Use Case

Identifying Consumer Trends & Patterns Faster with AI-Driven Analytics

For consumer goods companies, retailers, and ecommerce brands, success depends on their ability to identify and respond to shifting consumer trends, especially since customer behaviors and patterns change so quickly.

Monitoring metrics is crucial for consumer brands, who are asking questions such as:

  • How is sales trending across brands or sub-brands?
  • Why is revenue in store A less than revenue in-store B?
  • Which marketing channels caused a drop in product sales this year?

With Tellius, brands gain shopper insights and spot consumer behavior patterns from their data faster and easier than ever.

Where Traditional Analytics Approaches Fall Short

Even companies that have an arsenal of tools in their analytical tool kit still often crave more and more answers from their data, in a timely manner, in order to inform better decision making. For example, traditional BI Dashboards can tell you what happened in the past, not why things happened. To get to “the why,” data analysts endure the time-consuming task of manually slicing and dicing data, which can still lead to analytical blindspots because of the sheer volume of data to analyze. Traditional data science methods require skilled coders who work with fragmented tools in an inefficient workflow and generate findings that are not easily explained to business teams and decision-makers who need answers quickly.

Enabling Decision Intelligence with Guided Insights

When brands utilize AI-driven Decision Intelligence, they can more quickly and easily identify the most important contributors of changes to metrics, understand why metrics changed through root cause analysis, and identify target customers and marketing campaign attributes that will lead to desired outcomes. Decision intelligence applies statistical analysis, AI-driven automation and machine learning, with a natural language to help accelerate the process of turning data to improved decisions.

Enabling DI telliusLet’s take a deeper dive into how consumer brands can use decision intelligence to gain shopper insights faster. You can also watch our most recent CPG webinar on how CPG Brands identify consumer trends & patterns faster with AI-Driven Analytics in Tellius.

Goals For Analysis

In this use case, the goal is to explore key questions that help measure success. For example:

  • How are Sales trending across brands or sub-brands?
  • What are the top-performing brands in store x?
  • Why is revenue in store A less than revenue in store B?
  • How do sales compare between years?
  • What will sales look like across all brands & products in the near future?

Data From Multiple Sources

For this example, we’ll be using CPG Market share data spread across multiple sources. These sources include:

  • Sales Transactions
  • Market Research
  • Competition
  • Promotion
  • IRI/Nielson, and other 3rd party data

data from multiple sources

Exploring Data with Natural Language Search

Using the Natural Language Search capabilities, Tellius has the ability to ask questions of the data in plain English to give a best fit chart in return (in this example, Tellius has been asked to show dollar sales by brand with $ sales being the metric & brand being the dimension).

Exploring data

But let’s take it one step further and look at dollar sales by brand and sub-brand:

exploring data tellius

By drilling into the data, we now see where the largest drivers of sales volume are coming from by sub-brand within this stacked bar chart.

Let’s try something different now and look at a monthly dollar sales comparison chart between two stores.

comparison chart

Now, Tellius has shown us the comparison in sales volume between Food City & Target within the dataset. This allows us to understand where sales volume is highest over time and see that the trends are fairly consistent amongst the two stores. However, if any significant changes were discovered via anomaly highlighting from Tellius, you can dive into them deeper and leverage the insights feature to uncover a deeper level of understanding of the data. This allows the user to configure an insight to understand why a certain metric has changed over time.

metric insights

Uncovering “Why” with Guided Insights

From here, Tellius can show the top reasons for the change from the dataset and give a more clear picture of how these changes occurred. As we can see, Tellius breaks these changes down into the ‘What’ and the ‘Why’. By scrolling and exploring the ‘What’, we’re exploring what dimensions in the data changed the most in the time period where sales data went down. In this case, the plastic container volume is where we see a massive decrease in sales between one period and the next.  Guided insights

By now driving into the ‘Why’ we can take a deeper dive into the metrics that were correlated with the change the most, and see the correlations on a chart that allow you to dig a little deeper into the change drivers over time. In this example, we can see that dollar sales for people who only saw a display in the store were much lower than they previously were. With this insight, the store could increase in-store promotions to help drive those sales back to where they once were.  In store tellius

As you can see, guided insights allows users can ask questions in natural language and get the answers to their questions fast. Additionally, they can also click on points of interest (trend drivers) within their data visualizations and further explore from there. Tellius even learns what metrics you and your teams are interested in and proactively notifies you of changes within them in your insights feed. 

Forecasting Sales

The Tellius Predict feature for machine learning can kick off with one of two ways- Auto ML which lets Tellius run several algorithms to determine the best fit for your data/target for what you’re trying to predict, or Point & Click, which is intended for more experienced individuals in data platforms to customize models and train different types of models as well. This versatile approach to prediction is what makes Tellius such a user-friendly platform.

Forecasting salesSo let’s say you’re looking to get an idea of what to expect when it comes to sales volume moving forward. Here, we see a sales forecast that shows the total sales across all brands & products which additionally forecasts future sales to look like. This can be broken down by any dimension you would like (Day, Week, Month, etc).

Sales forecasting tellius

Conclusion

Now more than ever, the success of consumer goods companies, retailers, and e-commerce brands rely on the ability to make critical business decisions with more ease and more confidence. They need to enable more people to ask questions of their data, and more confidently make decisions on how they need to improve performance. Tellius gives organizations the flexibility to live query their data, perform live advanced analytics and machine learning – whether they are using a data warehouse or a data lake, or need to ingest data from multiple sources when the situation calls for it. We bring your team together for an all-in-one tool that gives your team an experience that takes insight-driven organizations to the next level, making it easier to make critical decisions faster and with more confidence than ever before.

tellius conclusion

Interested in honing the power of Decision Intelligence? Give Tellius a spin for free & reach out to us with what you think!

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