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From Paralysis to Profit

By March 18, 2020 No Comments
, From Paralysis to Profit

All too often, companies endure cycles of missed financial expectations beginning with over-optimistic forecasts in early fiscal quarters followed by cost-cutting and restructuring in later fiscal quarters.  This is especially common in consumer goods manufacturing and retail sector businesses as future sales volumes are very difficult to forecast. When in this cyclical mode, your business can become paralyzed and less innovative which is detrimental to long-term growth.

Many factors influence sales success of products and services, including consumer marketing, product mix, pricing and promotional factors.  All of these factors are measurable via data analytics which should readily be available to category and retail channel managers.

Do your data systems serve you or do you serve them?

The answer to this question can be complex but will often take the form of:

  • We have data but it is scattered in multiple systems and takes manual effort to coalesce.” This is an unfortunate reality for many companies with enterprise Customer Management, Financial Management and Supply Chain systems. Data lakes have helped consolidate data in the recent past but are not a cure-all.
  • Our data is incomplete.” This is a hard reality in which sales/volume data may only be available for certain customers or category products. Forecasting sales of new product innovations becomes even more challenging without historical data.
  • We have data but it is wrong so we just go with gut instinct.” When financial or historical reporting systems are proven to be incorrect, the end-user quickly loses trust in these systems causing them to seek other sources for information. Financial dashboards must tell a story and enable easy validation of numbers being presented.

The symptoms of these types of data challenges are often large variances in Forecast versus Actual sales performance, flat or negative growth in quarterly sales volume.

My analytics are a problem for me, so what do I do?

The road to greater profit and forecast reliability comes as a result of a sound sales planning processes supported by trustworthy data analytics presented in a meaningful and timely manner.  There is ample information technology available today in the form of Cloud computing, Modern Data Platforms, Robotic Process Automation, Natural Language Processing and Machine Learning to implement successful data analytics that can drive incremental Sales and Profit.

These new technologies can be overwhelming and require a solid understanding of the Business in order to be applied properly with minimal waste. Getting started on the road to enhanced sales analytics should begin with Business Analysis tasks similar to the following:

  1. Assessment of the “As Is” state of the business combined with the “To Be” desired state considering market Key Performance Indicators that define success.
  2. Identification of the Stakeholders (Personas) critical to the sales process and the types of data which they need to perform their tasks effectively.
  3. Inventory of internal IT data systems available to feed the new data analytics system combined with an understanding of external datasets which could be combined with internal data to enhance reporting.
  4. Elicitation of Business Rules which govern the classification and processing of customer and product data and ensure integrity of the resulting analytics.
  5. Development of a Data Dictionary and visual Report Mockups which represent the desired delivery of data analytics and reporting.

These initial steps are essential to evolving your sales data analytics to better support your planning processes to minimize your Forecast to Actual variance and improve incremental product selling.

Saama Analytics uses this proven process with its clients to define advanced analytics solutions that deliver insights enabling enhanced business performance.  The Saama Analytics’ Spend Optimisation solution, is an example of advanced sales analytics which optimise performance of retail channels and drive higher incremental profits.  For more information, see https://spendo.ai/.

 

This Blog was authored by George Shemas, a Principal in the Saama Analytics Client Care organization overseeing its Consumer Goods practice and having decades of experience building difference-making analytics.

All too often, companies endure cycles of missed financial expectations beginning with over-optimistic forecasts in early fiscal quarters followed by cost-cutting and restructuring in later fiscal quarters.  This is especially common in consumer goods manufacturing and retail sector businesses as future sales volumes are very difficult to forecast. When in this cyclical mode, your business can become paralyzed and less innovative which is detrimental to long-term growth.

Many factors influence sales success of products and services, including consumer marketing, product mix, pricing and promotional factors.  All of these factors are measurable via data analytics which should readily be available to category and retail channel managers.

Do your data systems serve you or do you serve them?

The answer to this question can be complex but will often take the form of:

  • We have data but it is scattered in multiple systems and takes manual effort to coalesce.” This is an unfortunate reality for many companies with enterprise Customer Management, Financial Management and Supply Chain systems. Data lakes have helped consolidate data in the recent past but are not a cure-all.
  • Our data is incomplete.” This is a hard reality in which sales/volume data may only be available for certain customers or category products. Forecasting sales of new product innovations becomes even more challenging without historical data.
  • We have data but it is wrong so we just go with gut instinct.” When financial or historical reporting systems are proven to be incorrect, the end-user quickly loses trust in these systems causing them to seek other sources for information. Financial dashboards must tell a story and enable easy validation of numbers being presented.

The symptoms of these types of data challenges are often large variances in Forecast versus Actual sales performance, flat or negative growth in quarterly sales volume.

My analytics are a problem for me, so what do I do?

The road to greater profit and forecast reliability comes as a result of a sound sales planning processes supported by trustworthy data analytics presented in a meaningful and timely manner.  There is ample information technology available today in the form of Cloud computing, Modern Data Platforms, Robotic Process Automation, Natural Language Processing and Machine Learning to implement successful data analytics that can drive incremental Sales and Profit.

These new technologies can be overwhelming and require a solid understanding of the Business in order to be applied properly with minimal waste. Getting started on the road to enhanced sales analytics should begin with Business Analysis tasks similar to the following:

  1. Assessment of the “As Is” state of the business combined with the “To Be” desired state considering market Key Performance Indicators that define success.
  2. Identification of the Stakeholders (Personas) critical to the sales process and the types of data which they need to perform their tasks effectively.
  3. Inventory of internal IT data systems available to feed the new data analytics system combined with an understanding of external datasets which could be combined with internal data to enhance reporting.
  4. Elicitation of Business Rules which govern the classification and processing of customer and product data and ensure integrity of the resulting analytics.
  5. Development of a Data Dictionary and visual Report Mockups which represent the desired delivery of data analytics and reporting.

These initial steps are essential to evolving your sales data analytics to better support your planning processes to minimize your Forecast to Actual variance and improve incremental product selling.

Saama Analytics uses this proven process with its clients to define advanced analytics solutions that deliver insights enabling enhanced business performance.  The Saama Analytics’ Spend Optimisation solution, is an example of advanced sales analytics which optimise performance of retail channels and drive higher incremental profits.  For more information, see https://spendo.ai/.

This Blog was authored by George Shemas, a Principal in the Saama Analytics Client Care organization overseeing its Consumer Goods practice and having decades of experience building difference-making analytics.

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