eCommerce Analytics

(Read the entire Customer Analytics: A Guide to Getting Started series)

The situation so far: We have a glut of women’s sweater inventory that we need to unload. We decided to identify the customers who are most likely interested in purchasing women’s sweaters so that we can send full priced offers to them while offering deep discounts to the rest of the customer base.

In part 4 we produced the relevant data and had our data modeler (or software solution) produce a list of customers and their relative probabilities of responding to a sweater offer.

Read Customer Analytics: A Guide to Getting Started (part 6) »

I will be the first to admit that our industry, predictive analytics to support retail marketing strategies, is dense with technical words, phrases and methodologies all wrapped up into various umbrella groups or plans such as “Market Segmentation Analysis”, “Customer Analysis”, “Predictive Marketing”, “Retail Pricing Strategy” etc. Although each sounds a bit different all of these approaches strive to reach the same outcome:

• Use available data to describe customers and understand their needs, wants and actions

and each takes a different approach but all the approaches are based on well established principles of computer science and statistics.

Read Market Segmentation Research: Science Fiction or Non-Fiction »

With the holiday season upon us, and with the specter of economic downturn no longer looming but now in full unfurling, the hue and cry for free shipping to draw customers has never been greater. Almost everyone insists that drastic price cuts and coupon-like free shipping are necessary evils to keep your customers from the competition. But I would urge smaller retailers to resist giving up margin and making deal-seekers of their entire housefile. Instead they should get more personal.

Read Retail Marketing Strategy Should Rely More on Personalization, Less on Free Shipping »

(Read the entire Customer Analytics: A Guide to Getting Started series)

Now that we’ve explored a couple of possible customer analytics goals and developed plans to achieve them, we can now start thinking about execution.  Before we do that, however, I want to touch on what is probably the biggest challenge in executing a customer analytics initiative: organizational behavior.

Read Customer Analytics: A Guide to Getting Started (Part 5) »

(Read the entire Customer Analytics: A Guide to Getting Started series)

Last time we left off, we put together a plan to help us to use customer analytics to understand which marketing campaigns were worth pursuing and which to kill. This is a valuable analysis that can yield real profit growth without requiring any advanced predictive modeling. But what if we want to do something with a little more sizzle that really boosts the bottom line?

Read Customer Analytics: A Guide to Getting Started (Part 4) »

I apologize for the long absence from the blog. For one, I was forced to spend a lost week in Maui. Then, when I returned, the rest of the guys at Istobe and I showed our wares at the MIT Technology Review emTech convention last week. It was a great time for a first look at our new product but a bad time to write a blog.

Read Ignite’s first tradeshow exhibit: our product meets the public »

(Read the entire Customer Analytics: A Guide to Getting Started series)

Yesterday we talked about one of the reasons it’s important to go into your customer analytics intitiative with a well defined goal and a solid plan.  So how do you put a plan together to achieve our customer analytics goal?

Read Customer Analytics: A Guide to Getting Started (Part 3) »

(Read the entire Customer Analytics: A Guide to Getting Started series)

In part 1 of Customer Analytics: A Guide to Getting Started, we talked about defining the goal of your customer analytics initiative.  We’re now going to spend some time over the next few days talking about planning.  But first, I want to briefly give an example of why it’s critical to have a well defined goal and plan before deciding which type of tools you need.

Read Customer Analytics: A Guide to Getting Started (Part 2) »

(Read the entire Customer Analytics: A Guide to Getting Started series)

More and more companies are looking to implement customer analytics as a way to better understand purchasing behavior and squeeze more revenue out of their customers. And why not? Over the past few years, companies have been investing in collecting tons of data about their customers through their CRM, web analytics, and transactional systems. But how do you get started in customer analytics?

Read Customer Analytics: A Guide to Getting Started »

I saw that some of my other brethren in the data analytics game broke out their parachute pants and shuffled MC Hammer into their blog lineup this weekend. And, of course, all of them had to make some snarky Hammer-based quip on how no one can touch his understanding of analytics or that Hammer was too legit to quit.

Read MC Hammer Declares Analyticstime »