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

A puzzle asked - “How many ping-pong balls could you fit into the room?”

Somewhere else, there was $26,000 prize for a similar question - “How many 76 antenna balls did we fit in a Chevy Trailblazer?”

And a little far away from all this - the Japanese were inventing square watermelons, probably thinking about a very similar puzzle. Or, maybe a little variant of these questions - After putting in as many round watermelons as possible, what percentage of the container space is wasted??
A little while back, Harish wrote about the impact of shelf-space allocation on brand performance and cannibalization. As I read about the square shaped watermelons that fit the shelf-space better in retail stores better, I almost went back to Harish’s post and thought- How will the sales of round watermelons dip because of the introduction of square watermelons? I am sure this product extension will displace some of the round ones off the shelf. Is round out of shape now? Or, is round being forced out of shape?

However, all said and done, assuming demand for watermelon to be static, a one for one replacement of round ones with square ones can help ­_corner_ more profits for sure!

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.

Recommendation Engines are being used by many online retailers today to cross-sell and up-sell products and services to online consumers. Amazon.com, Newegg.com, Buy.com seem to use some sophisticated algorithms around the recommendation engines at the heart of their online selling portal. They are also an integral part of any choice optimization strategy.

Without even going to the technicalities of recommendation engines available, we have put together a few points which can help businesses can harness the power of recommendation engines:

• Get the Basics Right
  Understanding some salient features of your products or services can help your business classify and categorize what parameters are important to track and assign weights to and make recommendations to users on. If you are a seller of toys, the most important parameters (with varying weights) may be price, toy features (learning toys or motion-based toys etc.) and toy accessories (batteries, replacement parts etc.). If you sell high-end luxury goods, the most important parameters may not include price and may just center on niche features and custom options.

• Take Baby Steps
  Unless your business is high up on the maturity curve (a la Amazon), you need not start with algorithms that are very sophisticated. Take a baby step approach to developing algorithms. Broadly speaking, recommendation engines can be classified into 3 categories:
1. Content Based filtering engines – those that are based on the contents of an item and tries to pair user interests (if known from previous web visits) to ‘contents’ of an item (can range from books to luxury cars)
2. Collaborating filtering engines or Social filtering engines – those that try to match user profile and interests with other users with similar profile and interests. Takes into account user reviews of products and services.
3. Knowledge-based filtering engines – the most complex of the three, these try to leverage knowledge of user needs and a target product or service that may not be directly related. For example, an engine may try to recommend you a GPS unit based on your interest in books related to Roadtrips or North American geography.
   
   

Ample literature is available on these recommendation engines and other emerging architectures (Few links here). The choice of what recommendation engine is best for the business should be entirely based on the business goals and the products and services being offered.

• Make them Adaptive
  No matter what the nature of the business is, recommendation engines should be built around the belief that there is never a perfect algorithm for designing them. They should be constantly capturing and storing usage data and patterns and must continuously learn from user website visits, time spent on web-pages, third party websites visited and so on. Every time the same user visits your website, the recommendations that are presented will be a little more relevant than on the previous occasion. A sample starting point architecture can be found here.


• Keep it True
  Last but not the least, objectivity of recommendations is valued by the savvy consumer. An online platform for freely expressing opinions (on user experience or product performance) can feed into your recommendation engine to make it better with time. Amazon.com and Buy.com provide not only good platforms for customer feedback but also opinions on where else shoppers can get the same product they are looking at cheaper or costlier rates. The neutrality of the ratings and objectivity of the price positioning influences a customer positively and presents an image that these sellers are not trying to peddle their wares necessarily, but want you to make a good purchase decision as per your needs, preferably through their own stores but not necessarily.


Photo Credits: ieatcrayonz, scott92007, Jmb, losiek

An interesting update on one of the examples we used in our Choice Optimization paper.

We mentioned how credit card companies offering a complex array of products risk overwhelming their potential customers with too many choices (‘purchase paralysis’). One way to reduce this risk is to ask customers questions about the product features that is important to them (reward card vs. low APR card) and then use the answers to narrow down the choice of products (explicit filtering). However, as we pointed out

“….this process indicates to customers that there are additional options available to which they are not being exposed. Also, explicit filtering still introduces the risk of regret…”

Capital One seems to have figured out a way to overcome the risk of regret through their new online Card Lab feature. Built for customers who are evaluating new cards from Capital one, it uses a very simple intuitive design to let a potential customer understand the various tradeoffs between features and choose the card that best suits his/her need.

As the customer tries out the various options, a huge amount of data will be collected. This can be used to generate insights to develop new products.

This tool is applicable to situations where consumers are making trade-offs between product features and options while shopping online. Whether it is buying auto insurance, cable/wireless service or a digital camera – it makes choosing the right product a simple and transparent process for the consumer and might result in less abandoned carts and more sales for the company.

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

Cannibalization can be defined as gain in market share of one brand at the expense of another brand/s in a company’s brand portfolio. Companies usually study cannibalization by measuring the sales volume trends of various brands after the launch of a new brand.

But does cannibalization start as soon as the consumer exhibits brand switching behavior or even before that? It starts manifesting itself when the manufacturer asks the retailer to stock the new product. The new product launch gets a priority and is at times stocked even at the expense of other brands.Hence to study cannibalization what should also be evaluated is the jostle amongst various brands to maintain their shelf space. Quantity of products stocked on the shelf not only showcases the push that the company is trying to create but also the potential effect on sales of other brands that are getting nudged off the shelf.

Before arriving at definitive cannibalization conclusions, the relation between shelf spaces and counter sales for key brands should be studied. In-store merchandising and product placement significantly affects sales of new brands (as has been studied by several researchers). Based on the placement-sales association, decision should be taken to de-prioritize display of certain brands and the vacant space should be allocated to a new brand. This should to a large effect discourage the new brand from eating into the sales of existing core brands. After all the performance of a new brand should hinge on whether it can help the manufacturer gain overall market share and not on it barely realigning the brand share of different products (unless of course the new brand is a product extension (such as Mach 3) or is launched to kill an existing brand or is launched to make it the core brand).

Webmetricsguru points to two interesting studies done on impulse buying. One of the studies uses a behavioral economics framework (with brain scan evidence) to explain the buying process.

..consumers trade off the immediate pleasure of making a purchase against an immediate pain: the pain of forking out the money for the item.

Furthermore, to increase the likelihood of ‘impulse’ buys, online merchants are suggested to follow some principles which are very analogous to the ideas we have put forward in our recent Choice Optimization paper.

1. Expose the target audience to the “stimuli”:
   Priming the audience through targeted advertising and product placement
2. Figure out what the price point for buying “stimuli” items are for the target audience(s):
   Avoid price risk for consumer through market research, transparent pricing (showing competitor prices like Progressive) or price matching schemes
3. Offer the item(s) (stimuli) at a lower price than what the target audience thinks it’s worth:
   Relative framing by comparing the value of the decision compared to an alternative.

To make the point in tangible terms, here is an interesting example of relative framing which we found while researching for the whitepaper:

The Economist magazine once offered its three subscription options on its website,
(a) Online only for $59
(b) Print only for $125
(c) Online and Print for $125.

16% of customers chose the “Online Only” option and 84% selected the “Online and Print” option. No customer chose the “Print Only” option, so the company removed it. However, when only two choices were presented (“Online Only” and “Online and Print,” at $59 and $125, respectively), the number of customers choosing the lower priced “Online Only” product increased to 68%, while the percentage of subscribers choosing “Online and Print” dropped to 32%. While no one chose the “Print Only” option, having it available made the more expensive “Online and Print” option appear to be a bargain, and this drove a higher percentage of customers to select it.

Photo credit: PaysImaginaire

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.