4 minute read
Thinking beyond CRM analytics
Managing a distributed sales team is difficult. Many managers not only have a team to manage, but often have the added responsibility of managing a key account or two.
Add to this the relentless pursuit of growth, changing portfolio of products and services, recruitment and retention challenges and reps fluctuating performance and you see the difficulties most sales managers face.
Pipeline management and sales forecasting are key capabilities for most B2B sales team and fundamental to a team’s success. Sales organisations operating best-in-class pipeline management capabilities out-perform competitors with standard capabilities.
Working with flawed data
Central to best-in-class pipeline management and sales forecasting is a combination of application generated data and self reported data.
Today however, many managers rely simply on a combination of CRM Dashboards, spreadsheets and updates from reps usually in the form of 1:1 meetings to help them create forecasts, manage pipeline health and understand the status of individual deals.
It is important to recognise that this information is entirely self-reported and no matter how well intentioned when you factor in the assumptions, recollections, bias and misreads it’s easy to see that this data is flawed.
Recollections of customer meetings change over time, some reps update their opportunities in the CRM sporadically, some are more disciplined, whether your reps are exceeding their targets or falling short they can feel the pressure so, a rep under pressure may be inclined to put a positive spin on a marginal opportunity or reduce pressure by playing down the chances of winning an important deal.
When you analyse this situation it’s easy to see that managers are working largely in the dark and that producing consistently accurate forecasts, building and maintaining a healthy pipeline and effectively coaching reps with this type of flawed data is practically impossible.
Missed opportunities
A consequence of working with flawed data is time wasted during 1:1s. Typically 1:1 calls or face to face meetings are 30 or 45 minutes but on average 80% of this time is spent gathering information, managers spend so much time on fact finding there is little time to spend on coaching.
Often only the most pressing deals get attention, leaving little or no time for reviewing the other factors affecting pipeline health and forecast accuracy, such as out quarter deals, portfolio mix, etc.
With only a few minutes of coaching time available it is important to get the most from your 1:1s but the quality or relevance of coaching can also be diminished.
If the next steps are misunderstood or important information has been missed and with no hard evidence to support or challenge your rep’s assessment your coaching can easily miss the spot.
Given the fact most managers have and are hired for their valuable experience, opportunities to put this experience to use are being lost.
What top performers do differently
Rather than working on self reported data, top performing organisations with best-in-class pipeline management capabilities use technology to automatically gather sales activity data from email, calendars and CRM, plus data from other systems such as proposal and demand generation applications, organised by pipeline stage this provides a single view of your pipeline.
This single source of truth provides clear insight into the overall health of your pipeline and quickly identifies gaps in the pipeline.
Spotting gaps in the out quarter allows you to take action before it becomes a current quarter problem. It enables you to inspect any deal and quickly identify its true status, easily see which buying tasks are completed or still to do, see engagement levels, progression, and close date accuracy.
By aligning 1:1 sessions to the month or quarter you are able to manage all the moving parts of a healthy pipeline.
Take for example the IT managed service business, they align their 1:1s to the quarter, in week 1, they inspect deals in the commit stage and deals pushed from last quarter, in week 2 they inspect deals in commit and the current quarter top 3 deals, in week 3 they inspect deals in commit and deals in the out quarter and so on.
This 1:1 structure combined with the system data has helped the business increase revenue by 6% through improved win rates and reduced sales cycles and a reduction in conversion costs of almost 10% through more targeted use of resources.