A data scientist analyzes and interprets complex data sets to inform business decision-making. They use statistical techniques, programming, and machine learning to extract insights and patterns from data. Data scientists often clean and preprocess data, create models, and develop algorithms to solve specific problems. They collaborate with cross-functional teams to identify business challenges and formulate questions that can be addressed through data analysis. They also communicate findings to non-technical stakeholders through visualizations and reports, aiding in strategic decision-making. Additionally, data scientists may be involved in designing experiments, testing hypotheses, and continuously improving models to enhance predictive accuracy. Their role requires a combination of analytical skills, domain knowledge, and proficiency in programming languages such as Python or R.
Most companies don’t have a holistic view of their business performance
Large proportion of the budget is spent on campaigns that generate little to no return
Monitoring business performance in real-time and swiftly halting initiatives with negative returns will give a company more time and cash to survive the unstable economy.
Why a business needs statistics
Identifying Opportunities
Find new markets, promote better customer retention, increase sales, and identify sales opportunities
Increase efficiency by finding duplication in the market or pinpointing areas that you want to eliminate from your current strategic plan
Understanding customer behavior
Looking at their buying patterns and how they use your products or services
Can make decisions on the type of products or services you should offer to your customers
Identify new opportunities for product development by looking at areas that may require further research and study
Determining the correct target market
Identify the best possible choice for your business because all decisions must be made around this key area
Help determine whether your current target market is as profitable as it should be
Evaluating products or services
Determine what your customers are using or how they are accessing your products to find new ways to improve or alter a product or service you offer
Making better decisions
Allows you to make better decisions about business changes, hiring new employees, marketing, and advertising strategies
In the beginning the value comes from creating a more automated, streamlined, and reliable process and developing metrics that better measure aspects of the business.
Insights are an iterative process which depends heavily on the quality, quantity, and predictiveness of the data
A data scientist automates and improves data-centric processes such as collecting, storing, accessing, analyzing, and reporting.
DS shrinks the haystack rather than finds the needle
“Using machine learning or data science to predict <thing> is just really hard. There are tons of reasons <thing> may not happen, many of which you have no chance of observing in your data. What you CAN do with comparative ease is predict relative likelihoods though. So you say find me the 20000 records most likely to do <thing> according to my model. Within them you’re going to find more of <thing> than you would in a random sample. If you can make / save money doing this, you win.” McBain
This is about understanding the levers that drive your business, then using them to improve operations. A key aspect is making data available and understandable to those who are making daily decisions.
Monitoring business performance in real-time allows businesses to swiftly halt initiatives with negative returns
Metrics are also ways for organizations to align stakeholders around one vision of the world and one common goal.
Metrics help companies have clarity, alignment and prioritization in what to build.
Metrics help a company decide how to build the product once they’ve prioritized what to build.
Metrics help a company determine how successful they are and hold them accountable to an outcome.
“Over 20% of Amazon’s North American retail revenue can be attributed to customers who first tried to buy the product at a local store but found it out-of-stock”
A demand forecasting system can help save a lot of dollars in term of workforce management, inventory cost and out of stock loss.
See inventory bkmk folder, SCPerf pkg. Has inventory/supply chain functions that utilize demand forecast as a variable.
Sales forecasts by store
Monthly units sold by SKU (item)
eCommerce
Daily or Weekly Visits by Channel, Source, and/or Medium using Google Analytics data
Daily Customers, New Customers, Revenue, and Units Sold by Channel
Increases marketing efficiency by helping to indicate which campaigns are more likely to succeed with certain groups of customers
The company can achieve a higher return on ad spend with a smaller marketing budget, yielding a higher profit margin and more room for market expansion.
Form marketing hypotheses based on cluster characteristics and test these hypotheses by varying campaigns based on the customer’s cluster membership.
Recommend a product they’re likely to purchase, a multi-buy discount, or on-boarding them on a loyalty scheme (e.g. rewards program)
Once you know who your customers are and what their value is to your business, you can:
Personalize your products and services to better suit your customers’ needs
Create Communication Strategies tailored to each segment
Focus Customer Acquisition to more profitable customers with messages and offers more likely to resonate with them
Apply Price Optimization to match customer individual price sensitivity
Increase Customer Retention by offering discounts to customers that haven’t purchased in a long time
Enhance Customer Engagement by informing them about new products that are more relevant to them
Improve your chance to Cross-sell and Up-sell other products and services by reaching out for the right segment when they’re more likely to respond
Test which type of incentive a certain segment is more likely to respond to (e.g. pricing discounts, loyalty programs, product recommendation, etc.)