• Written by Azhar Aliyar, Director

A sales manager checks the latest forecast, trying to understand why the numbers are not adding up, and wonders if the data foundations are in place to support accurate predictions. This is a common scenario, and one that highlights the importance of having the right data in place to support sales teams.

Field-level meaning and why column names are not documentation

Most people assume that column names in a database are sufficient documentation, but this is wrong. Column names can be misleading or unclear, and without further context, it is difficult to understand what the data actually represents. For example, a column named "sales" could refer to the total sales for a product, or it could refer to the sales for a specific region or time period. Without clear documentation, it is impossible to know for sure.

This lack of clarity can lead to mistakes and misinterpretations, which can have serious consequences for sales teams. If the data is not properly understood, forecasts and predictions will be inaccurate, and sales teams will not be able to make informed decisions. It is therefore essential to have clear and concise documentation for all data fields, to ensure that everyone understands what the data represents.

The systems that get left out of scope and the partial answers that follow

When implementing a system like AsscherAi, it is easy to focus on the main systems and data sources, and to overlook smaller or less obvious ones. However, this can lead to partial answers and incomplete data, which can be just as misleading as incorrect data. For example, a sales team may have access to data on customer interactions, but not on customer feedback or complaints.

This can lead to a lack of understanding of the customer's needs and preferences, and can result in sales teams pursuing the wrong opportunities. It is therefore essential to consider all relevant systems and data sources, no matter how small or obscure they may seem. By doing so, sales teams can gain a more complete understanding of their customers and make more informed decisions.

The sales data this depends on: historical sales at planning grain, order book and pipeline, inventory positions, promotional calendar

In order to make accurate predictions and forecasts, sales teams need access to a range of data, including historical sales data, order book and pipeline data, inventory positions, and promotional calendar data. Historical sales data provides context and helps to identify trends, while order book and pipeline data gives insight into future sales. Inventory positions and promotional calendar data help to identify potential bottlenecks and opportunities.

For example, a category manager compares two suppliers, taking into account their historical sales data, current inventory positions, and upcoming promotions. This allows them to make an informed decision about which supplier to use, and to optimize their inventory levels accordingly. By having access to this range of data, sales teams can make more informed decisions and drive better outcomes.

A shift supervisor checks the maintenance log, to ensure that equipment is properly maintained and that downtime is minimized. This is just one example of how having the right data in place can support sales teams, and sales teams can use this data to drive better outcomes.

What it changes about: a forecast nobody can interrogate and inventory out of step with demand

Without accurate and complete data, forecasts and predictions are essentially meaningless. They cannot be interrogated or understood, and they do not provide any useful insights. This can lead to inventory levels that are out of step with demand, resulting in stockouts or overstocking. By having access to accurate and complete data, sales teams can create forecasts that are meaningful and useful, and that can be used to drive better outcomes.

For example, a sales team may have a forecast that shows a significant increase in demand for a particular product. However, without access to data on inventory positions and supply chain constraints, they may not be able to meet this demand. By having access to this data, they can identify potential bottlenecks and take steps to address them, ensuring that they can meet customer demand.

Measured with: forecast accuracy, win rate, stock cover

The effectiveness of a sales team's data foundations can be measured in a number of ways, including forecast accuracy, win rate, and stock cover. Forecast accuracy refers to how closely the forecast matches actual sales, while win rate refers to the percentage of sales opportunities that are won. Stock cover refers to the amount of inventory that is available to meet customer demand.

By tracking these metrics, sales teams can gain insights into the effectiveness of their data foundations, and can identify areas for improvement. For example, if forecast accuracy is low, it may indicate that the data is not accurate or complete, or that the forecasting model is not effective. By addressing these issues, sales teams can improve their forecast accuracy and drive better outcomes.

What this does not do, or when it is the wrong choice

While having accurate and complete data foundations is essential for sales teams, it is not a magic solution that will solve all problems. It will not, for example, automatically generate leads or close sales. It will also not replace the need for skilled and experienced sales teams, who can use the data to drive better outcomes. If you are looking for more information on how to get the most out of your data foundations, please contact us.