When is a DiGA field force profitable? (by Marcus Bergler) (EN)
The uptake of Rx Digital Health Applications in Germany (DiGAs) is perceived to be very slow and manufacturers struggle to find the right sales/marketing mix to convince doctors to prescribe DiGAs to their patients at larger scale. On the other hand the focus of DiGA providers in the channel mix so far has been on DTC/patient marketing and digital marketing to doctors with only very few providers strategically focussing on a “traditional” field force to educate doctors on available DiGAs and patient benefits face to face. The article provides a framework of industry field force KPIs and applies them in the context of a DIGA field force, calculating the break even and a realistic prescription potential.
Let's talk numbers...when is a DiGA field force profitable?
As a short introduction for the readers who are not familiar with the German DiGAs (Digitale Gesundheitsanwendungen/Digital Health Applications):
Basically a DiGA is a digital health application that can be prescribed by a doctor and is reimbursed by the statutory health insurance system. Patients can download the app and receive an activation code as part of the prescription so that they can use it.
A DiGA has to be approved by the German BfArM (Bundesinstitut für Arzneimittel/Federal Institute for Drugs and Medical Devices) and the approved DiGAs cover a variety of indications e.g. depression, obesity, insomnia, smoking cessation, etc. with the typical duration for one prescription cycle being 90 days.
In my discussions with DiGA providers about sustainable and successful go-to-market models and the "right" channel mix, the general argument often comes up with regard to an own sales force that it is far too expensive and can never be used profitably - not to mention the attitude that a digital product must also be primarily marketed digitally...
In order to create a common basis of facts in this context, I have compiled a simple break-even calculation below. The assumed input factors are absolutely realistic and for me it is important to illustrate the order of magnitude and to offer a framework for flexible simulation of how the break-even analysis changes with changes in the input variables (especially DiGA price).
I assume the full costs for one sales rep to be €120,000 per year. Let's further assume that we employ 10 reps (this could cover, for example, the big cities/metropolitan areas), thus resulting in costs of €1.2 million per year.
As an average price for a DiGA in the first 12 months, I assume 600 €, which is absolutely realistic, given the prices of the most recently approved DiGAs.
For the vast majority of DiGAs, the marginal costs are very close to or equal to zero (there are a few with personal support and thus fixed-step cost), so that no product costs have to be deducted. The fact that not all prescriptions/activation codes are actually redeemed by the patients (for example, at TK the rate is 90%, across all SHI funds 80%) I neglect for this simplified view, as far as the improvement of the code activation workflow is concerned, all stakeholders involved are working on an optimisation and simplification of the processes and, besides, I am primarily concerned with the order of magnitude of the prescriptions needed for profitability and not with the correct reporting of the decimal places.
In order to break even at € 1.2 million costs for the entire sales force, it must then generate 2,000 DiGA prescriptions per year at a turnover (=revenue) of € 600 per prescription.
In terms of the ten field force staff members, this means that each member of the team has to generate 200 prescriptions per year from the doctors visited. With 180-200 field days, this results in an average of one prescription per day (which is not significantly changed by taking the redemtion rate into account).
Let us further assume that we consider an GP (general practitioner/practitioner/internist) field force that can easily execute 6-8, max. 10 visits per day in major cities/metropolitan areas. This is 30-40, max. 50 doctors visited in a week and 120-160, max. 200 doctors visited in a month, which is roughly a realistic territory size for a GP territory.
With a visit frequency of 4-6 weeks, the rep speaks to each of the up to 200 doctors in his territory 10-12 times (after establishing face-to-face contact, remote engagements can also be used substantially as part of an optimised omnichannel mix) and only needs to generate 200 prescriptions a year from these contacts to break even. In other words, on average, each doctor seen up to 12 times by a sales rep needs to trigger a prescription for only one patient in 12 months for the sales force to break even.
Now, of course, one can always "argue around" the assumptions, but this does not lead to such significant changes in the result that a sales force becomes uneconomical, because the profitability threshold is so low that a lot has to happen to produce a negative result. Let's say, for example, that after 12 months the price of the DiGA halves (this is not an unrealistic scenario...), now the rep has to generate an average of 2 prescriptions per doctor and year in order to become profitable...mind you, with doctors he has been looking after for a year.
Another option would be to share the costs for the sales force with a DiGA provider who has the same target group of doctors but is active in a different indication (comparatively easy to realise with GPs), then the rep details two DiGAS and the costs for each provider are halved.
In addition, with a predominant focus on an field force supported sales strategy, costs for other sales/marketing measures (e.g. patient marketing) can be significantly reduced.
I also often hear the argument that the DiGA companies don't have the experience and management capacity to set up and manage a sales force - for this, on the other hand, there are specialised contract sales organisations, which may cost a little more, but this again does not fundamentally change the magnitude of the ROI calculation.
So far, I have only looked at the "minimum" number of prescriptions needed to cover the costs of the field force, but the question of the prescription potential of such an approach is much more exciting.
In broad indications with a high prevalence, such as obesity or insomnia, one can easily assume that an intensively serviced doctor can generate an average of one prescription per month (instead of one per year for break-even); the patient potential for this is given in any case. This results in a total of 18,000 to 24,000 prescriptions for ten reps with about 150-200 doctors in each territory. The situation is more differentiated for other indications and (secondary care) doctor target groups, but for the large GP indications there is no way around the sales force as an elementary component of a sustainable channel mix strategy in my view. In the case of specialist indications, one will strategically focus on optimising the depth of prescribing as opposed to the breadth of prescribing in the case of GPs.
A quick note on targeting and segmentation: there are different approaches/providers that can support very precisely with targeting and segmentation data and services, so selecting the right doctors for an efficient visit strategy is not a major challenge.
Marcus Bergler is a globally recognized expert for KOL/HCP profiling and engagement in the pharmaceutical and digital health/DiGA industry and after a career as senior manager at IMS Health (now IQVIA), Cegedim and Veeva Systems, he now works in executive and non-executive roles at D2L Pharma Research Solutions, xircle.ai, Knowledge Gate Group and Exaris Solutions.