Analytical Engine

Segmentation initiatives often fail, not because of their design, but because of their execution. In many cases, management is so dazzled by the promise of an elegant design that they neglect to focus on the hard work of actually solving a business problem. Our recent work with a global pharmaceutical shows how to avoid that problem by deploying a simple yet powerful “build/plan/execute/monitor” model. The success of the execution depends upon key metrics around targeting performance (time spent per customer), sales performance (average order size) and resource productivity (effort to conversion).

The global healthcare company relied on a specialized sales force to sell a particular high-end drug directly to consumers. Success depended on the sales force building strong customer relationships and customizing product offerings to each customer’s specific needs.

Step 1: Build a ready-to-use targeting tool
In this case our segmentation approach relied on classifying customers based on two key metrics: by1) customer potential, which reflects a customer’s overall appetite to consume the client’s product, and 2) by customer’s share of potential which indicates percentage of potential captured in terms of revenue dollars.
However, independent of the segmentation approach one follows, the key factor is to translate the approach to a ready-to-use targeting tool which gives sales force reps the flexibility to view the customer classification. (In this case, the customer classification could be viewed on a 2X2 matrix by various filters such as ”geography” and ”last order month.”)

Targeting Tool Overview

Step 2: Plan for the roll out
A solid pre-execution plan can ensure that there are no surprises during the execution phase. We created an exhaustive checklist of key tactical planning steps. Significantly, we also assigned ownership and timelines for each activity. Some of the key areas to focus on as one thinks about a successful execution are:

Step 3: Execute the roll-out
Execution generally tends to be the most arduous phase and requires close tracking and monitoring. In this case, ensuring a means of capturing regular feedback from the sales reps as they got into the targeting and sales process was a critical component of the execution phase. There are two key elements here:

Step 4: Analyze results
A deployment story is half baked if management is not able to assess the performance of the solution over time. Building the right measurement plan is a start, but real value only comes when the data is analyzed in ways that deliver insights that will inform management decisions.
A critical part of the feedback loop, apart from what is discussed in Step 3, also falls in the analysis phase: incorporating the learnings from the metrics into the segmentation algorithm. The metrics and numbers give the real picture of the sales force performance. Beyond that, it also speaks about the efficacy of the target list. Then, the idea is to make refinements to the segmentation algorithm based on the learnings from the pilot process.

Execution Results

In this engagement, our build/plan/execute/monitor approach, along with the buy-in and required compliance from the sponsor of the program,ensured a successful roll out of the solution. Our segmentation solution identified “high maintenance” customers who took approximately two hours of a rep’s time during a particular sales visit, but also generated enough business (up to five products per order) to justify the extra attention . We also helped the sales force reduce the time and effort they spent with some low-potential customers and refocused efforts on the tele-sales channel which cost less and required only about 20 minutes of phone conversation to convert customers. That initiative freed up account managers to focus instead on high potential, low share-of-wallet customers.

John Sviokla recently wrote in his Harvard Business blog about our first analytics product called DemandEstimator. He cites an example of a client situation in insurance industry where we have used the product to understand agent profitability, and shares some of his other ideas on how business executives can use DemandEstimator.

Please read his full post

Market research is, but seldom treated as, something beyond reporting figures and displaying good looking charts. The intent of market research should be to provide in-depth insights and answer key questions around the business problem at hand. However, more often than not, post the survey execution, researchers and analysts end up fishing in the ‘numbers’ ocean, and with great difficulty, find their way to final insights and recommendations. What they leave the table with, often, are piece-meal insights that may or may not add up to strategic recommendations.

However, consulting as a profession requires quick and effective market research, most of which is conducted with specific end objectives in mind.
At Diamond, we extend the hypothesis driven approach (HDA) to conducting market research and survey analytics. HDA is the answer to most of the woes and worries faced by a market researcher

Let us use a simplistic scenario to explain HDA. Suppose, we want to conduct a study to understand the buying behavior of people towards personal computers and one of the hypothesis we want to test is that ‘price is an important attribute in purchasing a PC’.

This approach begins with hypothesis definition. In the example considered earlier, we want to test whether price is an important driver of purchasing a PC. This is followed by laying out sample analysis that would help prove or disprove the hypothesis, e.g. % respondents rating price as an important driver or average rank of price as a driver. Next, we would need to gather data on parameters such as relative importance of drivers, allocation of points between various drivers to support the analysis. The process till here would culminate into survey and questionnaire design (e.g. the exact question to be asked, the likert scale to be followed.) followed by survey execution (e.g. online vs. offline).
The beauty of the process lies in the fact that once the survey execution is complete, the collected responses can be directly fed into the sample analysis generated in the second step. The process closes with hypothesis validation and delivery of insights and recommendations to the end user.

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.

(There is no dictionary definition of P2P process, but often P2P is defined as a generic term used to encompass the payment for goods and services: The basic process of raising a purchase order, receiving the goods and paying the invoice is often put under P2P process .)

In a recently finished engagement with a manufacturing major, we leveraged Diamond’s procurement analytics solution to help the client identify dollar leakages and potential saving opportunities across their P2P process. This engagement had all the challenges associated with a large manufacturing setup viz. inbound and outbound from plants located across various locations, multiple vendors supplying the same raw material, a complex distribution and lack of unified IT systems. The list below can give you a feel of some the issues we identified –
1) Price variation/anomalies:
a. Procurement organization paying different price for the same raw material within the same time frame across different vendors
b. Price changes for certain non-contract items being anomalously higher (even in an increased demand scenario, where the organization might have had a chance to negotiate better prices)
2) Contract Adherence:
a. Shippers invoicing higher than the negotiated rates in purchase orders
b. Vendors charging prices which are not consistent with contract-negotiated prices
3) Freight Charge Variations:a. Different freight charges being charged for the same kind of delivery across supplies
b. Freight charges being consistently higher than industry standards
c. Variation in the freight charges for equal shipments from the same vendor Payment
4) Payment & Order Schedule:a. Payments made significantly prior to the negotiated deadlines leading to loss of revenue
b. Sub optimal volume discounts because of fragmented orders

Organizations are investing a lot of money and effort in IT solutions, designing data warehouse(s), and data collection methodology. These efforts, though, aimed at streamlining decision making processes, continue to exist in silos and are not creating the desired impact for the business. To help our client tackle this challenge, some of the things we tried to focus on, as part of the engagement were –

• Master Data Management: Integrating different data sources (General Ledger, Purchase Orders, Invoice, Vendor information etc.) and incorporating process information to come up with the final form of an analysis data mart
• Basic Data Profiling: Performing basic data validation, enriching/ cleansing and classifying required data, instead of building highly complex spend analyzers around existing solutions; The essence of all analytics done by a team should be the business benefit that can be derived, and not intellectual gratification
• Variance Reports: Leveraging the analysis data mart to create variance reports that help identify the variation in price/freights for a certain procured material
• Dashboards/Segmentation of the invoices : Developing a segmentation tool that helps identify where the money is invested/spent; Having a sense of where the dollars are going always helps businesses prioritize their spend rightly
• Payment segmentation: Identifying anomalies between the POs and Invoices, and keeping track of vendor issues such as reject rate/ quality, etc. can help renegotiate contracts, create appropriate procurement and penalty mechanisms for ongoing cost reduction
As can be seen, a significant part of our approach was about getting the basics right, and instituting basic analytics processes in place. Before moving on to advanced statistical concepts/techniques, we demonstrated the value of analytics through simplified dashboard and analytics reports,and showcased a potential saving of multimillion dollar to the client

Ian Ayres, in his book Super Crunchers, questions decisions made through intuition and advocates decisions made by optimal utilization of data. In the book, Ayres gives examples of how data driven decision making is impacting businesses, education, sports, government, etc. One such industry where a lot of data is collected but not necessarily used in decision making is the gaming industry. Despite Harrah’s being an analytics poster child, not a lot of gaming companies leverage analytics to its potential.

Casinos collect revenue and customer data from a variety of sources, such as player cards, slot machines, and gaming tables. They can also gather information from non-gaming sources: through call centers, surveys, hotels, restaurants, and events. Harrah’s is a pioneer in collecting detailed data on its customers’ activities. Harrah’s team sliced that data into finer segments ”identifying unique customer groups and targeting each group with pitch-perfect marketing strategies.” The results -  tremendous increase in customer loyalty that has turned Harrah’s from a relatively unremarkable player into an industry giant.

Recently, we worked on an engagement for one of the large gaming companies in the world, where the casino wasn’t able to capitalize the rich data it had on its players and slot machines. Direct marketing campaigns and slot machine floor layout optimization, were two big initiatives that were performed but without leveraging the rich information in the data. An analytics driven approach was needed to identify high potential players and optimize slot machine layout. In this post, we will talk about the approach we used to segment players and identify changing behavior patterns of players.

Though a lot of advanced concepts of customer analytics can be applied here, we used a basic RFM (Recency, Frequency, and Monetary) segmentation to effectively target players. RFM was tweaked to LFM (Latency, Frequency and Monetary) for a simple reason- unlike a website or a grocery store, where a person’s recent visit shows that he/she is likely to visit again in near future, a casino trip happens after some interval. So it makes more sense to calculate whether days between the consecutive trips are changing significantly. In an LFM cube the cutoff of each cell of cube can be determined using basic business rules based on frequency distributions.

One of the KPI that was tracked was average spend per trip, and due to this, some players with higher number of trips and medium spend are undervalued and under-targeted. The LFM cube gives us the visibility into such customers and helps us evaluate the true potential of such players. Such a cube can be run at regular intervals to capture the movement of players between the cells, and such movement is a good predictor of changing behaviors and can be an effective lever for targeting marketing campaigns.

In next post we shall talk about how analytics can be used around slot machines and how such inputs can be used to design the floor layout in a better manner.

Mobile phone operators are facing a rapidly saturating market, thus facing customer retention as the primary challenge.  Mobile operators, like almost all big retail operations, have their own predictive churn models. Moreover, mobile operators have the luxury of having a wider array of variables. The “Know Your Customer” questionnaire and the usage behavior of the mobile phone typically provide access to a few thousand variables.  Modelers frequently add some off-the-shelf segments (from providers such as Claritas or Acxiom) in the mix to make a better sense of and add more predictive power to the model.

But the large number of variables adds at least two complexities.  First, even after performing variable reduction, modelers usually are still left with a high number of components/ factors, a number high enough to make a concise business presentation difficult.  Second, the high number of variables also affects the actionability of model insights.  No single variable affects churn in isolation, so it is hard to determine the independent impact of individual variables in customer churn.

An alternative approach to comprehend and act on the model recommendations will be to bundle the variables in logical groups.  For example, the variables related to time can indicate when a customer is more prone to churn and can be grouped as a ‘when’ group of variables. This ‘when’ group will not necessarily tell us why a customer is more prone to attrition, and would rather tell us when she is more prone to attrition.  Similarly, the variables’ indication of issues faced by a customer (her interactions with call center might be a good source for this) such as handset problem or network issues can be grouped as ‘why’ variable.  The demographic variables that are significant in predicting churn help the modeler to profile the customers likelihood to churn and can be grouped as ‘who’ variables. An integrated view of these three groups is more palatable, and from a modeler’s perspective, it is easier to convey to the business ‘who’ are more likely to churn, ‘why’ they are more likely to churn and ‘when’ they are more likely to churn.

The above approach does not only make the models easier to comprehend, but also increases actionability by providing a structure to the model findings.

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 - 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 - 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.