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.

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 recent article in the New York Times discusses the impact of price on the perceived effectiveness of drugs.  The article describes an experiment where two groups of patients receive a placebo drug that they are told is a pain reliever, but the groups are told different price points.  After taking the placebo and receiving electric shocks, more people (85%) who were told it cost $2.50 reported pain relief than those who were told it cost only $0.10 (61%).  While the placebo effect is well-documented, this experiment highlights an important application to business – that a product’s price can be an effective marketing lever that can directly impact its effectiveness and value.

This is not the first time that cues such as pricing and packaging have been applied to marketing.  We recently collaborated with Professor Ariely (also quoted in the article) to explore the impact of applying behavioral economics principles to online marketing strategies.  One of the concepts we discussed in the resulting paper, is “Framing,” and we explored how consumers evaluate their options on relative terms, and make purchase decisions based on the cues given to them.  In the paper, we highlight the example of a magazine publisher who was able to steer consumers toward a higher-cost option simply by presenting a lower-cost one.  Even though no one chose the lower-cost option, it was an effective cue in that it showed the relative value of the higher-cost one.  In the drug experiment, price was the cue, and the higher price led consumers to find it a more effective (and valuable) product.

In our market segmentation work, we have found that different consumer segments require different value propositions, and that marketing messages need to emphasize factors such as features or pricing to appeal to their target markets.  Many generic, store-brand products are actually the same as the name-brand products, but are packaged differently and sold at a lower price to appeal to a different consumer segment.  The drug experiment highlights the same concept – and 61% of the patients who thought it was a cheap drug still reported that the product was effective in relieving their pain.

In developing marketing strategies, it is important to carefully consider the market segments you are targeting, along with the value drivers for each.  Understanding these drivers allows you to apply behavioral economics principles to maximize ROI through optimal pricing and product framing for each segment.