eCommerce Analytics

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 »

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.

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.

We talk a lot about the nuts and bolts of predictive analytics on this blog - how we build customer analytics models, how we interpret them, and how we establish whether or not they’re working in production. However, one item we have yet to tackle - and a very important item at that - is how to integrate data mining and/or predictive models into your organization and your company’s routines. Indeed, the black box in the middle should be the necessary data integration (getting the data into the right format for the models) and running a bevy of models against the data to see which one projects as the most effective. But it’s the before and after this black box that really make or break any attempt to incorporate predictive models into a company’s processes: data collection and incorporating data mining results into your processes.

Do you need to collect more data than you already have?
The main problem that companies run into is the need to collect more data in order to build the necessary predictive models. For example, if a company wants a model that tells them which of their products a specific customer might buy next, does it already collect the data necessary to support a model that does that? Ultimately, the question is: Is it worth the time and cost needed to invest in collecting more data that may not yield any better results when supercrunched? New collection methods take months to set up and sour clients on predictive models before the fun has even begun.

We believe that it’s important to try to work first with the data that our clients have and add new sources of data over time. This is a handy tip for business users who really want data mining to work in their organization: start small by using the data you have and get more sophisticated as you go. I know it may be tempting to put in that clickstream collection database right now so that you can use online behavior to segment and market to your customers. But trust me, get the buy-in from your organization first by proving that predictive customer analytics work. Then ask for the stars.

Are you ready to completely change the way that you do direct marketing?
Some companies would have you subscribe to a whole new way of doing business in order to use their models. Customer-centricity is my favorite new business philosophy. Though I firmly agree with the need for firms to be customer-centric, is it realistic to expect companies that use offers, coupons, and holidays to draw new and existing customers to their websites - and have for years - to change their direct marketing approach to accommodate a new set of tools? Not really. Company culture and routines are slow-developing and even slower-changing.

So the question at the end is really: What are you going to do with all this newfound predictive power? How are you going to fit in customer-centric model results - We’re 99% sure that Brad Pitt will buy two Baby Bjorns with lumbar support - to your next email blast that has a summer theme and features hats and sunscreen? And what about the extra creative necessary to relay that customer-centric message? You’re going to have to make a new email blast featuring the Baby Bjorn with lumbar support, in addition to the summer-themed email.

Well, I’m pretty sure that the summer themed email blast was probably going to draw some business but I’m also positive that luring customers with what they want when they want it is a lucrative way to market. The best practice is to initially skim the cream off of the predictions, to take those that have the highest probabilities of succeeding, and run with them. Collect a group of customers that has the highest probability of buying the Baby Bjord - Mr. Pitt among them - and send an intermittent email to just that group. Now watch your open rates and clickthroughs soar.

At Istobe, we’ve clearly been preaching the virtues of customer analytics for some time now, so it makes us happy when we read new research that shows an increased interest in customer centric software solutions to make better use of existing customer data. Specifically, a new report by AMR (The Customer Management Market Sizing Report 2007-2012) shows that spending on customer management applications and software has increased by double-digits for the first time since the 1990’s.

The report talks about how “an almost universal trend around customer-centricity and customer experience management will fuel continued customer management investments regardless of economic conditions” and how in the next five years “the customer experience will be the most important potential competitive weapon.”

Additionally, the author specifically singles out marketing analytics as being “even more critical in a more challenging economy as insights into customer behavior provide better visibility into future demand and the effect of promotions and campaigns” and also notes that marketing organizations “are feeling more pressure from executives to demonstrate better insight into return on marketing investment. Today’s marketer needs to rely more on technology to aggregate demand data, analyze customer behavior and close the loop on campaigns.”

The report goes on to talk about the growth in SaaS and other industry trends, but the biggest takeaway I found was that the current economic recession is a great opportunity for companies to gain ground on competitors by implementing customer analytics solutions that better focus on increasing customer retention (see our other post about that here) and improving marketing response rates. While many organizations have already planned for or implemented cost cutting measures in light of concerns over the future, I definitely believe the measure of success between firms in a given industry over the course of the next few years will largely be determined by how effectively they use their existing data to manage their business.

Last week in What is Customer Analysis? we found that the first step in a customer analysis is determining customer lifetime value across segments. Armed with this information, we can determine which customers are worth focusing our marketing efforts on and which customers should be “fired.”

The concept behind modeling customer lifetime value is relatively straightforward. We can group customers into segments which behave similarly and then based on historical data, determine how much a customer in each segment produces in profit over the course of his/her lifetime.

One thing to understand with calculating customer lifetime value is that there are many different ways to do it. Practically speaking, as long as you remain consistent in your usage across all your customer segments and across time, you should be ok using any of them.

With this in mind let’s look at one of the simpler customer lifetime value formulas:

Where,

m is the average gross margin
i is the discount rate
r is the customer retention rate

In using this simplified formula we to pick one average profit margin value and one average customer retention rate.

We can calculate m for each segment as

m = revenue - product or service costs - cost of servicing (includes acquisition and promotion costs)

over the course of a period (usually a year).

To find r, we calculate from historical data what percentage of customers in a segment repurchase in the next period (again, usually the next year). We then assume that this will also be the retention rate for subsequent years for the segment. This is generally not the case but that’s a price we’re willing to pay to keep things simple.

Finally, i is the cost of capital (sometimes called a hurdle rate). If you don’t know what your company’s discount rate is, your CFO will likely be able to give you the number. If not, you’ll usually be ok using a value between 8% and 15%.

Using the customer lifetime value formula to rank each of your customer segments will give you a solid understanding of which to court and which to forget about. In particular, any customer segment with a customer lifetime value less than zero is costing your company money. Shed yourself of these customers as quickly as possible!

Anxious to get started calculating customer lifetime value? Our customer lifetime value calculator will help you get started. If you sell online and are looking for a way to increase customer lifetime value across all your customer segments you’ll want to check out our recommendation engine.

Customer analysis is the process of determining customer segmentation, value, purchasing behavior and motivation in order to better target marketing and increase sales. Well, that sounds ok in theory but is perhaps a bit too abstract for practical use. Practically speaking, what is customer analysis, really?

The crux of customer analysis is that all customers are not created equal. Companies have some customers who are worth their weight in gold. They buy frequently and spend a lot. They are a pleasure to deal with: no returns, no complaints, no hassles. They also have customers who make life miserable. They inundate customer service centers with calls and constantly return merchandise. They buy infrequently and are happy to take their business to a competitor if it means they can save a dollar.

The top 20% of customers are the lifeblood of a business and contribute 80% of the profit. The bottom 20% at best contribute nothing to the bottom line, and at worst cost more than they contribute.

The purpose of customer analysis is to identify the top 20% of customers (gold customers), middle 60% (silver customers) and bottom 20% (lead customers). We then try to determine how to keep the gold customers happy and how to encourage silver customers to become gold customers. Then we have to figure out what to do with our lead customers.

We can do a basic customer analysis in four steps:

1. Who are my customers? Which customers are valuable? Which aren’t?

There are many ways to determine a customer’s value. One of the most accepted from is using a metric called customer lifetime value (CLV). CLV estimates how much a profit customer will contribute over his/her “lifetime”. By comparing CLVs among customers (or more often, among customer segments), we get a good idea of which customers are valuable and which are not.

Another method that can be slightly easier to use is a recency, frequency, monetary analysis. This method breaks customers into groups based up on how often they purchase, how much they spend on average, and how long it’s been since their last purchase. Customers who rank high in all three criteria are most valuable while those that rank low are least valuable.

Over the next few weeks we’ll talk more about these techniques and how to use them. The general idea, though, is that we separate our customers into gold, silver, and lead groups based on how much profit we expect them to contribute.

It is important to note that the gold, silver, and lead groups are not customer segments. Customer segments are a function of demographics and behavior, not value. Our gold customers can be made up of many different segments and the customers who comprise a particular segment may differ wildly in how much money they spend.

To make things a bit more confusing, customer segments can also have CLVs associated with them. Assigning a CLV to a customer segment is a useful technique in determining how much to spend to acquire a customer in that segment. We’ll touch on this more in step 4.

2. What do I do with my most valuable customers?

Congratulations! You’ve identified your lifeblood customers. These customers feel like they have a relationship with you and buy based on this relationship, not on price. From a marketing perspective they are already “maxed out”. You can’t spur them to spend any more with you — they are already giving you all of their business! All you can do is make sure that they are stay happy.

The single biggest mistake in marketing to these customers is to give them discount offers. These customers do not make their decision to purchase with you based on price. By giving them discount offers you simply throw money away and, worse, train these high value customers to wait until they receive a discount offer before buying.

One of the best ways to market to your gold customers is to offer perks that are not available to other customers. This is essentially what airlines do with their status levels. By treating these customers as special, they will be more likely to continue spending with you to maintain their special status.

Sometimes, however, it can be appropriate to give these customers special offers that aren’t necessarily discounts. This is something we will discuss in a future post.

3. How do I make less valuable customers more valuable?

We have to face facts. Some customers are too far gone to save. Lead customers are money losers. It is almost always best to stop spending marketing dollars on them. If you can entice them to go to your competitor it is a double win — not only do you get rid of a money losing customer, but now your competition is saddled with them.

The majority of your marketing budget should be spent trying to move your silver customers up to gold customers. This is where the real action happens. You can entice customers to step up their spending with a clever combination of incentives, promotions, and discounts. Since each segment can be expected to respond differently to your messaging, however, it is critical to really understand what drives these customers to buy. Often the best way to find the optimal marketing mix and message is through rigorous testing and analysis. No fun, maybe, but the payoff is huge if done correctly.

Again, we’ll talk about specific testing techniques in a future post.

The simple rule in customer acquisition is that you want to spend money to acquire customers who look like your current gold customers. These are the customer segments that are most represented in your gold group. It’s ok to spend to acquire silver customers, too, as long as the cost per customer is less than the expected lifetime value of the potential new customer.

This high level view of customer analysis is a good starting point for companies who are looking to use their customer data to increase sales and get better ROI from their marketing budgets. By following these 4 simple steps you’ll be well on your way to understanding your customers and better targeting your marketing spend.