Assessment of Digital Marketing Technologies using TOPSIS Method

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract The marketing industry is constantly evolving as digital technology and their applications spread. Consumer behavior, company structures, marketing approaches, and competitive capacities are all impacted by this shift. For large and medium-sized businesses, digital technology has made them more competitive than competitors in the market when it comes to sales. Public relations can be given a significant boost thanks to the widespread adoption of digital technology in marketing. There are many competing goals and numerous parameters to consider when evaluating digital marketing technologies. When it comes to solving these kinds of issues, multi-criteria decision making (MCDM) is a strong instrument. The purpose of our work is to develop an evaluation framework for digital marketing technology using MCDM techniques. MCDM approaches are utilized in the evaluation process after developing the evaluation criteria and alternatives. The weights of the criteria are determined by the Analytic Hierarchy Process (AHP), and digital marketing technologies are ranked using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Finally, an application of the proposed methodology is demonstrated.


I. INTRODUCTION
The marketing industry is constantly evolving as digital technology and their applications spread. Consumer behaviour, company structures, marketing approaches, and competitive capacities are all impacted by this shift. Digitalization is a key marketing concept for staying on top of the ever-changing landscape. Building a digital brand image and enhancing the brand's worth have become increasingly important. Utilizing digital platforms, companies can easily reach their customers, manage sales and orders, and maintain good client relations long after the sale has been completed ( For large and medium-sized businesses, digital technology has made them more competitive than competitors in the market when it comes to sales. If you're using digital technology in marketing, you can build a long-term, mutually beneficial relationship with your clients by enhancing the power of public relations the most. There are many competing goals and numerous parameters to consider when evaluating digital marketing technologies. When it comes to solving these kinds of issues, multi-criteria decision making (MCDM) is a strong instrument. It is one of the most often addressed issues in academic writing. Finding the best solution from all possible options is the goal of MCDM. Analysis Hierarchy Process (AHP), which derives its total values for each alternative from relative weights of the various elements, is an exceptional MCDM approach (Saaty, 2005). It is used to rank and assess options in terms of importance and benefit evaluations using COPRAS. Because of its simplicity, it is less time-consuming than other MCDM approaches such as TOPSIS. Qualitative as well as quantitative factors can be evaluated using this approach. According to the findings, MCDM methodologies can be used to develop an evaluation framework for digital marketing technology. MCDM approaches are utilized in the evaluation process after developing the evaluation criteria and alternatives. For establishing the weights of criteria, AHP is utilized, and TOPSIS is used to rank digital marketing technology.

II. LITERATURE REVIEW OF DIGITAL MARKETING TECHNOLOGY
There is numerous research on digital marketing in the literature, which has been a hot topic in recent years. In general, these studies focus on the marketing concept as a whole. A lack of academic research has been done on the usage of digital marketing with cutting-edge technology. On Tab. 1, you'll see these findings. There is no research in the literature that combines digital marketing with technology using MCDM methodologies. This study aims to bridge this knowledge gap by combining MCDM analytic approaches with a digital marketing technology review.
Major players have more than doubled their manufacturing capacity in India's sanitary ware business in the last six to seven years. The enterprises have also introduced battery casting, beam casting, and the latest imported quick burning cycle kiln technology to their manufacturing process. Furthermore, these businesses have improved their product quality and offered higher-priced items to the market, which have been well received. In India, demand for high-quality sanitary gear is on the rise. High-value manufacturing demands are being satisfied by enterprises, and as a result, the realisation per metric tonne is quite good, resulting in good profitability.
With the goal of persuading Indian consumers to buy only high-quality goods, corporations have launched an intensive advertising campaign. There has been an increase in the number of corporations offering showroom incentives and opening their own retail locations in key cities, as well. In India, the demand for sanitary products is increasing at a rate of 15% to 17% each year.

III. THE PROPOSED MODEL AND METHODOLOGY
The proposed methodology in this study consists of three basic steps: Step 1. Determination of criteria and alternatives for evaluation of digital marketing technologies.
Step 2. Determination of the evaluation criteria's importance degree in the proposed model by AHP method.
Step 3. Evaluation of digital marketing technologies by TOPSIS method according to the criteria.
As a result of the literature review and expert opinions, the digital marketing technology evaluation model is shown as in Fig. 1.
In this model, there are three main criteria: customer, company and market. There are three sub-criteria of this each main criterion.

Analytical Hierarchal Process (AHP)
The weights of the criteria are estimated by AHP, which involves 3 steps.
Step 1: Developing a hierarchal structure with a goal at the top level, the criteria at the second level and the alternatives at third level. Here the goal is nothing but finding weights for each criterion.
Step 2: To determine the relative importance of different criteria with respect to goal a pair wise comparison matrix is developed based on the scale of relative importance. Scale of relative importance: 1 -equal, 3 -moderate, 5 -strong, 7very strong, 9 -extreme strong, 2,4,6,8 -intermediate values.
All the elements in the column of pair wise comparison matrix are obtained by dividing the first element in a row with the remaining every elements in that row respectively. After creating pair wise comparison matrix, normalized pair wise comparison matrix is created by adding all elements in a column of pair wise comparison matrix, to get a value for every criteria in every column. Then every element in a column of pair wise comparison matrix is divided with respective sum value of that column. The prepared normalized pair wise comparison matrix.
Step 3: Calculating the consistency to check whether the obtained criteria weights are correct or not, for this pair wise comparison matrix is taken and the column elements are multiplied with the criteria weight. Then the weighted sum value is calculated by adding all the values in the particular row. After that the ratio of weighted sum value to the criteria weight are calculated for each row. By considering the average of these values the lambda max is calculated. Then consistency index (CI) and consistency ratio are estimated.
The obtained criteria weights are considered as correct when the consistency ratio is less than 0.1.

TOPSIS Method
TOPSIS (Technique for order preference by similarity to ideal solution) is established on the idea of finest alternative should have the shortest distance. That is the best distance from the ideal solution. This research deals with the selection of best blend out of all 5 alternatives and for which the CO%, HC (ppm), CO2%, O2%, NOx ppm, Smoke Mg/m3, BTE and Sfc (Kg/KWh) are considered as criteria.
The process of TOPSIS technique is as follows: Step 1: Normalization of the evaluation matrix: It is aimed to convert various units in different criteria into common units to allow comparisons among the criteria. For the alternative j on criterion I, of normalized values of alternatives Xij Xij is defined as follows: ̅̅̅̅ = √∑ ( ) 2

=1
, i = 1,2,…,m; j = 1,2,…,n. (4) Step 2: creating a weighted normalized decision matrix: The weighted normalized decision matrix can be prepared by multiplying the normalized evaluation matrix Xij with its associated weight Wj to obtain the result. = ̅ × (5) Step 3: Determination of the positive and negative ideal solutions: The positive ideal solution Vi + shows the utmost better alternative and the negative ideal solution Viindicate the least desirable alternative. Vi + is maximum value as a best alternative for beneficial and minimum value as non-beneficial. Viis minimum value as a worst alternative for beneficial and maximum value for non-beneficial.
Step 4: Calculation of the separation measure: The separation from the positive and negative ideal for each alternative can be measured by the n-criteria Euclidean distance.
Step 5: Calculation of the relative closeness to the ideal solution or Performance score: The relative closeness of the i th alternative with respect to ideal solution V+ is defined as = _ + + − (8) Step 6: Ranking the priority: A set of alternatives then can be preference ranked according to the descending order of Pi.  At the end of the AHP method, the most important criterion is found to be the "Customer satisfaction (C11)". The second important is "Customer loyalty (C12)" and the third ranked factor is "Image (brand value) of company (C21)".