Picture this: You’re sitting across the table from a prominent supplier in your category. They sell in many places around the world, at home and abroad, to your company and to your competitors. They have product to sell and you want to buy it, but it seems like “someone else” always gets the pick of the crop.“Overseas customers pay more” or “the buyer down the street has a better acceptance record,” says the prominent supplier in what seems like an attempt to force you to pay a higher price or to be lenient on quality control.
Sadly, it is a situation that I found myself in many times. And I’m sure like many other produce buyers, I was neither willing to pay more, nor at liberty to accept a lesser quality than my standards commanded. At times, it felt like a stalemate.
Thankfully, there was an antidote.
To be fair, this antidote didn’t materialize out of nothing. It was only after years of trial and error that I realized to end the stalemate and tip the balance in my favor, the one thing I needed was more knowledge than the person across the table from me. Simply speaking, I needed to know more.
And in our business of dollars and cents; cases, pallets and truckloads; and rejections and acceptance, knowing more meant having more data. Then it took me a few more years to realize data is no good if you can’t make sense of it.
Enter data analytics
Analytics means different things to different people. To some people, analytics means crunching billions of data points in the hopes of teasing out tendencies and determining cause and effect. Those people would be right.
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To others, it means analyzing the fruit and vegetable purchase habits of a group of people willing to answer a few questions, perhaps as few as half a dozen people or as many as thousands. They would also be right.
And others still consider analytics to include determining the average annual cost of a commodity, or the weekly volume needed to satisfy customer demand or the rejection rate of a primary supplier. Again, these people are correct in their assertion that these are examples of data analytics.
If you take pieces of related data and put some analytical rigor to it, you’ve got analytics — the antidote to the buyer-seller stalemate. But you can strengthen your hand further if you know how to use data analytics.
So what?
The simplest answer you can get from analytics is “the what”: a historical review of what happened in the past.
These analytics may include last year’s cost of a certain product, a focus group’s viewpoint on price thresholds or the average defect rate in a supplier’s product. It is good data to get your hands on, and it’s better to have it than to not.
Truth be told though, knowing “the what” will only get you so far if you don’t fully understand what this knowledge means for your business. Understanding “so what” provides insight into why “the what” is important. It usually comes about by the combination of two or more pieces of analytics.
This article was in the May-June issue of PMG magazine. Read more in the digital edition.
Imagine this scenario: The supplier across the table from you advises that their product cost will increase to $20 per case, from last year’s $17. An unarmed you can push back, prompting replies from across the table like, “the whole market is up” or “inflationary pressures have driven up input costs across the board.” At which point, the whole negotiation could turn into a battle of wills.
On the other hand, if you come to the negotiation with your data analytics homework done and prepared to explain the “so what” behind a cost increase, you might counter with something like “all other suppliers have increased costs by $1.50 per case or less,” or “your new cost puts the product beyond the price threshold established by our consumer research,” or “it’s detrimental for us to buy your product because your rejection rate is above average.”
Suddenly, your knowledge advantage created by better data analytics has put you in the driver’s seat.
The holy grail
The holy grail, though, of data analytics is to answer “now what.” Once your data analytics have informed you about “the what” and given you insight into “so what,” the next step is for it to enlighten you on where to go next. This takes more than just good data; it takes skill and experience with analytics. But it can be a powerful ally on your side.
Going back to our negotiating situation, suppose you realize that even after aggressively negotiating a lower cost with your supplier, you realize their total cost, when factoring in replacement cost due to rejections, forces a retail price that is over the price threshold established by your consumer research. You’ve just figured out, to be successful, you don’t need to negotiate a lower cost. You need to find a new supplier.
You’ve just figured out “now what.” You can thank data analytics for that.
Oh, and by the way, data analytics can be the antidote to negotiating stalemates on either side of the table. As a buyer, it can be your best friend or your worst enemy. It all depends on who’s wielding the knowledge advantage.
— Mike Mauti is the founder and managing partner of Execulytics, a consulting firm for produce professionals. He worked for Loblaw Cos. for 20 years, serving as a produce category manager, director and eventually merchandising, operations and analytics.
Related: Listen to this "Tip of the Iceberg Podcast" interview with Becky Eldredge, vice president of commercial customer media and loyalty at Kroger's retail-data science, insights and media company, called 84.51°. Learn how to use produce-shopper data to sell more produce, to gain and retain consumer loyalty and to keep shoppers engaged.