predictive analytics

7月 142015

Dust off that old aphorism about an ounce of prevention. Oil companies applying analytics for predictive maintenance can see a substantial downtick in the unanticipated equipment repairs that quickly eat into an oil well’s profitability. Maintenance is far from a trivial concern in the oilfield. A pumping oil well is […]

From downtime to upside with oilfield predictive maintenance was published on SAS Voices.

5月 082015

Not so long ago, I started my retail/merchandising career in the juniors division at the corporate office of a retailer. It was so exciting to be in a place where I could wear the clothes that I worked with, and I was sure that picking out cute clothes all day was what I was meant to do! SAS Analytics helps retailers get merchandising right.

Then about a year later, I moved to the "missy" sportswear division, which I thought would be great, too, because cute clothes are easy to pick out in any area, right? Well - I thought wrong. You see, this company was based in Florida, where the missy area for retailers is long on conservative and short on trendy.

My first red flag was raised during my first “Hit or Miss” meeting, where the merchants display their top-selling items (hits) and their slow movers (misses). As you might have guessed, the very first hit was very far from a hit in my book. I just could not imagine 3,200 or so ladies actually choosing to wear a bedazzled hot pink shirt with flamingoes and seagulls swallowing up the fabric!

SAS analytics helps retailers with merchandising.Sadly, one of the "miss" items was a very cute peasant top that I could see wearing myself. Suddenly I got a sickening metallic taste in my mouth as I realized how bad a fit I was for missy sportswear in Florida.

Merchandising is not as easy as one initially thinks because it involves selecting merchandise for different arrays of individuals who may not all have the same taste as you. So how do we go about figuring out what to buy? This is an age old question…

There’s competitive shopping, but relying on that always puts you behind the trend. The largest tactic for conquering this question has been analyzing what sold in the past. Many merchants try to bring items back in the next season that had mediocre performance the previous season but only to then find that they are this seasons dogs.

The customer is constantly changing and evolving. Do you buy items that you already have? No, of course not. So then how do you determine what the customer will want? That is the magical question to merchandising.

Unless you've been living under a rock, I’m sure you’ve heard the term “Big Data”. Through all of the customer transactions in store and online, there becomes a wealth of data regarding sales. Then you also have the wealth of information on social media. I think we all are still dying to know what color that dress was on Facebook. For those who might not remember, a photo portraying a dress in two different colors went viral on social media. But what if you could instead ask the audience which color they would prefer? You’d know what color of the dress to buy before you buy it! Jackpot!

Answering the magical question to merchandising is now possible through analytics. We are able to understand which attributes such as colors, patterns, fabric, or silhouettes drove your business. But most importantly, we are able to predict which combinations of these attributes will drive your business in the future, including combinations that you didn’t even have in your assortment last season! We are able to integrate social media data as well.

We are able to do this through the use of predictive analytics. Utilizing predictive analytics gives merchants the ability to predict the evolution of the trends. This truly takes the guess work out of buying! If I had these analytical insights at the start of my career, I would have been able to leverage the insights and know that rhinestones and hot pink were the next evolution of the trend!

If you want to understand how analytics can help your business and why SAS can help you, check out the 2015 Forrester Wave for Predictive Analytics where SAS is named a leader. For a broader sense of our industry-specific expertise, I encourage you to visit our Retail Industry Solutions page. Let me know what you think!


tags: analytics, customer preferences, merchandising, predictive analytics, retail

The post Predicting customer preference evolution in retail appeared first on Customer Analytics.

3月 302015

Bookies have long turned a trade in predicting the fate of our politicians in the general election. According to Ladbrokes, gamblers are set to spend a staggering £100m betting on this year’s result. The outcome of the May 7 vote is anticipated to be the hardest election to predict in […]

The post Get the inside track on the UK's General Election result appeared first on SAS Voices.

11月 212014
Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection, […]
11月 192014
Many vendors claim they have analytics, and a lot of users have embraced the belief that analytics is the way to go. But what does analytics really mean, especially to business users without statistics backgrounds, and how much do they need to know about analytics to be able to make […]