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How Predictive Analytics Improves B2B Sales and Marketing Effectiveness


When used the right way predictive analytics can provide B2B sales and marketing teams with valuable insight into what companies have a higher likelihood of becoming customers and which companies have a lower likelihood of ever converting. This form of predictive analytics is not a lead scoring system that sits inside a CRM system or marketing automation platform, and it’s not intent data either. What it is, is a tremendously valuable tool that analyzes data patterns across a company’s customer base and then identifies companies within a target market that exhibit those same patterns. The net result is a greater level of effectiveness which is realized in several areas across sales and marketing.


Improved budget and resource allocation: Companies will often direct sales and marketing budget and resources towards the channels that have proven to be more effective. In an age where companies have data and tools to market to a “segment of one” this approach is quickly becoming obsolete. Instead, companies must allocate sales and marketing budget and resources to the companies that are more likely to engage and become customers and less to those that won’t. Predictive analytics is the tool that should guide this approach. The net result is a higher ROI from sales and marketing activities, and the ability generate more revenue with the same number of resources and budget.


A well-defined target market: When sales and marketing teams build a target market and ideal customer profile based on a qualitative analysis they’re building a half-baked target market. That’s not to say that a qualitative analysis does not have its place in this exercise, it does. But a data first approach will yield a far better target market and an ideal customer profile that’s grounded in objective scientific analysis. Predictive modeling will play a significant part in this process by clearly defining what data attributes are most important when looking for the best target accounts, as well as data attributes that indicate a particular company may not be a good fit.


Increase opportunity win rates: By targeting companies that have a higher likelihood to buying, opportunity win rates will increase over time. Sales people will have better insight into where they should and should not be spending their time. And budget and resources can be directed towards the right companies.


Reduce customer acquisition cost: The ability to understand higher and lower propensity to-buy before engagement ever occurs means budget and resources can be redistributed towards high propensity accounts. By redirecting resources and budget towards these companies and realizing higher opportunity win rates, customer acquisition cost will naturally decrease.


Optimize account-based marketing programs: Predictive analytics makes perfect sense for companies running account-based marketing programs. An effective ABM program relies on not only having the right strategy in place, but also a strong list of target accounts. It’s also important that these target accounts are tiered and ranked from highest to lowest strategic value. Insights gleaned from predictive analytics models applied across a list of target accounts should guide the process of stack ranking these accounts as well as much of the ABM program strategy.


What it takes to build an effective model:


Besides having access to the right technology and data science expertise, there are several factors that determine whether or not a reliable propensity to-buy model can be built in the first place.


Firstly, there must be enough historical data on existing and previously acquired customers.

In addition to the necessary quantity and quality of data, when looking at why companies converted to customers, organizations must be able to decipher what variables and data points are most pertinent and should be analyzed. The more variables that are used in developing a predictive model the more accurate and reliable the outputs become.


Lastly, it’s important to understand that for a predictive analytics model to provide a long-term benefit it must be continuously updated. As product offerings, the competitive landscape, and target markets evolve, so does the makeup of the companies that are converted to customers. That being said the validity of a model will likely diminish over time if it is not updated with fresh data on companies that were or were not acquired.


The benefits well-developed predictive analytics models can provide sales and marketing teams can be significant. However, it’s essential that organizations first have the proper data infrastructure in place. As well as an understanding that the data they do have on their customers and sales history must be accurate and complete.


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