Problem Statement:

Our current digital marketing campaign requires a thorough analysis to assess its effectiveness and identify areas for improvement. We need to determine at what point our marketing efforts have reached saturation, where we might be overleveraged in certain aspects, and understand the likelihood of achieving more than 8 conversions per day. By gaining these insights, we aim to optimize our marketing strategies and drive better results.

Analysis:

Overleveraged:

Omni-Channel : Users may be introduced and converted on different channels.

Nearly 91% of our traffic comes from organic search. Paid Marketing is 24% of Conversions but 5% of Traffic. We need to understand users' conversion funnel. We are overleveraged.

Saturation:

Analysing Paid channel data, we created 3 new fields:

Session Sum: Running total of Sessions. (First value is equal to first record session. From second record onwards, Session Sum = SUM($E$1:E2))

Conversion Sum: Running total of Conversions. (First value is equal to first record session. From second record onwards, Conversion Sum = SUM($I$1:I2))

Slope: First value is 0. From second record onwards, Slope =SLOPE($I$2:I3,$E$2:E3)

The growth of traffic continued while the conversions declined. Our campaign traffic took more than 3 months to find audience. We should speak with the advertising campaign team to understand the optimizations during this period. Our campaign peaked between May and August. With August being an inflection point.

Relationship between Traffic and Conversions:

Slope: A negative or flat slope indicates the campaign saturation.

If the learning during the optimization can be more readily employed, we can increase the productive period of the campaign. Paid traffic also showed negative slope and saturation as time passed.

More than 8 Conversions a Day is Unlikely:

Average conversion 3.8 daily, Max conversion are 16

For Reverse Probability, sort conversions from smallest to largest. There are 1119 conversion count. Create an Index column, Frequency =B2/1119, Reverse = 1 - C2

The distribution of conversion indicate that most of the conversions will be under 7 conversions daily. We can see this in the histogram distribution and cumulative distribution plot. There's 14% probability to get more than 8 conversions per day.

Recommendations:

1. Omni-Channel Evaluation: It is imperative to thoroughly assess the customer journey from introduction to conversion. Utilizing our analytics attribution models will enable us to gain insights into whether users are being introduced to the brand and converting through a single channel.
2. Addressing Overleverage: We should exercise caution regarding overreliance on organic traffic, particularly when targeting generic keywords. Sudden changes in the Google algorithm could lead to traffic losses. Employing Google Search Console will aid in comprehending keyword intent and mitigating potential risks.
3. Optimization Analysis: It is essential to gain a comprehensive understanding of the optimization strategies implemented during the initial three months of the campaign. Reproducing successful tactics will extend the campaign's productive period, fostering continued effectiveness.