Promotion Analysis (Customer Counts, Products, and Stores)

 

What is the Promotion Analysis module?

Utilize these three dashboards to analyze how well a campaign did by comparing store groups to one another or by comparing product groups to one another.
 

Why would you want this?

To get a true, data-supported, answer to determine if a marketing campaign was successful or not.
 

What questions can you answer with this?

• My brand ran a campaign across a few stores, was there a sales lift in those stores compared to other stores?
• My brand ran a campaign on a group of products across all stores, was there a lift in sales with those products compared to a group of other products not in the campaign?
• My brand ran a campaign with the goal to increase new customers, was it effective?
  
   

Example Analysis For Customer Counts

We ran a campaign in Dockside stores in Seattle during the first two weeks in January and we want to compare it to The Reef stores in Seattle. The goal of this campaign was to increase the overall customer count.

The filters will be set up as the following:

 

This graph will be the meat of this analysis. It is easy to see that the campaign failed at increasing the number of customers in our experiment stores. In fact, the control stores even increased during the campaign. Based on this data, the campaign was not successful and we shouldn’t run this again.

 

 

To determine if this campaign had any impact on the customers that did shop simply scroll down to the last two charts.

 

 

 

 

Example Analysis For Products

We ran a campaign on Lemon Garlic with the goal to increase units sold. We find that this strain sells similar to Sour Tsunami. We will use the sales for Sour Tsunami and compare Lemon Garlic to that using the Control Products field.

 

The filters will be set up as the following:
 

 

The lift table shows that there were negative sales during October when we ran the promotion.

 

Further down the dashboard, this graph shows that the experiment products, Lemon Garlic, have been consistently decreasing in sales through the pre-campaign, campaign, and post-campaign period. This trend is apparent in Sour Tsunami as well. Unfortunately, this particular campaign did not increase unit sales for Lemon Garlic.

 

 

 

 

Example Analysis For Stores

We ran a campaign at the beginning of the year at the Reef stores around Seattle and maintained business as usual at Dockside stores with the goal to increase basket

 

The filters should be set up as the following:

 

This table shows that the experiment stores (Reef stores) had a decent lift, but the lift is comparable to the lift from the control stores (Dockside). This is an indication that this campaign was not as successful as we would have wanted. However, the post-campaign lift was high. This raises some questions as to what promotions were running in the experiment stores (Reed stores) after the campaign concluded.

 

 

Finally, we can review discounting in the experiment stores during the campaign compared to the prior period and post period. We can also compare the experiment stores to the control stores.