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The Diamond Information Advantage Team

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.

Today - Mr. Smith opens a new credit card account with an attractive APR and minimal annual fee. After the first four billing cycles, Mr. Smith has already defaulted once on the minimum payment due, thus becoming a riskier proposition for the issuer. While banks in general generate fee and interest revenue on such accounts, the risk of default (60-90 DPD/write-offs) starts increasing. At minimum, issuing the card at that particular “teaser” APR seems like a wrong decision. Damage control begins with the card issuer increasing the APR, which slowly helps the issuer cover its losses.

February, 2010 – Ms. Jones follows Mr. Smith’s bad example and quickly becomes another high risk customer. However, since CARD Act regulations are now in place, the issuer cannot increase the APR in the first six months of issue and without 45-days notice. The probability of default and hence, the loss-given-default amount starts increasing in the wake of a few decisions gone bad, and because the bank now lacks the flexibility to re-price.

These scenarios highlight implications of the CARD Act and UDAP regulations, legislation that aims “…to establish fair and transparent practices relating to the extension of credit under an open end consumer credit plan, and for other purposes.”

Among other things, UDAP and CARD:

• Restrict all interest rate increases during the first year:
• Restrict interest rate increases on existing balances;
• Allocate payments in excess of the minimum payment first to the balance with the lowest APR:
• Treat a payment as late unless the issuer provides a consumer with a reasonable amount of time to make payment:
• Place limits on fees and penalty interest:
• Increase notice for rate increases on future purchases.

The CARD Act takes effect in February 2010, and has significant cost and strategic implications for credit card issuers, not just in terms of their acquisition strategy, or risk profile of their current/future base, but also a fundamental shift in their strategic outlook towards the credit card business.

We think the implications for the use of analytics within the card industry are profound.

• One of the first and foremost thoughts is: “Will the card industry shrink?” Issuers must carefully consider their exposure vis-à-vis the risks associated with it, given the new limits imposed on them. How should issuers evaluate their risk exposures differently, given the new regulatory constraints?

• Do we see “revenue-at-risk” models (a term more commonly used in telecom) becoming the latest analytics investment for credit card issuers, to better understand the pockets of revenue that are most at risk for issuers? How are the issuers going to deal with the challenges of revenue replacement? How will the issuers treat the transactors vis-à-vis revolvers?
• Will Lifetime Value (LTV) or profitability modeling supplement or supercede risk/price modeling as related to the overall underwriting function, both for acquisition and base management?
The big strategic question is this:How should issuers look at their portfolio and their products now to determine the customers they want, and the products and pricing they offer in order to retain profitability and position for competitive growth in the new era?

For analytics thinkers, here is an example where numerous forms of credit card analytics (pricing, underwriting, risk management, customer acquisition, customer management, and customer attrition, ) are suddenly in play, and all at the same time, together. Everyone wishes for a quick solution to this development, realizing fully well that there is none. The risk models and pricing strategies used by issuers have been built and stabilized over decades with great attention to detail. Even so, they require careful handling every few cycles as the underlying economic and social structure of the population changes. Given all this, is there a good near term fix that can be rolled out for testing?

The road ahead will be strewn with a series of analyses and tests that issuers will experiment with to assess the impact of the changing regulatory environment on the credit card industry.
In a subsequent post, we will talk about the different analytics that we think will be of immense value in these changing times. But for now, let us all think about the change and its impact.

Please read his full post

In June’07, the National Institute for Health and Clinical Excellence (NICE) of UK decided that Johnson and Johnson’s multiple myeloma drug, the one year treatment of which stands at around USD 48,000 a year, can be defrayed for if the company is willing to refund the cost in case of not showing a pre-decided level of cure.

This move towards sharing the risk of treatment outcome did not go unnoticed. Providers are all the more willing to share the risk of no-outcome with the drugs manufacturers. Biogen Idec, maker of Avonex for Multiple sclerosis drug that costs around $18,000 a year has made a dynamic pricing agreement with UK’s NICE. Under the plan, 5.000 multiple sclerosis patients are being followed for 10 years to see the effect of the drugs in slowing the progression of the disease. The prices of the drugs will be adjusted based on improvement of performance. Merck-Serono has offered to refund the primary care cost if its drug Erbitux if the patient does not respond within 6 weeks.

Although Europe seems to be leader in this development, but US companies are definitely taking note. United Healthcare have made an agreement with Genomic Health with United paying for the $3,460 genetic test to determine whether a woman with early-stage breast cancer would benefit from chemotherapy. If tests don’t have the intended impact of lowering chemotherapy United Healthcare will negotiate a lower price for the tests.

Essentially this development adds a new dimension to the pharmaceutical pricing, i.e, risk. Before pricing a drug, the point of paramount interest to both manufacturer and providers is what drugs are the ideal candidates for outcome-based pricing? The high cost drugs which are new in the market or have relatively low success rate seem to be the ideal target from the developments so far. The principal risk that companies should take into account is financial risk, which in turn will depend upon probability of no- outcome and amount of dollar exposure. Apart from regular variables such as physician’s ability, patient’s specific variables etc. there may be exogenous factors such as pollutions level for certain treatment which will affect the probability of outcome.

There are several interesting questions already. Will the companies have different refund policies for different locations? Will there be selection criteria? How will regulatory environment affect the risk-sharing?

We are at an interesting corner here. I will wait and watch how the pricing territory shapes up in the near future.

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 our recent stint with a Europe based retail chain for electronics goods, we helped our client use analytics to better price cell-phone accessories. A lot of advanced sophisticated pricing models can be built and deployed; however, when efforts and incremental value of such models are weighed against the approach of using elasticity and bundling, the latter option seems to be an optimal choice to get some quick hits for the business. For the uninitiated, here is a quick review of elasticity and bundling:

— Price elasticity of demand: The measure of responsivenesses in the quantity demanded for a commodity as a result of change in price of the same commodity.
— Product bundling: A marketing strategy that involves offering several products for sale as one combined product

Price elasticity approach: We calculated the price elasticity, and accessories that are having extremely high or extremely low elasticity along with reasonable business impact (sales volume or margins or life-stage) are picked for analysis. With the help of gross margins, we came up with the best price change for such accessories. In order to evaluate the financial impact, we built a business case to find out the potential revenue impact of such price changes.

Product bundling approach: A basic market basket analysis can help find products customers like to buy together. However, bundle needs to be evaluated for elasticity as well as likelihood of success before taking it to the customer. In order to increase the sale of some of the underperforming accessories, such accessories can be strategically bundled with highly elastic products. Gross margin of the bundle can be used for driving the price of the bundle.

Both these methods fetch actionable recommendations which can be tested in a very short time-frame in a test environment. One of our recommendations led to an additional £100K revenue impact per week for selected SKUs. A similar approach can be used for other items to better understand the nature and the monetary potential of the products lying in a store.

Question: Can you tell us something about the type of problems you are solving in the organization using analytics?
Bill: Given our current strategic priorities, we are using analytics for reducing out of stock at retail locations. There are three key components that we are focusing on:
• Better Inventory forecasting (having the right product at the right delivering location)
• Divisional Dispatch & Voice Pick (having the ordered product picked and delivered correctly)
• Suggested Order Solution (ordering the right product and quantity at the right time to fill the customer demand)

Question: How do you quantify the value realized by an analytics project?
Bill: Analytics has several quantifiable and measurable benefits for us, as we have realized during the learning process:
• Reduced out of stocks at retail locations
• Reduced inventory levels are at the retailer back room
• Optimized delivery schedules suitable to better meet demand
• Less time for sales reps. in the ordering process and more time focusing on selling

Question: What are some of the key challenges that you face in analytics focused projects?
Bill: PepsiAmericas did not have a lot of experience in handling analytics projects which presented us with some challenges. The key learning for us was to understand that analytics projects have a strong R&D component inbuilt into them. While our organization is very comfortable with execution projects, the test and learn cycles inherent in an R&D process took some time to get used to. Another challenge for us was selecting the assistance of right resources that bridge the gap between business, statistics and common sense, and can communicate between these groups effectively.

Question: How do you see analytics growing within your organization and within the CPG industry?
Bill: If you ask me about the present, I see analytics growing in two main areas, optimizing the ordering process and helping in pricing and trade promotion spend

Question: Since selection of the right analytics resources is a key component for the success of the project, what do you look for in an ideal analyst that you hire into your team?
Bill: From my perspective, there are three key ingredients to a key analyst hire in our team. First and foremost, the analyst should fit into the culture of the team . He/She should be good at understanding business problem and incorporating it into the statistical tools to solve the problem. Last but certainly not the least, effective communication skills are very important. I talked about it earlier when I mentioned the need to bridge the gap between business, statistics and common sense through effective communication.

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.

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