Data visualization tools have grown in popularity in the recent years. The market is flooded with business analytics and data visualization tools.
In this article, we will talk about Tableau.
Tableau is one of the leading platforms used by the majority of the top 100 retailers and more than 7,000 retail and consumer goods companies globally.
The platform helps business and organizational leaders visualize and understand data, and create actionable insights for better decision making. Tableau does this by providing easy ways of exploring data, building dashboards, and performing ad hoc analyses with a few clicks.
Through the Tableau retail dashboards, you can visualize your data and get a broader understanding of your business no matter where you are in your data analytics journey. Dashboards also give you the business intelligence you need to create a massive impact on your organization and gain a competitive edge.
We’ve curated a collection of Tableau retail dashboard examples so that you can learn how to use visual analytics for competitive advantage, and test run them to experience the power of visual analytics for yourself.
Best Tableau Retail Dashboard Examples
1. Store Operations
Despite the mobile world being a big influence over the retail operations, physical retail stores still account for a large percentage of retail sales, making them critical for success.
Store operation dashboards for retail businesses can deliver rich and valuable insights to region or store managers, and/or the corporate office about the business’ performance and execution. These dashboards give managers the ability to benchmark their stores’ performance better in a region against traffic, sales, average transaction value, year over year growth, and units per transaction.
Managers can also drill down to specific and unique store-level operations data such as weekend vs weekday sales, compare space productivity in the store, performance of the product department, and evaluate top-selling brands.
The store operations Tableau retail dashboard shows an overview of operations with trends in sales and growth in sales, customer traffic and conversion rates, and the average size of transactions. This gives managers the ability to compare their multiple stores versus their peers in their specific region to identify underperforming stores quickly.
The dashboard and visualizations show:
- Key performance indexes with current values of key retail metrics
- A graphical representation of the conversion rate and performances in regards to conversion
- A quick view of daily sales for their stores to compare sales effectiveness around seasons, events, holidays, weekdays, or weekends
- Sales distribution for weekday vs weekend to ensure you use the correct mix of labor used for peak demand
2. Product Availability
One of the biggest challenges retail stores face today is stock level product availability. This entails having the right product at the right stock levels in the right stores for the customers to find and purchase.
If customers can’t buy the product they want because of incorrect inventory levels or being out of stock, the retail stores measure this as lost sales.
Supermarket customers in particular want to find everything under one roof whenever they visit a store to complete all their shopping in one store.
When this isn’t possible, the retail store not only measures lost sales but also risks losing out on the whole basket and any future baskets they would have received from the one basket.
The supermarket and grocery stores sector loses out on 5 percent of distribution points with an average of 95 percent product availability. This 5 percent can account for billions in lost sales.
Retailers typically measure and analyze the product availability in percentages, though this doesn’t highlight the issue as accurately as it should be when measured by the missed distribution points.
Consequently, retailers end up wasting resources in fixing low sales products with low percentage availability or low distribution, instead of finding the products and stores that deliver the biggest impact possible.
With this Tableau retail dashboard, retailers can solve the problem of having the right product, inventory levels, and stores as it helps them identify gaps in efficiency and drill into the details interactively.
It also features a color palette that draws the user’s attention quickly to the biggest problems so that the retailer can interact to drill into the insights shown.
Some of the insights you’ll get from this retail dashboard include:
- Data for different categories
- Worst problems by product
- Marks sized on lost sales in sample currency
- Worst offenders to filter store regions and product to take action with
- Stores that need more stock
- Last seven days of availability
- Product details with low availability
You can also collaborate with your team and/or export the products to a .CSV file. This file can be shared and/or sent to any relevant team for further action.
3. Merchandising assortment
In order to make profitable decisions around the products customers want, category managers and merchandisers need a quick way of analyzing the demand data.
They can use a retail dashboard to analyze the demand data by sales and profit margin performance by sub-categories, departments, or brands.
This merchandising assortment retail dashboard from Tableau allows managers to:
- Get a quick overview of relative sales performance of different categories and their sub-categories including those that can contribute a huge percentage of sales
- See the share of national and private label brands, plus richer insights on attributes like color, shape, product type, and style that can lead to higher conversion.
- See outliers and large volume sales items whose costs can be re-negotiated with the vendor
- Shift underperforming items to other store areas marked down for clearance
- See seasonality trends
- Identify and focus on top products with the most revenue
All these demand data can be used in real-time by managers for decision making.
4. Waste Manager/Optimization
Data visualization can help identify food waste on fresh products that are either reduced in price or thrown away owing to expiring shelf life. Waste costs retailers a lot of money in real revenues, which is why they would want to reduce any waste and save on them to impact on profits.
Corporate responsibility and sustainability also place huge pressure on companies to reduce waste, especially for retailers and quick-serve restaurants (QSRs).
Thankfully, it’s possible to properly identify wasteful products and eliminate the hassles of consuming large text files in spreadsheets. Retailers can look at waste at the store level instead of in aggregation so they can take appropriate action.
By identifying the problems in specific stores where there’s high wastage, retailers can take corrective action such as changing product forecasts, facings, and delisting or removing them from the menu completely. All these measures can impact on the reduction of waste, but also cause customer churn.
This waste manager/optimization retail dashboard from Tableau shows the following details:
- Products that are selling
- Main cause of the wastage problem
- Break down by store cluster to identify if the issue is in one or multiple store types
- Consistency of the problem over time, when promoted, or in relation to specific events
- Retail stores can use the Waste Manager dashboard to interact with data, see stores and clusters that drive waste. As stores are selected, it’s possible to export the data or share it with the merchandising team or vendor.
The company can then make choices such as living with waste so that customers aren’t affected, removing the product from allocation at the risk of losing or annoying customers, or enticing customers to pick ready-made items.
Other choices that can be made include reducing stock levels to low affluence stores, lower prices, or engineer costs down while maintaining margins, or reducing pack/case quantities.
5. Retail Scorecard
A retail scorecard shows all the key performance index screens in one place because retail data tends to be siloed information that is hard to access, analyze, or make actionable.
Top management can see the entire business across functions without navigating to a different document or software.
The dashboard shows high-level cards with snapshots of each KPI, and when accessed, the manager can access a drill-down dashboard developed specifically for that function.
This way, managers can view the business health with detailed action items, and don’t have to have much expertise in using Tableau Desktop to achieve this. The dashboard can be designed and/or maintained by any client team though.
With such automated data analytics, managers get more time to focus on the bottom line. The system is easy and simple to build and can be applied to all retail scenarios with speed-to-insight in mind.
6. Weather Response Predicted Demand
A weather response predicted demand dashboard enables retailers to predict extreme product demand spikes, including the locations that will be impacted and the specific products and quantities.
Retailers have to deal with digitalization, changing consumers, and increased competition. However, extreme weather events and the response to these events are important to consider.
This Tableau retail dashboard enables retailers to funnel new consumer demands into their supply chain processes and execute faster in times of need.
The dashboard is powered by machine learning, which makes it analyze and distill insights quickly from more than 73 billion historical NOAA weather data points, psychographics, inventory history, regional demographics, and retail sales history.
This way, the retailers can be equipped with concise snapshots of the demand to anticipate during emergency weather events including tornadoes, hurricanes, or floods among others.
Retailers can use the on-demand weather data alongside inventory data and get a clear summary of predicted and baseline demand, on-hand inventory, remaining gaps in needed orders, and orders en route. This enables them to allocate inventory and resources well to ensure supplies are delivered at the right time and to the right place.
Larger retailers with store footprints across the nation or region can drill down the data by region, the timeframe to zero in on demand at the SKU, product category, day levels, and store levels. This allows them to get data for quicker decision making and mobilization of the next steps.
The data is also useful in accelerating emergency response and recovery efforts so that customers receive help when they need it the most.
7. Retail Store Heat Mapping
Retail store heat-mapping also improves sales where grid-based and/or numbers-only sales flash reports could not do. Modern retailers work with visual analytics to get more insights within their physical store spaces.
With store heat-mapping analytics, retailers can understand a lot about physical store layout and performance without sending out teams to the field to each store and wasting valuable time.
Heat maps show the store layouts in an optimized manger without the previous manual guesswork. These maps leverage industry-standard mapping technology and read overlays analysis and store CAD maps such as:
- Product locator services
- Sales per square foot
- Seasonal reset location and performance
- Planogram compliance
- Sales velocity
- Shelf/fixture profitability
8. Regional Manager Dashboard
As a regional manager, one of your priorities is to drive better decision making based on the regional data and analytics you receive daily, weekly, monthly, quarterly, bi-annually, and annually.
Such reports allow you to derive operational insights from the point of sale databases, which can inform decision making for your region and organization as a whole.
This Tableau regional manager dashboard allows analysts to build guided visual analytics that are simple, easy, and quick to digest. They also broadly distribute data over large retail networks.
With the written analytics in this dashboard, it’s possible to explain the context of the visualizations and make sure that information is consumed consistently with the goals and strategy given by the leadership.
The dashboard combines visual analytics and written explanations to reduce the amount of training needed for interpreting the information.
Whether you are sharing analytics on inventory with the supply chain team or distributing KPIs for sales to physical stores, you can gain a lot from simply creating a culture that’s driven by data.
The regional manager dashboard shows:
- The bigger picture of nationwide sales and merchandising
- What is going on along with a description of the broader context of why it happens and its relevance
- Visual and written analytics for easy exploration of data in meaningful ways thereby enabling users to efficiently identify events, trends, and KPIs without misinterpreting the data
- Role-based insights for complete context for managers to drive action
- Store performance in the context of the greater enterprise
With all this data, regional managers can drill into the underperforming stores while focusing on the products and departments that need attention.
9. Marketing Mix Models and ROI Engines
For many organizations, it’s hard to combine marketing performance measurement and sales activity. In fact, these two metrics are usually analyzed separately.
However, measuring actual sales performance and marketing spend together can help the organizations identify the marketing campaigns that deliver impact based on direct sales.
This dashboard applies machine learning to the underlying combined datasets thereby helping businesses optimize the marketing mix to realize maximum return on investment (ROI).
By combining the two distinct and often siloed sources of data, it’s possible to identify the effectiveness of sales and marketing. Machine learning can be used to maximize the marketing mix and drive ROI so that the campaigns have a better impact on increasing sales revenues.
10. Marketing Consumer Segmentation
Retailers need to analyze, scrutinize, justify, and optimize every dollar they spend. Not only that, but they also need to know who their customer is, their likes and dislikes, wants, and their location.
There’s huge pressure right now around marketing budgets, making it important and essential to optimize spending and know which customer segments to prioritize.
With granular insights from Tableau’s marketing consumer segmentation dashboard, you can improve the quality of decision making.
Marketing executives will be able to measure KPIs by sales impact, designated market area (DMA), and the dominant cluster type. This dashboard has no guesswork but offers a hyper-focus on key metrics that ensure marketing gets to maximize every dollar spent, expand key customer segments, or win back consumers whose loyalty may be wavering.
11. Online Shopping Cart Analysis
The online shopping cart analysis helps businesses analyze how marketing campaigns, traffic sources, or behavior of specific customer segments among other variables affect the customers’ shopping cart habits.
This dashboard describes how often customers remove items from their carts, and why they do so in order to understand their behavior. It also shows average shopping cart removals plus the standard deviation visual band, so that users can focus on true outliers.
You also get to see information such as:
- Upward trends of removals
- Identify potential issues that could lead to strategic price discussions and analysis, or whether you’ll opt to dropship from a vendor where warehouse inventory is low thereby increasing delivery days
- Customers deciding how long it takes to get the product and opt for alternatives
In a turbulent environment where it’s necessary to have customer-centric experiences and customers’ behavior is always changing, savvy retailers need an approach driven by data to help them make more informed decisions.
These 11 Tableau retail dashboard examples combine and use data from various sources to help retailers view and understand data differently while using the strategic competitive advantage that leverages granular, actionable insights to build massive value.
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