Computing Customer Lifetime Value

By Pooja Ranganathan

A few days ago, Avinash Kaushik and David Hughes published a post on Customer Lifetime Value. David was kind enough to provide a downloadable Excel template to get readers started on their own LTV analysis. The post and the worksheet provided a perfect opportunity for us at Medill IMC to vet our techniques against those used by industry practitioners. Last quarter, Professor Ed Malthouse taught several models and approaches to computing customer lifetime value at Medill.

The basic premise in both techniques is the same:

1. Segment your customers. On this note, David’s idea of segmenting by acquisition channel for optimal allocation of your marketing budget is excellent. He also suggests segmenting by purchase behavior, using differences in average order value, frequency of purchases, retention periods, etc.
2. For each segment, compute retention rates. What percentage of the original segment do you retain year-on-year?
3. Estimate the net profit of customers retained each year. This is a function of frequency of purchase, average order amount, and costs.
4. Discount each year’s net profit to get its net present value, less acquisition costs. The sum of all NPVs less acquisition costs is the customer lifetime value.

I won’t cover this process in more detail as it has been wonderfully explained in the original post. It’s almost exactly the same as the technique taught at Medill, with a few differences:

• David’s technique accounts for different average order amounts, as he uses it as a basis for segmentation (“best” vs. “average” customers).
• The technique taught at Medill accounts for “recency,” which is the period in which the customer last made a purchase. Your “active” customers in the current period would therefore include the customers that you retained from the immediately preceding period, as well as those you lost earlier that renewed their relationship with you in this period (after an absence of a few periods). The retention rate for the latter would vary based on recency as the longer a customer is inactive, the lower his probability of renewing the relationship in a later period.
• By accounting for different recencies, we can vary our marketing expenditure accordingly, and spend less on customers that have been “inactive” for a longer time.

A happy union of David’s technique and the approaches taught at Medill is a small step away. One way of doing it is to segment customers based on revenue or average order amount (deciles are a common way of doing so), and replicate the spreadsheet for each of the top deciles.

How would you go about estimating LTV? Are there any further tweaks you would recommend?

***

Pooja Ranganathan is a blogger at Vitamin IMC and a student in the Masters in Integrated Marketing Communications program at Northwestern University’s Medill School. She can be reached at PoojaRanganathan2010@u.northwestern.edu

The science of marketing: What makes IMC different

By Daniel Hindin

What is IMC and how is it different than traditional marketing? We Medill IMCers get this question a lot.

Part of what sets our graduate program apart is the diversity of knowledge we develop. By the time I graduate this December, I expect to have taken courses in five subject areas: Branding & Advertising Strategy, Media Management, Direct & Interactive Marketing, Marketing Analytics and Corporate Communications & Public Relations.

Though marketing is both an art and a science, I find that most people only think of the art, the right-brained side, the creative message. Marketing agencies have long had the reputation as a place where you hole up a bunch of creatives until they come up with the magic message that will get your product flying off the shelf.

It turns out there’s a lot more to it than that. Sure branding and corporate communications are important tools for any well-rounded marketer. That’s where the message is ultimately formed. But making decisions within those areas should be far more than the gut instinct that determines the fate of too many marketing budgets.

The science, the left-brained part of marketing, is what should drive any smart marketer’s decisions. This is where hard data comes into play. Who’s spending? When? On what? Do the profits from each customer exceed their costs? Yes? Well, these are the people to target with your messages. No? Then they’re just costing you money.

Once you know who your ideal customer is, you can take the data a step further and figure out what types of messages spur them to action and in what form those messages can be delivered most effectively. Through analytic tools such as multiple regression, cluster analysis and factor analysis, you can figure out what is likely to work and why.

Now you’re on your way to understanding how to deploy your creative team. Marketing will always be part art. But when you start to use data to form the basis of your art, that’s where science comes in. That’s how you get results. And that’s how you speak the language of the CFO and other budget decision-makers.

***

Daniel Hindin is Managing Director of Vitamin IMC and a student in the Masters in Integrated Marketing Communication program at Northwestern University’s Medill School. He enjoys using as many different parts of his brain as possible. You can reach him at DanielHindin2010@u.northwestern.edu.

CVS Kiosks get Intuitive: How behavioral data can tailor coupons to individuals

By Brad Mild

We can all think back to a time when our parents would sit down with a Sunday paper, pull out the coupon insert and begin to snip out one promotion at a time. Then, they would place the clips of paper into a little envelope and set it aside for the next trip to the store. The whole process took at least an hour to complete only to be foiled by forgetting the envelope at home. Thanks to CVS the days of tireless coupon clipping are at an end.

CVS stores began to roll out interactive coupon kiosks in 2007. These intuitive red boxes scan a consumer’s ExtraCare shopping card and pop out a money-saving offer based on prior purchases.

Both CVS and the consumer benefit from this relationship. Kiosks entice consumers to share their shopping behaviors in return for personally tailored coupons, and their use increases revenue for CVS. It’s a win-win.

For customers, the red box meets two needs. It saves them money and time. Tailored deals save customers money on items they want to buy without having to look for coupons elsewhere.

This process can also be fun. The shopper does not know what promotion they will receive. All they know is that it will be relevant to their needs. The uncertainty behind each scan encourages customers to scan their card more often. This phenomenon is called variable ratio reinforcement. Can you say Vegas?

So what does CVS get from the deal? Data! They collect mounds of data on purchasing patterns. This info can tell marketers about trends in seasonality, time of day, recency of purchase, and the dollars spent. All of which can help in strategic and managerial planning.

However, that is only the icing on the cake. The major gain is the ability to automate decisions and test promotions instantaneously. A computer automatically decides on a promotion and offers it when the customer is in the mood to buy. If a consumer doesn’t use the coupon that day, the computer knows that the promotion was not relevant and it adjusts. Or, if they buy a product on a regular basis and then stop, the program will notice that trigger event and alert the operator.

The question now is whether CVS has the means to analyze this data. If they do are on the right path to understanding their consumer and meeting their needs. If instead they are letting the data rot they are missing out on a solid competitive advantage .

So the moral of the story is that win-win situations exist. With a little data marketers can tailor offers to consumers and increase returns.

***

Brad Mild is the Visual Communications Manager at Vitamin IMC and a student in the Masters in Integrated Marketing Communications program at Northwestern University's Medill School. He daydreams of wakeboarding in the early morning when the water is glass. He can be reached at mild@u.northwestern.edu

Data Mining vs. Science: How OkCupid translates data into dates

By Anne Mahoney

If you’ve ever marketed yourself online in the romance department, you likely have painstakingly analyzed every part of your profile. Is the photo attractive? Do I say the right things about myself? How should I word my first email to another enticing product in the dating aisle?

An article in the New York Times looks into how OkCupid, an online dating site, shares user data with its registered love-seekers to provide advice on how to develop and market their personal brands. To find the data, it analyzed 7,000 user profiles, noting photos, number of responses and content of those responses. One useful finding was that being “fascinating” or “cool” is more important than the initial physical attraction factor. For instance, OkCupid says a woman using a photo portraying her playing an instrument or on an exotic beach receives more responses than focusing on physical assets.

If true, this certainly is valuable information for site-users. The study has, however, strictly focused on pure numbers through data mining. To gain additional insights, I sought out the opinion of the foremost expert I know in statistical data analytics: Medill IMC professor Edward Malthouse. Professor Malthouse brought up the scientific question still at large for OkCupid: Why do these tactics work? He thinks marketing can help to explain.

“Some physical beauty is a point of parity,” he said. “Differentiators will make you stand out, at least among a segment that values such activities. So, marketing theory predicts that those who are differentiated will be more successful.”

That is the scientific way to view the findings of OkCupid’s study. It hypothesized that differentiators would increase or decrease response rates, which the study confirmed. Yet there are other variables that have not been taken into account. Malthouse points out an example as the experiences of the customer, or date-seeker browsing through profiles. These experiences are not directly focused on the “product,” or person trying to find a date. If each individual created a first-impression experience for the type of person they are trying to attract – the “targeted consumer” – Malthouse theorizes it would have an even stronger effect.

Boiling down an intimate subject with numerous and oftentimes-mysterious factors, such as dating, into pure numbers surely has its challenges. Do you think there’s truth to the OkCupid study? How can marketers benefit from activating these types of analytical tactics?

***

Anne Mahoney is the Social Media Director at Vitamin IMC and a student in the Masters in Integrated Marketing Communications program at Northwestern University's Medill School. She can be reached at annemahoney2010@u.northwestern.edu

5 Costs of Social Media to Consider

Are you a marketer considering trying social media to connect with customers and stretch your marketing budget? Are you intrigued by the low entry and exit costs and the potential to become a viral sensation or social media darling? Before you take the social media plunge there are 5 costs you should be aware of:

Design Costs. From your blog to Facebook to Twitter, your social media presence should be consistent with your brand. You can save money by repurposing current assets. However, you’ll still need resources for reformatting and/or creating new artwork, uploading art, and programming and designing pages.

Paid Model. Just because something is free today doesn’t mean it will be tomorrow. In March of 2009, Twitter co-founders announced they will probably move to a paid model and just need to figure out when. So if you’ve gained a big following on a free service, what is your plan if the service moves to a paid model? Find some room in the budget and pay up? Dump your customers? Try to migrate them to another free service? If you’re investing in a tool, be sure to consider what you’d do if it moves to a pay model.

Maintenance Costs. So you’ve got your account set up, your page is looking fabulous, and you’ve added some content. Great! Now you need to continuously engage with customers by posting content, replying to comments and questions, and monitoring chatter about your brand. The setup costs for social media are relatively low compared to other forms of advertising, but the high commitment level is unique to the medium. Do you have a budget for the hours needed to maintain your social media presence? Do you have someone in your organization with the time and skills to handle this responsibility or do you need to hire someone? Will you monitor chatter yourself, or pay for a service such as Sysomos or Radian6.

Costs of Doing it Wrong. Diving into social media and then abandoning it is a waste of time and money. But even worse is making a social media blunder that could damage your brand.

Costs of Not Doing it at All. You’ve heard it once, you’ve heard it twice, but in case you forgot: Your customers are out there talking about you already. They are in control and are increasingly expecting engagement and quick responses from companies they do business with. Consider some the consequences of not engaging with customers and listening to what they are saying: missed selling opportunities, lost consumer insights, angry or disengaged customers, misinformation spread about your brand, and fleeing customers. Recently Delta Airlines learned the hard way by not having a good strategy in place.

--Marina Molenda