Big data is the collection of a massive amount of information that regular data tools cannot process. To store the data, you might have to break it into multiple chunks and spread it across several computers. In the digital marketing world, big data means storing, processing, and analyzing consumer behavior. The reason it is big data is because it covers almost the entirety of the internet.
Characteristics of big data
For some information to qualify as big data, they need to have the 4V’s.
Volume refers to the sheer size of the data. The data generation spans machines and consumers. The metric is often petabytes and terabytes.
Variety refers to the type of data and the source of data. This means all kinds of technological devices, social media websites, websites, apps, emails, images stored for search, and so on.
Velocity refers to how fast the data is stored, generated, analyzed, and utilized to get the meantime.
Veracity refers to the trustworthiness of the data. This is a considerable risk in digital marketing, as people often tend to enter false information into the system.
The role of big data in digital marketing
Big data and digital marketing have the same goal, which is to attract as many people as possible. It is why the two facets are codependent.
Customer insight in real-time
Big data helps you analyse the sentiment of your customers. You can analyse the unstructured feedback of customers through conversations on social media, random reviews, etc. It’s to look at the feedback and figure out whether the customer sentiment is positive or negative towards it.
For example, the film, 'Morbius' got lots of promotion through ironic memes on social media. The production company mistook it for positive sentiment and released the film twice. It ended up performing poorly at the box office.
A big data sentiment analysis could have avoided this result.
You don’t have to sort through every response in a survey of thousands to understand whether customers found the product sufficient or not. The sentiment analysis tools will automatically concur the level of satisfaction with the product. It goes far enough to have a grading system for the negative to the positive scale of sentiments.
The same tool can be used for comments all over the internet in response to your brand and product. You can pinpoint the exact reason or close to it on what frustrated the customer and change your production quality to get rid of the problem. Aspect-based sentiment analysis helps find the relation between the texts and the product.
If there’s an overwhelming amount of negative response to a campaign, the sentiment analysis tool can help you learn the cause of the negative emotion. The same goes for a Google business customer review on your service.
You don’t have to sort through the data yourself as each emotion is graded automatically by the tools. As some words can mean something different based on context, such as ‘sick’ is often slang for ‘amazing’ you can also code analysis tools to research deep into the sentimental context.
Sentiment analysis tools can further sort data into how the sentiment differs based on topic, ratings, overall feelings, and the changes over time. The change in sentiment over time is especially important as it helps brands find the cause of the shifting emotion all year due to their product or service.
Increase in Sales
By analysing big data, brands get to know customers' preferences, their buying habits, and their chosen payment methods.
You use this data to provide the best service to your customers. For example, Netflix used to only allow payment through credit cards. Now, they allow users to pay with wallets and with debit cards. It is because the current customer base prefers this payment method, leading them to buy the subscription.
Big data from social media can be filed into recommender tools.
These systems check a customer’s profile and their overall interaction on the internet. When the person explores your site, they get recommendations based on their purchase history. This helps increase repeat sales.
Big data helps you create social campaigns.
The insight you gain from users’ perspectives on your product along with their interest on a broad, social level means you can create content which would attract attention of non-customers. You can expect a rise in sales from pure curiosity.
Big data analyses current trends and trends over time.
You can predict products which would be popular in the future, in certain locations, and products which will gain a niche following. You can provide customers with what they will need in the future before they figure it out, thus increasing their reliance on you.
Price Optimization
Overpriced products don’t sell and underpriced products harm your margin. Big data analysis helps brands determine the right price of a product through general consumer opinion. You get reports on market price and the brand’s best price for profit.
You can also determine how to improve product quality to suit the right price.
Analysing the pricing and inflation strategy of a large number of competitors can help you understand the overall price preference of the users of a brand.
Big data analysis tools such as Hive help determine what customers are willing to pay for each and every product, rather than for a broader range of categories. Customers might be willing to pay more for coke over Pepsi, even though they’re both sweet drinks.
Predict the future demand for a product and sell it at a lower price now so you can increase the price in the future when it’s hot. Customers are already too addicted to move away despite the rise in price.
Location, age demographic, etc also play a huge role in determining the price and big data analysis can understand how much a user base can afford.
Stock clearance is inevitable. However, companies have a hard time determining the discounted price which will still result in a profit. Big data can analyse the optimal price at which the product will sell.
Big data analysis also helps price optimisation during the manufacturing process, determining workforce pay, and analysing how financial resources should be distributed between the departments.
Target Audience
Big data helps you find your target audience. When you create digital marketing campaigns, you want to ensure it reaches people who have an actual interest in your product or service.
A 20-year-old is unlikely to show any interest in classical jazz music. So, it wouldn’t be smart to send them recommendations for that kind of album.
Marketing towards the target audience includes:
Personalised emails with the person’s name and discounts they will fancy.
Product recommendations based on their search activities.
Analysing the future demand of a product based on the current activities of the consumers.
You can use big data to create a visual representation for the analysis and conclusion.
Final Thoughts
Big data in digital marketing is analysing consumer behaviour on a particularly broad scale. It’s not easy for anyone to do and requires both tech and digital marketing knowledge.