I’m bringing this topic to the top of your inbox, so to speak, but not (just) because I’m self-promotional. Lack of alignment really does continue to plague B2B sales and marketing organizations.
Last year, I began to explore this challenge with analytical methods front and center. My hypothesis: B2B marketers and sales can use a breadth of analytics techniques to align at the very core of their go-to-market strategies — and continue that alignment through post-sale — to improve revenue outcomes.
I introduced revenue growth analytics and shared a simple framework organized simply by four common revenue objectives. Here’s a refresher.
- Go to market purposefully to achieve efficient long-term growth. Marketers and sales peers who start with total addressable (TAM) and ideal client profile (ICP) analyses align early on market opportunity. The components of account scoring and prioritization — desirable attributes and high-value behaviors — are discovered jointly at this stage. These insights not only prevent marketing from “qualifying” ill-fit accounts and sales from pursuing business that is unlikely to close or be profitable but also drive target account selection and content development. Bonus? The analysis serves as a foundation for a mutual beneficial lead management model, which assures that accounts with the perceived propensity to take action upon get prioritized and accelerated and that accounts that require further cultivation get segmented and nurtured.
Tech tip: Predictive models identify valuable attributes that might not be identified with human intuition.
Pro tip: TAM and ICP are not just for ABM — these are fundamental methods for basic qualifying of prospects.
- Target intelligently to design revenue-optimized customer journeys. Various analytical approaches further examine accounts that meet basic qualification criteria and consume deeper behavioral and fit data. They leverage broader data sources, derive correlations between specific attributes and propensity to act, and may reveal contextual, logical product pairings for specific customers. These analyses — like multitouch attribution — inform outbound strategies and journey design, as they inform predictions such as what behaviors, conversions, and interactions exist in the fastest and most valuable deals.
Tech tip: Infuse attribution analytics with machine learning for enhanced predictability.
Pro tip: Provide personalized experiences (boosted by product affinity and recommendation analysis) to drive engaging journeys.
- Maximize customer lifetime value to boost margins. Customer lifetime value (CLV) is a financial analysis that forecasts future revenue streams from individual customers or customer segments. A function of investment, retention, and profitability data, CLV can optimize marketing spend and guide retention efforts. Firms segment customers by CLV to focus higher-cost acquisition efforts on programs that engage and retain high-value customers.
Tech tip: Use next-best-action analysis to provide desirable experiences to high-potential-value prospects throughout their journey.
Pro tip: Make sure the “action” is the next best one for the prospect, not for your bottom line
- Anticipate and prevent churn to protect existing revenue streams. Churn analysis examines past client or segment behavior to understand trends that precede attrition. Churn analysis and cross-sell analysis directly drive key metrics such as retention and average dollar value.
Tech tip: Apply machine-learning algorithms to uncover patterns and trends in data that are less obvious.
Pro tip: Combined with CLV, this method helps marketers determine what level of investment is appropriate to retain customers.
To go beyond these highlights, check out the full report: “Master The Mechanics Of Analytics For Revenue Growth.”