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Practical AI for partnership analytics (Advanced)

PracticalAIforpartnershipsNavigating Complex Partnership Data with AI



In the realm of partnerships, accessing timely and relevant insights can often be a challenging endeavor. Today, I'll share how a Python script I developed enabled a client to unlock new insights into their critical ecosystem metrics.

Background



Our client aimed to strategically prioritize partners and optimize their time allocation across a multitude of partnership opportunities. We focused on key metrics such as Average Annual Recurring Revenue (ARR), Win Rates (with and without a partner), the number of partners providing exactly one opportunity year after year, and the close rates for those opportunities. However, the data extracted from Salesforce reports was inconclusive, requiring significant cleanup to fill in blanks and refine the information.

Getting the Right Data with AI and Python


We used Python scripting, particularly the pandas library, to streamline our data analysis process. Here's a simplified overview of the steps we took:

1. Loading Data: Imported partnership and opportunity data from a CSV file containing relevant columns.
2. Filtering Data: Selected data for the last and current fiscal years to analyze recent trends.
3. Calculating Win Rates: Determined the success rate of opportunities with and without partners.
4. Identifying Key Partners: Pinpointed partners who consistently provided one opportunity per year and calculated their success rates.
5. Ranking Industries: Listed industries based on the number of opportunities from one-time partners.
6. Finding Top Contributors: Identified partners responsible for 80% of opportunities in each fiscal year.
7. Calculating Average ARR: Computed the average Annual Recurring Revenue from successful opportunities.
8. Reporting Results: Summarized findings, including win rates, key partners, and top industries.

This approach not only saved time and minimized errors but also equipped our client with valuable insights for informed decision-making in their partnership ecosystem.

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Want to know how to do this yourself?

Here is the prompt:

Create a Python script using pandas to analyze partnership and opportunity data from a CSV file in the Salesforce financial services industry. The script should:

  1. Load a CSV file named 'yourfilename.csv' containing columns for 'Created FY', 'Partner', 'Stage', 'Industry', and 'ARR'.
  2. Filter data for the last fiscal year (2022) and the current fiscal year (2023).
  3. Calculate the win rate for opportunities with a partner and those without a partner, expressed as a percentage.
  4. Identify partners who provided exactly one opportunity in the last and current fiscal years, and calculate their close rates.
  5. Rank industries by the number of opportunities provided by these one-time partners for both fiscal years.
  6. Find partners contributing to 80% of opportunities for each fiscal year and overall.
  7. Calculate the overall average Annual Recurring Revenue (ARR) of 'Closed Won' opportunities.
  8. Print the results, including average won ARR, partner and no partner win rates, the number of one-time partners, their close rates, and the top industries.

Python partner stats example

Here is the output.

Partner stats example

Conclusion

Automating the analysis of partnership data using Python can significantly streamline the process, saving time and providing valuable insights for decision-making. By leveraging tools like pandas, we can efficiently filter, calculate metrics, and generate comprehensive reports that offer a clear picture of partnership performance.

Next week, we will delve into generating Slack notifications from this automated analysis, showcasing how technology can further enhance data-driven strategies in partnerships.
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