Marketing - product management and market deep-dive w/ margins

The artificial turf industry has undergone several significant changes in the last decade. What used to be the easiest predictor of success (volume) is now but one of the many different dimensions by which we evaluate our success. Our sales strategy is includes several elements, including but not restricted to: product, channel, region, partner and many more. This evaluation matrix is loosely defined, and coupled with the large volume of data involved - much of it dirty - and the limited data fluency within the org, we struggle to cube our sales into meaningful metrics and compare it to our high-level sales strategy. We are seeking a team to perform a lookback of the last 10 years of sales, define the most relevant metrics, build a regional and consolidated dashboard in Power BI that is meaningful to senior business leaders with minimal analytical training. As part of their journey to successful deliver the final deliverables, the team will need to perform these steps: Gain an understanding of the business Perform exploratory analysis to identify which business drivers are the most significant and relevant for our senior business leaders Identify the relevant data sets - most of which are not integrated Build any simplifying SQL queries if the load on Power Query is too heavy Perform an analysis of the dirty data & identify reasonable assumptions that are relevant to our business as placeholder data points for the dirty data Building a supporting dashboard with metrics on the data quality that is easy to navigate and can be drilled down so that users can know exactly which data records require cleaning, why, and what the expected result should be Final deliverables include: A presentation the covers key methodology, findings and reasoning behind selection of key metrics and visualization techniques The exploratory analysis (10-year lookback) The sales dashboard in Power BI The data quality dashboard in Power BI

Marketing: automating the collection of external data for an internal KPI

Any process that requires significant human intervention is bound to produce data with quality issues. Our order management process currently depends on 3 external data points that must be collected and inputted by humans: The prospective field's address Whether or not the prospective field is a replacement Y/N Whether or not we won or lost our bid, and to whom Our current process is highly manual, relying on: Our sales reps awareness of their region Competitors posting wins on social media Google Alerts or Google Maps Industry journals that discuss high-profile projects We are seeking to automate both the collection of this external data and the recording of it in our CRM. It is important to note that we currently do not operate on the cloud. The selected team to lead this project will be given a data set that can be used as testing data and or training data. Final deliverables include: The automation of the process Cleaning of the testing and training data set (>= 90% completeness) A presentation on problem identification and definition, alternatives considered and why, final methodology, and any business recommendations deemed relevant

Defining ETL integration architecture

What is data analytics without clean-ish data and data architecture? Nothing. Easier said than done. As we seek to define a 3-year data strategy for our organization, the challenge of identifying our desired data architecture looms large. We are seeking a team of highly-motivated students to gain an understanding of our current architecture (consisting entirely of non-centralized databases and legacy systems), our business and industry, our data maturity level, and help us define the alternatives to achieving an ETL integration architecture that works for us. Final deliverables include: A detailed position paper with the top 3 alternatives that have been retained by the team, the decision matrix, and the recommended selection A presentation detailing an overview of what the implementation of the recommended alternative would entail for our business, risks and challenges, resources required.

Marketing - product management and market deep-dive

The artificial turf industry has undergone several significant changes in the last decade. What used to be the easiest predictor of success (volume) is now but one of the many different dimensions by which we evaluate our success. Our sales strategy is includes several elements, including but not restricted to: product, channel, region, partner and many more. This evaluation matrix is loosely defined, and coupled with the large volume of data involved - much of it dirty - and the limited data fluency within the org, we struggle to cube our sales into meaningful metrics and compare it to our high-level sales strategy. We are seeking a team to perform a lookback of the last 10 years of sales, define the most relevant metrics, build a regional and consolidated dashboard in Power BI that is meaningful to senior business leaders with minimal analytical training. As part of their journey to successful deliver the final deliverables, the team will need to perform these steps: Gain an understanding of the business Perform exploratory analysis to identify which business drivers are the most significant and relevant for our senior business leaders Identify the relevant data sets - most of which are not integrated Build any simplifying SQL queries if the load on Power Query is too heavy Perform an analysis of the dirty data & identify reasonable assumptions that are relevant to our business as placeholder data points for the dirty data Building a supporting dashboard with metrics on the data quality that is easy to navigate and can be drilled down so that users can know exactly which data records require cleaning, why, and what the expected result should be Final deliverables include: A presentation the covers key methodology, findings and reasoning behind selection of key metrics and visualization techniques The exploratory analysis (10-year lookback) The sales dashboard in Power BI The data quality dashboard in Power BI