eCommerce Analytics

Recently, I’ve noticed that the vast majority of articles and blogs out there on marketing and customer analytics primarily focus on how to use data-mining to increase revenue and cut costs in B2C oriented companies. Today, I wanted to take 5 minutes to outline some ways B2B companies should be using analytics to increase their top line while minimizing costs.

Knowing Your Leads

Many sales people will tell you that they already know which companies and decision makers they should be selling into. The marketing folks will tell you they already know who matches their ideal lead profile. Funny part is that in many cases these two ideals don’t match up.  A recent marketing research report concluded that in nearly 50% of companies out there, marketing and sales do not have a direct view into what the other side is doing. Data analytics can clearly help bridge this gap by following a customer through - from lead to purchase to maintenance - showing which leads result in the most profitable customers - not just in terms of initial sales, but also in terms of potential lifetime sales. It’s the best way to make sure both departments stay on the same page and the entire company increases its overall hit rate.

Better Cross Sell and Up Sell

It shouldn’t surprise anyone that better targeted email results in better cross-sell and up-sell rates. Personalization is a hot topic in the B2C space, but there is no reason why B2B companies shouldn’t have the ability to tailor their emails to match the company profiles of their customers.  A CTO shouldn’t be receiving the same sales pitch as the marketing folks and someone in the energy business shouldn’t get an email that is designed for online retail. These seem like easy rules to follow, but the trick comes in knowing how to segment these customers by industry, contact, and previous purchase history to minimize the work needed to pitch the right offer at the right time.  No one has time to draft up 100 emails to each type of customer - which is why predictive analytics is critical for optimizing the segmentation process to forecast which customers are going to want similar products in the future.

The True Cost of Support

Many times support personnel get relegated to the back office and we tend to forget about them when factoring in the true costs of sustaining a relationship with a customer. If a customer spends $1,000 on one of my products, but then uses up 100 hrs of one of my support staff, are they still a profitable customer? Factoring in and then forecasting support costs can be an extremely valuable way to judge the lifetime value of a customer and help management decide whether they should renew contracts or continue maintenance agreements.  Working this back into lead generation can also help marketing make sure they focus on customers that will not only buy product, but also won’t cost the company an arm and a leg once that customer comes onboard.

These are really just a few ways B2B customers can make use of data analytics. There are many more, and next time I’ll try and focus on some leading edge cases out there that show how B2B companies are using analytics to increase revenue and cut costs.

When we talk with customers about improving the success rate of cross sell and up sell opportunities on their existing products, many reference Amazon.com as the retailer they believe is doing the best job in the industry.  While I agree that Amazon does a fine job with following up on self-generated leads, I don’t agree that they do an especially good job of anticipating or prompting additional purchases.

Let me give you an example. I bought my mother a cookbook three weeks ago for her birthday. I wrote out a gift card, opted for the additional gift wrap, and shipped it to my parents’ home. Additionally, because I’m particularly anal when it comes to data organization, I tagged it within Amazon as a present that was bought for my mother. Now, knowing all of that info, any guesses as to the next product Amazon decided to pitch me on? Cookbooks. I can maybe understand the tendency for someone to purchase the same product again after purchasing it once…maybe…but in this case, really? After doing everything I could to signal to their system that the purchase wasn’t for me (different ship to name, different address, tagged as belonging to a different person), I still get an offer for cookbooks?

Now, don’t get me wrong, Amazon does do many things right. They are one of the best on following up on customer clickstream data. A recent visit to the site to research GPS systems for an upcoming trip resulted in two emails offering deals on GPS systems I actually might be interested in.  My problem with Amazon, and more specifically, the perception of their analytics is that their email marketing model is based on being reactive - the customer is responsible for coming to the site and narrowing down the type of product they want to buy - and Amazon reacts to this behavior with an appropriate offer.  But for those retailers that aren’t Amazon, who can afford to sit and wait for customers to tell you what they want?

The key to predictive analytics is being just that… predictive.  Most retailers don’t have Amazon’s budget and need to have better information about who is going to buy next month or next quarter, and more importantly, what they’re most likely to buy. And for most, the surprising fact is it’s not much of a leap to get there since the majority of companies are already sitting on all the information they need to better target email blasts by matching offers with customers who are willing to buy.

I praise Amazon for their ability to follow up with customers that express interest in certain products, but as far as predictive analytics….there are better solutions out there.

In the age of Amazon, up-selling a customer has become tantamount to suggesting more products during checkout time. In fact, Amazon’s recommendation engine is a relentless upseller and its success has spawned a breed of companies dedicated to building recommendation engines that they license to online storefronts. The truth, however, is that good upselling seeks to harness a larger percentage of the customer wallet over a longer period of time.

While Amazon and other storefronts are undoubtedly suggesting some of the best items to customers when those customers are at the point of purchase, this type of selling also has the feel of the chewing-gum rack at the grocery store checkout counter, attempting to capitalize on customer impulse. Checkout-time recommendations are great if you’re a grocery store and selling a product that is quickly consumed. But what if those checkout-time items have a shelf-life? More likely than not, you’ve saturated your customers for the foreseeable future. If you’re not selling a consumable, upselling with shopping-time recommendations is tantamount to cannibalizing future revenue from that customer.

Think about it. If someone adds a third t-shirt to their purchase because they react impulsively to an upsell recommendation, are they likely to buy a t-shirt from you two months later? The risk of checkout-time up-selling, of course, is that while you do sell more in that instance, adding one more needless box to an already-tipping armload, you are not delighting your customers. Worse yet, you may give them a sense of buyer’s remorse, an association that your company most definitely does not want.

To truly delight your customers, isn’t it better to give them what they want when they likely want it, not when you feel they are in a captive position? In other words, truly satisfying your customer doesn’t mean compelling them to engage in impulse buying. Instead, your company should be seen as a trusted advisor that brings the right information at the right time. Oftentimes, this is not when they are on your website. Let’s take the case of a customer who purchases yarn. Would you rather sell them so much yarn - in colors that they may not ultimately want - at one time that they feel buyer’s remorse? Or would you like to be there with an email right around the time that our theoretical knitter runs out of yarn? Which approach do you think will turn that customer into a loyal customer who buys at your store again and again?

This is the customer service side of marketing that seems lost with checkout-time recommendation engines. I believe that the data mining behind recommendation engines is unquestionably the future of marketing. In fact, as a co-founder of Istobe, I bank on it since our specialty is helping to understand customers - and their buying patterns - based on their purchasing history and their demographic information.

Up-sell today is not necessarily what up-sell will be tomorrow. As customers become more savvy in their online purchasing, bridging the gaps between site visits will be increasingly necessary to establish a relationship with your customers and to signal to the customer that your company is a trusted advisor not a relentless upseller.