What Is Big Data In Marketing?

Basically, big data is a collection of information given to businesses by them from their social media and other digital channels, to interactions with customers, and more. Today, this collection of information is called big data, which plays an important role in decisions made for successful marketing campaigns. By using big data in marketing, businesses will be able to extract insights on consumer behavior, preferences, trends, and more to make better decisions and adapt their marketing strategies to best affect.

Understanding Big Data in Marketing

Big data in marketing refers to large and complex datasets derived from multiple sources (for example, online transactions, social media interactions, customer feedback, browsing behavior) and that includes both structured (e.g., purchase history) and unstructured (e. g. social media posts). Big data is typically classified by the following three Vs:

Volume: The sheer amount of data generated every second.

Velocity: The rate at which new data is created/processed.

Variety: The variety of data (textual, visual, video, etc. ) collected.

Big data is all about collecting, analyzing and using massive amounts of data which helps marketers discover patterns, customer preferences and opportunities By understanding the data businesses can design better marketing campaigns, offer a better experience to their customer and ultimately grow.

How to Use Big Data for Marketing Success

Hundreds of things need to be done to actually implement a big data marketing strategy that works

1. Data Collection and Integration

Assimilate all the data available (on various points of interaction (websites, social media, email campaigns, customer service, etc) into a central system to get the full picture of what customers are doing.

2. Data Analysis and Interpretation

Utilize the latest statistical techniques to process and analyze data (including machine learning, artificial intelligence, etc. to find patterns, predict trends, and discover insights that could otherwise be difficult to derive from standard data processing techniques.

3. Personalization of Marketing Efforts

Use the insights from the data to segment your audience and create personalized marketing messages and offers based on their information. A 2016 study found personalized marketing increases engagement rates and conversion rates by 78%.

4. Real-Time Decision Making

Real-time data gives marketers the opportunity to react quickly when making marketing decisions (perhaps if one product is currently trending on social media, it will result in marketers making a promotion to capitalize on the popularity).

5. Continuous Monitoring and Optimization

Monitor the results of your marketing campaigns from time to time using big data analytics. This way, you can make any adjustments and optimizations you need (on a timely basis), so your marketing strategies are always up to date and effective.

Benefits of Big Data in Digital Marketing Strategies

Incorporating big data into digital marketing strategies offers numerous advantages:

• Easier customer identification: Learn about your customers’ actual needs, preferences, and behavior to better target them.
• Optimisation of Marketing Campaigns: Based on data-driven insights, marketing campaigns can be more relevant and effective.
• Better ROI: By reaching the right people at the right time with the right message, businesses can end up with better conversion rates and better ROI on their marketing investments.
• Competitive Edge: Companies that use big data strategically can compete more effectively by quickly reacting to market changes and customer demand.
• Successful Resource Allocation: A need to define the marketing channels and strategies with the best customer return in order to create an efficient resource allocation.

Examples of Big Data in Marketing Campaigns

There are some companies that have used big data to enhance their marketing strategy successfully:

Netflix: Personalized Content Recommendations

Netflix uses data analytics to deliver personalized recommendations. Netflix’s recommendation engine is driven by your viewing habits, ratings, and metadata, which then informs recommendations. It’s a game changer since around 80% of content discovery on Netflix comes from recommendations.

Amazon: Customized Shopping Journeys

Basically, Amazon uses information collected from the customers, like past purchases and browsing history, to create a shopper’s experience based on their specific needs. This builds customer loyalty and increases sales.

Kroger: Targeted Coupon Campaigns

Kroger uses customer shopping behavior information to create specialized couponing ad campaigns that yield 66% higher coupon redemption rates than the industry average.

The Economist: Better Audience Insights

The Economist uses a customer data platform to study reader behavior. From this data, they can create targeted marketing campaigns that cause them to get more subscribers.

Airbnb: Regional Marketing Optimization

Airbnb has a diverse hiring pipeline and it clearly recognizes that the hiring process is an important part of its growth. By using big data analytics, they discovered customers in Japan, Korea, China and Singapore had a different customer journey compared to the rest of the world.

It all started back in 2014 when the company noticed that the bounce rate of visiting the homepage was slightly higher in certain Asian countries. After doing further research, a team came to the conclusion that visitors from that region had to click on the “Neighbourhood” link to see some pictures and never return.

Also, these thoughts were taken over by the website developers, and they acted immediately to change the version of the website to be best suited for users from that region, and this resulted in a 10% higher conversion rate.

Predictive Marketing Analytics: Anticipating Customer Needs

Predictive marketing analytics includes historical data, machine learning, and statistical algorithms for marketing purposes to forecast future customer behavior and trends. Marketing companies can use this insight to proactively personalize their strategies.

For example:

Product recommendations (e-commerce websites use past purchase behavior to recommend products to customers).

Churn Prediction: Subscription services can tell you when it’s likely to happen for a customer to cancel and thus target some retention efforts.

Dynamic pricing: Retailers can set prices in real-time based on demand forecasts and competitor pricing.

Predictive analytics uses algorithms to inform and optimize marketing tactics. So marketing messages are tailored to the consumer at the right time.

Conclusion 

As we have reached here, it is clear how big data is changing the market landscape by offering various opportunities to businesses to understand their customers. By implementing different measures like a big data strategy, taking help from predictive analytics, and using custom data platforms, by using such measures companies can create even more effective and creative campaigns for their businesses, improving their outcomes and also the possibility of generating leads.