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Technology advancement has allowed consumers to be reached at all times. They create touchpoints on multiple online and offline mediums using their smartphones, computers, and other modern devices. The data mined from consumers is valuable to digital marketers because it helps them carefully develop marketing strategies that have high chances of success.
Without big data and data analytics, marketers will operate blind and without much guidance. Their marketing efforts will be based on gut feeling and trial and error. This can make them waste most of their ad spend before finding something that works.
Big Data is an exponentially growing compilation of data sets from different sources. The data sets that make up big data always have extensible variety, high velocity, and large volume. Big data tends to be too voluminous for humans to analyze, understand and decipher effectively.
When broken down with the appropriate tools, it gives valuable insight into consumer behavior and preferences. This insight can tremendously improve business operations and marketing exploits in the following ways:
Customer engagement
The way customers view your brand is vital to your marketing efforts. Analytics of big data will guide you on the steps to take to paint your business in a more positive light. This can be done by improving your brand image, existing products, marketing efforts, and making promotional offers.
Holistic view of the target audience
When targeting a specific demographic, it is best to know how that group behaves. This allows digital marketers to target them through different forms of advertising successfully. Companies can use a platform like Skai that has cross channel marketing features to effectively manage all the concurrent advertising campaigns they run.
Improved brand awareness
Big data analytics helps businesses to create customer-specific content at the right time to display to their target audience and improve brand awareness. The insight from big data allows companies with small marketing budgets to compete with the leaders in their space, as they will be able to adjust their marketing exploits on the fly.
Better consumer acquisition
By consistently gathering and analyzing personal consumer data from multiple sources, including mobile apps, websites, blogs, and emails, businesses can use the insight they get to improve their customer acquisition efforts.
Saves money
Companies can use cloud computing to leverage real-time data to make quick changes to their marketing campaigns on different platforms. Big data can obtain, process, and examine real-time data accurately and quickly enough for marketers to take immediate actions based on their new findings. This rapid big data analytics helps optimize marketing performance, saving a company time, money, and resources.
Data is valuable to marketers and sales employees, so companies need to source and store it effectively. There are various ways to do so, and the method companies use depends on the tools at their disposal or preference.
Some of the data they need are age, consumer preference, financial status, purchase history, and their competitors’ standing in the marketplace. All these data sets play significant roles in planning how to attract new customers and increase sales. These are four common ways companies collect data from customers:
Asking them directly
In some cases, customers are comfortable giving their personal information as long as the business requesting it is legit and the demands are not too intrusive. It is okay to ask them for their name, date of birth, location, gender, and contact information when they purchase an item from a website.
Customers can also be asked to complete surveys after they receive an item they ordered from an e-commerce store. The survey questions should be clear, concise, and structured to profile customers and make them detail their preferences.
Special offers
Offering rewards to repeat customers on e-commerce websites incentives them to open accounts on those sites. The reward can be redeemable points that accumulate every time customers purchase items. While customers are creating their accounts on the website, they can be required to enter their preferences or specific information to complete their profile.
Purchase from data companies
Some third-party companies sell the consumer data they have at their disposal to businesses in need of profit. Businesses aiming to expand their marketing endeavors can buy data sourced from consumers interested in their products or services.
Social media
Billions of people worldwide spend time on social media, making such platforms fertile grounds for gathering massive amounts of consumer data. People interact on social media posts that interest them and comment on topics they resonate with.
Their activities indicate their preferences, forming a basis to target them with social media advertisements. Social media is also great for observing consumer sentiment and gathering feedback because people often discuss their experiences with different brands there.
There are three types of big data namely:
Structured Data
This is any data that can be stored, retrieved, and processed in a fixed format. Technology experts know how to effectively work with and extract value from this kind of data. Structured data can become voluminous and require large storage space to hold it if a company consistently collects data from leads and people interested in what they have to offer.
An example of this data is a table listing people’s names with their gender, job titles, salary ranges, and the state they live in. Marketers can work with such information to target prospective buyers of their company’s products.
Unstructured Data
Unstructured data usually have an unknown structure or form and can be challenging to process and interpret. Unstructured data is heterogeneous in nature and consists of a mixture of images, texts, and videos. Companies that regularly collect data might have a treasure trove of unstructured data at hand but might be unable to use it in its current format.
To analyze unstructured data, data analysts should first identify the data sources most relevant to the marketing campaign in question and get rid of the noise (irrelevant data). Software programs that can effectively filter noise from the relevant data are still relatively new. Some are being developed and trained with artificial intelligence and machine learning.
Once the relevant data is extracted, marketers can use it to further their campaign goals and target potential leads with their advertisements.
Semi-structured Data
This contains the two previously mentioned forms of data. Semi-structured data have a structured appearance; however, they are not defined. This type of data is usually presented in XML files.
Big data has four main features:
Variety
Structured and unstructured data components have a variety of sources, and are of different natures. Databases and spreadsheets used to be the only data sources recognized in the tech world. Nowadays, videos, photos, audio recordings, emails, and PDF files are being considered in data analysis. Unfortunately, this unstructured data format makes data mining, analysis, and storage difficult for marketers.
Volume
Big data is huge, as the name implies. The larger the volume, the more value marketers can extract from it. For a data set to be considered big data, it needs to be voluminous.
Variability
The content of big data can be inconsistent, making it tough for analysts to mine relevant data for marketers to use for their advertising campaigns. This variability attribute also hinders the smooth management and handling of big data.
Velocity
Big data velocity should be high enough to deal with the enormous and continuous flow of data being generated online. Most of this data is generated from social media, IoT devices, application logs, smartphones, computers, and business operations. The data should also flow fast enough to tech experts to process it in real-time and deliver the results marketers rely on to run their ad campaigns effectively.
The more you know about a person, the better you can predict their behavior. This principle applies to big data and marketing. Extensive big data analysis indicates the product or service prices consumers will approve of. Prices can attract or deter potential customers, so companies should gather insight to avoid pricing themselves out of the market.
Big data also determines the advertisement content that will positively resonate with a company’s target audience. Their marketing team can use it to determine trends to infuse into their campaigns to make them go viral and increase brand visibility. Big data helps improve business-consumer relationships. With the insight companies have about consumer decision-making processes, they can plan ahead and make the purchase process as smooth as possible.
Marketers that use big data mainly focus on consumer, operational, and financial data. These three types of data are obtained from different locations and stored separately. When combined, these data sets can provide valuable insight to create a solid multi-faceted marketing strategy.
Consumer data helps them know more about their target audience. They use financial data to measure performance, so their company can operate more efficiently. Financial data also accounts for competitor product pricing and operational costs. Operational data relates to information from CRM systems, logistical operations, feedback from IoT devices, and more.
