Several months ago, I wrote a post about why providers are moving away from the traditional lead scoring model and using predictive analytics to better determine which leads are most likely to purchase. Since that time, I’ve talked with both sales and marketing executives and practitioners at a number of B2B companies (not limited to technology) and it’s clear that predictive analytics has much broader applicability across the buying journey and the accompanying sales cycles. A few days ago, I published a Market Guide for SaaS-based Predictive Analytics Applications for B2B Sales and Marketing (subscription required).
I spoke with (and profiled) 16 different providers of applications across a broad range of categories including:
- Lead Scoring
- Opportunity Scoring
- Customer Renewal/Upsell/Cross-Sell
While (aside from pricing), the adoption of predictive analytics for these purposes is pretty recent (and likely at an early stage in the Hype Cycle) and the market is fluid and dynamic, it’s clear that B2B companies are seeing significant value. The buying cycle has clearly shifted in a way that puts more power in the hands of buyers at the expense of sellers and the sellers have to get smarter in how they respond.
While the attention has been focused on what this means for marketers vis a vis lead scoring, the buying cycle changes have also affected the sales organizations. This means that new models need to be used once the lead reaches an opportunity stage, both in terms of likelihood to close, but also the optimal price to get the deal done. And the same logic can be applied both pre-funnel (to find companies that best match the characteristics of customers) and post-funnel to identify churn risks and expansion opportunities.
I would encourage any B2B sales or marketing leader (especially those in demand generation and sales operations roles) to start looking at whether their organizations might benefit from a SaaS-based predictive analytics application. If you are a Gartner client, the Market Guide is a good starting point. Like any other software market, there are a range of different approaches from best-of-breed to more “suite-like” solutions. Entry costs are (generally) low, proof of concepts are available, out-of-the-box integrations exist with CRM Lead Management and Sales Force Automation (SFA) systems, ROI can be quite rapid and the applications are designed for sales and marketing users as opposed to data scientists or business analysts.
While this paints a rosy picture, there are definitely cultural and organizational challenges to consider, especially when you have people that have done things a specific way (and been successful at it) for a long time. There will clearly be new entrants, acquisitions and the normal shakeout with any new market. Finally. there some tinkering that will inevitably need to be done (as the machine learning kicks in). But as adoption continues to expand, companies that stand back and idly watch could be putting themselves at a competitive disadvantage.