As the US and the world economies encounter a downturn and firms look to scale back, Marketing is often one of the first places to face budget cuts.  Forrester reports that many companies expect to cut their marketing budgets by 3%.  But how do you maintain or grow your customer base and revenues when consumers are spending less and your message isn’t getting into the marketplace as loudly?

We think the use of online coupons deserves a harder look.  Emailing your customers and prospects with newsletters, product updates, and coupons is certainly nothing new, but it’s now well-positioned for even greater success:

• Companies are getting good at it. After dabbling in techniques like SEM and direct email, firms have gotten better at driving profitable growth from these methods, and many are increasing their focus on online advertising as a cheaper way to spend their marketing dollars. 
• Consumers want more of it. During these uncertain times, consumers plan to increase their use of coupons to save some money.  Sending these options straight to their inbox or mobile phone accomplishes that goal and positions you as a preferred provider.
• Consumers who use it are attractive prospects. Compared with consumers who only use offline coupons, Forrester reports that users of online coupon tend to have higher incomes, shop online, like to try new products, and influence peers.  Younger consumers also use coupons, and they can be a good avenue to get the word out about your product.
• More data is available to help you win at it. More firms sell marketing lists (or can help you run campaigns to get new lists), segmentation data helps you understand consumers’ preferences and desires, and syndicated data helps you understand purchase behavior.  Combining this data gives you incredible insight into consumers to tailor unique marketing messages.

You don’t just want to throw promotion dollars at existing customers to give them discounts on things they were already going to buy; rather, you likely want to use those dollars to deliver positive returns and achieve business goals – such as acquiring new customers, increasing market share, or increasing wallet share.  Doing this requires targeting offers to customers based on their stage of the customer life cycle:

1. Acquire. Coupons can be a good tool to help consumers overcome the risk associated with trying a new product; if a new product is cheaper than the one they normally use, the savings might be worth trying.  You can use them to attract entirely new customers to your firm, or to get your existing customers to try a new product line.  Targeting early adopters can also help generate buzz, as they will influence friends and family to buy the product as well.
2. Grow/Stimulate. Once you’ve acquired a customer, you want them to maintain or increase their purchases.  Two ways of stimulating usage are encouraging them to try a different variety (e.g., color, size, flavor) or showing them new uses for the same product (e.g., using Q-tips for craft projects in addition to hygiene).  In this stage, the focus should be on the marketing message, the coupon being used to help seal the deal and drive the customer to the store.
3. Manage. In this stage, your customers are steady-state users, and couponing may not be required to retain them. However, these consumers present a good opportunity to test new offers on an already loyal customer base and measure the response before using them on the general public.  You might test them using different demographics, layout, or wording, perhaps even running controlled experiments to determine which of two offers is more effective.  We’ve done some research on the use of Behavioral Economics to improve offer design, which might be helpful in performing this testing.
4. Reclaim. If customers reduce their consumption or begin to try competitors’ products, you can use targeted offers to reintroduce your product and retain them as customers.  However, depending on their needs and your product pipeline, you may otherwise opt to move back to the beginning of the life cycle and acquire them as customers of another of your products.

This strategy requires a high level of customer insight to understand preferences and stages in the life cycle.  You can gain this insight by applying segmentation schemes to your lists of customers and prospects, and by analyzing your customers’ history of purchases and coupon redemption.  Applying a rigorous testing approach will help you identify the most effective offers for each customer and stage.

Applying this framework to understanding your customers and targeting coupons will deliver several benefits, including:

1. Strong ROI potential.  Campaigns that are more effective and lower-cost, targeted at attractive customers, have a stronger potential to deliver a positive ROI.
2. Better data to analyze results. Results of online campaigns are easier to track and measure than traditional campaigns, particularly if your coupons lead customers to purchase from your own website.  Analyzing results from campaigns that involve multiple partners may require a different approach, as Vishal outlined in his earlier post on trade promotions.
3. Better customer relationships. You can use the insight you’ve gained about your customers’ behaviors, preferences, and purchase history to continually develop targeted offers.  This level of personalization will help you deliver the right offers to the right customers at the right time, and ensure that your promotion dollars are spent most effectively.

Some recent academic literature has shown that companies using the standard Net Present Value (NPV) approach to calculate Customer Lifetime Value (CLV) and allocating their marketing spending might be losing substantial profits.

The standard CLV approach calculates the net present value (NPV) of all the anticipated cash flows coming in (revenues) and going out (expenses) over the projected life time for a given customer. Customers with a positive NPV are aggressively marketed and the ones which do not have a positive NPV are dropped. This is a pretty logical choice at the point of calculation- but only then. The existing conventional structure of calculating CLV is static and has the potential to leave money on the table for the company; because it does not account for the fact the company has the flexibility of abandoning a customer at any future point in time. Flexibility means options and options have a value.  The paper lays down a methodology (based on dynamic programming) to explicitly estimate the value of such options in the CLV calculation.

The authors of the paper cite the example of a specialty catalog company. They took data of 12 years and around 100,000 customers, and the difference in CLV using the traditional versus real options was as much as 20% in some cases. In fact, certain customers who had negative CLV’s turned positive when the value of real options was considered.

Retailers, communication companies and consumer finance companies who frequently use CLV as a decision making metric, are strongly recommended to read this paper.

A set of whitepapers for the executive who wants to leverage the data to make smart marketing investments:

1. Identifying attractive markets and understanding your customers
2. Increasing online sales
3. Focusing on profitable distribution partners
4. Improve customer experience through call center data
5. Retaining your customers

The Net Promoter Score (NPS) methodology uses a 10 point Likert scale to calculate the all important metric. I am not sure whether any research was done before a 10 point scale was chosen, because the scale certainly can influence the metric. In general, this caused me to wonder how to decide whether a Likert scale survey question should have a 5 or 10 or any other point scale.

A quick google search pointed to some academic research. While there are no rules of thumb, here are a few guidelines to consider when you are deciding on the scale.

1. Respondent knowledge of subject matter:
1. If the respondents are not very familiar with the subject matter – they tend to abuse the endpoints of a longer scale.
2. Respondent frames of reference:
1. More response options introduce error when respondent group has very different frames of reference.

So if you are asking about a tax filing software and the respondents have just used the product to do their taxes, then their knowledge of the software and their frame of reference will be relatively similar – so a 10 point scale can provide more nuanced results.

However, if you are doing a survey to determine whether the respondents will sign up for a new wireless phone service – where wireless usage and needs of the group might significantly differ and also their knowledge about various services – a shorter 5 point scale might be the better option

Update: NPS does have a whitepaper on the topic where they justify their scale

Steve Shu had quoted his wife (Suzanne Shu, a Professor of Marketing at Southern Methodist University) on the issue of indefinitely delaying reward redemptions.

Shu’s comment is interesting from a behavioral model for rewards redemption point of view. A credit card holder will be interested in those redemptions that suit his choice and utility function. True- a rewards program in itself manages to alter the choice function available for the credit card holder.

The linked article and another US News article highlight some interesting attributes of rewards redemption by consumers

1. Incentives –
1. Leading to purchase acceleration
2. Guilt/Excitement of opting for something that they would not otherwise
2. Disincentives –
1. Indefinitely delaying an aspirational redemption for a special occasion
3. The Cost Benefit Analysis -
1. Cost of Card
2. Benefits from Reward
3. Cost of payments/credit card usage

The closest we can come to creating a consumer choice/utility function in the database driven world is by collecting and integrating 5 different sources of information –

1. Demographic Data – Age, Gender, Ethnic Background, Education level, etc. Available through census, application forms filled by customers, etc.
2. Economic Background – Income levels, saving and investment data. Available through application forms, relationship level view of a customer, etc.
3. Behavioral Data – attitude, interests, etc. Can be collected through surveys and lifestyle data bureaus.
4. Bureau Data – e.g. risk bureaus such as Experian, FICO score, or lifestyle data from Claritas.
5. Transaction Data – prior credit card usage data, payment data, etc.

Demographic and behavioral data together would be ideal to define a person’s wants, while to build a choice model, it would be a good idea to include Economic and Bureau data as well. The transaction data can be used for validation and refinement of the choice model.

Back to the articles, a choice model can help us explain and manage all three aspects–

1. Incentives – through a greater understanding of choice limitations and utility functions, we can customize offerings that maximize the utility for a customer at the lowest possible cost
2. Disincentives – any alternative outside the “want” area and significantly beyond the choice area suffers from possibility of being delayed, as pointed out by Suzanne Shu.
3. Cost-Benefit Analysis – an economic model of choice can bake in the tangible costs of a reward program to optimize profitability.

Some of our other posts on credit card analytics are here

A recent paper in the Journal of Marketing reports on a study done to examine the Net Promoter Score (NPS) and firm’s revenue growth and found that NPS is not the best measure to predict growth as claimed by its proponents.

NPS methodology recommends conducting the customer satisfaction survey with one question: How likely are you to recommend company or product X to your friend or colleague and the answers are measured on a 10 point scale. People selecting 9 or 10 are called promoters and those selecting between 0-6 are called detractors. The net promoter score is the difference between the two numbers and claims to predict firm growth.

The major issue with NPS is not that it does not predict growth, but the fact is that it is not actionable. NPS does not link to any specific business metrics which the company executive can influence.

As Larry Freed points out:

What you measure will determine what you do
The results of your measurement will determine where you decide to allocate resources. If you’re asking survey questions about the wrong area, then you’re going to invest in improvements to the wrong area without improving what you set out to improve.

Customer satisfaction is a complex metric based on multiple dimensions along which the customer interacts with the company. A carefully designed survey questionnaire based on hypotheses which link to clear business action is the first step towards understanding root causes of customer pain points and identifying actions which will ameliorate them.

Photo credit: Zoom Zoom

Not many of us think about Amazon’s free shipping offer as a rewards program. That is exactly the point Jack is making as he classifies Amazon’s free shipping offer as a continuous reinforcement schedule. He uses a schedule of reinforcement framework proposed by psychologist B.F Skinner and applies it to rewards programs.

A continuous reinforcement schedule is dangerous. At best, the promotion shows a little lift in the short term and a huge decay in the long term (doing nothing in terms of real loyalty). At worst, you unintentionally create new business rules for your company. When those rules inevitably change, you alienate people who migrated to your company because of them. You even risk teaching users new behaviors that are bad for business

Apart from continuous reinforcement, the other reward categories are:

• Fixed ratio (SUBWAY’s “buy 12 feet, get one foot free”) and variable ratio rewards (lottery)
• Fixed interval (Restaurant happy hours) and variable interval rewards (radio contests that grant prizes “sometime this hour”)

It is an interesting framework to generate hypotheses for rewards programs, which need to be then quickly tested in the market and analyzed to determine the most financially positive program structure.

We earlier wrote about some recent academic literature on customer loyalty and highlighted the importance of a good market testing and campaign management capability to determine the optimal rewards program structure.

In any customer facing business, the channels used to reach out to your customer as well as the channels your customer use to consume your products and services have a strong correlation with overall profitability. 

From the B2C perspective, advertisements, stores, online, mailers, charity, etc. are different ways of reminding customers about your offerings. From a C2B perspective, again, stores, online, home delivery orders, etc. are ways in which consumers consume your products. 

However, the key here is to understand which customer prefers which B2C and which C2B channel. Right-Targeting Customers is as important as Targeting Right Customers! Someone who spends 14 hours a day in front of his computer and has no time to go to a store 5 days a week may prefer a home delivery channel. On the other hand, a student in a college is only interested in the bargain channel, irrespective of the inconveniences, maybe. This report also mentions how retailers need to manage their investments across channel against the scale and timing of their expected return. I would go beyond Ron’s Right Channeling [read post] to include all aspects of targeting under the concept of Right-Targeting. Having said that, I agree that today’s world is about multi-channel customers, and the need of the hour is to optimize channel returns, rather than just channel re-alignment/phase-out. 

Targeting Right Customers- Its equally important is to understand how channel profitability gets affected if you are not targeting the right customer. For instance, Wal-Mart, even with its Everyday Low Prices (EDLP), must be making money on some products/ some SKUs and these would drive the overall positive profitability. However, what if your customers are not buying your profitable SKUs? What if the draw that brings them there is not luring them to buy more? What if there is no up sell/cross-sell/bundling that happens there? And suddenly the business realizes that channel profitability is coming under immense pressure! (One of our earlier posts tries to answer the question of market basket analysis and product bundling). 

Re-Targeting - And last but not the least– taking the difficult decision of phasing a channel out. If it’s not generating any returns (directly or indirectly), the business needs to reconsider the cost of the channel. But what about retaining those customers who are loyal to the channel and have helped you get some mileage out of it? They need to be Re-targeted with a new offering/communication. Traditional retargeting refers to targeting customers who did not convert the last time, though!

This report (PDF) here talks about issues to be kept in mind when moving customers from one channel to another. I am sure Kevin would want to talk about Multi-channel customers being better than single channel customers, and without refuting his argument I would classify my argument as being restricted to customers that are being phased out of a channel. This example demonstrates one case where the retailer has effectively leveraged multiple channels over a period of time by effective use of catalogs and internet channel. 

In Summary

• Right Targeting – Know your customer. Cater to their needs through their preferred channel. It increases the share of wallet, as well as longevity of the customer
• Targeting Right – Pick your customers for each channel-product combination well. Know what and how you are selling and who will buy it.
• Re-Targeting – If it’s imperative that you move away from a particular channel, think about retaining your loyal channel customers. A little caring goes a long way in creating customer loyalty.

The qualifying argument behind all this is that if you are thinking about channel optimization, it needs to be a more concerted discussion based on your strategy/business objective, your data, your needs and your concerns. What we know for sure is that strong data analytics is known to help not only Targeting Right (which is the most common application), but Right Targeting, as well as Re-Targeting.

Ron’s post made me think about the implications of this suggestion on the global banking system. Getting a dynamic price optimization algorithm at a relationship level in place requires an integrated view of the customer.

Almost every bank that I’ve worked with or have talked to, suffers from the problem of disintegrated views, LOBs competing amongst each other, and the customer being valued at the product level and not the relationship net worth level. What’s surprising is that every bank is aware of this problem, is trying to address it in its own way, and still far from being successful.

Ron talks about the futility of this pursuit and the importance of Loyalty Programs in achieving it. Let me look at another side of the story – The strategic focus required for solving this problem. Banks are either sales focused, or profitability focused.

Sales focused banks play the volume game with hundreds and thousands of cards getting shipped every day to unsuspecting customers. The same customer may have a silver, gold, platinum card from the same bank. The idea is to carve out a bigger and bigger share of the wallet without getting bogged down by the profitability levels, as long as even marginal amounts are being made. The same customers are being approached for mortgages, auto-loans, refinancing schemes, personal loans, etc. The pricing of each of these products is done individually, and I mean “individually” at product level – each product is priced separately, The debate is around moving to each individual getting priced separately for her/his bouquet of products.

Profitability focused banks operate on the P&Ls and margin levels for each product. They keep the back-end and front-end of each of their products separated. There is positive money and higher margin, but not a lot of it (in some cases, there might be a lot of money in niche operating areas). However, at some point all such players realize that scale comes from the masses. A seemingly premium American Express card gets offered free for a lifetime in some of the developing countries.

Now, having said that there are two kinds of banks, both of them eventually will try to move in one direction – the integrated view of the customer followed by a relationship management approach. Loyalty programs may help get more data and act as an incentive for customers to proactively get their unified integrated presence in the bank databases. However, loyalty programs are still a piece-meal solution. The effort required is higher, turnaround times longer, and vigilance is incremental. Changes need to be introduced gradually and their effect/success needs to be monitored.

Compare that to the “break the bank apart” module before reorganizing it. The upfront investment is higher, the complexities overwhelming and the implementation time can be long initially. What the banks need to realize is that the investment required to upgrade to an integrated view of the customers is a strategic investment to be amortized over several years. Any bank ready to take the pains and go through the organizational and technical barriers will find itself way ahead of its competition. The proof of this lies in the gradual advent of Direct Banking, which forces an integrated view on the customers for all the products offered since there aren’t multiple human-to-human touch-points.

At Diamond, one of our key strengths has been the ability to think about, create plans for, and help our client implement both. Helping client redefine their data systems, architecture, program managing it to successful implementation, followed by the ability to create a relationship view and management approach. Our analytics approach has been geared at looking not just at the problem at hand (increasing sales, improving profitability) but also at increasing the impact of the business for overall success.

Ron (commenting on Larry’s post) identifies loyalty program as a key reason for the holiday season success of online channel of retailers. Apart from driving sales, loyalty programs generate valuable data which can be analyzed to determine tactical strategies like cross-promotional programs, estimating discounts on bundles etc. Here is a four step actionable process to exploit the loyalty card data, and use it to broadly target customers with effective offers, even those who are not members of the loyalty program.

Step 1
Append an off-the-shelf segmentation schema (such as Claritas Prizm NE or Acxiom Personicx) to the loyalty card customer data (matched on name and address information) to get an understanding of segments of your customers and associated demographic and lifestyle behavior

Step 2
Identify cross-promotional opportunities and determine optimal discounts by using the purchase history data of loyalty card customers to determine which products are bought together by customers (product correlations in the market basket). Use data from past promotions to identify customer’s sensitivity to discounts (paying full price vs. buying at maximum discount) and overall volume and profitability of bundles. A simple visual will show a set of product associations (at various discount levels) helping to identify products that can be bundled. Green boxes in the diagram below represent recommended bundles.

Step 3
Using the augmented loyalty data identify customers who purchased the recommended bundles and build a propensity model to score segments based on their likelihood to purchase the bundles. Rank order the segments based on their propensity to respond to particular offer bundle.
Step 4
Integrate this segment ranking information into your online and direct marketing targeting schemes.

Now, not only do you know what to bundle, but also which members to offer the bundle within your loyalty program. Since you have used an industry standard off-the-shelf segmentation schema, you can append your prospect list with the prioritized segment information and more effectively target even the non-members.