Article | March 21, 2020
In today’s digital revolution, the realm of data is growing at an unprecedented rate and will continue to rise as businesses will leverage more smart technologies or devices. However, maintaining and processing these myriad amounts of data require massive computing power and the knowledge to use it. Moreover, companies these days are utilizing data to make data-driven decisions and this pursuit of data-driven decision-making can make them to seek out data science.
Article | March 21, 2020
We know that data and analytics play a role in everyday products from recommendations on what music we might like to hear to automated re-routing by our GPS system. But how might the power of analytics be brought to bear on a disease that is currently threatening the health and economic welfare of people across the globe?If we rewind the clock to the 1850s, there are two significant examples of how early pioneers in data science made incredible impacts on the world that can provide some insight into what we might see happen next.
Article | March 21, 2020
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 | March 21, 2020
With the increasing amount of data in modern businesses, data science has been receiving a lot of attention. A growing number of companies are, nowadays investing in data science researchers and experts to implement technologies like artificial intelligence and machine learning in their organisation in order to derive actionable insights. But, to place such a massive transformation in an organisation, one has to ensure complete business readiness for data science. Although it is interesting to imagine the potential benefits data science can provide for your organisation, it is worth evaluating how much your organisation is prepared to accommodate a team of data scientists.