective digital transformation. Digital strategy focuses on leveraging technology to enhance business performance, specifying the direction where organizations can create new competitive advantages with it. Despite a lot of buzz around its advancement, digital transformation initiatives in most businesses are still in its infancy.Organizations that have successfully implemented and are effectively navigating their way towards digital transformation have seen that deploying a low-code workflow automation platform makes them more efficient.
Article | March 9, 2021
For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions.
Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics.
From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment.
Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings.
Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource.
In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement.
Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data.
The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread.
To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests.
For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials.
In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough.
AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic.
Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests.
Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level.
Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation.
This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer.
This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months.
As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks.
AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation.
AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid.
Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at www.wasrahman.com