Diagnostic Analytics: The Art and Science of Data-Driven Problem Solving
What do true crime podcasts and diagnostic analytics have in common?
I promise this isn’t the start of a bad joke.
If you’re an armchair detective, like myself, then you’ll know the power, and lure, of a good true crime story. When listening, it’s easy to get lost in the specifics as you try to solve the crime before the narrator. You may consider different angles, evidence, or theories.
If only there was a way to do that for our biggest business pain points….
Enter: Diagnostic analytics!
Both true crime podcasts and diagnostic analytics approach a problem from different angles and use different methods and tools. While true crime podcasts use storytelling and journalism to explore evidence, diagnostic analytics applies statistical models and algorithms to analyze data and uncover insights for improving business performance.
Diagnostic analytics is transforming the way businesses operate.
According to McKinsey, companies that extensively use data analytics are 23 times more likely to acquire new customers and six times more likely to retain them. Talk about a reason to “like + follow”!
Let's dig deep and discover the secrets of diagnostic analytics!
What are Diagnostic Analytics?
Diagnostic analytics is a branch of data analytics that focuses on examining past data in order to identify the causes of specific events. It involves analyzing data to understand why something happened or to find patterns and relationships that may help explain a particular outcome.
In other words, diagnostic analytics is about examining data to gain insights into what has already happened, as opposed to predictive analytics which is about using data to make informed predictions about the future.
Diagnostic analytics can be used in a variety of industries and contexts, such as healthcare, finance, and marketing. By understanding what factors contributed to the outcome, businesses can then take targeted actions to improve performance and achieve better results in the future.
Why are diagnostic analytics important?
Diagnostic analytics is essential in marketing because it allows businesses to identify and understand problems in their marketing strategies. By analyzing data, businesses can uncover insights into consumer behavior and preferences, the effectiveness of their campaigns, and the performance of different marketing channels.
With this information, marketers can make informed decisions about how to optimize their strategies and improve ROI. Additionally, diagnostic analytics can help businesses identify and address any data quality issues or biases that may be impacting their marketing efforts. Overall, diagnostic analytics are key to data-driven marketing.
Example of diagnostic analytics:
A retail store analyzes its sales data to identify the reasons behind a recent decline in sales.
Business questions diagnostic analytics help with:
- Which products are selling poorly?
- Are there any trends in customer demographics or buying patterns?
- Are there any issues with the store's layout or merchandising?
- Are there any external factors, such as changes in the economy or increased competition, that may be affecting sales?
By analyzing sales data and answering these questions, the store can gain a deeper understanding of the factors contributing to the decline in sales and develop strategies to address them.
For example, the store may decide to adjust its product mix, redesign its store layout, or launch a new marketing campaign targeted at a specific customer segment. Without diagnostic analytics, the store would not have a clear understanding of the root causes of the problem and may not be able to effectively address it.
Are diagnostic analytics and marketing attribution the same thing?
Sorta, but no.
Diagnostic analytics looks only at past data to understand an outcome. It’s very focused on cause and effect. Marketing attribution, on the other hand, is a tool that is used as a part of diagnostic analytics. Marketing attribution is the process of identifying marketing channels and touchpoints that lead to an outcome.
How are diagnostic analytics different from predictive analytics?
The main difference between diagnostic analytics and predictive analytics is that diagnostic analytics focuses on understanding what happened in the past, while predictive analytics focuses on making predictions about the future. Diagnostic analytics and predictive analytics are, ultimately, two different types of analytics that serve different purposes.
For example, diagnostic analytics can be used to identify why sales decreased during a specific time period or why website traffic decreased after a website redesign predictive analytics can then be used to forecast future sales or customer behavior.
While diagnostic analytics is useful for identifying problems and their causes, predictive analytics can help organizations to anticipate future events and take proactive measures to address them.
How can you integrate diagnostic analytics and predictive analytics?
Short answer: Seer!
Integrating diagnostic analytics and predictive analytics can help organizations gain a more complete understanding of their data and make more informed decisions about the future. Here are a few ways to integrate these two types of analytics:
- Use diagnostic analytics to identify patterns and relationships in historical data that can be used to inform predictive models.
- Use predictive analytics to identify future scenarios that can be tested using diagnostic analytics.
- Use a combination of diagnostic and predictive analytics to monitor performance and make ongoing adjustments.
Not sure where to start or now to do any of that - don’t worry, we’ve got you covered!
Example of diagnostsic analytics + predictive analytics:
An example of using both diagnostic analytics and predictive analytics in marketing is to analyze the performance of a marketing campaign and use the insights gained to make predictions about future campaign success.
First, diagnostic analytics can be used to analyze the performance of a recent marketing campaign. This may involve looking at metrics such as click-through rates, conversion rates, and sales figures to identify what worked well and what did not. The goal is to understand what factors contributed to the success or failure of the campaign.
Once this data has been analyzed, predictive analytics can be used to make predictions about future campaign performance. For example, identifying patterns in customer behavior and preferences. This may involve analyzing data such as customer demographics, purchase history, and engagement with previous campaigns.
Using these insights, you can make predictions about which marketing campaigns are likely to be most effective in the future. For example, if the data suggests that customers in a certain demographic are more likely to respond to a specific type of campaign, the company can target those customers with similar campaigns in the future.
How does Seer use data analytics?
Seer’s Analytics & Insights team uses both diagnostic analytics and predictive analytics to optimize our client’s marketing efforts.
The Analytics & Insights team uses diagnostic analytics to conduct comprehensive website audits and identify areas for improvement. They analyze website data to determine which pages are performing well and which ones need to be optimized. Seer's team then works with their clients to implement changes that can improve website performance and increase conversions.
But wait! There’s more!
The Analytics & Insights team also uses predictive analytics to help clients make data-driven decisions about their marketing strategy by analyzing large data sets and identify patterns that can be used to make predictions about customer behavior and campaign success.
The Diagnostic Analytics Process
Diagnostic analytics is a process that involves identifying and analyzing data to diagnose problems and improve performance. This is a very simplified version of what Seer does when we analyze data.
Here are the steps in the diagnostic analytics process:
- Identifying the Problem: The first step in diagnostic analytics is identifying the problem that needs to be addressed. This could be a decline in sales, low website traffic, or a decrease in customer satisfaction. Defining the problem is critical because it determines what data needs to be collected and analyzed.
- Defining the problem is critical because it helps ensure that the analysis is focused and relevant. Without a clear understanding of the problem, the data collected and analyzed may not be useful.
- Collecting Data: Collect data that is relevant to the problem being addressed. This could include sales data, website traffic data, customer feedback, and more.
- Cleaning and Preparing Data: Once the data has been collected, it needs to be cleaned and prepared for analysis. This involves removing any irrelevant or duplicate data and formatting the data for analysis.
- Run the Data through Tools: At Seer we use tools like bigquery, powerbi, and Supernova to populate them into dashboards and visualizations that we use to find to insights. These use statistical analysis, data visualization, and scripts and algorithms to identify patterns or trends.
- Analyzing Data: The final step in the diagnostic analytics process, and the most magical one, is analyzing the data!
By following a structured process for collecting, cleaning, and analyzing data, we’re able to analyze data at scale to craft data-driven marketing strategies that lead to improved performance.
Business Applications for Diagnostic Analytics
The Analytics & Insights team at Seer has successfully implemented integrated analytics to solve various business problems for our clients. Some of the examples of business problems solved with integrated marketing analytics are:
- Improving E-commerce Conversion Rates: We used diagnostic analytics to identify the barriers that were preventing an e-commerce client from converting visitors into customers. By analyzing data on user behavior, they were able to identify the issues and provide recommendations that increased the client's conversion rate by 168%.
- Enhancing Paid Search Performance: A client was struggling to see the desired results from their paid search campaigns. By using integrated marketing analytics to identify the low-performing ad channels, and suggested changes that resulted in a 8% increase in click-through rate (CTR) and a 69% increase in conversions.
- Improving Organic Traffic: A client's website was experiencing a decline in organic traffic. The Analytics & Insights team used diagnostic analytics and predictive analytics to identify what triggered customers to make a purchase and the type of content that drove those conversions. By creating the correct content they were able to increase organic traffic by 264%.
The benefits of diagnostic analytics for businesses include:
- Improved decision-making: Diagnostic analytics provides businesses with valuable insights into their operations, enabling them to make informed decisions.
- Cost savings: By identifying and resolving issues, businesses can save money and improve their efficiency.
- Increased revenue: Diagnostic analytics can help businesses identify opportunities for growth and optimize their strategies to generate more revenue.
Challenges and Limitations of Diagnostic Analytics
.Here are some of the key challenges and limitations of diagnostic analytics:
- Data Quality Issues: If data is incomplete or inaccurate, it can lead to flawed conclusions and poor decision-making.
- Data Privacy and Security Concerns: It is important for businesses to take steps to protect their customers' data and comply with data protection regulations.
- Legal and Ethical Considerations: Another challenge of diagnostic analytics is ensuring that the analysis and resulting decisions are legal and ethical.
- Human Limitations: While diagnostic and predictive analytics both rely on advanced algorithms and technology, it requires skills to analyses and interpret correctly.
- Biases and Subjectivity in Analyzing Data: Human biases can be introduced into the analysis at various stages and can lead to inaccurate results and flawed conclusions.
This is where having a team like the Analytics & Insights team at Seer comes into play! Let’s chat.
The future of diagnostic analytics
As technology and data continue to evolve, the future of diagnostic analytics looks promising.
According to a report by MarketsandMarkets, the diagnostic analytics market is projected to grow from $7.8 billion in 2020 to $18.7 billion by 2025, at a compound annual growth rate (CAGR) of 19.8%. This growth is driven by the increasing demand for data-driven decision-making, the growing adoption of big data and advanced analytics tools, and the rising need to address data quality and governance issues.
The future of diagnostic analytics will likely involve more automation and integration with other data analytics processes, such as predictive analytics and prescriptive analytics. (Something that Seer is ahead of the curve on :wink wink:) This integration will allow for a more holistic approach to data analysis and decision-making allowing for increased efficiently.
Lastly, with the rise of artificial intelligence and machine learning, diagnostic analytics will likely become even more sophisticated and accurate, enabling businesses to gain deeper insights and make better decisions based on their data.
Conclusion
According to McKinsey, "analyzing and improving processes with diagnostic analytics can deliver cost savings of up to 30% in operational processes and increase revenue by up to 20% in marketing and sales processes."
It’s more important than ever to have a data-driven approach to your marketing strategy. That's where we come in. Our team is passionate about using diagnostic and predictive analytics to help businesses like yours drive growth and stay ahead of the competition.
By leveraging the power of data, we can help you make informed decisions, identify new opportunities, and optimize your marketing efforts for maximum ROI.
So why wait? Contact our team today and let's work together to navigate big data and emerge stronger than ever before.
