There are many questions organizations would love to be able to answer:Â
How can we become more profitable?Â
How can we serve our customers better?Â
Where should we build our newest distribution center?Â
These are tough questions that, if answered by someone uninformed, can become disastrous for any business. That’s why companies have turned to the experts at phData to be able to answer these questions and more through the use of data-driven facts and predictions.Â
In this blog, we’ll discuss some of the questions you and many other retail and CPG businesses ask daily and how phData can answer them using data.
Retail and CPG Questions phData Can Answer with Data
Why are Customers Abandoning Their Carts Online?
One of the most frustrating occurrences for a retail business is customers loading up a cart online and abandoning it for no apparent reason. It leaves you thinking, what did we do wrong, and how can we fix it so that customers abandon their carts less in the future?Â
To answer those questions, phData can:
Analyze website analytics
Identify pages or specific steps where customers are leaving the site
Measure how long customers are on the site for each step
Check the bounce rate on key pages
Collect and analyze customer feedback
Receive feedback directly from customer forms online
Review chat transcripts or call logs
Break down all this data into different segments to identify patterns.
Data could be segmented by geography, device type, or traffic source to better understand why customers abandon their carts.
Identify and catalog common issues.
Provide an in-depth analysis of the pain points that customers may be experiencing that cause them to leave the site without purchasing.
With this information, your business could dramatically increase the number of customers leaving your site having purchased a product, improving your customers’ experience while driving sales.
What are the Upcoming Trends in Consumer Demand?
What are my customers going to do next? If answered correctly, that question can make or break a business. Predicting even a bit of where your customer demand is heading can potentially drive sales and save costs. This is a perfect use case for machine learning algorithms that predict metrics such as sales and product demand based on historical and environmental factors.
With our experts in data engineering and machine learning, we at phData can help predict consumer demand by:
Bringing in all relevant data into a central data repository
Historical sales, geographic data, competitor and market data can all be landed in a central data warehouse through data pipelines that are updated regularly for the most up-to-date analysis.
Cleaning and preparing the data
Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction.
Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together.Â
Develop machine learning models.
Using this cleaned data, our machine learning engineers can develop models to be trained and used to predict metrics such as sales.
Create dashboards to allow stakeholders to view the output of models to make informed decisions easily.
What is the Risk of Stockouts or Excess Inventory?
The costs of having too much or too little inventory can be detrimental to any business, especially in retail and CPG. Losing out on sales because there isn’t anything to sell, having to discount, or even throwing away overstocked products severely affects the bottom line. Luckily, there are a few ways we at phData can help you make informed decisions when purchasing inventory and save you money:
As mentioned earlier, we have expert data engineers to collect and clean the relevant data needed for inventory analysis, including sales, current inventory levels, seasonal/promotional, and market trend data.Â
Once the data is prepared and cleaned, we also have a team of analytic engineers to perform exploratory data analysis to determine:
Peak seasons, slow periods, and overall demand patterns
Supplier lead times and reliability
We can then create a visualization dashboard to let stakeholders better understand their inventory levels based on the analysis.Â
However, why rely on human eyes to determine the best inventory levels for your products when machine learning can be used to optimize stock without human intervention?
Our machine learning engineers can predict product demand as mentioned above, and that data can be used to:
Calculate the optimal order quantity of products to minimize total inventory costs
Determine the inventory level at which a new order should be placed
Calculate the probability of running out of stock based on demand variability and lead time
Determine the amount of extra inventory to hold to mitigate the risk of stockouts
Analyze the cost implications of holding excess inventory and the likelihood based on demand forecasts
All of this while continuously learning from previous predictions to achieve increasingly accurate results over time, leading to more sales and cost savings for you.
Who are our Most Profitable Customers, and What Drives Their Purchases?
Every business has customers they love. The ones that buy the margin-heavy items bring huge profits to your organization. What if you could know what drives them to buy your products and could use that to bring in more customers like them? This can be achieved by, you guessed it, analyzing the data.Â
As we’ve seen before with these other questions, phData can create data pipelines to bring all the data on your customers together to determine the profitable ones and what makes them tick, including:
Transactional data
Demographics
Behavioral data, such as website interactions and email engagement
Customer feedback
Call logs and chat transcripts
Then, that data can be cleaned and organized to prepare to analyze and segment your customers and determine their Customer Lifetime Value (CLV) to identify who exactly makes up your most profitable customers. From here, our analytics engineers can perform behavioral analysis to determine purchase patterns and engagement metrics of the high CLV customers.Â
This information will allow our machine learning engineers to create predictive models of what other customers could turn into high CLV ones. This can be used to create:
Personalized marketing campaigns based on customer preferences and behaviors
Implement loyalty programs to reward high-value customers and keep them around
Make personalized product recommendations to drive even more sales
Why phData?
There are many data engineering consulting companies out there that could also answer these questions for you. Maybe you think you have the talent on your team to do it in-house; why should you choose phData to help?Â
Expertise
Here at phData, we strive to be experts in data engineering, analytics, and machine learning. Our engineers have worked with many businesses like yours and can use that knowledge to answer questions with data you may have never thought possible. We continuously learn and grow as a team to adapt to the ever-changing data landscape and ensure we’re current on all the latest technologies.Â
Partners
We are partnered with dozens of companies that create data and machine learning engineering tools and have advanced knowledge of them. This allows us to recommend the best tooling for the job, which can make DE and MLE faster, more efficient, and cost-effective for you and your team.Â
Automation
With all our experience with projects, we have created in-house automation tools for many DE tasks, especially when using Snowflake AI Data Cloud as your cloud data provider. These include (but are not limited to):
SQL dialect translation
Data migration validation
Synthetic data generation
Information Architecture provisioning
These automation tools are free for all phData customers in perpetuity.
phData Retail Case Study
phData helps many retail businesses answer these questions and more by utilizing their data to the fullest. Here is just one real-life example of what phData can offer:
Problem
How Can We Mitigate Risk in a Cost-Efficient Way?
A consumer packaged goods company analyzes, reports, and mitigates risk by gathering trading positions in commodity markets worldwide. Moving this application to Snowflake Data Cloud will reduce the delivery analysis timing and outages experienced on its legacy platform.
Solution
phData re-architected their application for the Snowflake Data Cloud. The solution leveraged Snowpark, UDFs, and external functions.Â
Results
An agribusiness company we worked with observed a cost reduction of almost $300,000, time-to-value, and increased operational efficiencies.
Closing
In the highly competitive retail and CPG industries, making informed decisions within your organization has become more critical than ever. Not having the right experts on your side can lead to massive mistakes.
By partnering with phData, you can be sure that your decisions are guided by accurate, comprehensive data insights. This enables you to answer questions, drive profits, and save money.
