How To Determine The Outcome Of Your Sales Process

Sales forecasting is agony for most sales managers. Since managers are expected to deliver accurate sales forecasts, although at times they lack the large volume required for statistical forecasting of their efforts.
Therefore most managers rely on subjective appraisals of their sales pipelines for business growth. That means what the pipeline consists today and how that compares to their experiences with the same sales pipeline in the past.

Now, while this type of sales forecasting by comparisons are less scientific and more of art, nevertheless such comparisons also leverage solid insights, which include designing, monitoring and analyzing the sales processes which are the key to improving B2B sales forecasting for your brand.

Hence, validating the link between sales pipe and its results is thus an enticing challenge for most B2B organizations, whereby mathematically speaking it is like looking for the function “x” in the equation:

Sales forecasting = x*(sales pipeline)

FRAGMENTING THE SALES PIPELINE

The term “Sales Pipeline” is a feeble input for a potential equation that relates to your business growth . Hence we must fragment it into more specific parts since doing so will also increase the data availability for analysis once we are using an easy to use CRM for boosting sales and revenue for the prosperity of our businesses.

For example, viewing sales pipeline transitions (opportunities moving from one stage in the pipeline into another) instead of looking just at the pipeline outcome (closed opportunities) increased data volume collected in the CRM database breaks down the information by a factor of three.

These three-dimensional attributes are:

Attributes of the opportunities

The sales pipeline is filled with opportunities, all of which has numerous attributes that are potentially infinite which affects the outcome of any sales process, such as customer types, product lines, deal sizes and more.

Attributes of the sales process

The number of stages in the pipeline, their durations, the corresponding tasks and the number of participants involved are some of the structural attributes in the sales process that influences accurate forecasting.
For example sales pipelines with just two or three stages, and that concludes in a few weeks and involves only one decision-maker, will obviously be an easier model than more complex sales processes.

Attributes of the sales rep

Lastly, the sales rep actually running the sales process will also influence its outcome. Per say, over here we are looking for indicators that help in measuring effort and competency, which in most organizations are found in metrics that are measure by activity volume or quota attainment.

THE UNREALISTIC EXPECTATIONS

Therefore in an ideal situation, the function “x” would include:

• A minimum of 3 to 4 opportunity attributes
• A maximum of 3 to 4 factors summarizing the structure of the sales process
• A minimum of 3 to 4 proxies for salesperson attributes

We would then replace function “x” on previous sales process data, to recognize the statistically important factors and their significant coefficients, and use that calibrated function for the purpose of forecasting.

Now that is severe data crunching, which is accompanied by equally serious limitations, since:

• This may only be possible with vast computing and consulting resources (for most sales managers which is a dream).
• There is a trade-off between robustness and sophistication. This is because; the more sophisticated this model is the more sensitive it will be to the company’s environment and would require frequent calibrations.
• As we also want the salespersons to understand the corresponding calculations, since just producing accurate forecasts is not enough, hence “black box” forecasts would be perceived as just another enforcement tool.

Therefore, let us resist the golden algorithmic fallacy and find out rational and practical ways to improve the accuracy of sales forecasting as suggested and can be implemented with no trouble in context of traditional easy to use CRM software.

PRACTICAL ALTERNATIVES

1. Refine your sales process

The more ‘realistic’ is your sales process the easier it will be to determine its outcome. Over here ‘realistic’ implies measuring the dynamics (like conversion rates, time to wins, durations) that reflects the decision making process of the buyer.

2. Explain your assumptions

It is inevitable that B2B sales forecasting include judgmental elements. Now, while your sales reps may be biased, they also possess undisputable market knowledge.
Hence, writing one-liners explaining the reason for such judgments increases the reliability of their forecasts significantly.

3. Build detailed sales simulations

Opportunity-by-opportunity simulation is of great help, going beyond the traditional best-case and worst-case scenario and building details, especially when you have an opportunity that is so large that can make or break your forecast.

4. Use multiple forecasting methodologies

Instead of relying on a single, more sophisticated sales forecasting methodology, for more better results it is advisable to average 3 to 4 simple forecasts to find an “enlightened judgment”.

5. Monitor forecast errors

To forecast accurately B2B organizations are recommended to measure forecasting errors rigorously.

Since closing dates are so important in a B2B context we recommend brands to measure opportunity amount and closing dates that should provide you with useful indications on how to improve your sales forecasting accuracy and hence not only make way for fewer mistakes in the times to come leading to consistent business growth.