B2B Predictable Prospecting In 2019
- With a wide and relatively non-uniform prospect pool, combined with visitor anonymity, B2B lead generation tactics require insights beyond the capabilities of predictive analysis (analysis of historical data).
- User intent data analysis is used to fill the gap. Predictable prospecting is B2B user intent data paired with predictive analysis to form a set of marketing tools that can help companies maximize the effectiveness of and return on their sales and marketing efforts.
B2B Predictable Prospecting
When looking at the various B2B sales pipeline stages for a prospect to become and remain a customer, it’s clear that marketers need to be able to:
- Interpret user data at every stage of the customer journey and deliver personalized services—such as content or recommended products—based on intent and “next action” assumptions and predictions. Recommendation engines and artificial intelligence modules may be used for this.
- Prioritize B2B leads or prospects, and then focus on accounts with the highest potential ROI through methodologies such as predictive lead or engagement scoring. Marketing approaches like account-based marketing may use these methodologies.
- Access insights on behavior and trends. Often, increased account insight leads to better before- and after-sale services and easy renewals that finally end with loyal customers.
The backbone of all of this is data analytics: Data, coming from a variety of sources, are continuously captured, cleansed and consolidated. Mining techniques are then applied to find patterns and build appropriate models. Data analytics may be predictive (based in machine learning techniques) when voluminous historical data are available or descriptive, producing an easy, quick and indicative conclusion.
Processing of Unstructured Data
Search and discovery tools allow marketers to extract information from large unstructured databases. Unstructured data often have a higher level of ambiguity and higher difficulties in terms of integration and analysis. Improving the performance of this data source will improve accuracy in B2B user intent data, and then, subsequently, better prediction accuracy.
Enterprise Data Integration
Many B2B companies are faced with the challenge of multiple and distributed sales and marketing systems and applications, each with its own silo of data. Integration of customer data is critical. If data from various sources—external or internal—can be effectively correlated and analyzed, prediction accuracies will improve.
Predictable B2B Prospecting Tool
Software systems that combine the predictive engine with analytics and transactional processing are the industry leaders. Enterprise-level CRM systems like the Salesforce platform, powered by the Einstein AI engine and with integration capabilities and strong analytics, fulfill all predictive analytics activities. Its tier-based architecture allows separation of analysis infrastructure with tools that may use its output. Learn more on Salesforce Predictive MarTech Alignment.
B2B predictable prospecting, using machine-learning techniques, is the most widely used application of artificial intelligence. It has proven to be a highly effective and popular tool for marketers, as demonstrated by the year-over-year doubling of implementations and the expectation for investments to quadruple in 2021 compared to 2017. However, with a wide and relatively non-uniform prospect pool, and visitor anonymity, predictive analysis (analysis of historical data) can only go so far in the B2B marketplace. User intent data analysis can fill the gap. Using predictable prospecting—user intent data paired with predictive analysis—marketers can more effectively identify, engage with and convert prospects. Although figures show a significant increase in area of application and investment, there is room for improvement in 2019 in performance and accuracy and in expanding application in other areas.