Most distributors would probably say they make good use of their data. I’m going to stick my neck out here and say they don’t.
Imagine you’re an executive at a top-tier cable network. Every morning, you pore over the previous night’s ratings, deciphering what worked and what didn’t. The ratings are up. But is it because the football game on a rival network underperformed, driving viewers to your show as an alternative entertainment? Or was your show good enough to be the first choice of your audience? The reality is that you don’t know. All you see is the aftermath, the results of a broadcast already aired, and the audience’s reaction to content already produced.
This approach isn’t data-driven — this is looking at data.
How can a distributor move from level one – reviewing the data and making some assumptions – to the top tier: being data-driven?
Netflix: Predictive Analysis for Binge-Worthy Sales Numbers
Consider Netflix’s approach. They used their extensive data to predict what viewers would want, even before those viewers knew it themselves. This predictive, data-driven model grew Netflix’s paying subscriber base to over 200 million worldwide, pushing them to win the war against cable TV. They didn’t just look at data, they used data to inform their decisions.
Netflix is data-driven and winning because:
- They understand the audience: Netflix knows almost everything about its viewers: demographics, what they watch and when they pause or stop watching a show, what device they use when watching, what time they watch and for how long, and more. All of this data is analyzed to give Netflix data-driven insights into the type of shows to produce or buy rights to.
- Offer personalized recommendations: Every subscriber to cable TV gets the same generic lineup. Netflix personalizes every viewer’s experience based on what they know about them. Netflix’s recommendation algorithm uses data gathered about each subscriber to suggest shows viewers might like, making it easy for them to find movies, shows and documentaries they’ll love.
Take the sci-fi hit Stranger Things. Many don’t realize that artificial intelligence actually predicted the show would be a success. It didn’t matter that the cast was unknown. Netflix knew it would do well because the proof was in the data and Netflix embraced it. Stranger Things ended up breaking a lot of viewing records:
- In 2022, Stranger Things broke the Nielsen streaming view record; viewers streamed 7.2 billion minutes watching the show in one week.
- 26.4 million viewers watched the series’ third season in the first four days it aired.
- The first season had 79 consecutive days in the top 10—the longest popularity streak of any new TV show.
Everyone was surprised by the show’s quick popularity — everyone but Netflix.
While a lot of networks passed on Stranger Things, Netflix leaned into data to decide the show was a good bet. They also used data analytics and predictive modeling to market the show to viewers they knew, based on the data they had on those viewers, would appreciate the show’s ‘80s nostalgia, engaging storytelling, and supernatural elements.
Netflix pulled off a similar coup in 2018 with its first original series House of Cards. By listening to what the data analytics revealed they knew their viewers were more likely to binge-watch complicated, high-production shows. Armed with this knowledge, Netflix released all 13 episodes of House of Cards at the same time, an unusual move at the time. It created a lot of buzz and even more viewership.
Distributors can learn a lot from Netflix and their data-driven approach to creating and promoting content.
Here’s a lightbulb moment: What if you could do this in your distribution business?
Predictive Analytics in Distribution
To illustrate how to apply predictive analytics in distribution, I’ll share a recent interaction with a vice president of sales at a large distribution company. His business was highly transactional, and their acquisition of new customers was impressive. The sales reps were excellent at nurturing and growing new accounts. But there was a hidden problem: customer retention.
When it came to data, top-level managers would often focus on top-line revenue. At first glance, there were no red flags. Sales were healthy, and customer spending trended upward. But a closer look revealed a significant issue: Sales reps poured their energy into low-hanging fruit – newer accounts. They assumed that because a company was one of their top customers, they were “safe.” They no longer needed to nurture them.
But scratch beneath the surface, and you’d find a problem. The sales team had fallen for the easy wins – new accounts. They figured their long-time customers were loyal. They didn’t need extra attention, right?
This mindset meant they didn’t notice when a customer started looking elsewhere. Maybe this customer started picking up a thing or two from a competitor, little by little chipping away at the distributor’s share of wallet. Or a sales rep might have missed a chance to provide something new – like a part for a new machine or supplies for a different kind of project.
Recognizing these issues might give you the illusion of being data-driven. Yes, you’re poring over the figures and dissecting each rep’s sales. But is that sufficient? The answer is a resounding no. You can’t fix a leak if you’re only looking at the puddle it’s left. By then, the customers will be long gone.
The distributor was only getting half the story from their data. It didn’t offer insight into account health or potential churn risk. They weren’t using the data to guess what customers might need or buy next. So, they always felt like they were playing catch-up.
The distributor was looking at data when they really needed to be data-driven.
They needed to take that data, analyze the hidden trends – and use those insights to make changes in their business to reduce customer churn.
Just as Netflix redefined broadcasting by employing predictive analytics and machine learning, distributors can transform their approach to, for instance, customer retention by harnessing the true power of their data.
To be genuinely data-driven means two things:
- Accurately predicting future customer behavior.
- Implementing appropriate strategies based on these predictions.
For distributors, moving from level one, reviewing data, to making data actionable shifts the focus from reactive to proactive. Distributors can use their customer data to guide every strategic decision.
But to begin this transformation, distributors must embrace predictive and prescriptive analytics. What’s the difference?
Predictive vs. Prescriptive Analytics in Distribution
There are three levels to how you use your data. Many distributors remain on level one, descriptive data – which tells you what has already happened – to make best guesses based on what you see. The next two levels are predictive and prescriptive analytics.
Predictive analytics forecasts outcomes from existing data, and prescriptive analytics gives you a roadmap for how to respond.
Predictive Analytics: This branch of analytics employs statistical models and forecasting techniques to understand future behavior. An example for distributors is predicting customer churn. Predictive analytics provides a competitive edge in a market where understanding customer behavior is critical to business growth. It’s especially important in distribution where relationships are king.
Prescriptive Analytics: While predictive analytics provides a view of what could happen, it doesn’t offer a course of action. This is where prescriptive analytics comes in. Prescriptive analytics fuels recommendations on the best course of action. Let’s go back to that customer churn example: Prescriptive analytics would help a distributor devise a targeted engagement strategy to bring less-active customers back into the fold.
However, these analytical techniques require vast amounts of data processed quickly and accurately — a task perfectly suited for artificial intelligence (AI). AI, and in particular machine learning, can analyze massive datasets faster and more accurately than any human analyst, with continually refined algorithms for increasingly accurate predictions.
For instance, an AI-enabled tool can spot patterns and correlations in customer behavior that might take humans months or even years to discover. AI can track thousands of data points for each customer — from the frequency and volume of their orders to the time of the year they buy specific products — to predict their behavior. Imagine this at work in your business.
- Recommend what products to pitch to your customers.
- Target the highest-potential customers.
- Don’t miss reorder opportunities.
- Identify which customers need attention or are at risk of churn.
- Suggest strategies to re-engage those who have become inactive.
Transitioning to a genuinely data-driven approach will involve adopting and implementing AI-powered analytics tools. But this transition is more than just a technological shift — it also involves a cultural change. Distributors must be willing to trust the insights derived from AI and use these insights to guide their decision-making.
The choice is yours. Do you want to end up being the cable TV executive who isn’t sure what’s driving numbers or the Netflix executive who accurately predicts the next hit? When you choose a data-driven approach, you can proactively address issues before they become problems, uncover new sales opportunities, and provide a level of customer service that will keep their customers coming back time and time again.
Benj Cohen founded Proton.ai, a growth engine for distributors. His company’s mission is to help distributors harness cutting-edge artificial intelligence (AI) to drive increased sales. Benj learned about distribution firsthand at Benco Dental, a family business started by his great grandfather. He graduated Harvard University with a degree in Applied Math, and speaks regularly at industry events on the benefits of AI for distributors. Benj has been featured in trade publications including MDM, Industrial Distribution, and Industrial Supply Magazine. His company, Proton.ai, announced a $20 million Series A round of funding in 2022, led by Felicis Ventures.