THEORETICAL JUSTIFICATION OF A METHODOLOGICAL APPROACH TO ASSESSING THE IMPACT OF MARKETING ON THE PHARMACEUTICAL MARKET

ABSTRACT

The article provides a theoretical justification for a methodological approach to assessing the impact of marketing on the pharmaceutical market as a systemic factor in the transformation of the competitive environment. Marketing influence is proposed to be analyzed through changes in key market parameters, namely price sensitivity of demand, switching barriers, prescribing patterns, the intensity of generic competition, information transparency, and the role of intermediaries. An integral index of marketing-driven transformation at the therapeutic class level is developed, allowing for the consideration of the complementarity of influence channels. A model linking the index to price dynamics while accounting for market concentration is proposed, along with a methodology for empirically determining weights based on panel regression with regularization. The study demonstrates that the index can serve as a tool for comparative segment analysis and competitive environment monitoring. The results provide a foundation for further empirical validation and for developing market-based alternatives to administrative price regulation.

АННОТАЦИЯ

В статье представлено теоретическое обоснование методического подхода к оценке влияния маркетинга на фармацевтический рынок как системного фактора трансформации конкурентной среды. Предлагается рассматривать маркетинговое воздействие через изменение ключевых параметров рыночной среды, т.е. ценовой чувствительности спроса, барьеров переключения, структуры врачебных назначений, интенсивности дженериковой конкуренции, информационной прозрачности и роли посредников. Разработан интегральный индекс маркетинговой трансформации терапевтического класса, который позволяет учитывать комплементарность каналов влияния. Предложена модель связи индекса с ценовой динамикой с учетом концентрации рынка, а также методика эмпирического установления весов на основе панельной регрессии с регуляризацией. Показано, что индекс может использоваться как инструмент сравнительного анализа сегментов и мониторинга конкурентной среды. Полученные результаты формируют основу для дальнейшей эмпирической апробации и разработки рыночных альтернатив административному ценовому регулированию.

Keywords: pharmaceutical marketing, pharmaceutical market, generic competition, price dynamics, integral index, competitive environment, market concentration.

Ключевые слова: маркетинг в фармацевтике, фармацевтический рынок, дженериковая конкуренция, ценовая динамика, интегральный индекс, конкурентная среда, рыночная концентрация.

Introduction. The pharmaceutical market is among the most institutionally complex markets, characterized by the fact that price and consumption volume are determined not only by classical supply and demand mechanisms, but also by a multi-level system of intermediaries, regulators, and professional agents (including physicians, pharmacy chains, insurance companies, procurement mechanisms, etc.). Under these conditions, marketing becomes a system that influences the configuration of the competitive environment, the structure of preferences, the dynamics of drug substitutability, and the distribution of market power among participants. The problem of assessing the impact of marketing on the pharmaceutical market is further complicated by the fact that the end consumer often does not make decisions autonomously, but within an interaction framework between the manufacturer, the physician, and the patient, where, against the background of information asymmetry and quality uncertainty, conditions arise for pronounced informational effects. In particular, existing evidence on the promotion activities of pharmacological and pharmaceutical companies shows that communication with physicians can alter prescribing behavior under conditions of quality uncertainty and incomplete information; that is, marketing influence manifests itself as a mechanism for shaping beliefs and expectations rather than through direct price pressure [2]. The evidence base on the influence of promotion on prescribing behavior is also supported by longitudinal evaluations of physician-directed promotion, which document pronounced changes in prescribing patterns under exposure to promotional activity [3].

At the same time, marketing in the pharmaceutical sector is closely associated with the issue of potential conflicts of interest and financial incentives, since payments and compensation from companies may serve as a factor influencing prescribing decisions. Studies based on Medicare Part D data demonstrate that company payments correlate with prescribing behavior and may be interpreted as a channel of influence on demand through the professional agent, thereby reinforcing the need for rigorous quantitative identification of such effects and for distinguishing informational influence from incentive-driven effects [1]. However, the impact of marketing on the market is not limited to the physician channel. Consumer-oriented channels are also present, such as direct advertising, which are capable of altering the structure of demand and prompting patients to request specific therapies; this, in turn, affects the overall level of consumption, the composition of the consumer basket, and the intensity of competition among therapeutic equivalents [7].

The main macroeconomic effect that makes the study of marketing in the pharmaceutical market significant is associated with the competition between branded and generic drugs and price dynamics. Competitive pressure from generics, on the one hand, results from institutional and technological factors, and on the other hand, may be substantially accelerated by marketing mechanisms that increase substitutability and reduce switching barriers. An analysis of the pricing behavior of branded drugs in competition with generics confirms that the structure of competition and the parameters of entry and/or substitution shape price trajectories and allow competitive effects to be considered as a function of the market environment [4]. Therefore, if marketing alters the market environment (awareness, switching behavior, preferences, prescribing practices, availability of generics in pharmacy chains), it is capable of exerting an indirect but pronounced influence on price levels and on the economic efficiency of maintaining monopolistically high prices.

Additional complexity is created by the infrastructure of intermediaries and reimbursement mechanisms associated with incentivizing the selection of specific drugs. A systematic review of the literature on PBMs (pharmacy benefit managers acting as intermediaries between insurance companies, employers, pharmacies, and drug manufacturers) indicates that the institutional system of incentive allocation and access control may influence availability and prices; therefore, assessing the impact of marketing on the market must account for intermediaries as independent actors transforming promotional effects into market outcomes [6]. Furthermore, the pharmaceutical industry is characterized by variability in the effectiveness of promotional activity, depending on the drug class, lifecycle stage, competitive density, and regulatory conditions. Aggregate assessments of the effectiveness of promotional marketing expenditures demonstrate that promotional effects systematically differ and require the development of a comparable measurement scale for cross-class comparison [5].

Thus, the problem underlying the present study lies in the insufficient development of methodological tools that would allow for assessing the impact of marketing on the pharmaceutical market as a whole, i.e., as the aggregate effect of transforming the market environment, leading to changes in substitutability, prescribing structure, generic competitive pressure, transparency of choice, and, consequently, price dynamics.

To address this problem, the development of an integrated model appears promising, one that would be capable of:

1) identifying the main channels of marketing influence;

2) formalizing them into measurable factors;

3) aggregating the factors into a single index;

4) linking the index to market outcomes (price pressure, savings, changes in concentration);

5) establishing the limits of applicability and interpretative boundaries.

Accordingly, the objective of the study is to develop and theoretically substantiate a methodological approach to assessing the impact of marketing on the pharmaceutical market.

Research methodology. The proposed methodological approach is based on the premise that marketing affects price not directly, but through changes in the market environment. Therefore, it is appropriate to measure not marketing expenditures per se, but the integral state of the environment that renders the maintenance of monopolistically high prices economically inefficient, i.e., price sensitivity increases, switching barriers to alternative drugs decrease, the structure of prescriptions changes, generic competition intensifies, informational transparency improves, and pharmacy chains and intermediaries become more actively involved.

The therapeutic class c (for example, ATC3/ATC4) and period t (quarter, year) are proposed as the unit of analysis. The index is calculated by class and aggregated into a market-level indicator. The target market variable for interpretation is the average therapy price P_c,t (preferably converted into DDD (defined daily dose) or treatment course) and the consumption volume Q_c,t (DDD, packages, prescriptions). Such a choice ensures comparability across classes and allows the result to be interpreted as price pressure or savings, ceteris paribus.

To operationalize the index, a system of normalized factors x_k,c,t ∈ [0,1] is introduced, reflecting the main channels of marketing influence. The selection of factors is theoretically grounded in empirical findings on the impact of promotion on prescribing behavior [2; 3], on the role of incentives and payments in physician behavior [1], on the pricing consequences of competition between branded and generic drugs [4], on the heterogeneity of promotional expenditure effectiveness [5], on the significance of the institutional infrastructure of intermediaries [6], and on consumer demand channels through DTC advertising [7]. Let us consider each of the factors.

Factor 1. Price sensitivity of demand PS_c,t Substantively reflects the extent to which demand within the class exhibits responsiveness to price, that is, the degree to which information and comparability of alternatives transform price into a significant determinant of choice. For calculation, Formula 1 is used:

                                                                     (1)

where: ε_c,t is the estimated price elasticity of demand (if data are available); ε^ref is a benchmark value (for example, the market median or an expert-based estimate). If direct elasticity estimates are unavailable, proxy indicators are used, such as the share of purchases in the lower price segment among therapeutically equivalent alternatives, the share of cases where the minimum price option is selected in the pharmacy receipt, or the share of users who compare prices online.

Factor 2. Switching (reduction of switching barriers) SW_c,t. Reflects the speed and frequency of transitions between therapeutically equivalent drugs (brand–generic; generic–generic). Substantively, the factor is associated with the role of marketing in reducing switching barriers through information provision and normalization of equivalence, as well as through communication with physicians and pharmacies [2; 3]. The factor is calculated using Formula 2:

                                                          (2)

where: SwitchRate_c,t – is the share of switches between equivalents during the period (based on prescription data, pharmacy sales data, or survey/expert assessments), and SR^ref is the benchmark level.

Factor 3. Shift in prescribing toward generics RX_c,t. Since the physician is the principal agent, marketing influence in the pharmaceutical industry often manifests through the structure of prescriptions [2; 3]. The factor is calculated using Formula (3):

                                                  (3)

where: GenericRxRate_c,t – is the share of generic prescriptions (or the share of prescription-based sales), and GR^ref is the benchmark for the market and/or class. The factor should be interpreted with caution, since the share of generics is influenced by regulatory and procurement mechanisms; therefore, it is advisable to separate the marketing-induced component from the administrative one through the use of control variables and fixed effects.

Factor 4. Intensity of competition associated with generic influence GC_c,t, which represents a structural outcome of an environment characterized by a diversity of active competitors – their actual sales share generates competitive pressure and affects the brand’s price trajectory [4]. A simple form of estimation is proposed (4):

                                   (4)

where:  – is the number of active generic competitors in the class, N^ref – is the benchmark level; GenericSalesShare_c,t – is the share of generics in total sales volume.

Factor 5. Informational transparency and digital comparability TR_c,t. In the context of this factor, it is important to note that digital channels may increase the transparency of prices and alternatives, thereby contributing to additional price sensitivity and reducing switching barriers, which is consistent with the idea that DTC and other forms of communication influence preferences and the structure of demand [7], as well as with the general principle of heterogeneity of promotional effects across channels [5]. The calculation is performed using Formula (5):

                 (5)

where: OnlineShare_c,t – is the share of online orders and/or sales in the class; PriceCompareCoverage_c,t – is an estimate of the coverage of comparison tools (0…1).

Factor 6. Assortment and merchandising pressure of pharmacies and intermediaries AP_c,t. This factor reflects the ability of pharmacy chains and institutional intermediaries to shift choice, increase the availability of generics, and alter the economics of pricing [6]. The calculation is performed using Formula (6):

                             (6)

where: GenericShelfShare is the share of shelf space or assortment allocated to generics, PrivateLabelShare is the share of private label products (if applicable), and PL_ref – is the benchmark value.

Taken together, the six factors form an integral index. The Marketing Transformation Index of the class IMKD_c,t is defined through geometric aggregation of the factors (7):

                                             (7)

The multiplicative form reflects the complementarity of channels. For example, if digital transparency is high but switching or generic competition is absent, the systemic effect on the market is limited. Thus, a “penalty” is imposed for weaknesses in individual channels, which corresponds to the nature of the pharmaceutical market with its multiple decision-making nodes.

For aggregation across classes at the market level, Formula (8) is used:

                                 (8)

where the weights s_c,t reflect the significance of the class in terms of therapy volume.

To translate the index into an interpretable estimate of market impact, a concentration coefficient C_c,t ∈ [0,1] is introduced, reflecting the initial market power (for example, through a normalized HHI) (9):

                                                         (9)

It is assumed that under higher concentration, the same level of environmental transformation produces more pronounced price pressure, since the effect disrupts the mechanisms sustaining the price premium.

A simple model of relative price change is calculated using Formula (10):

                                         (10)

The parameter β determines the calibration and may be estimated empirically using a panel of classes or adopted as a conservative scenario parameter; β is preferably estimated from data, since the literature indicates substantial heterogeneity of promotional effects [5], and the role of physician incentives may vary across classes [1; 2; 3].

The estimation of savings (conditional benefit for the system, the consumer, or the payer) is carried out using Formula (11):

                (11)

The exponential form ensures an upper bound on the effect and provides for realistic diminishing returns.

Equally important is the determination of weighting coefficients, since the weighting scheme constitutes the core methodological challenge of the index, as it defines the contribution of each channel. As a promising approach, supervised (target-oriented) weighting is proposed, based on an empirical model in which weights are derived from the predictive power of the factors with respect to the target market outcome.

Stage 1. Selection of the target outcome. The most substantively meaningful targets are: a) change in therapy price ΔlnP_c,t, b) change in generic share ΔGenericShare_c,t, c) change in concentration ΔHHI_c,t. It is preferable to combine a) and b), since brand–generic competition constitutes a channel of price dynamics [4].

Stage 2. Construction of a panel model with fixed effects (12):

                (12)

where: z_k,c,t are standardized factors (z-score),, α_c the class fixed effectа, δ_t is the period effect, Z_c,t are control variables (cost inflation, regulatory changes, availability of subsidies, etc.). This specification reduces the risk of bias due to pronounced differences between classes.

Stage 3. Regularization and validation. To enhance the stability of estimates, it is proposed to use Lasso, Ridge, and ElasticNet methods with cross-validation, since the factors may be correlated (for example, digital transparency and switching). Regularization reduces overfitting and makes the weights reproducible.

Stage 4. Transformation of coefficients into weights (13):

                                                     (13)

This rule ensures interpretable weights as the shares of factor contributions to explaining the target effect. If multiple targets are used, an aggregated weight vector may be constructed as the average (or weighted average) across models, thereby accounting for both price and competitive aspects.

The advantage of this method lies in the fact that the weights are not assigned arbitrarily but are derived from the data, while taking into account the heterogeneity of promotional effects [5] and institutional constraints related to physician incentives and intermediaries [1; 6]. In the absence of sufficient data, weights may be assigned heuristically (for example, equally) or determined through expert judgment.

At the same time, it is important to highlight the limitations of applicability and interpretation of the proposed index, which are as follows:

‒ endogeneity of marketing, manifested in the fact that companies intensify marketing in classes where they expect demand growth or where competition is intensifying. Without proper controls, this leads to biased estimates. Panel fixed effects and instrumental approaches reduce the risk but do not eliminate it completely [5];

‒  regulatory and procurement factors, since the share of generics and prices in certain segments are strongly determined by administrative mechanisms, which necessitates accounting for institutional conditions [6];

‒ the distinction between informational and incentive-driven influence, as payments and physician incentives may distort estimates of market marketing effects and require additional interpretation [1];

‒ the DTC channel is not equally applicable across countries, since depending on regulation, advertising of prescription drugs may be prohibited or restricted; therefore, the consumer pressure factor must be adjusted or conditionally applied [7];

‒ the existence of situations in which marketing increases price; for example, if marketing strengthens brand preferences without enhancing substitutability and without strengthening the position of generics, a price premium may increase. In the methodology, such a dependence is addressed through interpretation rules, as the index reflects specifically competition-transforming marketing rather than marketing in general. In practice, this circumstance is identified through consistency checks of the factors, since if SW, GC, and PS are low while promotional activity is high, the effect may not lead to a price decrease.

Results and discussion. To illustrate the applicability of the methodology, let us consider three hypothetical therapeutic classes (A, B, C) in period t. The factor values are specified on a normalized scale in the range 0…1 (Table 1).

Table 1.

Normalized factors and the IMKD index

Based on the estimated values presented, it can be established that classes A and C are characterized by a high level of marketing-induced competitive transformation (high switching and significant generic competition), whereas class B is characterized by low switching and weak structural competition; therefore, the overall index is noticeably lower, even with moderate digital transparency. At the same time, the absence of competition cannot be compensated by a single communication channel.

For market-level aggregation, let us assume the following conditions: the shares of the classes in total therapy volume are s_A = 0,40, s_B = 0,35, s_C = 0,25. Then IMKD_market will be 0,40 * 0,67 + 0,35 * 0,43 + 0,25 * 0,66 = 0,58. If in the subsequent period t+1, as a result of digitalization and the strengthening of generic programs, the values of TR and GC in class B increase (for example, to TR = 0.75, GC = 0.50), the class B index rises, say, to 0,52. In that case, the market index would increase to approximately 0,61 units.

The purpose of such a comparison is that the index can be used as a metric to track the dynamics of the market environment rather than as an indicator of advertising activity.

From the perspective of assessing price pressure and savings, suppose that normalized concentration across classes equals CA = 0,70, CB = 0,85, CC = 0,50. Let us take a conservative calibration β = 0,12 (a scenario-based value subject to empirical estimation). Then the forecast of relative price change is:

ΔP/P ≈ −0,12*IMKD*C

For class A: −0,12 * 0,67 * 0,70 ≈ −5,6%;

For class B: −0,12 * 0,43 * 0,85 ≈ −4,4%;

For class C: −0,12 * 0,66 * 0,50 ≈ −4,0%.

Here, an important advantage of the methodology becomes evident, since even with a lower index in class B, price pressure may be comparable due to high concentration, which is consistent with the assumption and existing findings that where initial market power is higher, the dismantling of switching barriers and the growth of competition alter the economics of price maintenance.

If we assume conditional expenditure volumes P_c,t−1*Q_c,t – A=100, B=80, C=60 (in arbitrary units), then the savings according to the exponential formula will amount to:

Savings ≈ ∑(BaseSpend) * (1 - e ^{−0.12 * IMKD * C} )

A: 100 * (1 - e^−0,056) ≈ 5,4;

B: 80 * (1 - e^−0,044) ≈ 3.4;

C: 60 * (1 - e^−0,040) ≈ 2,4.

Accordingly, approximately 11.2 conditional units, which provides an interpretable estimate of the potential effect of environmental transformation.

Let us note the features and limits of applicability of the methodology. The advantages of the methodological approach include the following:

1. The index considers marketing as a systemic factor whose impact operates through the market environment rather than as a partial expenditure indicator, which is consistent with the observed heterogeneity of promotional expenditure effectiveness and the dependence of the effect on competitive structure [5].
2. The methodology reflects the specific features of the pharmaceutical market, where the physician is the principal agent and marketing effects manifest through prescribing behavior, informational uncertainty, and incentives [1; 2; 3].
3. The inclusion of brand–generic competition as a separate factor links the index to a proven channel of price dynamics [4].
4. The consideration of intermediaries and pharmacy infrastructure makes it possible to integrate institutional mechanisms of access, drug prescribing, and the influence of economic incentives, which is important for the correct assessment of price effects [6].

The limitations of the approach are as follows:

1. The factors may be measured with error, especially when different indicators are used, which will affect the accuracy of calculations.
2. The endogeneity of marketing requires careful identification and empirical estimation of β and the weighting coefficients.
3. It is advisable to account for national (country-specific) constraints when calculating the index, taking into consideration the particularities of the pharmaceutical market [7].

Conclusion. The proposed methodological approach is based on the thesis that marketing in the pharmaceutical sector affects the market primarily through the transformation of the market environment, namely through the formation of price sensitivity, the reduction of switching barriers, changes in prescribing practices, the strengthening of competition from generics, increased informational transparency, and the involvement of intermediaries and pharmacy chains in the redistribution of demand.

The methodology ensures computability and comparability of estimates across therapeutic classes and makes it possible to link the index to market outcomes through a simple model of price pressure that accounts for concentration. A key element of the approach is the proposed prospective method for determining weights based on supervised weighting (a panel model with regularization and validation), which enhances reliability compared to purely expert-based schemes and accounts for the empirically observed heterogeneity of promotional effects. The index is suitable for comparative analysis across segments and periods and is potentially applicable as an analytical tool in the assessment of competition policy and soft market alternatives to administrative price regulation.

The prospects for further research are associated with expanding the set of factors to include institutional and clinical components, refining the role of intermediaries, and implementing dynamic evaluation models. The practical value of further development lies in the possibility of establishing standardized monitoring of the competitive environment in pharmacological and pharmaceutical markets, identifying segments at elevated risk of monopolistic pricing, and assessing the conditions under which marketing mechanisms genuinely enhance competition and reduce the economic inefficiency of maintaining monopolistically high prices, as well as the cases in which they, conversely, reinforce brand preferences and increase the price premium. Therefore, empirical testing of the proposed theoretical approach appears to be a promising direction for future research.

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