eCommerce Analytics

Internal acceptance of customer lifetime value as the primary metric to use in marketing decisions is an important milestone in the growth of any marketing organization. Using an online customer lifetime value calculator is a good first step in understanding how much an average customer is worth and how much should be spent in acquiring the next customer. The real value in using CLV will be realized, however, by organizations that calculate customer lifetime value across customers, segments, and marketing campaigns.

Marketers are consistently faced with questions that one average CLV alone cannot answer:

• Which customer acquisition campaigns should we spend more money on and which less?
• What are the demographic and behavioral attributes that define my best customers?
• How much should we spend to retain a specific customer? A customer in a given segment?

Imagine, though, if you could slice and dice customer lifetime values to answer these questions. You would be able to decide with confidence exactly how much budget to allocate various customer acquisition and retention activities. Even better, you could look at your sales pipeline and predict which leads are likely to become valuable customers and which are a waste of time based on attributes like location, industry, size, title of contact, etc.

Unfortunately, calculating customer lifetime value on anything but an average basis can get tricky. It requires a good understanding of SQL and dedication to hammering out many little details. How should we calculate a customer who bought for the first time within the last few months? Has a customer who hasn’t bought in two years expired or just between purchases? How do we find the statistically significant attributes that predict customer lifetime value?

We understand that dealing with these issues is not easy so we’re helping solve them by offering a Premium Customer Lifetime Value Analysis. Our goal with this service to provide a jumpstart to organizations that want to move to a more analytical marketing approach. By keeping the price point low at $495, we hope to remove price as a barrier to what we think is the most important first step in understanding customers.

We’re excited about offering this service and hope that it helps companies solve the CLV problem in a way that is affordable and easy. We’ll keep you posted on the results.

I was going through my usual news download this morning when I happened across the following blog article (here) from the washingtonpost.com that talks about the recent disclosure by major search providers that they are tracking ever increasing information on their users without their users’ explicit consent. The reason why their collecting this information? Better ad targeting.

For many online retailers, this may not seem like the worst idea - better information from search providers means a better understanding of who’s buying their products, when they’re buying them, and what they’re most likely to buy next.  For many consumers though, there is an increasingly fuzzy line between companies collecting information about their buying habits to better meet their needs and companies invading their privacy.  Rep. Ed Markey (D - Mass) is actually proposing new legislation that would limit what businesses could collect and ultimately do with their data, whether it be analyzing clickstream data to track customer behavior on their own site or trading information with data providers and other retailers to get better qualified lead information.

As someone who is entrenched in the analytics business, I fear the backlash from overzealous companies that trade on customer trust in return for increased revenue streams. There is much discussion online over NebuAd’s deep packet inspection technology being deployed by many ISPs to track individual user’s web activity for micro-advertising. While they provide an opt-out service for consumers, it’s still unclear whether you are opting out of receiving micro-ads or opting out of being tracked by the service. There was a similar outcry with the launch of Facebook’s Beacon technology which sent data from external websites back to Facebook for better ad targeting. Although they too now provide an opt-out for users, many bloggers and websites have resorted to publishing step by step instructions for completely blocking the Beacon technology through your browser.

Clearly, there is a middle ground where both consumers and businesses will feel comfortable with the amount of data being tracked and analyzed - my hope would be that this is can be found through best practices and not legislated through Congress. In the meantime, companies should focus on the following to maintain a level of trust with their customers:

• Transparency - Most important, let your customers know what information you intend to collect and how you intend to use it - and more importantly make this statement prominent and easily accessible on your website. Many customers don’t mind being targeted as long as they know up front how the company aims to target them.
• Opt-Out and Opt-Down - All companies should provide an opt-out of targeted advertising, but most should also provide an opt-down - the ability to decrease (or in the rare case, increase) how often or which types of products get advertised to the individual user.
• Anonymization and Obfuscation - While email targeting usually comes down to the individual level, most data analysis is done in aggregate and doesn’t necessarily need personally identifiable information to come up with important rules and formulas. If you’re working with a data analysis provider, they should be able to outline what information they need and what information can by anonymized or obfuscated to hide the identity of the underlying customer.

If all companies could follow these simple guidelines, I think we could find a happy medium between information collection and consumer privacy that wouldn’t necessitate legislative intervention.

Just finished getting through the backlog of my Don Dodge RSS feed today and I’m happy to report that venture capitalists seems to think that businesses like Istobe are about to break out. Let me qualify that. Venture capitalists seem to think that using data to improve e-commerce is an industry that clearly needs some maturing and that maturing time is nigh. Istobe represents that maturing, combining hundreds of models and data integration routines into a package that lets you target the right customer with the right product at the right time.

Investors believe that the maturation in this industry will occur in the next five years. Well, so does Istobe. We believe that it’s time to put your data to use. If you don’t use it, data is no more than the new shelfware: that software you just had to have before you realized you lacked the in-house talent to unlock its value. Istobe is your outsourced in-house data analysis talent that lets you ask simple questions and get answers without analysis, questions like: I need to sell this overstock of shirts, to whom should I market them? In reply, Istobe gives you a list of your customers that are most likely to buy your shirts and the probability that they will buy. This is the new paradigm in predictive modeling that Gartner calls the data mining packaged application. Let’s just take a quick look at the moment in time at which we are poised.

Data collection methods are clearly refined

Nowadays, everyone is sitting on a pile of customer data that they don’t know what to do with. As Rob Hayes, partner at First Round Capital, says in the Stefanie Olsen article from which Dodge draws his inspiration, “Everyone talks about all the data that’s being created and how valuable it is, but the way you make it available is by doing something actionable with it.” The glut of data is due, in part, to years of CRM implementations and the current CRM zeitgeist. You can’t turn around without being inundated by a flood of marketing for “next generation” CRM systems. In fact, I used to be one of those marketers at a not-too-small company that builds Dynamics CRM.

Of course, online everything has made data more prevalent as well. Returning some kind - any kind - of functionality in exchange for your profile is no longer new hat. Every widget and social network known to man requires you to divulge information before you start using it. And then it collects your clickstream as you use the app. Heck, I’ve got at least 50 different login/pass pairs that I need to remember now.

Purchases, as well, fall into this category. With online shopping increasing at a terrifying clip, all of your purchases are more seamlessly collected and tied to your profile and your clickstream, meaning that now, more than ever; pre-purchase behavior and purchaser characteristics - on a large scale - are at a marketer’s fingertips.

Data analysis tools have matured but haven’t turned the corner

So there are various types of data out there right now that need tying together and, ultimately, analysis. But have the tools to merge and make sense of that data improved? Not appreciably. Really, when you get right down to it, the tools used to perform data analysis are still catch-all tools that can build any model you want or merge any type of data you want. But they can’t help you with specific business problems. In other words, the tools exist for database experts (data integration) and PhD statisticians (statistical modeling tools).

For years, these tools have gotten easier for experts to use but haven’t gotten any easier for business users. This means that your $300K worth of in-house data integrators and PhD statisticians have become slightly more productive over time but translating their language into the language of business is as difficult as ever. And turning the data they spit out into a meaningful business strategy is just as tough.

With today’s news that the retail sector is experiencing a slowdown, now is a better time than ever for multi-channel retailers to do two things: turn to cheaper forms of advertising (email) and use quick-return customer analytics to compete with gargantuan discounters like Wal-Mart that threaten to swallow retail whole. The truth is that Wal-Mart will continue to invest in analytics during the tough economy because they will see immediate ROI from understanding which customers are poised to buy, which items they want, and how much those customers are willing to spend. I can think of two, good reasons for smaller multi-channel retailers to follow suit.

Harvest your current customers
Most would say that the thick of a poor economy is a poor time to invest in new marketing projects. If these projects are tied to new customer acquisition, I might agree. It’s damned expensive to acquire customers and you tend to forget what you already have while you’re out prospecting, buying lists, etc. Sometimes, the answer is in front of you. In a poor economy, isn’t it imperative that you retreat to your base? Multi-channel retailers need to figure out ways to:

A. Not lose your current customers to competition (like Wal-Mart)
B. Harvest your existing customers by making them feel as though you understand them

Really, achieving B is the answer to question A. A redoubling of your customer service effort will always make your customers more loyal and less likely to jump ship. But we have to remember that larger players can always offer deeper discounts in an effort to combat your superior customer understanding. One way around this is to deepen your customer understanding on the marketing front with timely, personalized emails to your customer base. Ultimately, if you can address your customers’ needs first - make your customers offers at the cusp of when they need those products - then you are likely to win their business. This is the advantage that predictive models based on your customers behaviors provide you: the ability to beat your larger competition on timing as opposed to discounting.

Quick ROI
Customer analytics like those that Istobe proposes are great because the analysis takes advantage of data that you, as a multi-channel retailer, already possess. You’ve already got a record of your cusotmers’ purchases. In other words, there is no up-front infrastructure or talent investment. What this ultimately means is that your ROI emerges quickly. How quick? Well, let’s just say that you’re in the black (or, green) around month two. This is especially true if you’re already used to sending your customer data to a co-op database (like Abacus or NextAction); you’ve already made your data collection and transfer investment. Now it’s simply about turning those investments to a different use - customer development not acquisition - by focusing how that data helps you pull in the monetary margins in your current customer base.

These days even the most technophobic consumers have inboxes full of marketing from companies they have interacted with. As responsible marketers, we have ensured that these customers have opted in to our communications and we know that we must promptly remove them from our house file when they no longer want to hear from us. However, according to Marketing Sherpa’s Email Marketing Benchmark Guide 2008 (summary here), ensuring opt-in may no longer be enough to keep our company’s image clean.

In a survey of over 4000 consumers, half consider email to be spam if it arrives too frequently, even if it comes from a known sender. This has serious consequences for email marketers using “carpet-bombing” strategies to spur customers to purchase. Even if consumers have opted in and know a company well, they may come to think it as a spammer if they are receiving marketing emails every day or every week.

The sentiment that, regardless of permission, frequent email marketing is spam will only grow as inboxes become even more flooded. Marketers will be forced to migrate to a “surgical-strike” strategy where customers are targeted with highly personalized messages only at the most likely time to buy, and probably no more than once a month.

In an environment where consumer trust is hard to gain and can vanish with one misstep, nobody wants to be seen as a spammer. Unfortunately, the risk of marketing too frequently is now beginning to outweigh the benefit. If email marketers do not adapt through better targeting, they may find themselves relegated to the junk folder for good.

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.

I noticed that the RRW Consulting blog alluded to an article on Friday that I have been promoting to my peers: a research report by the Aberdeen Group (abstract here) that discusses the importance of email personalization. The one-to-one marketing emphasis in the article is precisely the kind of email targeting that we espouse here at Istobe. Today, I want to expand on one aspect of the Aberdeen report that we spend extra time on at Istobe: the importance of the buying cycle in determining what kind of email message to send your customers.

In the Aberdeen article, Ian Michiels mentions that web analytics provide great clues to assessing where customers are in the buying cycle. For example, if a customer invests a vast amount of time clicking about a product group, that customer is likely doing research and is in the market to buy a product in that area. A discount offer, Michiels says, would likely get this customer - who is now highly qualified and advanced in the buying cycle - to act on their desire and make a purchase.

I totally agree with this sentiment. But as Chris mentioned in detailing his experience with GPS systems at Amazon, there is another way to do this. Customers can clue you into what they want via their clickstream. But even if you don’t have clickstream data, transaction histories, once supercrunched, can give you a leg up on finding customers who will likely buy next. In other words, this supercrunching can help you locate the customers that will likely buy before they locate you.

How does this work? Well, other customers have come before them and laid out patterns that aren’t perceptible to you and I but are very perceptible to Istobe’s predictive models. Istobe’s models throw out those customers that are not likely to buy again and then work with those who are. From there, Istobe’s models assign the products that are likely to be purchased by these likely buyers.

I won’t argue that this method is more statistically powerful than clickstream data, which is a solid indicator of future behavior. But I will argue that clickstream data takes vast amounts of resources to capture and use, a difficult proposition for online retailers who are just dipping their toes into analytics. And using transactional data to predict who will buy next is a more proactive approach. So what do you get from that proactivity? Probably a two- to three-month head start on your competition. You can focus on targeting your “most likely” customers with act-now offers while your competition waits for these customers to visit their web site.