Mining meaningful and actionable insights from business data are oftentimes a massive hurdle for marketers. However, technologies like Artificial Intelligence (AI) and Machine Learning (ML) are changing this scenario for better outcomes; by spreading their innovation across diverse industrial realms. One such innovation is SaaS-based predictive analytics – an effective tool that enables B2B marketers to reinvent and ameliorate their go-to-market strategies and marketing efforts. How exactly does it help marketers?
Looking from a business standpoint, predictive analytics aims to narrow down an organization’s overall marketing efforts. It uses predictive modeling, machine learning, and data mining to analyze current and historical data to generate meaningful insights and predict future business outcomes. Thus, marketers can see a clear path laid in front of them. All they need to do is create more relevant content, and marketing campaigns targeted at each customer segments’ specific needs—focusing on the prospects who are most likely to convert into high-value customers. Consequently, the entire process becomes more precise and targeted.
According to MarketsandMarkets, the predictive analytics market size is expected to grow up to USD 12.41 Billion by 2022, rising from USD 3.85 Billion in 2016 at a Compound Annual Growth Rate (CAGR) of 22.1%. This pinpoints the industry’s growing inclination towards advanced analytics focused on predicting future business outcomes.
So, how does predictive analytics help your business build a better marketing strategy? Here is how it helps your marketing and sales teams utilize the available data in an intelligent way and focus their efforts accordingly to drive better ROI.
Identifying patterns and acquire prospects
One of the features that makes predictive analytics powerful is its ability to identify prospects and leads that exhibit similar behavior patterns through their actions and create an identification model based on these patterns. These actions may be making a purchase intent, downloading a certain content offer or even renewing an existing contract. Such predictive models can help the marketing and sales teams identify similar prospects early in the sales cycle, prioritize them, reach out to them faster, and create new opportunities and markets for their offerings.
Prioritizing prospects and leads
The primary and an important feature of predictive analytics is predictive scoring. Contrary to traditional lead scoring which uses experimentation, speculation, and iteration to derive weightings, predictive lead scoring takes a scientific and mathematical approach to assign weightings and scores to your prospects and leads. This type of scoring helps the marketing and sales teams to identify accounts and leads who have a higher chance of making a purchase, prioritize and focus their efforts on them while spending less time on leads who have a lower chance of converting.
Automating lead segmentation
Traditionally, most B2B marketers tend to segment their prospects and leads based on generic attributes such as industry and job role. Moreover, most of this segmentation was manual and laborious. However, predictive algorithms can take segmentation one step further and automatically identify as well as create valuable and meaningful segments. This enables the marketing teams to create and deliver campaigns and communications that are targeted and meaningful to the leads.
Although the applications of predictive analytics are endless, CMOs first need to identify their problem statements that they are trying to solve or the business goals that they are trying to achieve. Adopting this approach can help them make the best use of predictive analytics.
Originally published , updated April 10 2018