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Still Forecasting With Spreadsheets?

Here are Five Problem Symptoms to Watch Out For

At many companies, product managers and analysts are still using spreadsheets to create the bottoms-up product forecast (also called the marketing forecast). Their managers may think everything is fine. But in reality, working with spreadsheets can hurt productivity, impede collaboration, and impair good decisions.

Five problems symptoms to watch out for that indicate it's time to find a better way:

  1. Forecasts are difficult to explain

  2. Too much time is spent on spreadsheet maintenance

  3. There is too little collaboration

  4. Forecast accuracy is consistently unacceptable

  5. Gaining management consensus is painful


Symptom #1:  Forecasts are difficult to explain

Causes:

  • Many forecasts are based merely on the trends seen in the orders history time series. That may be fine for minor products. But trends alone do not explain what is driving demand. 

  • The historical orders data that is used for analysis is oftentimes stripped of variables that may provide insights into the actual drivers of demand.

  • Even if variables are not stripped, multi-variable analysis using spreadsheets is too cumbersome when there are many products. Also, the inability to easily chart analysis results impedes fast, interactive analysis.

  • The actual orders experience resulting from demand shaping activities, such as promotions and new markets penetration, are difficult to record and track in spreadsheets. Consequently, organizations do not learn from past activities and do not create models that can be applied to forecasts.

  • Without good product sales analysis and learning from past experiences, forecasts are difficult to explain.


Symptom #2: Too much time is spent on spreadsheet maintenance

Causes:

  • The amount of time that product managers spend on creating, updating, and maintaining spreadsheet workbooks may come as a surprise. This is a hidden productivity sink that many managers don't understand.

  • Managing spreadsheet data is labor-intensive, requiring manual copy-and-paste or formula links to add the latest orders data. This consumes time and injects possible errors.

  • Mapping products and rolling them up into product families and product lines takes time and care. Also, the perceived simple task of summing bookings and order quantities by fiscal period (fiscal month, quarter, and year) becomes difficult when managing many products.

  • Modeling forecasts using spreadsheets can be complex. Formulas linking different spreadsheet rows may need to be created. This leads to modeling complexity which makes maintaining spreadsheets difficult, time-consuming, and error-prone.

  • In addition, forecast workbooks are not easily decipherable and transferable to another product manager. Many times, whenever a different product manager takes over a set of products to forecast, he/she will scrap the old spreadsheet and start from scratch.

 


Symptom #3: There is too little collaboration

Causes:

  • A company or division that has multiple product lines may have a different product manager responsible for each. When using spreadsheets, each product manager typically creates their own workbook that includes only their products.

  • Workbook data silos are created that are difficult to share with other members of the team.

  • Product managers lose valuable insight into the linkages or correlations that product lines have with one another.

 


Symptom #4: Forecast accuracy is consistently unacceptable

Causes:

  • With spreadsheets, it is very difficult to track deviations between forecasts and actual orders. Therefore, most organizations do not systematically measure deviations and set forecast accuracy objectives.

  • Consequently, no quality control plan is put into place to improve forecast accuracy.

  • Poor product sales analysis results in a lack of understanding of the true drivers of demand and poor forecast accuracies.

 


Symptom #5: Gaining management consensus is painful

Causes:

  • Most organizations rely on an analyst to import and “roll-up” separate forecast spreadsheets in order to view the total bookings forecast. This takes time and effort.

  • Roll-ups many times contain errors or are held up because of inconsistencies between the spreadsheets.

  • Much time and effort is spent preparing formal presentations for the Forecast Review, where forecasts are presented to get management feedback and gain management consensus.

  • Presentations may not represent the slices of data needed to gain management consensus quickly. For example, the presentation may show only the worldwide forecast; whereas, the VP Sales may want to see forecasts by region. 

  • More time is spent re-doing the spreadsheets and the roll-ups to reflect the feedback from management.

 


 

 

 

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Demand Analytix helps companies develop product, marketing, and sales strategies that drive and shape product demand to meet and exceed financial goals. We do this through our consulting practice, professional services, and Business Analytics applications, which include Third Party software and our ForecastWorkbenchTM SaaS (Software-as-a-Service).

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