Analytical Engine

In addition to a very technical perception as a method of predicting a continuous variable, Linear Regression is also, and equally importantly, an analytical method to understand the business drivers of dependent (which is being predicted) variable. Because of its inherent nature, regression has been used to understand how a variable is influenced by the interaction of multiple variables. In this post, we will talk about a couple of such scenarios where we used linear regression in the context of strategy formulation.

Casino – Slot Machine Optimization

In our previous post we talked about how a simple LFM (Latency, Frequency, & Monetary) segmentation can be used for customer management across casino industry. In this post, we shall talk about how linear regression can be used for floor layout optimization of casino.

Recently, we worked on an engagement for one of the largest casinos in the world, where the casino wasn’t able to capitalize on the abundance of slot machines; moreover, the machines were spread across the floor without much thought or analysis behind why a machine should be placed at a certain location – and hence, were not tapping the right potential of either the machines or the location. However, even from a gut instinct, a lot of us who have been to a casino, know that most of the players have their own preferences when it comes to picking a machine – be it the corner vs. central isles, the red machines vs. the blue machines, the spinning wheels vs. the talking genies, and so on.

A simple profiling of slot machine performance across various variables like denomination of machine, jackpot, type of game, location of machine, etc. can fetch interesting results. However, all these variables need to be evaluated simultaneously to create a holistic picture.

Even though a lot of optimization concepts can be applied, a linear regression can be used to determine the drivers of performance and better understand how these drivers interact with each other. The drivers of performance help to determine an optimal floor layout, so that maximum returns can be obtained from the given slot machines on floor. We found that certain themes with certain colors drive the performance while for some types of machines it was progressive nature of machine payouts that drives money into them. Certain machines if placed around restaurants seem to do well, while others if placed around some attraction tend to perform well.

Sales Territory Prioritization - Distribution Performance:

For companies with retail distribution, it is critical that revenue is maximized while expanding business in hitherto untargeted geographies, and therein lies a need to prioritize geographies based on their expected worth. Such a strategy requires analysis for not just one factor but for multiple factors and an area where linear regressions is useful.

While working for a Financial Services firm, we helped our client prioritize territories where they did not have any presence. Based on the current product offering and existing market attributes, a regression model was built and untargeted territories were scored (prioritized). This not only helped to reduce the focus but also helped in designing a quick and efficient marketing strategy that targets the ‘right customers’ of such territories.

We found that, for some territories the major driver of revenue was the abundance of a certain segment of population, while for other territories, the key driver was the strong presence of a particular type of business in that territory. These drivers helped us tailor the marketing strategy in a customized manner by understanding the ‘needs’ of each territory.

This is not all by any means. At Diamond, we have effectively used regression in launching new products, managing churn, managing portfolios, etc. to not only come with a target(scored) population but also come up with precise recommendations based on the detailed study of drivers.

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.

A few weeks ago, Shantanu wrote on recommendation engines and how user feedback and ratings can be a part of recommendations you provide to your customers.  But if you have ever looked through user recommendations while shopping online for a product, stock, or movie, you know that they aren’t all helpful.  Ideally, user ratings would accurately represent the population, but not all feedback is created equal, and there are some inherent challenges in these systems:

1. Not everyone will rate.  People may read the ratings when shopping for an item, but they won’t always come back to rate.  Unless a site offers an incentive for rating a product, a customer’s only real incentive for doing so is to talk about how much they love or hate it; moderates may be under-represented.
2. Ratings will be biased.  People’s individual biases produce variances in ratings, even if strict guidelines (think about employee performance reviews) are presented.  In addition, new raters tend to rate high.  Their average ratings decrease over time as they rate more items, presumably because they are exposed to more items and have a better sense of an item’s value relative to alternatives.
3. Ratings are averaged, masking the underlying data. Because people often only rate items they feel strongly about (love it or hate it), and an average of those extreme ratings may not truly represent the actual sentiment surrounding a product.  For example, a review of rankings on Amazon.com revealed that the reviews for the majority of the products have an asymmetric bimodal distribution. For these products, the mean of the online product reviews does not necessarily reveal the product’s true quality, resulting in misleading conclusions about the product’s future success.  In addition, established products are at a disadvantage against new ones.  Consider a product that has received five rankings, four and one giving it an average rating of 4.8. If a new product enters the space and receives one rating, it will be ranked higher than the other simply because it has fewer ratings. 
4. Ratings may be false.  Take the case of the Whole Foods CEO who posted disparaging comments about a competitor on a message board while talking up his own company, later stating “Sometimes I simply played ˜devil’s advocate” for the sheer fun of arguing.  Other visitors may post false comments to artificially affect the rating, and these are not always removed from the calculation.

As more products are marketed and sold online, feedback-based ranking systems are increasingly common components.  Amazon and eBay were among the first to use visitor ratings to rank products and sellers, and in February, Yahoo launched its Buzz service, which asks readers to click on their favorite stories, then uses those ratings to determine the most popular articles on the web.

Why is this important?  Because accurate product ratings help predict that product’s success, and higher product ratings lead to more sales - or, as Allen & Appelcline state, the value of individual items (most frequently goods) rise or fall based upon the largely subjective judgment of individual users. So what can you do to ensure the rating system on your own website accurately reflects your customers’ views and the value of your product?

We’ve seen some thought around using analytical techniques and Bayesian mathematics to create better product rankings.  Some of the solutions explored include:

1. Adjusting ratings based on known biases.  Since some people rate higher or lower than others, one approach is to assign users a “User Optimism” value based on their rating history, and adjust the product’s overall rating based on the raters’ optimism value.  Another approach is to remove all ratings from people who have only rated 1 or 2 items, helping to eliminate new-rater optimism or “drive-by” fraud.
2. Weighting a product’s ratings based on the number of ratings received.  When a product has a small number of ratings, these ratings should count less than those for a product rated many times.  To achieve this, you can add a “magic value” into the algorithm that calculates a product’s average ratings.  This “magic value” brings products with few ratings closer to the average ratings of all products, then reduces its effect as more ratings are received, allowing established products’ ratings to float freely and more closely reflect the average of its ratings.
3. Adjusting the algorithm to account for bimodal distributions.  To account for products that only have ratings at the extremes, one approach is to use a dual-point estimation model to more accurately reflect customers’ views of the product and predict the product’s success.
4. Analyzing a customer’s rating history for fraud.  Someone who rates several products on your site should have a predictable rating pattern.  You can analyze individual customers’ accounts to identify and remove anomalies that might skew product ratings.
5. Encouraging customers to leave text-based feedback.  When a person takes the time to write out a product review, and knows that her name will be attached to it, she generally does a better job in her rating.  These reviews tend to be closer to the average than those without.

Although your customers’ product ratings may be imperfect, they can still yield insights and value with the application of a good set of analytical techniques.

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.

We were working for a large Fortune 500 company to assess the performance of its sales team’s incentive program. A two tier incentive structure was in place for the sales force.
·        The incentive was directly proportional to the revenue generated by the salesperson
·        The salesperson generating revenue beyond a certain cutoff was given extra perks and monetary rewards
Prima facie this incentive structure made sense but when we analyzed it carefully, an array of systemic problems was observed. We mention few of the issues with potential ideas to tackle them
1)      Setting and maintaining goals – The incentive revenue cutoffs were predefined, static and the senior stakeholders had the power to override them. The cutoffs and tier allocations were not reviewed on a regular basis, making the tiers passive for the candidates who are performing well over a period of time. 
The key is to better understand the performance drivers of the sales force by  defining and measuring appropriate metrics  (using correlations, principle components, and factor analysis). Appropriately segmenting the sales force, defining the segment cutoffs using historical data and then reviewing them on a periodic basis is critical to achieve a better return from an incentive program.
 

2)      Quality Vs. Quantity of Money - Sales force were rewarded based on the volume (revenue) of the business and not the quality of the business (margins). Targeting the wrong prospects by the sales representatives sometimes led to value negative customers.
Using lifetime value calculation (LTV) of customers instead of absolute number of customers or amount of revenue, as the performance metric will solve this problem. Most organizations do understand the theory but do not use the metric, because calculating customer lifetime value is tricky and the data is not always available to do so accurately. In our opinion, it is better to have an incomplete understanding of the lifetime value (using averages and proxies for certain aggregated costs) rather than not making an attempt to calculate and use the metric. A flawed metric is better than a wrong metric.
 

3)      Poor and inadequate data - Data to track performance and understand the drivers of success for sales representatives was not readily available. The data was collected was mostly in standalone excel sheets. They were manually reported and coded making it difficult to link the independent tables.
A culture of measurement fosters investment in better data capturing systems. As analytics is used to calibrate the incentive structures on periodic basis, investment in centralized systems to standardize the data collection process will reduce analysis cycle time and increase accuracy of the analysis.

An integrated view of the incentive program helps in incentivizing the right people for the right reasons with the right benefits. Analytics is part of the answer. However, as emphasized by most of our projects, we believe that a sustainable solution is a combination of a robust understanding of business processes, technology infrastructure and analytical techniques.
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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.

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

Professor Dan Ariely of MIT has done extensive research on human decision making using the framework of behavioral economics (go here to pre-order his forthcoming book on the subject). We recently collaborated with him to apply some of his research ideas in the online world. The result of the collaboration is the white paper: Predictably Irrational Customers: Optimizing Choices for How People Really Buy, Not How We Think They Buy.

As customers begin to make more financial decisions online either by conducting a transaction or researching a product online before buying at a store, it becomes critical for companies to have framework to understand customer decisions to generate potential ideas to improve the website. This coupled with the ability to rapidly baseline, test and evaluate the ideas using web analytics makes for a potent capability for companies to gain competitive advantage.

You can download the white paper here

PS: You can also set up sometime to speak with our resident choice optimization experts by sending an email.

A wireless service provider wants to determine where it should open new stores to attract customers. A paper products company wants to determine whether it is setting the right quotas for its sales people responsible for generating business within their geographies

Companies are often interested in determining the potential size of their market in a particular geography or for a specific product to better manage their sales, distribution and marketing resources. This market sizing problem broadly falls into two categories: whether the customer is a business or a consumer.

The analytical approach to determine addressable markets happens to be very similar. It starts with identifying a very rich data source for the universe of customers, and then applying a layer of filters (e.g. companies with >100 employees, or families with children) based on the specific business needs, to define the target market. This is followed by a estimating the amount the target customer will spend on the service/product – to calculate the potential market size.

Here are two papers which clearly articulate the analytics process, and are a must read before you start any market sizing activity. The first one is for those who sell to businesses and the second one is for the consumer market.