Today’s digital marketing decisions are now determined by big data. Through analytics, you can improve marketing campaigns, show relevant content, and more.
· 3 Examples of Big Data in Digital Marketing
· Leveraging Big Data in Digital Marketing with Stefanini
The future is exciting for digital marketers. Rather than make decisions based on experience and intuition, today’s marketing decisions are now determined by big data. For those unfamiliar with the term, according to Forbes, “big data” refers to the study and application of big, complex datasets, which cannot be processed by traditional data-processing applications. These figures generate insights that can lead to strategic moves and better decisions made by business owners.
Companies are faced with huge volumes of both structured and unstructured data that are based on the increasing number of outputs and customer characteristics. This level of processing is beyond the abilities of traditional software techniques and databases.
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Digital Marketing Analytics
It’s not the amount of data generated that is important – it’s what organizations do with data-driven information that matters. According to Medium[MM1] , while the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is not. The theory first gained momentum in the early 2000s when industry analyst Doug Laney laid out the now-well known definition of big data as the three Vs:
1. Volume – Organizations collect data from a variety of sources, such as social media, business transactions, and information from machine-to-machine or sensor data. In the past, storing this huge amount of data would’ve been an issue, but new technologies have made it easier.
2. Velocity – Data is generated at extraordinary speeds and must be dealt with in a prompt manner. Sensors, RFID tags, and smart metering are driving the need to deal with torrents of near-real time data.
3. Variety – Data is produced in all types of formats — from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
3 Examples of Big Data in Digital Marketing
Where can you start when using big data in digital marketing? Here are three suggestions of how big data can be leveraged for digital marketing efforts:
1. Creating Improved Marketing Campaigns – By developing rich and informative content, big data enables companies to better target the core needs of customers and helps companies collect data about customer behaviors. For instance, cookie files collect information about customers’ activities as they browse the internet, which generates personalized data in the process. Further, campaigns that use big data are more efficient than the aggregative advertising that was used in the past. Using big data to create marketing campaigns takes the guesswork out of pinpointing what customers want. Using data like customer behavior, purchasing patterns, favorites and background, marketers can develop different buyer personas. While applying big data to digital marketing is almost necessary in today’s landscape, it is just as crucial to use good analytical tools to make sure the data presents useful conclusions. A good analytical tool should be able to access all types of data including cloud, social media data, log files, websites, emails, and other unstructured data. At the same time, it should support campaign attribution tracking, real-time analytics, funnels, and third-party testing and integration tools.
2. Producing Better Pricing Decisions – Companies typically price products and services using basic information like competitor pricing, product cost, demand, and the perceived value of the product from the customer. Big data enables companies to use many other factors to make pricing decisions, such as data from completed deals, incentives, and performance-based data. Big data enforces making pricing decisions as clear as possible; this outlook is particularly true in the business-to-business (B2B) sector, as each deal is different from the next. When relying on big data for price setting, companies need to remember that they may already have plenty of unused data at their disposal, such as general economic information and customer preferences. The goal (and subsequent challenge) is to gain usable insight from this information. Further, big data also allows companies to automate with artificial intelligence, which can save time in price settings and lead to more accurate pricing decisions. This benefit is due to the fact that there will be no human assistance and as a result, less chance for mistakes.
3. Serving Customized Web Content – By tapping into their knowledge base to determine which content will be more engaging to each visitor, online marketers will be able to show appropriate content to their website visitors. When designing your website, refrain from thinking of your page as a static site. For instance, check out the “time spent on page” data to determine what the visitor is interested in; the next time that particular visitor comes to your website, you can reference their browsing history to show them relevant content. Similar to the way search engines return different results when you search for a term in different locations, your website will look different depending on who is looking at it. Though it is a technical challenge to show customized content, more and more of today’s consumers expect personalized experiences. Craft a personalization strategy by using inductive research, deductive research, and customer self-selected methods. Ultimately, the consumer will decide where to click and what to purchase. Companies that can attract and serve those consumers better will do the best.
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Leveraging Big Data in Digital Marketing with Stefanini
Our data analysis insights provide clients with the best customer experience. We are focused on strategy, data science, media, and advertising, using data intelligence to boost business, connect people, and create experiences. Using our expertise, best practices, and statistical techniques, we provide actionable business insights based on data mining and predictive models. We provide these insights through research, analytics, and social insights.
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