"Unleashing the Power of Data Analytics: A 6-Step Approach to Solving Complex Digital Marketing Challenges (In Business Context)"

Hello there! If you're reading this, chances are you're on a mission to help your company overcome its challenges and achieve its goals. You may feel overwhelmed by the complexity of your issues but fear not - with the right approach (Using Data Analytics), you can uncover the root causes of your company's problems and develop practical solutions.

One of the essential aspects of uncovering the root causes of your company's problems is the use of data analytics. Gathering and analyzing data from various sources can gain valuable insights into your company's processes, customers, and market trends.

With the right Data Analytics tools and methods, you can identify patterns, trends, and anomalies that may contribute to your company's challenges and improve financials (either reduce cost or increase overall revenue).

The following are the six steps I adopted (from the HBR case) to solve a business problem.

?

1. Learn the root causes of the company's problems: The cost per acquisition (CPA) for a client's Facebook ad campaign has increased by 20% over the past month, despite the same ad settings. We identified the key metrics that need to be tracked: CPA, click-through rates (CTR), conversion rates, ad spend for previous month.
2. Consider prior research in relevant domains and implications: We looked at the client's historical ad performance data to identify any trends or patterns that could explain the recent increase in CPA. In the context of the example provided, we were trying to understand why the client's cost per acquisition (CPA) had increased. We first looked at the client's historical ad performance data to do this. This involved gathering data on ad impressions, clicks, conversions, and metrics from the client's past ad campaigns. Once we had this data, we analyzed it to identify any trends or patterns that could help explain the recent increase in CPA. For example, we also looked for changes in ad creative, targeting settings, or bidding strategies that could have impacted CPA.
3. Modelling problems' solutions with a suite of algorithms: We used classification modelling to model the situation, where the focus was predicting outcomes such as whether a customer will convert or not based on specific variables such as demographics, time of the ad being shown, ad creative, type of ad (Text based, hook based, Image based, carousel, video) or browsing behaviour.?
4. Gather necessary data: We gathered data on the Facebook ad campaign, including ad impressions, clicks, conversions, targeting settings, and bidding strategies, by analyzing the data from the client's Facebook Ads Manager account. We also collected data on the client's target audience, including demographics, interests, and behaviours, by conducting surveys and analyzing social media analytics tools. To ensure the accuracy and validity of the data, we used multiple data sources, including primary sources such as surveys and secondary sources such as social media analytics tools(SEMRush).?
5. Analyse and Inquire into the data (source of data, whether there were outliers, how did the outlier affect the overall situation?): After gathering and verifying the necessary data, we analyzed and inquired into the data to identify any correlations or patterns that could explain the recent increase in CPA. We also examined the data for any outliers or anomalies that could have affected the results. Thankfully, there were none.
6. Present (Narrate the tale and act on the findings) (Narrate the story and act on the results) (Recount the events and take appropriate measures): During the data analysis stage, We discovered that specific ad targeting settings were leading to a higher CPA. Specifically, they found that ads targeting audiences outside of the client's desired age range and geographic location were generating more clicks but fewer conversions, resulting in a higher CPA. To address this issue, We recommended adjusting the targeting settings to focus on the client's core audience and exclude any demographics that were not converting well.

We also conducted A/B testing to determine which ad creatives were most effective at driving conversions and lowering CPA. They tested different images, ad copy, and call-to-action and analyzed the performance metrics to identify which variations resulted in the highest conversion rates and lowest CPAs. Based on this analysis, we recommended specific ad creatives to the client and suggested adjusting the targeting settings to show these ads to the most relevant audience.

( To apply logistic regression in this case, we first identified the variables that they believed could influence the CPA and conversion rates, such as ad creative, targeting settings, and bidding strategies. They then collected data on the performance of these variables and used it to build a logistic regression model.

The logistic regression model allowed us to analyze the relationship between the independent variables and the probability of a conversion occurring. By looking at the coefficients of the independent variables in the model, we could determine which variables were most strongly associated with conversions and low CPA.

For example, if the coefficient of the ad creative variable were positive and statistically significant, it would indicate that specific ad creatives were more effective at driving conversions and lowering CPA. On the other hand, if the coefficient of the targeting settings variable were negative and statistically significant, it would suggest that certain targeting strategies contributed to higher CPA and lower conversion rates.

We identified the key drivers of CPA and conversion rates using logistic regression. We used this information to make informed recommendations to the client on optimising their Facebook ad campaign.)

As a result of these changes, the client saw a significant improvement in ad performance. The CPA decreased by 20% within the first month, while the conversion rate increased by 15%. The client's ad spend remained consistent, but they were able to drive more conversions at a lower cost. We continued to monitor ad performance metrics and refine their strategies as necessary, resulting in ongoing improvements in ad performance over time.

?In conclusion, data analytics is essential for understanding the root causes of your company's problems. Using data analytics tools and techniques, you can gather and analyze large amounts of data to uncover valuable insights and patterns that may not be immediately apparent.

The process of analyzing your company's challenges involves the following:

• Researching relevant domains.
• Gathering necessary data.
• Modelling problems with algorithms.
• Analyzing and inquiring into it.
• Presenting the findings.

With the insights gained from data analytics, you can identify the underlying causes of your company's challenges and develop practical solutions.

Data analytics can help you identify trends in customer behaviour, analyze any data, and improve decision-making processes. It is a powerful tool for comprehensively understanding your company's challenges and developing informed strategies for overcoming them.

In short, data analytics is a critical component of analyzing your company's challenges and achieving success. By harnessing the power of data analytics, you can uncover valuable insights and develop practical solutions to drive your company forward.

#dataanalysis #digitalmarketing #businessanalytics