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Be the Jerry Maguire of Analytics: Show Me the Money

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Finally, your data science team is up and running time to unleash data insights, lead critical business decisions, and drive fast ROI. But before you put the pedal to the metal on analytics, make sure you won’t end up driving around in circles. This starts by mapping out what you should work on first. So, where do you begin your analytics journey?

Many organizations start by automating the insights that their business spent significant time manually churning in the past, such as improved forecasts, sales projects, analytics on attrition rates, downtime reporting, etc. While these do provide value to the organization, both in the insights delivered and the time saved from manual efforts, it’s akin to chasing nickels around dollar bills when there are bigger savings and revenue initiatives to tackle.

Follow the Money Trail

The goal is to transform the business, not to cover the cost of the team, so where can companies find the big money projects?

Typically, large-scale savings come from projects that are not descriptive in nature, but instead drive an action in a highly prescriptive way. A system that only shows the pattern of spending will likely not save as much as one that intercepts orders that appear high before they go out and forces a secondary review.

But what areas of the business will have the best opportunities for these projects? Here are four high-value use cases to get you started.

1. Targeted Marketing: If You Want to Hit the Target, Don’t Shoot Blindfolded

There’s often rich data both internally and externally that can improve targeted marketing, making it an important initiative for fast and significant ROI. Through customer analytics — knowing which customers to contact, finding the best way to engage customers, understanding customer needs/wants/values/buying preferences, and determining ideal new markets — companies can boost their marketing response rates, improve customer relations, and gain a competitive edge.

This leading print and advertising agency manages accounts for some of the most recognizable names across the United States and Canada and helps its clients get to market faster, giving them a real competitive advantage. Leveraging analytics, the agency optimizes budgets across multiple media products to make sure every dollar is allocated appropriately. Data helps them gain the strategic insights required to help their clients target the right customers, with the right media vehicles, at the right times.They’re a leader in the highly competitive advertising industry because their quick insights enable clients to capitalize on opportunities faster, make media decisions in near real time, and gain an advantage for their customers.

2. Price Optimization: Stop Leaving Money on the Table

According to McKinsey & Company, 30% of the pricing decisions companies make each year fail to deliver the best price. One mistake companies make is only offering a single price to customers for their products. This is essentially assuming all their customers are the same, have identical needs, and will use the product the same way. Using data, companies can create detailed customer segments with different price points for buyers. By driving pricing to a 1:1 level and providing offers to each individual based on their likelihood to purchase, they’ll be able to capture more revenue.

For one LA-based fashion company, a new pricing model helped them do just that and return from bankruptcy. One problem they wanted to tackle was over-discounting items, a process which had eroded profit margins. They created a pricing probability model to determine which customers were most likely to pay full price. If a customer was willing to pay full price, they wouldn’t offer them discount prices and would capture more revenue. In their first marketing campaign with the pricing probability model, they delivered a 275% lift in revenue compared to the same campaign a year earlier.

3.Quality Analysis: Did You QA That?

Failed parts, customer complaints, long lead times, process issues, and low efficiency, among other issues, all have real costs. In fact, quality-related issues can cost as much as 15-20% of sales revenue, and sometimes even 40% of total operations. By analyzing quality-related patterns, companies can reduce recall size, improve customer satisfaction, lower churn rates, improve inefficiencies, and save money on warranty costs.

A human resources software company built a predictive model to identify customers likely to be dissatisfied with the company. Thanks to their model, they were able to identify four out of five at-risk accounts before they churned — making it a powerful tool for retention. Instead of waiting for a customer complaint, account managers can now use data to convert a potential detractor into a promoter and proactively make sure clients are happy. And, as research from Bain & Company shows, increasing customer retention rates by just 5% can increase profits by 25%-95%.

4.Put the Logic in Logistics

The world of moving goods is becoming increasingly important as companies ship products globally. Not only do companies need to efficiently produce and store their products, they also need to deliver them in a timely manner. The bar keeps being pushed higher by companies such as Amazon that can offer features like 2-day shipping, and only the companies that leverage their logistics data will be able to compete. Luckily, there are a myriad of ways companies can do this. They can conduct detailed cost benefit analyses, simplify the supply chain, use real-time data to make decisions, optimize rates, routes, shipping modes, packaging density, and more.

For one major retailer, analytics helped optimize parcel routes to drive costs and time savings. The team leveraged order data to get a clearer picture of which items were going into each package so they could properly calculate the size of the box. Prior to their logistics optimization, each package had the same length and height. Now, with different lengths and heights, teams can easily determine which mode is optimal based on package dimensions. With new supply chain visibility, the team identified cost savings opportunities of up to 11% of the total inbound freight cost and spend. Additionally, they were able to go from a 98.5% on-time percentage to 99.4%.

The list of analytics could fill a book, but these are a few that will “show you the money” and help you gain the biggest wins.

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Written By
Alan Jacobson
Alan Jacobson
Chief Data and Analytics Officer, Alteryx
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