CASE STUDY

What Does Your Real Sales Cycle Look Like?

ABSTRACT

Executives at a leading US healthcare provider faced unexpected shortfalls and volatility in revenue, in addition to inconsistent forecasting. Sales pipeline metrics indicated a level of robust activity at logical odds with the decline in sales revenue. Examining their sales pipeline in greater detail helped them to understand revenue variations and eventually enabled better forecasting and investment allocation.

 

Clearly Define Your Business Objectives

A leading US healthcare firm was experiencing high variance between actual and forecasted revenues, despite having a sales pipeline-driven revenue forecasting model.

Their primary goals were to understand the drivers of the variance and to be able to allocate their sales resources relative to pipeline bottlenecks.

 

Acquire & Synthesize Relevant Data

A key breakthrough occurred when they examined historical Salesforce data by lead vintage. This helped them identify pipeline characteristics that were difficult to observe in aggregate, such as saturation point and lead half-life (Fig. 1).

Additional insights were gained by examining conversion rate variance according to various deal characteristics: product, sales channel, deal size, and new vs. existing clients. This provided a deeper understanding of how conversion rates have changed over time, trends in conversion times, half-life of the opportunity and ideal engagement window (in number of months) for an opportunity.

Fig. 1 – Conversion Rate by Lead Vintage

 

Develop an Action Plan

A snapshot of sales leads does not always yield an accurate view of a typical sales cycle. Some reasons for this include a) significant change in number of leads generated over time, b) several large leads accelerating through the pipeline over a short period of time, and c) changes in product or pricing that can impact the probability of closing sales.

Devising a tenured view of leads by creation date, probability of closing the deal (influenced by its stage in the pipeline) and conversion time provided insights on 1) which stage of the sales pipeline required concentrated effort of the sales team, 2) when a lead turns stale, 3) trends in average conversion time quarter over quarter and 4) more granular methods to better forecast accurate revenues.

Partner with You to See it Through

 

Partner with You to See it Through

This analysis enabled a deeper understanding of timeframes and probabilities associated with a lead’s progress through the sales pipeline, ultimately providing a new metric for overall health of the sales pipeline itself. These insights opened the door for planning, forecasting and budgeting more realistically.

Keywords 

Revenue Forecasting, Sales Pipeline, Sales Cycle, Investment Allocation, Healthcare Investment, Healthcare, Data Modeling, New York Consulting, Management Consulting, Healthcare, FischerJordan, Data Analytics, Quantitative Analysis, Analytics, Healthcare Management, Healthcare Case Study

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