Every brand I have talked to has said their most important marketing activity is reaching out to beauty reviewers (including bloggers, mainstream publications, vloggers and social media channels). It’s very important to them that they reach out to the right reviewers, and send them relevant products to review.

Brands currently struggle to understand who are the most relevant reviewers to engage with for each product, and they get bombarded with new blogs and websites who say they can review their products and promote it to their audiences. The brands do not have the resources or tools to accurately assess how influential a review from a particular website will be and who is the best person to review a product.

The beauty industry has a vast amount of new content being published each day that could be crawled and analysed to predict which reviewers are likely to write about specific products types, specific beauty concerns and specific brands.

By analysing content can we work out the following?

• Who is likely to write about a specific product type?

• Who is likely to write about specific beauty concerns?

• Who is likely to write about a specific brand?
• What beauty products types and brands will be popular in the next X months/years?
• How much influence does each review have over how successful a product will sell?

If we can work out these key questions we can then do the following:

• We can start to build an algorithm to work out the best content to serve ads on for specific brand campaigns. This is effectively the beginning of the Beauty Brains ad network.
• We can provide brands with insights into who the best reviewers are to send products to (even more brains!)….we can even proactively send products to reviewers and track how much ROI they generate as part of the Beauty Brains platform.
• We can launch this as a data product very quickly which we can use to attract brand’s interest into Beauty Brains before the launch of the main platform.
• We can provide review websites with new value propositions:
  • we can help them receive more relevant products to review
  • we can provide them with our information about their website’s content based on our analysis
  • by implementing our tag we can also provide them with information about the beauty concerns, products and brand preferences of their audiences.
  • we can help them increase the revenue they generate from selling more targeted advertising space (to us!)

Questions?

• How do you suggest we go about this project?
• Do we need to define some sort of methodology for selecting reviewers (as well as identifying new reviewers on an on-going basis)?
• Do we need to define beauty taxonomy, or will this emerge as we crawl sites and identify trends in terminology?
• How do you go about crawling the content of these websites?
• How do we assess how influential or authentic each site is (e.g. social media likes/followers? inbound links?)
• What do you think can be delivered for the initial $5,000?
• How long would you anticipate a project like this to take?

For a given client website we want to recommend the most effective content sequence (web pages) to lead to a goal conversion e.g. sign up for a demo. Our clients are mostly B2B businesses and the B2B sales cycle compared to B2C has more key touch points and a time duration in months. For now, a goal is a URL and we are dealing with only website content, not other channels like emails. Because a goal is typically a generic thank you page, the visitors also need to be segmented. For a client like iron mountain, the segments would be like SMB, Healthcare, etc.. The input data is the http clickstream data (page view events). These events sometimes do have segment identifiers but we are finding they are not very useful in the current setup (we may need to run some clustering algorithm to identify “useful' segments). Other input data available includes Google Analytics and Site Catalyst. The output data we want to generate is for a given segment and goal, what is the recommended content sequence.

We are begining a project to build the next generation of our SaaS application in a specialized retail space to better allow our clients to better use their data and for us to build data and analytics capabilities into our products.  We will be looking at the large cloud providers (AWS, Azure, Google Compute Engine) as well as NoSQL technologies and are looking for someone to help us select the right platform and architect the solution.

We currently have a highly transactional system that is using MS SQL, and we will be exporting the data from there as well as building the new application to write to our new data architcture directly.  We currently have over 400 million records and expect that to scale significantly.  We are looking help defining a Big Data solution to store that transactional data, and then a plan for processing it and reporting on it both for internal use and for our customers.  We have historically been a Microsoft Development shop, but are open to other platforms to find the best fit for our needs.

We would be looking for someone who:

    Has at least 8 years of Data Architecture and Data Modeling experience

    Has worked with multiple cloud providers

    Has worked with multiple NoSQL database engines

    Has worked with multiple data visulaisation tools

    Must be able to work United States Central Time Zone hours

I need the answers, visualized in Tableau ideally, with supporting data, by May 22 at the latest. 

Data:

• The free Crunchbase data is available on Tableau Public. 
• I have also purchased two commercial data sets from CB Insights that may have better data. Revenue data will need to be found from another source — I believe there are some. 

Goals:

• Illustrate what it takes in venture capital, revenue, headcount, and time, to build a successful big data analytics company versus a social listening analytics company
• Illustrate the relationship between capital raised and exit valuation
• Illustrate the relationship between capital raised and revenue attainment
• Illustrate the relationship between capital raised and time to exit
• Illustrate the relationship between capital raised and exit ROI

Detailed Questions:

Big Data Analytics

• What is the average amount of funding that companies in big data analytics have raised at each round of funding (Seed, A, B, C, D, E)?
• What is the average amount of funding that companies in big data analytics have raised in total, by year since founding?
• What was the average annual revenue per each size funding round (Seed, A, B, C, D, E)?
• What is the average number of years from founding that it takes big data analytics companies to raise $30M, $50M, $100M, $200M
• At each round of funding, what pre-money valuations were achieved on average?
• What was the average multiple of revenue and prior funding raised, at each round of funding (A, B, C, D, E)?
• For various levels of total amounts of funding raised, what exit values (at IPO or acquisition) were achieved?
• How many years from founding to exit did it take on average?
• How many employees did companies have on average at each round (A, B, C, D, E) of funding?
• What is the average revenue, pre-money valuation, headcount, and new funding raised, and total funding to date of B round, and C round companies?

Social Listening / Analytics

• What is the average amount of funding that companies in social listening / analytics have raised at each round of funding (Seed, A, B, C, D, E)?
• What is the average amount of funding that companies in social listening / analytics have raised in total, by year since founding?
• What was the average annual revenue per each size funding round (Seed, A, B, C, D, E)?
• What is the average number of years from founding that it takes social listening / analytics companies to raise $30M, $50M, $100M, $200M
• At each round of funding, what pre-money valuations were achieved on average?
• What was the average multiple of revenue and prior funding raised, at each round of funding (A, B, C, D, E)?
• What were the average multiples of total funding raised, and revenues at exit, to exit price achieved in this category? (for example 6x total funding, 10x revenues)
• For various levels of total amounts of funding raised, what exit values (at IPO or acquisition) were achieved?
• How many years from founding to exit did it take on average?
• How many employees did companies have on average at each round (A, B, C, D, E) of funding?
• What is the average revenue, pre-money valuation, headcount, and new funding raised, and total funding to date of B round, and C round companies?

Financial Literacy Assessment Data (4-5 years worth) and need the following questions answered:

Financial Literacy Assessment Data – 2011 vs. 2015

The zip file contains two spreadsheets:

1. ‘2011 & 2015 Assessment Records’: contains all assessment data from 2011 and 2015 with student IDs as respondent identifiers.

Please note: Not all students who took the assessment in 2015 also took the assessment in 2011 (some students transferred in or were waived through the requirement). In the same way, not all students who took the assessment in 2011 also took it in 2015 (some students left the college early or held back and are not graduating this year).

Each assessment includes:

• 1 Respondent Date field (Note in the 2011, the date is not available. Instead, there is a “Respondent ID” field that sorts by earliest response to last response)

• 1 Student ID Question

• 10 Demographic Questions

• 20(*) content knowledge questions (marked in light red) on the following topic categories:

• Credit (7 questions)

• Income (3 questions)

• Saving and Investing (6 questions)

• Insurance (2 questions)

• Personal Finances and Budgeting (2 questions)

(*)The 2015 assessment also includes 4 extra questions that were not considered when scoring (to have an equal comparison against 2011).

• 5 Background and Interest questions marked in tan

• 7 Behavior questions marked in purple

1. ‘Event Attendance Record for Seniors’:

1. First tab contains all the events current seniors have attended throughout their journey at Champlain.

2. Second tab contains the details for each event: date and type.

3. Third tab contains the topic categories covered at each type of event. A few events cover more than one topic. For example, Check It Out covers ‘Personal Finances and Budgets’ at an intermediate level and ‘Saving and Investing’ at a basic level.

Questions to Answer:

Main:

Are students getting a higher score in topics that are covered in workshops they attended, versus topics they did not attend?

Are students who increased their financial knowledge also improving their behavior?

Other Questions:

Do students who tend to attend workshops/events in the fall semester (August – December) have higher scores in their 2015 assessment versus students who attend in the spring semester (January-May)?

Are there any considerable trends in any demographic groups or any particular majors? Do students who score higher also have better behaviors (lower credit card debt, don’t get overdrawn, etc.)?

Are students who worry about their finances more than average also scoring higher than average? Do they have better behaviors or worse than those who don’t worry as much?

Additional Information

• A summary of your business:  private 4-year college in Burlington, VT
• The problem that you are trying to solve:  Understanding the collected financial literacy data we have collected.
• The kind of expertise you require:  Data analysis / comprehension
• The data sources at your disposal and their formats (Pricing data in CSV format, etc.):  data in an excel file.

To analyse and extract in medical record documents and provide feasibility assessment for longer term medical coding automation.

DATA SOURCES & INFORMATION EXTRACTION:

We will focus on the following two data sources

-          Scanned OCRs

-          XML Documents

For both of them, the information to be extracted would be as follows

-          Date of Service (DOS)

-          Medical History

-          Assessmeent

-          Page number from which data was extracted

Currently, we would focus on a single template to extract information.

DELIVERABLES:

The final deliverable would consist of a web interface which allows the user to upload a chart and see the outputs, as above. 

We have a well-defined Excel-based financial model that is used by by dairy producers, bankers and other industry professionals to complete financial analysis on dairy farms that includes Cash Flow Projections, Actual to Budget Comparisons and Balance Sheet and related financial measurements/reports. These are all intended to boost the business acumen of the user and provide them with improved insight into the financial health of a dairy operation. It can also be used as a tool to develop various “What If?” scenarios, guiding a milk producer or banker to make more informed financial decisions.

For example, “What if I obtained longer term (e.g. a 20 year Real Estate Loan) financing, supported by real estate collateral, versus using short term lines of credit to expand my business? What are the financial implications and potential impact of these various scenarios?” “Is my dairy business cash flowing? If not, what cost areas do I need to reduce? Are there other financial management items that need to be refined?”

Using our program, an individual can obtain a better grasp of the finances of any particular dairy operation. In the case of a banker, he or she will be better equipped to make financing decisions. For a producer, they can make better investment, expansion and financing decisions, as well as obtain a much clearer picture of their actual financial performance in comparison to their budget numbers.

Our goal is to take this Excel-based financial model and create a SaaS-based platform.  This project here is the first phase in which we develop an alogrithm that reflects our current model.

Project for Experfy Expert

You will take our existing Excel model and turn it into a Python-based algorithm.  You would need to understand all the calculations in Excel and then build a data model in a relational database. We would like the alogrithm to update the data entered by the user in the relational database and provide the outputs.

Since we will be building a front-end for the SaaS-platform later, we do not see the need to develop an extensive UI at this stage.  We would like to build a basic interface to input the data and then have the output either printed on the screen or downloaded in a CSV file.

We would test the validity of your alogorithm again the results in Excel and if the two match, then the project will be considered complete.

In your proposal, please be very specific about 1) your approach; 2) the technology stack that you would use; 3) whether you will deliver the results in a CSV or on an online dashboard; 4) how you see your alogrithm being consumed by our future SaaS platform; 5) milestones and dates.

I have built a robust backend data mining and recommendation platform that provides audience analytics and partnership recommendations for commercial brands and recorded music artists. I now need a leading edge UI built that is intuitive and extremely visually appealing.

CUTTIME.FM is a marketing platform that empowers agencies, PR firms, and the music and entertainment industries with audience analytics and partnership recommendations for a growing list of more than 4K brands and 1M recording artists.

CUTTIME's technology combines big data and direct consumer insights to uncover, evaluate, and expand partner marketing opportunities by drawing correlations between bands and brands, establishing audience affinity profiles for each, and then presenting the data in a very compelling digital format.

I am looking for a front-end designer.

I have a time series data derived from exposing cancer cells to radiation and clinical drugs (perturbations) in a combinatorial fashion with appropriate controls. I would like to understand this data better as I am confounded by the component of time. The data consists of 182 proteins that has been interrogated with the above perturbations for 11 time points. A basic workflow beginning with clustering, PCA, p and z scores, volcano plots, parametric or non parametric analyses, Bayesian analyses and differential equations comes to my mind. I would like some assistance in generating this data with some explanations as well.

We understand we are a data driven company.  We rely on buying media for offers and products (some of these products and websites we control, some others we do not). We need to have clear metrics available to us at a glance. Some of this data includes: Earnings per click, CTR (click-through-rate) from different positions: Banners and Ads, Presells to Offers, Offer to Order Page, Upsell Take, etc. These are just some samples. What we realized is that we do understand these metrics but we want a data analyst expert help us be more efficient without much of our time and input while having the team on the same page understanding where their time needs to go. I am open to solutions, my job is to run this company and delegate what others can do better than me and my team. I look forward to speaking to you and understanding how you can help us.

A summary of your business

AdBullion.com is an affiliate network that focuses on gathering exclusive offers. Some of these offers we control and some others we do not control. Our goal is to control more and more of these offers by developing them which we have 45 years of combined experience developing offers. AdBullion focuses on the traffic distribution side, we have inhouse media buyers and affiliate managers whose whole job is traffic maintainining profitability goals.

The problem that you are trying to solve

Identify gaps and opportunities in the day to day media buying while also monitoring metrics that can tell at a glance the position of their traffic distribution.

The kind of expertise you require

I require data analyst to provide the best opportunities driven by a dash board and syncs everyones data into one place.

The data sources at your disposal and their formats

All the data is available in platforms and CVS's files.

What is your current technology stack?

PHP mostly and spreadsheets.

What is the deliverable?

Dashboard and any applicable algorithms to run it

Does the deliverable need to be deployed in the cloud or your infrastructure?

In the cloud

Attach a sample data file, if you can

No samples available right now