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Case Studies in Patient Incentives

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One Offer Optimized for Multiple Brands

Situation:

A leading dermatology manufacturer had three complimentary products in the same space with 3 similar offers but was not happy with the results  The first brand was the largest one, the second and third brands were much smaller  The ROI on the three programs was negative and the client didn’t feel they would be continuing the copay offers on the two smaller products.

Our Solution:

  • We recommended our National Optimal Offer Model to look at options for combining the three products into one offer that would benefit all three brands
  • We looked at data from all three products going back two years
  • Eight different offer configurations were examined
  • After analyzing all the data we found even though each product had their own unique problems and was used for a completely different purpose they were complimentary products that could be used together

Result:

  • The model recommended the offer that best met the client objectives weighted at 50% sales and 50% profit
  • We proposed a unique offer where patients were given higher discounts with the more products they bought “PNMT $25 for one, PNMT $40 for two, and PNMT $50 for if the patient bought all three products together
  • The program results were outstanding:
    • Product #1: sales up +9%, Profit up +3%
    • Product #2: sales up +13%, Profit up +9%
    • Product #3: sales up +21%, Profit up +18%
  • And one of the best things about the program is we were able to work within the existing budget by consolidating the three programs into one which reduced costs and allowed them to fund the additional patient discounts

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Optimize My Entire Product Portfolio

Situation:

A leading pharma manufacturer had almost a dozen products all with copay programs. Spending had gotten out of control and their finance department forced an initiative of saving 30% across the board in the upcoming year. Sales had to be maintained/increased, ROI increased, while cost per incremental unit reduced.  

The company was also looking for a standard way for brands to plan and evaluate all future copay offers. The company sent out a comprehensive RFP and Alpha 1C was chosen

Our Solution:

  • We recommended all brands go through at least the first step of our process which was our National Optimal Offer Model
  • Once phase #1 was complete each brand would be looked at to identify if they would see value in running our additional models:
    • Market Level Geo-Targeting Copay optimization Model
    • HCP Level – Asset Allocation Model
  • Each brand worked with the Alpha team on their own objectives & budget

 

Result:

Phase 1

    • National Optimal Offer… the entire process took eight weeks and all brands offers were optimized
    • A total of 287 offer configurations were forecasted across the brands in that timeframe
    • A total of $35MM in spend was identified as savings based on offer reconfiguration
    • All brand offers configurations were changed – some minor and some considerable
    • Many brands were found to have caps that were too high (spending more money than they needed to).
    • We were able to redistribute spending to move money away from patients with small OOP costs and spend more against patients with higher OOP levels reducing spending awhile still attracting more incremental volume  

.Phase 2 (Upcoming)

    • Certain identified brands who have market level managed care or affordability issues to implement our market level offer model
    • Other brands to go through our HCP level model to further optimize their coupon distribution  
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Launch for a Blockbuster

Situation:

A leading pharma manufacturer was launching a new entry into the very competitive pphthalmics category. Although they had many active copay programs with other brands this new launch was super important because the drug had the potential to grow to blockbuster status and it was important to do it right as millions of dollars were at stake.

The company investigated options to structure their program and based on both unique data and technology, Alpha 1C was chosen for the important job.

Our Solution:

  • We took the brand team through the first step of our process which was our National Optimal Offer Model
  • We have a very unique process for handling launches because they take special since data is scarce and there is much benchmarking to do
  • We had the brand team estimate what the status of their managed care coverage would be at launch, 6 and 12 months post launch as it is important to set a program that works in partnership with their contracting strategy.
  • The brand team worked hand in hand with us on their specific objectives & budget requirements

Result:

  • The entire process took six weeks to develop and implement our National Optimal Offer model
  • Offers were derived for both physical cards delivered to their targeted HCP’s and for use by patients via the web
  • A total of sixteen different offer structures were forecasted across the brand in that timeframe to see which ones best met the brands objectives for launch
  • The brand is off to an excellent start!
  • Brand is performing very close to expectations on all aspects of their plan
  • Next step will be optimizing the allocation of their physical cards to their targeted HCP’s to ensure they get in the hands of the patients that really need them

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Market Level Success

Situation:

A top Pharma brand was becoming increasingly dissatisfied with their current program which had run for 3 years but they wanted to increase their ROI productivity and reduce spending.

The brand had issues with their managed care coverage in pockets of markets around the country.

Our Solution:

We ran a two-step process; the first step was to run our “optimal offer” modeling model to ensure they got the biggest “bang for their buck” based on their available budget

We looked many offer constructs with the objectives being to allow more patients to move their larger size which had been proven to keep patients more adherent

The second step was to run our geo-targeting model to identify the markets which were under performing, over performing, and neutral to come away with a specific offer for each market that better met each market’s conditions.

Result:

Based on their brand objectives of 75% profit and 25% sales the model recommended their optimal offer.

Their national offer was increased (less lucrative for patient), their cap was reduced 20% resulting in a $3MM savings, no sales loss was predicted and a healthy margin increase is the result

The geo-targeting analysis resulted in both increased sales by millions and added additional profit of almost a million dollars.

Being the brand leader in the category we have seen that their competition is beginning to follow suit and decrease the amount of their patient offers which is still meeting patient needs and allowing them to increase their margin.

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Putting Offers in the Right HCP Hands Increases Program Productivity

Situation:

In 2014, a manufacturer of a leading drug had already had a co-pay program in market for several years. They were pleased with the results and the program had become an important part of their overall marketing strategy. Their program had just been renewed for another year.

Although they saw the program as productive, they wondered if they could squeeze out some additional sales and profitability by optimizing the way they were distributing their coupons to their targeted physicians.

Our Solution:

We recommended our Optimal Distribution Model to develop a new distribution methodology for the sales reps to replace the current blanket methodology they had been using.

We utilized our industry HCP database which contains coupon usage and fill data for over 75,000 HCPs as some of the key data for the analysis.

We loaded all 15,000 of the brand’s targeted HCP’s along with their addresses into our model which plotted them on our integrated map.

Result:

By looking at the data in a map format and by overlaying all the key data points, the brand team utilized our model to come up with a new distribution plan. Each targeted HCP was given a minimum number of coupons, and then the remainder were allocated based on HCP’s “earning” additional coupons. More coupons were given to HCP’s who were:

  • Operating in areas with unfavorable managed care coverage
  • Considered “brand loyalists”
  • In the top 40% of coupon usage (across all brands)

The result of the new allocation methodology was that the brand was able to put more coupons in the hands of the HCP’s who were utilizing them, resulting in an additional $373K in bottom line profit.

 

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Showing Tremendous Value as part of the Launch Process

Situation:

A major pharma manufacturer was launching a new product into a very competitive pain marketplace. This market segment contained many competitors with lucrative offers with two having PNMT $0 offers in place. To maximize trial in this highly competitive migraine market, the brand team was interested in developing a patient incentive offer strategy to help speed the adoption of the brand.

Our Solution:

The brand implemented our National Co-Pay Optimization Model which would not only forecast different offer scenarios but give them a complete financial picture of each of the proposed offer. This model also recommends the best offer for the brand based on their specific brand objectives during each portion of its lifecycle.

Result:

As a part of the data gathering process market research information from the brand was compared to information in the Alpha 1C database. We found:

  • Current forecasted results for items like expected patient persistency and refill rates to be out of line with what was actually happening in this category
  • A miscalculation in the translation of their expected managed care data which resulting in a lower than what would be actual average patient copay. The difference was an average copay of $70 verses their expected $50 patient copay. Catching this error was of vital importance as the cost of what the brand was originally interested in implementing would have resulted in a drastic overspending situation

Based on these findings we were able to adjust the brand’s offer structure enough (type, amount, duration, cap, and patient eligibility) to provide a well-balanced offer which has met the brands pre-launch objectives showing that it’s vitally important to get an objective 3rd party with industry level data to review current expectations and forecasts.    

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Re-structuring a Complex Offer to meet their Patients “Donut Hole” needs for a Major Specialty Pharma Brand

Situation:

A major pharma manufacturer has a small share product in the highly competitive RA category. The brand had one of the highest growth rates of all the RA brands

The current offer (essentially a pay $0 program) provided the greatest patient savings over a one year period in the category, but several large competitors also offer attractive savings via their patient incentive programs (covering >95% of patients at no out of pocket expense for the year).   A more productive offer was sought that still maintained or increases that OOP number. Another key objective was to investigate any drops or tolerance level trends for OOP payments at different levels.

Our Solution:

The brand implemented our National Co-Pay Optimization Model which would not only forecast different offer scenarios but give them a complete financial picture of each of the proposed offer. This model also recommends the best offer for the brand based on their specific brand objectives during each portion of its lifecycle.

Result:

A full analysis was performed and it was discovered that the most productive change it the brand’s offer was to redistribute their annual cap. Instead of having a structured monthly and additive annual cap where many times the patients exceeded the cap on their first or second use, we suggested a more flexible cap that started higher on the first fill and then was reduced over the course of their therapy.

This ensured that the cap was high enough when the patient had not yet met their deductible and was reduced when co-pay deductibles had been met. There was however a “sticking point” which was the client could not meet their profit objectives by maneuvering the cap. In order for this strategy to work the brand team needed to realize that their patients needed to have “some skin in the game”. The model showed the brand didn’t need to have a $0 offer and they could come up to at least a $10 “patient pays first” co-pay offer. The savings gained on the this offer was more than enough to fund the cap structure change and deliver the increased profitability the brand was looking for!  

 

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New Product Launch

A Case Study for Securing the Optimal Offer for a New Product Launch

Situation:

A leading pharma company was preparing for a major product launch and wanted to implement a productive patient incentive program as part of their launch plan.  They had a lot of disparate data from multiple sources including market research studies, market share and managed care estimates.

The company was missing several key pieces of data which were needed to do an effective analysis to determine the optimal patient incentive offer. The brand team had several major objectives:
  • Increase trial beyond initial expectations
  • Understand both their average co-pay and the potential incremental volume that the various offer options might deliver
Our Solution:
  • Alpha 1C produced a unique “Patient Incentive Optimal Offer Model” for the client  - specific to their product launch
  • Surrogate data from Alpha 1C’s proprietary database was reviewed with the client and incorporated in place of any missing data.
  • We provided the client unique functionality and flexibility which tailored the model specifically to a launch brand. This flexibility was needed because the inputs frequently changed as new data became available.
  • We examined over 12 unique offers, focusing on the ones that produced the most trial for the most cost effective spend.

Result:

  • Alpha 1C utilized the client specific information combined with key performance data from their co-pay vendor and unique data from the Alpha 1C database to produce a custom simulation model.
  • The new model allowed the brand team to enter and change many of the needed inputs.  This changed the estimates on the fly, ensuring the brand had constant feedback utilizing the most recent data.
  • The resulting model showed that some of the brand group’s initial assumptions were not correct.  For example, the model highlighted that the brand’s assumed average patient co-pay estimate was underestimated by $20. This was a critical finding because the brand had been overestimating their market share and underestimating the cost of their co-pay program as a result.
  • Two main types of offers were tested: “free trial” and “zero co-pay” offers. The brand group was able to see the impact of these seemingly similar offers and was surprised by how different the results actually were.
  • The brand team utilized the model to find their “optimal offer” while also refining  their market share, incentive program budget and overall P&L estimates.
Timing:
  • The Alpha 1C finished the entire analysis in less than two weeks!
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Predicting RM Program Opt-Ins

A Case Study for Predicting Opt-Ins to a Patient Relationship Marketing Program

Situation:

A leading pharma company had just implemented a new patient relationship marketing program and wanted to ensure that their new patient incentive offer provided their patients a good discount while also driving patients to sign up for their RM program.

The brand team wanted to understand the value of getting a patient into their RM program and then find the best offer  to drive the best mix of patients into their RM program.

Our Solution:
  • Alpha 1C produced a unique analysis leveraging both our history in setting up RM programs and our unique “Patient Incentive Optimal Offer Model”.
  • We examined the data and determined values for both the average patient and the patient who also enrolled in an RM program.
  • We then examined over 10 unique offers and simulated the impact on not only trial and adherence but also on opt-in rates for their RM program.
  • We provided customized functionality which allowed the client to adjust each of these variables.

Result:

  • Alpha 1C utilized the client specific information, combined it with key performance data from their co-pay vendor and unique data from the Alpha 1C database to produce a custom simulation model including patient opt-in estimates .
  • The resulting model showed a side by side analysis of  many offer types including different approaches to driving opt-ins into the RM program.  For example, requiring the patient to sign up for the RM program was modeled against other options that gave greater incentives to those patients who signed up for the RM program vs. those who didn’t sign up for the program.
  • In addition, many types of offers were tested, including “graduating discounts”, “pay no more than”, “save”, “free trial”, ‘zero co-pay” and “combination offers” .  We also included their current in-market offers for comparison purposes.
  • We were able to produce a unique simulation model specific to the client’s combined brands and current market situation.
  • As in all of our models, we presented a “mini case study” on each of the scenarios showing all the KPI’s side by side.
  • The model’s flexible simulation capabilities allowed the brand team to run many more patient offer scenarios “on-the-fly” to quickly determine the impact of additional offer variations on brand results .
  • The brand team was able to utilize the analysis to find the optimal offer and program approach that provided an attractive patient discount, drove opt-ins to their RM program and delivered the best financial return.

Timing:

  • The Alpha 1C finished the entire analysis in less than two weeks!
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Three Brands One Offer

A Case Study for Three Brands Needing One Offer

Situation:

A top pharma company had just acquired two new brands which were complimentary to their current brand.  Each brand had its own strategy and a different co-pay discount offer in the form of a co-pay card and an on-line discount coupon.

The brand team had three major issues:

  • They wanted to save deployment funds by finding one offer that would work effectively across their entire three brand franchise.
  • They needed to understand how the brands would perform together as a group and wanted to promote multiple product use by their patients.
  • The brand team wanted a competitive offer but wanted to find the “tipping point” where a further increase in the offer would begin to show a lower ROI.

Our Solution:

  • Alpha 1C produced a unique value added analysis using our unique “Patient Incentive Optimal Offer Model”.
  • We examined the history of each of the brands to be included in the new multiple use strategy.
  • As additional background for the analysis, we met with the brand group and reviewed a series of strategic and tactical brand & market specific issues as well as each brand’s financial information.

Result:

  • Alpha 1C utilized the client specific information and combined it with key performance data from the client’s co-pay vendor to produce a “composite brand” which was a combination of the three brands.
  • We were able to produce a unique simulation model specific to the client’s combined brands and current market situation.
  • The resulting model showed a side by side analysis of  many offer types including “pay no more than”, “save”, “free trial”, ‘zero co-pay” and more including their current in-market offers.
  • The model presented a “mini case study” on each of the scenarios showing all the KPI’s side by side.
  • The model was customized with new features that allowed the brand to change the estimated mix of sales between the three products as well as change the estimates for patients buying multiple brands at the same time.
  • The model’s flexible  simulation capabilities allowed the brand team to run many more patient offer scenarios “on-the-fly” to quickly determine the impact of additional offer variations on brand results.   This simulation capability also allowed the brand team to quickly react to changing market conditions.
  • The brand team was able to utilize the model to find the optimal offer that would best meet the needs of all three brands as well as ensuring the best financial return for the company.

Timing:

  • The Alpha 1C finished the entire analysis in less than two weeks!
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Enhancing a High Performing Program

A Case Study for Using Predictive Modeling to Drive Better Decisions

 

Situation:

A leading drug had implemented a very successful patient incentive program for two years in a row. They recognized the program was successful because they had a very high redemption rate verses others in the industry, but they had no idea if the productivity of their program could be improved

Our Solution:

We recommended that the brand go through our process to determine the “optimal offer”. We completed the analysis based on the claims data that the brand provided, evaluating approximately 15 different offers with varying:

  • Patient offers
  • Frequencies
  • Vehicle types

Result:

  • Twelve different offer configurations were presented to the brand team three weeks later.  Each offer scenario produced a different financial outcome which was compared against the brands objectives
  • By analyzing the data provided we found the brand could actually decrease their patient offer by $5 and still produce the same patient trial and adherence rates
  • The forecast showed that the brand could save over $1.5 Million dollars simply by changing their offer slightly
  • The brand decided to change their offer, improve their return and re-invest their savings by putting even more cards into the marketplace
  • An additional 9,000+ new users were acquired, helping to grow the brand even faster than anticipated

Timing:

  • The entire analysis was completed in just 3 weeks because the brand had an offer in market and had all the data needed for the analysis
  • The new offer was put into market just 5 weeks after receiving the recommendation and was repeated for a second year