Which lead becomes a customer and which does not: Do not guess, learn from the data
The lead pipeline shows where leads stand and conversion management shows how to move them forward; lead analytics is extracting patterns from the data these two processes produce. Leads from which source convert into customers at a higher rate
and in a shorter time? Which segment, industry, region, and company size produce more valuable customers? Which behaviors of a lead — which products it looks at, which campaigns it responds to,
how many contacts it takes to progress — are reliable signals of purchase intent? At which stage and for which reason are the most leads lost, and to which competitor do these losses go? Lead analytics not only answers these questions with retrospective reports;
through lead scoring, prioritization, and conversion probability prediction, it also guides the decisions of the coming period.
Without analytics, leads are treated equally; yet leads are not equal. The sales team calls the leads on the list in order or by intuition, and the most valuable lead waits behind the loudest one.
Marketing invests in the channel that brings the most leads, but that channel’s leads have the lowest conversion rate. Even if loss reasons are recorded, because they are not read collectively, the impression “we lost on price”
overshadows the fact that the loss was actually due to delivery time. When lead data sits separately in the sales and marketing systems, the arrival of the lead and the outcome of the order cannot be connected, and analytics
never reaches the question “which lead became a customer.”
In Minerva, lead analytics is fed directly by the fact that lead, opportunity, quotation, order, and campaign data reside on a single identity and a single database; no separate data warehouse or transfer is needed.
Conversion rate, time to convert, stage-based loss, and the distribution of loss reasons and competitors by source, campaign, channel, segment, region, product interest, and representative are monitored on real-time dashboards;
cost per lead and the subsequent turnover and profitability of the won customer show source quality by value rather than by count. User-defined scoring rules prioritize leads according to profile and behavior data;
artificial intelligence and machine learning capabilities learn from past won and lost leads to predict the conversion probability of new leads, rank the sales team’s list by that probability,
and recommend to marketing which segment and channel to focus on. All analyses can be extended with user-defined reports and dashboards according to the company’s own criteria.
Handle leads not in the order they arrive, but by their probability of converting and their value; build a marketing and sales function that learns from lead data.