Five Essential Data-Driven Marketing Techniques to Help B2B Marketers Stay Competitive

    Cody Shankman
    Cody Shankman

    Data- driven marketing techniques are accurate because data is a fundamental component of marketing. B2B marketers need to identify the most efficient methods for collecting, analyzing, and utilizing data to acquire insights that assist in reaching the appropriate audience at the right time and elicit the optimal response.

    A company’s marketing initiatives will be more effective the more information it has about its consumers and potential buyers for its products. Converting the company’s data assets into sales is the aim of data-driven marketing. Here are five ways that marketers utilize the knowledge they gather about their target consumers from internal and external data sources.

    “Data is essential for a successful marketing campaign in today’s crowded and highly competitive marketplace. But it’s not just about being data-driven or even data-first – you need to embed data as a cultural underpinning of everything you do”, says Cody Shankman, CMO of NowVertical. He further adds, it needs to empower your team to think and act bigger, bolder, smarter and more tactically than the competition. Its application needs to be seamless and attributable, and you need to have the systems in place to provide easy access throughout the entire marketing organization. Too many organizations invest in their data only to have it bottlenecked with one or two analysts.

    “By employing domain-centric analytic tools, marketers can find greater success by bringing more data to the fingertips of more employees, opening up access and cutting down on the time between insight and action,” says Cody.

    Choose and gather the appropriate data

    Marketers must first be certain of the type of data they need to collect. A surplus of unnecessary data can confuse marketers and unnecessarily complicate matters. Marketers must thus establish their primary objectives, the KPIs to measure their performance, and the types of data that will be required for the process in order to make the best use of the data at their disposal.

    Analytics tools will be the best friend of marketers at this point. Marketers will work with data that is precise, complete, current, and relevant to their purpose if strong data quality procedures are in place.

    Hyper-personalization

    Big data and advanced algorithms are used by enterprises that use hyper-personalization to deduce critical information about the wants and preferences of their customers. Making the best use of implicit information (gathered from data streams), overt data (intentionally provided by the visitor), Customer Relationship Management (CRM), social media engagement, customer surveys, and more enables them to reach new audiences, predict behavior, boost precision targeting, and build healthier relationships.

    There are several opportunities for customization during the buyer’s journey, including devices, interactive channels, peak engagement hours, etc. Brands can utilize data to tailor offers, discounts, and other promotions for customers so that they are shown to them at the appropriate time.

    Using Artificial Intelligence (AI) to power the business landscape

    A recent report – PwC’s AI Predictions 2021  found that 86% of businesses consider AI to be their “mainstream technology” and that their investments are showing tangible results, like more revenue, smarter choices, and enhanced customer experiences. Artificially intelligent systems are always at work behind the scenes developing popular products and services. The writing style and data insights used in AI-generated content are based on the rules and formats that work best for the company. Natural language generation can be used by firms to produce content for social media posts, tailored reports, and emails.

    Also Read: Data Driven Marketing – Why Businesses Need to Update their Strategy

    Marketers can produce compelling content for disengaged users who are going to churn by adapting churn ML algorithms to the company. AI may assist marketers in creating a predictive model that shows the stage a client is in before they churn, allowing them time to provide incentives for the consumer to remain with their brand.

    Leverage predictive analytics

    Data can also be helpful since it can be used to forecast future events. The use of data, statistical algorithms, and Machine Learning (ML) approach to determine the likelihood of future events based on prior data is understood as predictive analytics, according to industry experts. Brands may get a peek at the kinds of consumers who are more likely to convert, the marketing strategies that are more successful, and how market changes may affect marketing initiatives. Marketing technologies with many applications include predictive analytics.

    The objective is to go beyond simply understanding what occurred in order to offer the most accurate forecast of what will transpire in the future. Predictive analytics is widely used mostly due to its ability to handle increasing amounts, types, and instances of data, the increased interest they generate in using data to gather insightful knowledge, the present state of the economy, and the requirement for competitive differentiation. In essence, companies employ predictive analytics to strengthen their financial position and competitive edge.

    When developing a new campaign, marketers and marketing teams may use predictive analytics to learn how customers would respond. To determine if the present product mix will entice clients into making a purchasing choice, they might analyze changes in demography. Analyzing data from previous occurrences, on the other hand, helps avoid duplicating mistakes and disasters in the future.

    Predictive analytics can help marketers in a variety of ways, including anticipating outcomes when no other options are available and determining the likelihood of success or failure surrounding a product before it is launched.

    To be more precise, predictive analytics provides businesses and marketers with a competitive edge by identifying significant trends and creating models that forecast the likelihood of upcoming events. Using past consumer behavior as well as the conduct of other customers who have similar traits, for instance. By examining the behavior of people, machines, or other things, many firms utilize predictive analytics to lower risk, optimize activities, increase effectiveness, and build strategies that result in competitive advantage.

    Marketers may evaluate future possibilities using data sets with a respectable degree of reliability, and this type of CRM procedure naturally varies by industry, field of expertise, and organizational maturity.

    Also Read: Shifting B2B Marketing Focus from Solution Based to Customer Centric Marketing Approach with Personas

    Design retargeted adverts

    Retargeted advertising is a great way to entice people to come back to the business. People frequently show interest in things but are hesitant to make a purchase. Retargeted advertising influences their decision to buy and frequently leads to conversions.

    Data-driven marketing aids in a better understanding of target audiences by marketers. Marketers are aware of consumers’ buying patterns and the elements that influence their choices. Because they will be aware of what their audience enjoys, desires, and needs, marketers can utilize this knowledge to produce more effective retargeting advertising.

    It’s a terrific technique to tailor advertisements to the audience’s preferences. Marketers can regain their attention and encourage them to make a purchase by retargeting them. Retargeted advertising can help marketers increase conversions for their company.

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