From Paralysis to Profit with Advanced Sales 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 ones. This expectation mismatch is especially common in consumer goods manufacturing and retail sector businesses as future sales volumes are very difficult to forecast. While following this cycle, your business can become paralyzed and less innovative, which is detrimental to long-term growth. However, advanced sales analytics can be of great value to a business.

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

Do your data systems serve you or you serve them?

The answer to the question can be a little complex, but often takes the form of:

• “We have data, but it is scattered in multiple systems and takes manual effort to coalesce.” This dilemma is an unfortunate reality for many companies with enterprise customer management, financial management, and supply chain systems. Data lakes have helped in the recent past but are not a cure-all.
• “Our data is incomplete.” This predicament 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 in the significant variances seen in Forecast vs Actualsales performance leading to flat or negative growth in the quarterly sales volume.

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

The road to higher profit and forecast reliability comes as a result of sound sales planning processes supported by trustworthy data analytics presented in a meaningful and timely manner. There is enough advanced technology available today to implement successful data analytics that can drive incremental sales and profit such as Cloud computing, Modern Data Platforms, Robotic Process Automation (RPA), Natural Language Processing (NLP), and Machine Learning (ML).

These new technologies can be overwhelming and require a solid understanding of the business in order to be appropriately applied with minimal waste. Getting started on the road to enhanced sales analytics typically begins with business analysis tasks similar to the following:

1. Assessment of both the “as-is” state of the business and the “to be” or the desired state with respect to the market’s key performance indicators (KPIs) that define success
2. Identification of the stakeholders (personas) critical to the process and the types of data that 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 harmonized with internal data to enhance reporting
4. Elicitation of business rules which govern the classification and processing of the data and ensure the 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 Optimization solution is an example of advanced sales analytics that optimizes the performance of retail channels and drives higher incremental profits at lower internal costs. For more information, see https://spendo.ai/.

Author: George Shemas
Principal at Saama Analytics Client Care organization overseeing its Consumer Goods practice with decades of experience building world-class analytics

Originally published on saamaanalytics.com