The B2B Marketer's Predictive Analytics Vault: Key Drivers and Business Significance for 2021 and Beyond
Predicting future activities is important for B2B marketers as it helps them plan for the crisis to come or to better adapt to dynamic B2B conditions. Accurately forecasting factors such as operations, budgets, supplies, or product demand is critical to the success of any organization. A marketer’s ability to forecast the ultimate cost of a policy determines how accurately they market and prices their product and the extent to which an adverse selection can be avoided.
Predictive analytics has quickly evolved as industry’s best practice and B2B marketers are increasingly using predictive analytic techniques to target potential clients, to determine more accurate pricing and to identify potential clients showing a higher intent of conversion.
What is Predictive Analytics?
Predictive analytics is a broad term describing a variety of statistical and analytical techniques to develop models that predict future events or behaviors. The form of these predictive models varies depending on the behavior or event that they’re predicting. Most predictive models generate a score with a higher score indicating a higher likelihood of the given behavior or event occurring.
The most predictive examples of predictive models used by three credit bureaus include Experian, Equifax, and TransUnion to develop credit scores for individuals. The higher the credit score, the more likely the individual is to pay his/her debt.
Data mining is a component of predictive analytics that entails analysis of data to identify trends, patterns, or relationships among the data. The information can then be used to develop a predictive model.
Predictive analytics, along with most predictive models and data mining techniques, rely on increasingly sophisticated statistical methods including multivariate analysis techniques such as advanced regression or time-series models. Such techniques enable organizations to determine trends and relationships that may not be readily apparent, but still enable it to better predict future events or behaviors.
Drivers of Predictive Analytics
Predictive analytics is not new for businesses and though businesses for many years have employed predictive analytics, several drivers have increased their prevalence in the B2B sector.
Needless to say, with the advancement of digital analytical techniques and the proliferation of digital transformation, especially in the post-pandemic era, predictive analytical techniques across the globe have evolved; however, the key drivers of predictive analytics remain the same, including:
- Technological advancements
- Data availability
- A desire for growth in slow markets, and
- A search for competitive advantage
The statistical techniques used in predictive analytics are computationally intensive. Depending on the amount of data used by the businesses, some may require performing thousands or millions of calculations.
Advancements in digital transformation and software design have led to the development of software packages that quickly perform such calculations, allowing marketers to effectively analyze the data and produce and validate new predictive models.
The validity of any predictive model depends on the quality & quantity of data available to develop it. Converting data to a usable format can be time consuming and costly.
There are numerous third-party sources of data that the marketers can use to develop predictive models. These sources include rating bureaus, predictive modeling companies and other data gathering organizations.
A Desire to Win-Over Slow Markets
Slow markets at times seem invincible to win-over. During such times some predictive insights may work. B2B marketers can use predictive analytics to develop more accurate pricing and improve how they target their services. Thus, the B2B marketers who use predictive analytics can use this information to claim market share from their less efficient competitors.
A Search for Competitive Advantage
The use of predictive analytics further gets fueled due to marketer’s search for competitive advantage. Predictive analytics provides marketers information about the target audience groups that their competitors do not possess.
Marketers can efficiently define a target market, develop pricing more accurately, and can do a ton of other tasks which provide competitive advantage over competitors who don’t use predictive analytics.
How Predictive Analytics Benefits B2B Organizations
Whether an organization is a wealth or asset management firm or a B2B tech enterprise, predictive analytics presents an innovative way to capitalize on the data a business has about their prospects and clients. This data can be used to deliver a predictable and profitable pipeline of new business.
Forrester research substantiates that predictive marketing initiatives have been under way for some time and are delivering exceptional results:
- 97% of predictive analytics users benefit from predictive analytics
- 83% of predictive users state that they’ve experienced a considerable or high business impact by using predictive analytics, and
- 58% of respondents have surpassed their business marketing goals
Predictive analytical capabilities are increasingly becoming an operational priority for marketers especially in the time of the pandemic crisis. Here are some of the benefits that the predictive analytical techniques offer for the B2B marketers:
- Lead Prioritization: Predictive intelligence facilitates double digit increase in sales conversions by analyzing and decrypting the emerging needs and interests of buyers as they research and consume content on client sites. One can tap into the signals of the decision-makers and predict when they are going to buy, and what they are going to buy.
- Net new leads: By understanding the common attributes of the most successful and high-value leads, predictive analytics helps marketers analyze some attributes in any new audience that engages with their marketing message. This technique is particularly useful for improving leads from an external customer data supplier.
- Augmented customer data: The more information is available about the buyers, the easier it gets for them to tailor their messaging and communications to the pain-points of the buyers.
Amalgamating buyers’ information with predictive analytics allows marketers to trace the right patterns or traits including the emerging needs or the traits of the buyers.
- Real-time CRM Segmentation: The CRM data is growing for businesses day by day transcending the firmographic details to real-time needs. This allows more sophisticated segmentation which then results in a laser focus on the right leads with the right message. Real-time segmentation makes campaigns more successful as the budget and resources are focused on who all in the market are willing to buy.
- Content Recommendation: Predictive analytics allows companies to analyze the next-best content that can be shown to the target audience groups. Content recommendation comes from algorithmic personalization – by passing the unsophisticated and arduous preset automation rules that typically power marketing automation and content recommendation.
- Retargeting: Retargeting based on predictive data means that organizations can possibly remarket to their internet audiences based on their likely purchase intent, rather than just working on the basis of predictive data.
- Identify Buyers’ Needs: Knowing where your buyers are allows you to target your budget for optimum results. Predictive events allow you to determine the events to participate in, where to go next with your content strategy and even how to improve your digital marketing results.
Predicting the future can actually give you more control over it, making you achieve new business heights, especially during the uncertain times following the pandemic crisis.
This will help you sustainably develop your business, as well as, optimize your revenue goals. B2B marketers must use predictive data to their respective advantages to increase pipeline and buyers’ engagement while reducing marketing complexities.