With over 61% of B2B marketing leaders already implementing solutions, it’s clear that Predictive has progressed from a nascent marketing tool to an essential solution in the marketing technology (martech) stack. However, even while innovators and early adopters spend or allocate budget toward predictive, marketing buyers cannot clearly answer, “What do we do with Predictive?” Sure, it’s broadly assumed that Predictive will help maximize revenue, but questions arise from both current users and marketers still in the pre-implementation stages about the tangible use cases of Predictive.
Our “The SO WHAT of Predictive: 5 Essential Use Cases” webinar (watch recording) explored the predictive landscape and defined the key use cases. In this post, let’s start by reviewing the common Marketing and Sales challenges across a classic funnel and how Predictive responds to those challenges with various use cases.
We’ve also built a scorecard that you can use to determine which use case you should consider and prioritize for your organization. Find that scorecard in part 2 of this blog post, coming soon….
Before the funnel comes the strategic stage when Marketing, Sales, and the executive teams build a company made up of people, products, and processes based on a data-driven understanding of their customer and target market.
Problem: Lack of Customer and Market Insights
Objective truth about the size of your total addressable market requires a massive, constantly updated data set built from a deep understanding of your current customer base. In addition to their addressable market, marketers struggle to grasp insights into the addressable market subsets where they perform best. They might ask: What are the more granular characteristics of our top markets? How large are those micro-segments? What is our current penetration within those markets? How many of the top accounts are currently in our CRM versus new accounts we have yet to identify? Are our sales territories or verticals optimized and fairly distributed? These strategic questions are some of the hardest for even the most sophisticated enterprises to answer. As a result, teams stick to simple industry and company size segmentation that limit their ability to optimize company-wide direction.
Unless you know the number of companies in the entire industry, you can’t judge whether your current customers from that industry are a small or large percentage of the whole, let alone how many additional companies from that industry are likely to buy in the future.” – David Raab, How Predictive Insights Unlock Explosive Growth in New Markets
Predictive Use Case: Go-to-Market Insights
Predictive solutions can aid in generating Go-to-Market insights. By analyzing your data and connecting it to external data sources, a Predictive solution can uncover your total addressable market. And that’s not all: with Predictive, the combination of external data, algorithms, and analytics can create clusters of your top customers based on their common attributes. Some predictive solutions will show you those clustered attributes, which help you create ideal customer profiles and personas. Flexible segmentation capabilities can also assist in slicing up the addressable market into segmented territory/vertical plans and account lists for your sales.
For the top of the funnel, Marketing’s primary objective is to drive awareness and demand with the right audiences. We’re not just hoping to improve campaign conversion metrics—we need to achieve campaign lift with more high-propensity buyers.
Problem: More, Higher Quality Leads for Outbound
Marketers face a broad range of challenges when assessing the top of the funnel. Most frequently we hear about too few leads, or challenges in identifying which leads are similar to current customers. The concept seems simple: find more accounts that look similar to current customers. However, minimal and siloed buyer data, when coupled with basic reporting capabilities, limits Marketing’s ability to efficiently identify target accounts. Many teams turn to list vendors, which fail to provide sufficient data or insights and degrade CRM system health. One of our customers put it best…
It’s the simple 80/20 rule–80% of our qualified leads come from 20% our marketing activities. I just don’t know who they are and where to find more of the them.” – Financial Services Company Executive
Predictive Use Case: Predictive Acquisition
Predictive can help guide prospecting and offer look-alike targeting so you can drive demand with the highest quality leads into and through the top of the funnel. By analyzing your historical customer data, Predictive applies ideal customer / lead profiles to identify new target accounts and contacts. The insight ensures that reps are going after best prospects and that marketing campaign investments are allocated toward audiences that are most likely to convert.
Someway, somehow, you have created a large prospect database. Now what? Continuing to efficiently allocate resources into nurturing programs and optimizing sales productivity is key to accelerating revenue growth. Here’s how Predictive impacts your existing database.
Problem: Inefficient Prospect Management
This problem is twofold:
In both nuanced scenarios, marketers have a low confidence in the correlation between their lead qualification and the actual likelihood of a lead to buy. Therefore, it is difficult to prioritize targets for sales outreach or to determine where to invest further in moving target accounts down the funnel. Whether you have a high-velocity approach targeting SMB accounts or a high-touch approach targeting enterprise accounts, a perfect formula that sets and reconfigures priorities is the missing link in maximizing revenue.
94% of your MQLs never convert.” – SiriusDecisions
Predictive Use Case: Predictive Prioritization
Many predictive solutions were founded in this use case. This is where you’ll find lead scoring functionality. Again, predictive prioritization offers two specific solutions for a marketer with too many leads:
Each solution analyzes existing records by how likely they are to convert. The common misconception is that only companies with an inbound marketing machine can benefit from this use case. With predictive prioritization, any marketer with existing records in their CRM or marketing automation system can uncover and act upon hidden opportunities.
The bottom of the funnel has received less attention from predictive vendors and buyers. However, there are still relevant challenges and upcoming solutions that help management with pipeline predictability and campaign attribution.
Problem: Inaccurate Forecasting
The three key inputs for an accurate forecast are opportunity amount (future revenue), likelihood, and close date. Organizations—from sales management to the board and directors—rely on the aggregation of current opportunities to forecast expected revenue in a specific time period. However, all those inputs are typically determined by the best guess of the sales rep or a generic likelihood percentage associated with the opportunity stage. In addition, teams experience added uncertainty caused by constantly changing buying cycles that are not reflected in the forecasting model. The result is a pipeline forecast that is often wildly imprecise.
Predictive Use Case: Pipeline Forecasting
Pipeline forecasting looks to solve this challenge by providing a constantly updated opportunity score, which reflects how likely the deal is to close as well as its amount. The models leverage internal transactional data very effectively in addition to external data that could change the likelihood of a deal to close. Some vendors even factor the past successes of each salesperson working the deal or the nuances of that particular account to more accurately forecast whether it will close and for how much.
Your Sales team has closed/won a new customer. Now it’s about growing your share of their wallet and retaining your customer base in an increasingly competitive ecosystem. Predictive marketing vendors have started aiding post-sale demand. Here’s how.
Problem: Low LTV or High Churn
Again, the problem is twofold:
While the pressure for Marketing to own post-sale demand is growing, many enterprise B2B teams lean heavily on Sales, Customer Success, and Support departments to maintain account management. The result is a reactive approach to growing valuable customers for life.
Predictive Use Case: Upsell/Cross-Sell and Churn Analysis
Selling more to existing customers is easier when you know the characteristics and behaviors common to companies that have bought in the past. Similarly, it’s easier to identify “at-risk” customers with a system that looks for shared traits and proactively identifies similar customers across your install base. Predictive solutions let you model not only for top- and middle- funnel opportunity identification, but also whom to target next for new products within your existing customer base.
Read our next post to download your Predictive Use Case Scorecard!
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