Watson Analytics Use Case for Marketing: Driving the Success of your next Marketing Promotion Campaign

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Watson Analytics Use Case for Marketing: Driving the Success of your next Marketing Promotion Campaign

Quick access to the right insights between your marketing promotions and resulting sales has been elusive for most marketers—unless you happen to be a data scientist or business analyst. Now, with the power of self-service analytics and smart data discovery in Watson Analytics everyone can harness their inner data scientist.

In this use case I’m developing a marketing campaign for a fast food restaurant franchise. We have been experimenting with the roll out of a new menu item and we just completed a 4-week pilot promotion across different store locations and markets.


Now I need to analyze the results of the promotions and find the connections between the campaign and the related sales.  How did the promotion do during the different weeks? Did the promotion do better in one market or store location compared to others?

Finding the right marketing promotion is a challenge, but when done right, it can lead to a whole new audience of hungry customers walking through your doors. Follow this use case to see how Watson Analytics can help you find the most important insights from your customer data so you can implement the right promotion plan and draw in the customers and sales you are looking for.

See the video version of this use case here: https://www.youtube.com/watch?v=Urur84JY-h4

STEP 1: Add the sample data

This example is based on the “Analyze Test Market Campaigns“ sample data available directly within Watson Analytics. To get started, add the sample data to your Watson Analytics environment.

  1. On the Watson Analytics > Data home page, tap New data.
  2. On the Import tab, tap Sample Data.
  3. Select Analyze Test Market Campaigns and tap Import.


You can also find out more about this data set and download a local version of it here:

SAMPLE DATA: Marketing Campaign, Promotion Effectiveness – Fast Food Chain


After the data is loaded, it appears as a data set tile in your Personal folder.


STEP 2: Ask questions to explore your sales and promotions data

Asking questions about your data is the key step to diving in, exploring and visualizing your data.

To see how the promotion campaign went, I start by asking some questions about the data just using plain language questions based on the column titles in the data – no SQL or complex queries needed here.
For example, I enter the question:  “Show me Sales by Week”.


Right away, Watson Analytics does some of the thinking for me (that’s the cognitive part) and offers a number of starting points based on the Sales and Week columns.  This helps to lighten the cognitive load on my own brain while allowing me to interact with the data in a more natural way.

I decide to look at the Sales by Week bar chart suggestion, but there’s not too much there – sales looks pretty flat across each week. That’s ok, because I’m just iterating and exploring here. Let’s try something different.


From here I have a couple different paths I could take. I could look at the discoveries listed on right side, change things in the current visualization or ask a new question.

Let’s stay here and change the current visualization using the interactive title to change the fields in the visualization. How about sales by promotions?

I tap on Week and change the column to Promotion. The results are a little better here and I can start to see some interesting results between the different promotions.


Using the interactive title I can quickly iterate through different visualizations to explore different combinations of fields.

Now I try visualizing sales by these other fields:

Sales by Market Size

Sales by MarketID

Sales by Age of Store

TIP: Instead of over-writing the same visualization and tab, use the tab copy feature to duplicate the current visualization on a new tab and then change the field there.


Step 3: Customize your visualizations to take a deeper look

After asking questions and using the suggested visualizations, I start building some of my own custom visualizations to explore deeper into the data. I’m interested in how the three different promotions breakdown between the different market sizes and market IDs, so I try the following combinations:

Sales by MarketSize with Promotion set as the Color by option.


Sales across the 10 different market IDs with Promotion set as the Color by option.


Multiplier option: Then I try the multiplier option with a packed bubble visualization to plot Market ID (bubble label), Sales (bubble size) and average Age of Store (bubble color intensity) across each of the three Promotions.

In this case, promotion 3 and market ID 3 stand out with the highest sales. The lighter color intensity of the bubble also shows that this combination has a lower average store age. I might want to think about evaluating and possibly updating some of my older stores to increase sales.


STEP 4: Use predictive analytics to find the top drivers of sales

OK, so I spent some time exploring and visualizing. Now it’s time to run some predictive analytics on the data to see how Watson Analytics can tell me which parts of the promotion were the main drivers of sales. Was it market size?  Store location? Or something else? Let’s see.

I ask a new question: “What drives Sales?”

The resulting spiral diagram shows the predictive insights for the top drivers of sales during the promotions. Watson Analytics also displays a table of the top results. I scroll the list to see the top one and two-field combinations that impact sales.


Scrolling through the list of drivers, I see the top single fields that impact sales:

  • MarketSize
  • MarketID
  • LocationID


Here are the top two-field combinations that impact sales:

  • MarketSize and MarketID
  • LocationID and MarketSize
  • Promotion and MarketSize

Let’s take a closer look at these highlights to see the relationship between market size, market ID, promotion and sales.

Looking at the impact of MarketSize and MarketID on sales, I can see that small and large market size have the highest sales overall for market ID 3 and lower.


A certain set of location IDs (405 and below) in the large market size have the highest amount of sales overall.


Finally, looking at promotions and market size, I see that these drive the most sales when promotion 1 and 3 were used in the large market size stores.


STEP 5: Build a dashboard and share your insights

Now that I’ve explored the data and ran a predictive model, I can pull all that together in a dashboard to communicate the results with others.

The Display builder gives me access to all of the visualizations and discoveries I found along the way. I browse and drag the key visualizations onto the canvas to create the following dashboard.


TIP: Need another visualization? No problem, add one on-the-fly using the Add discovery button.


To share these insights with my team, I simply click the Share icon. I can email or download the dashboard as different file types. I can also post a tweet about the dashboard or share it as a link with other users in my Watson Analytics account.


So just by starting with some simple, plain language questions I was able to explore and visualize my marketing campaign data to find valuable insights between the promotions and the sales that were generated. I can now use these insights and visualizations to drive my next marketing campaign to even more impressive returns by using what I learned right here in Watson Analytics.

Now that you’ve seen the power of Watson Analytics in action, try using your own data sets and continue exploring the capabilities of Watson Analytics for your next marketing campaign.

If you haven’t already, you can register and learn even more about the free version of IBM Watson Analytics here.