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.

It appears that utilizing ATMs as a sales channel is not even a near future thing as we thought it was, it has already arrived.
Last Friday on my way home, I decided to withdraw some cash from an ATM of one of the larger private banks in India.  When I was almost done with the transaction and was about to collect the money, there was a surprise waiting for me in the screen! The ATM offered me a reasonable amount of home loan and requested me to click a radio button written “Y” if I want to avail or click a radio button with instruction of reminding me later if I want to think about it later or click “N” in case my answer was an outright no.
But then neither am I the only one, nor India the only country where it’s happening. One of our blog readers, a few continents away from my city, has already come across and availed one such offer from Chase!
Things surely are fast nowadays!

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.

How many times one has deleted that email offering that free gold credit card or a pre-approved loan? How many times one has curtly disconnected the phone call from another tele-caller with a pre-approved check for that awesome holiday in Hawaii? In a market, where retail banks have exhausted almost all the channels to reach the target customers, ATM machines can be a powerful alternative for cross-selling.  ATM machines also provide the opportunity to reach out to the otherwise difficult to reach customers such as expats. 

So, how would it work? Almost every bank has pre-approved offers for certain customers.  The bank can load the offers for some of the pre-approved customers to the server that interacts with the ATM machines.  The moment the ATM recognizes a preapproved customer, it can show her the pre-approved offer through a blinker or an additional screen at the end of the transaction. If the customer is willing to go ahead with the offer, all she needs to do is to press “Y”.  The moment she presses “Y”, the bank gets notified and can track the lead through different mechanisms. For example, an SMS can go to the sales manager nearest to that ATM with the customer detail, who can take up the matter further.

Surely, it’s not as easy as it sounds. While the technology for such a process is available in the market, it’s still a challenge to implement it. Prediction accuracy is another big challenge.

·         It is not feasible to upload the entire set of pre-approved offers to the server.  And hence the need for a model, which can forecast with high accuracy the probability of a customer visiting a cash machine.

·         Each ATM screen on an average takes 5-7 seconds, which includes the time taken to interact with the server.  So, the time to pitch the offers must be chosen judiciously, especially for locations with long customer queues.  But restriction of timing will in turn affect the prediction accuracy.

·         The ATM cannot close the sell. The completion of the process depends on further integrations of channels.

Nevertheless, ATMs can still be used as an excellent channel and in the near future itself, with the advent of technology, it can be expected to be one of the primary channels.

Societe Generale has been in the news recently and not quite for reasons they would like to be in the news for . In one of the biggest cases of Insider Trading Fraud, an SG employee managed to cause a dent of over $7bn. And this is hardly the first ever case of such use of material non-public information. In one of our earlier posts, we talked about a project where we had built filters to tag suspect insider trades to help our client with identifying potentially suspect trades for manual evaluation. The system has been used retroactively so far, and is being considered for proactive tracking as well.

The efficacy of what we proposed was based on the simplicity of filters that we built. Using tribal knowledge for identifying suspect transactions, and augmenting it with statistical rigor and tests, we were able to generate efficiencies in the compliance/audit process. These same rules can be implemented in a real time fashion to flag suspicious activities for monitoring.

Update: Julie wrote a note to us and pointed out that SoGen case is not a case of insider trading but one of outright fraud. Thank you for catching it and pointing it out to us. Nonetheless, a set of logical and statistics based filters can be a useful part of effective control environment which may have been able to detect such fraudulent activity.

Investment Banks, with activities encompassing trading as well as deal making, are susceptible to information leakage across activities.  Traders often have access to the material non public information (MNPI) i.e., insider knowledge from the deal makers, which if used can generate significantly higher (illegitimate) profits for the trader or the employer. In recent past, a few traders from leading investment banks have been found guilty of using MNPI and punished. It is beneficial for the banks from a pecuniary as well as reputation point of view to track insider trading activities and take actions against offenders. 

However, it is difficult and resource consuming (financially and intellectually) to manually eyeball millions of trades done by employees and flag potentially fraudulent activity. What may help, though, is a set of robust logical and statistical filters that are designed to reduce the overall number of trades/traders subject to manual review, thus reducing the overall cost of regulatory compliance.

The approach described above requires a non-trivial effort up-front to acquire, aggregate, and clean the trading data. After the required cleaning of trades’ data and imputation of missing value of trades in certain time periods (assuming no trade), we build a general understanding of the traders’ behavior. We supplement this understanding with the help of logical/statistical filters which are a combination of basic measures (such as directionality of trading transactions, means and variances of trade values), trend analysis (such as correlation between trade and market movements, deviation from trader’s historical trading pattern) and statistical models (such as time-series models).

Fig 1: Example of correlation between market movements and trading
action

Fig 2: Example of analyzing directionality of trading actions

Some of the complexities of this approach lie in the inherent biases in any technique that the analytics as well as business teams should be aware of. Whether an OLS model will work as well as a time-series model to identify suspect transactions is a hotly debated question, for instance. The key to this problem, as in most of the advanced analytics
problems, is a strong coordination between business and analytics. Subject matter expertise around investment banking, trading, stock price movements, etc. must be built into a data-driven approach to solving the overall business problem.

A recent article by Neil Davey echoes our perspective on segmentation ‘only a limited number of organizations are actually using segmentation to its true potential’. He talks about ‘The seven perils of segmentation’ which includes some excellent rules of thumb to keep in mind during a segmentation exercise:

• Keep it simple: A case in point is the adoption of ‘off the shelf segmentation schema’ which when combined with primary research can provide valuable insights and help create a rapid, scalable and actionable segmentation strategy. Read our white paper on rapid market segmentation here
• Size matters: ‘For all the planners, segments of varying sizes can pose difficult problems at planning and execution level’ says Julian Berry, managing director of Berry Consulting and director of the Customer Partnership. So next time when you perform a cluster analysis or a CHAID, keep a keen eye on the ‘n’; rather take the statistics with a pinch of salt
• Take a holistic view: In our recent engagement on web analytics, we identified core groups on the basis of ‘when was the last time you visited the website’, ‘how frequently you visit the website’ and ‘how much time on an average you spend in a visit’
• Don’t let market research results grope in dark: Always tie your market research survey results with other critical elements such as customer satisfaction scores, and acquisition and retention metrics across segments to get a comprehensive view of current and potential customers

For more of our posts on segmentation, click here

There is a fair bit of academic research demonstrating that customer satisfaction (usually the ACSI metric) is a driver of firm profitability, and a recent paper from Journal of Marketing proves that customer satisfaction has a positive influence on a company’s future advertising and promotion efficiency. This is because customer satisfaction induces customer behaviors such as free word-of-mouth advertising making firms more efficient in future marketing communications investments. The authors also go on to show that the relationship is stronger in industries with high market concentration (less competition)

Two reasons the study is interesting and worth a read:

1. It quantifies the impact of customer satisfaction on an operational marketing metric, rather than a broad financial metric like ROI or return on assets, which are more difficult to manage from an executive’s perspective.
2. It uses Data Envelopment Analysis (DEA) as a technique to assess the efficiency of resource utilization. It is a simple yet powerful methodology to perform any comparative or benchmarking analysis across industries.

What once was a good ole’ boy sport that relied on tribal knowledge buried deep inside a crew chiefs’ head is becoming a hi-tech, data rich, and analysis driven sport. More and more teams, crew chiefs, engineers, and even pit crews rely heavily on granular data elements that are used to feed complex models to extract the slightest bit of knowledge that can be used to save thousandths of a second on the track or in the pit. The sport of NASCAR is no longer defined by car lengths that won the race rather it’s becoming a sport defined by hundredths and in some cases thousandths of a second that decides between the winner and the first loser, a.k.a. “second place”.

After having an interesting and thought provoking conversation with Ken Brown, former engineering director at Dale Earnhardt Inc. and lead engineer for GM’s Corvette Racing program, I found myself drawing parallelisms between what I do in marketing science and statistics at Diamond for our clients and what NASCAR teams are doing to extract value from information. Not in the ROI sense but in the thousandths of a second sense. However, this in the end does translate into ROI for the thousands of sponsors that make the sport go round and round, no pun intended!

According to Ken, granular data elements such as wedge, nose weight, spring rates, tire pressures, camber, and gear ratios (just to name a few), all play a critical role in determining the how good a car will perform at a particular race. However, the key decisions are often made long before the car arrives at the race track. Essentially through track testing, wind tunnels, shaker rigs, and simulation exercises, the engineer and crew chief can decide which and how much to change each variable to make their car the best on the track. In marketing science I call these things covariates!

So how are teams doing this? Although the details of the process are unique to each team, the general process that goes into setting building and setting up a stock car is as follows:

1. Know the track (flat track, road course, banked)

2. Define/identify the significant set up parameters (or covariates) relevant to that track

2. Gather test data (prior race data, testing, wind tunnel, etc) and validate its quality

3. Analyze preliminary data via data visualization techniques (histograms, pareto charts, and cross-tabs)

4. Build models (e.g., DOE, Pareto Analysis)

5. Analyze results of simulation techniques

6. Set-up car for the race

7. Test and refine!

This process that is followed by the top teams is not unlike the process we follow in our information and analytics practice at Diamond when trying to identify the key variables that will attract and retain the customers or segments a business is going after. Moreover, the quality of the testing data is one of the most critical components to the entire process.

So the next time you see a professional stock car race, it’s important to know that before there was the driver in victory lane, there was a data geek in the background making it all possible.

Firing customers is not something companies prefer to do for profitability. Fierce wireless reports that Sprint recently fired some customers because of their frequent calls to customer care.

Ever wonder how many calls to care can actually get one fired from the wireless company, now that the precedent has been set? We did some back of the envelope analysis to understand it better.

Assuming that the fired customer paid an average bill of $60 per month and that Sprint has 30% margin and average cost per call of $4, it will take 5 calls in a month to make one a negative value customer. However the trick of all lifetime value calculations is to estimate future value of customers because you can be unprofitable in one month but go on to upgrade to a highest plan down the road. Since there is no reliable way to predict it, we will be conservative giving the benefit of the doubt to the customer. So to get fired, the customers must be making way more than 5 calls a month (probably more than 10-15) and that too for a few consecutive months; so that Sprint had no hope of making a profit in future from these customers.

While it is certainly a sound financial decision, it will be useful to determine whether these customers had anything in common (type of problem they were calling for, expectation setting at sales channel etc.) which led them to call more often.

Photo Credit: Pappalicious