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Home > Insights Premium > ADD-ON: DEMAND PLANNING, Hourly + Day of the Week
ADD-ON: DEMAND PLANNING, Hourly + Day of the Week
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To access the DEMAND PLANNING, Day of the Week dashboard, expand the ADD-ON: DEMAND PLANNING module, and select the Day of the Week link

When / How to use this dashboard

This dashboard is meant to help you analyze hourly trends within the market. Hourly data is collected from transaction data with sold hour identifying the hour in which the transaction occurred. This dashboard can help you understand consumer trends within a market and plan resources accordingly. By identifying times in which store sales are more likely or less likely, you can target consumers, plan potential vendor days, and know which days and times that you need to have products available.

Use the filters on the top of this dashboard to select from the categories in your subscription. By default the Segment and Brand filters are set to ‘All’.

Cannabis Market Sales by Sales (Units) vs. Filter Selection Sales (Units) By Hour and Day
In the image below, we’re comparing the total market on the left to the Topicals market on the right. Notice the hours of the day along the left side of the maps, and the days of the week along the bottom. In this case we see activity for Topicals earlier in the day on Friday, and brighter activity on Saturday compared to the overall market.

The darker purple areas represent times in which a lower % of sales (units) occur within a week. By comparing these tiles, you can compare the % of Sales (Units) between times to understand how demand for your filter selection differs to the total market. Use these heatmaps to understand when your filter selection products are purchased, when to ensure appropriate stock levels to meet peak weekday demand, and compare these to the total market.

Index of Hourly Sales (Units)
This graph is meant to give a highly visual picture of your Filter Selection sales compared to total market sales. The metric is [% of Filter Selection Sales]:[% of Total Market Sales].

Example: 11:00 AM on Monday, Topicals sees 1.2% of its total weekly sales compared; the Rest of the Market sees .8% of it’s total weekly sales. The index is 144 (1.2/.8). This data is for Washington, and the date range is from January of 2017 through June of 2020. Notice the red arrow indicating this deep green-colored hour of the day.

Interpretation: Numbers over 100 are the days and times where your selected products see more sales than the market. These are the times where store sales are more likely to be to your Filter Selection and are colored in darker greens.
Numbers under 100 are the days and times where Filter Selection products sees less sales than the market. These are times where store sales are less likely to be to the Filter Selection products and are colored in red hues. Use these visualizations for determining the best time to engage with your customers in stores.

% of Sales (Units) Compared to Total Market by Hour
This graph compares sales activity by hour of the day, with the darker colored line indicating our filter selection and the lighter colored line the overall market. In this and the following graph our selection is Topicals. We see that until 3:00 PM, Topicals are ahead of the overall market roughly 62% of our Topicals sales will be completed by this time on a typical day.

% of Sales (Units Compared to Total Market by Weekday
This graph also compares our filter selection to the rest of the market. We’ll certainly want to have our shelves fully stocked and our budtenders well prepared for customers to buy Topicals on Friday and especially Saturday. Notice the days of the week along the bottom of the graph.

Related Content:

  • Period Over Period will help you understand seasonal trends across longer time frames.
  • Discounts - Demand Planning will show whether filtered products sales are driven by discounts at different times of the day and week.
  • The Holiday Demand Planning dashboard will help you compare a number of days leading up to and optionally including a holiday to the average of those same days over the previous 4 weeks.
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