eCommerce Analytics

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.

Although predictive analytics systems have become more popular in the last couple of years the term and the systems themselves still have a great deal of mysticism behind their definitions and operations. In this post I will reveal the best-practices based process we follow when delivering our predictive analytics solution in an effort to remove some of the mysticism surrounding these valuable systems.

Let me set the stage by defining what predictive analytics is and what information is needed. As the name suggests, predictive analytics systems attempt to forecast trends and behavior based on historical information. Essentially they predict what will happen given past experience. A good marketing example is product bundling or cross selling. If many customers are buying Blue-Ray DVD players and a Spider-Man DVD then the predictive analytics system will report the correlation and possibly drive a new campaign to offer a movie-player bundle.

Not surprisingly, a predictive analytics solution is built on a foundation of data, specifically operational data. Operational data is a collective term having several definitions but for now we will define it as any data originating from a business operations system. Customer order information, on-line shopping activity and direct-mail responses are all examples of operational data.

There you have it. Predictive systems use your operational data to prophesize the future. In our case we are forecasting customer specific trends and predicting how your customers will behave in various marketing scenarios. Now that we know what we are dealing with let’s get into how your data is turned into a valuable analytics solution. The first step involves finding the right data to work with.

Data Selection and Retrieval

As you can imagine even a small business generates vast amounts of operational data so we must filter out the noise by locating and identifying the data relevant to our predictive analytics solution. Just like preparing to buy groceries this step requires a human to review the available data sources (on-line traffic logs, historical orders, and customer portfolios) and then grade each data source by fidelity and quality. The data grading checklist used by Istobe is too comprehensive to discuss in this post but here are some example questions to help you do the same:

• Is the data redundant (e.g., do multiple account or customer numbers exist?)
• Is the data updated by a human or a machine?
• What is the data’s lifetime? Or how long does the data stay intact?
• If the data is related to another source how is the relation made?
• Does the data drive any business decisions or is it directly used in any reports?

After each data source is graded we can start to figure out what to keep and how to improve it. For the data sources that we want to keep it is usually necessary to filter out dirty data by running it through a cleansing process. You may be surprised to hear that your data is probably very dirty but even in 3rd party systems dirty data exists. Imagine these scenarios and you should get a feel for the hundreds of other ways dirty data can get inserted into your data sources:

• Users trying out new features in a CRM system
• Test data inserted for quality control
• Data entry errors
• Historical data that was updated but never removed
• System upgrades or merges

In its most basic form the cleansing process sets out to eliminate the dirt by:

• Standardizing specific values e.g. date and time formats
• Removing duplicate information
• Removing inconsistent data (e.g., orders which were never completed)

The Data Selection and Retrieval phase is the most intrusive (as it requires collaboration between the custodian(s) of the data and the group building the predictive analytics system) but it is also the most important as it sets the foundation from which everything else is built.

In the next post I will discuss how the cleansed data is used in the Knowledge Creation phase.

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.