Forecast Settings: Overview And Setup Guide

The Forecast settings page is where you control how Vividly calculates your future sales projections.

You can access it by clicking your profile icon (top-right) → Account Settings → Forecast tab.

This page has four main sections:

• Forecast Parameters — controls which promotions influence your forecast

• Averaged Weeks — controls how much historical data is used to project the future

• Seasonality Buckets — groups products and customers with similar seasonal patterns

• Seasonality Indexes — sets the weekly multiplier values for each bucket 

Forecast Parameters: Which Promotions Affect Your Forecast?

1. Go to Account Settings → Forecast tab → Forecast Parameters tab.

2. Review the list of fund types shown — these are all promotion types with Lift as a required field.

3. For each fund type, check or uncheck Include in Forecast:

   1. Checked = promotions of this type will add lift to the forecast.

   2. Unchecked = promotions of this type won't add incremental volume (commonly used for EDLP).

4. For each included fund type, select which date range Vividly should use to display Lift and the Promotion indicator in your forecast:

   1. Buy-in: lift appears during the weeks distributors are purchasing the product.

   2. In-Store: lift appears during the weeks the promotion is active at the retailer shelf. This is the most common selection, as it aligns with when consumers are actually buying.

   3. Scan-Back: lift appears during the weeks the scan-back redemption period runs.

In short, this setting answers the question "when should this promotion show up in my forecast?" Choose the fund type that will be use as date range reference that best reflects the moment you consider the promotional activity to be happening.

Averaged Weeks: How Much History Is Used To Project The Future?

This setting controls how many past weeks Vividly averages when projecting future velocity and store counts. After generating a new forecast, all future weeks will use this average as their starting point.

1. Go to Account Settings → Forecast tab.

2. In the Averaged Weeks section, select a lookback period for each customer type in the Averaged Weeks column (4, 8, 12, 16, 26, or 52 weeks). 

Selecting a certain amount of weeks can depend on different variables that you would need to evaluate according to your retailers and AOBs behavior. Here are some guidelines to consider which amount of weeks to select. 

Seasonality: Account For Predictable Sales Fluctuations

Seasonality lets you adjust your baseline forecast to reflect predictable patterns throughout the year — like higher beverage sales in summer or increased snack sales around the holidays. It works through two components:

• Seasonality Buckets — groups of products and customers that share the same seasonal pattern.

• Seasonality Indexes — weekly multiplier values applied to each bucket (e.g., 1.2 = 20% above baseline; 0.8 = 20% below baseline).

Seasonality only affects baseline volume. Promotional lift is calculated separately on top of it. Set up buckets first, then assign index values. 

Step 1: Create Seasonality Buckets

1. Go to Account Settings → Forecast tab → Seasonality Buckets tab.

2. Click "+ Add Seasonality Bucket".

3. Fill in the following fields and click "Save":

   1. Bucket name: A descriptive label for the group (e.g., "Holiday Snacks," "Summer Beverages").

   2. Customers: Select which customers (retailers or distributors) this bucket applies to.

   3. Products: Select which products belong to this bucket.

Note: Each product-customer combination can only belong to one bucket. Overlapping assignments will trigger an error.

Step 2: Set Up Seasonality Indexes

1. Go to Account Settings → Forecast tab → Seasonality Buckets tab.

2. Select the year you want to configure by clicking the Select Year dropdown.

3. Click any index field and enter the multiplier value for that week (1.0 = no change, above 1.0 = uplift, below 1.0 = decline).

Additional actions:

• Copy indexes: To copy last year's data as a starting point, click "Copy Previous Year Seasonality".

• Export indexes: To download your seasonality data, click "Download Seasonality". 

Best Practices

• Start with a small number of broad buckets and only get more granular over time once you've confirmed different segments have meaningfully different patterns.

• Review and update seasonality once a year using the year-over-year copy feature — avoid frequent mid-year changes.

• Never use seasonality indexes to compensate for data quality issues or planned promotional activity. 

• When in doubt about what amount of Averaged Weeks you should set up, start at 12–16 weeks for Retailers and 16–26 weeks for AOBs, then adjust based on how closely the forecast aligns with your actual data over time.

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