Predict your Future from the Past: Top 5 Uses of Historical Sales Data

When considering valuable and powerful ingredients of a strong sales organization, what immediately comes to mind?  Strong sales reps?  Absolutely.  Great products/services?  Certainly.  Brilliant sales operations folks?  Obviously.  Customers willing to open their checkbooks?  That always helps.  Historic sales data extracts from your CRM?  Crickets…..                      

Many sales leaders are very focused on day-to-day operations as well as tending to fire drills that inevitably arise.  Developing products, grooming the next generation of sales reps, and attaining quotas and budgets are frequently viewed as top priorities within any sales organization.  When faced with the decision to invest in products or in sales data analysis, the majority of leaders are certainly likely to opt for the products.  

Organizations have multiple systems, each containing hundreds of fields of data.  Typically the data from these systems might get pulled into various reports and might get distributed via email to several recipients.  But how many companies can truly say that they leverage these massive amounts of data and view their usage of the data as a strategic tool?  While a large number of companies drown in data and become overwhelmed, some effectively utilize the sales data as a powerful asset.  

And those that use data as a powerful asset are very likely to view historic data extracts as a key ingredient to enabling a sales organization and guiding decision-making.  So why are these historic extracts of CRM data so powerful?  They:

 

1) Help determine healthy pipeline levels

Many sales leaders use an old-school methodology to determine the health of a pipeline by listening to what their gut is telling them and/or by performing some back-of-the-envelope calculations.  While that will work for some, the prevailing sentiment is that examining historic sales data extracts has a stronger relationship with evaluating the health of a pipeline.  

For example, a manager might see $400 in his pipeline while needing to close $100 and deciding due to his 4.0x coverage metric that he is very, very likely to close at least $100.  However, when looking at past extracts, we might learn that he typically only closes 10% of his pipeline.  This would indicate that he is most likely to only close $40, which is less than half of his $100 requirement.  So, while an initial gut read on the $400 pipeline suggests that the manager’s pipeline is healthy, an examination of historic data actually indicates that the manager does not have nearly enough pipeline.

 

2) Lead to various investment decisions

When selling a seemingly never-ending list of products across a heavy list of clients, a sales team frequently loses visibility to  what’s selling and what’s not selling as well as who’s buying and who’s not buying.  An analysis of past data might indicate that penetration in a certain geography might be very light in comparison to past months/quarters.  This realization might suggest that launching a marketing initiative to drum up interest in a particular product is required.  

Another possibility might be to evaluate the need to hire additional resources in order to more effectively serve a particular market.  Suppose a team’s reps currently average 18 accounts per rep while an analysis of data from the previous year suggests that reps averaged 10 accounts per rep.  A manager very well might decide to hire an additional rep or two in order to reduce the 18 accounts to something closer to 10 in order to allow the rep to focus more intensely on his/her client base.

 

3) Identify reps and/or clients who might be over/under performing

When evaluating the performance of a rep or client, a manager frequently relies on gut and/or budget.  However, another way to calibrate performance is to examine data from previous periods.

Suppose a rep has attained 80% of annual quota through September.  While entering the fourth quarter of the year, a manager might be very confident that this rep will attain 100% of quota before the year ends.  But an inspection of this rep’s performance from the past couple years sheds light on the fact that the rep’s lowest performing quarter is in fact the fourth quarter.  This realization could suggest that the manager’s confidence that this rep is on target to attain annual quota is unwarranted.

 

4) Indicate the quality of pipeline

Pipeline amounts can be evaluated simply by the total amounts.  That is a fair approach for some businesses.  However, the majority of organizations will also consider the quality of the pipeline.  Suppose an organization uses forecast categories of Commit, Best Case, and Pipeline (in order from most likely to close to least likely).  By reviewing past extracts of CRM data, a team can determine the quality of the pipeline by analyzing the pipeline spread across these forecast categories.

Some organizations are negatively impacted by reps who might not include real data in their pipeline.  Sometimes reps might carry over deals from one quarter to the next and to the next even though the deals have no chance of ever closing.  In that case, sure, the rep is carrying some low-quality pipeline.  But by examining the quality, leaders should realize that this pipeline is highly unlikely to close.

 

5) Yield predictions

While effective sales reporting is a valuable asset for many sales organizations, some view predictive capabilities as taking this sales reporting to the next level.  When leaders must deliver on certain sales targets, accurately predicting sales figures can help set expectations and temper any instances of happy forecasting.

Predictive models are often constructed while yielding minimal variance between the predicted sales and the actual sales.  Contrary to popular belief, the accuracy of these predictions is not due to luck and chance.  But rather it is typically due to a well-constructed predictive model that leverages various inputs and applicable logic.  And the core ingredient of these predictive models happens to be data from historic extracts of CRM data.  While past performance does not always predict future performance, it can be a very good indicator.

 

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