Managing customer lifetime value in client communities
Empirical implications of CLV measurement in client communities. Client communities implementation. Impact of client communities on companies’ performance. Factors of success derived from content analysis. Customer lifetime value: Marketing models.
| Рубрика | Маркетинг, реклама и торговля |
| Вид | курсовая работа |
| Язык | английский |
| Дата добавления | 26.08.2017 |
| Размер файла | 81,4 K |
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3. Research on client communities' implementation and CLV growth interconnection
3.1 Research methodology
For the purposes of the current research stage, 107 respondents were asked to fill out the questionnaire with 41 respondents indicated their membership in at least one client community and 66 respondents as not members (38.3% and 61.7% of the sample respectively). The sample also differed by sex, age, and region.
Another important factor needed for verification is frequency with which customers refer to the technical support depending on their attachment to a client community. It is important to mention that from five possible answer choices, respondents made a decision only between two of them: either “Never”, or “Less than once a month”. From the bar chart, it can be concluded that customers who joined a client community, are more likely to never ask for help from the firm's technical support, whereas non-members are much more likely to apply for the support less than once a month.
It might be expected that the dataset provides evidence for the following hypothesis:
Hypothesis. Companies with a client community gain on average larger CLV than companies without a client community.
There is a great variety of means at our disposal treating solution of CLV measurement and several approaches were already discussed in the first chapter of the paper. For the purposes of the current research, it was decided to use the formula for CLV according to one of the last models discussed (Zhang et al., 2010):
The model presented above meets two key requirements of the current research being its two primary advantages simultaneously. The model is both:
1) predictive;
2) detailed.
The first positive point is reached by considering in the formula such element as Discount Rate, which helps to take into account the concept of time value for money. Moreover, the formula contains complex elements such as Margin and Retention Rate, which makes it rather detailed. On the other hand, the model is not overloaded with distinct calculations of CLV in every period anticipated - which would definitely make the research results more accurate, but a lot more sophisticated to obtain.
In order to make the research process more rational and concise, CLV was calculated without taking into account Costs of Acquiring and Retention, because this information (according to Jive cases) is not indicated in open sources and needs to be gathered from the company's representatives. Discount Rate was evaluated as a constant value demonstrating the average rate of the secured loan at the moment the research was conducted - 11% (Bank of Russia, Official Website). In addition, after the survey, CLV indicator had to be transformed from absolute value to relative value, and therefore, the final CLV obtained from the questionnaire was divided by the average customer lifespan.
3.2 Results and discussion
The hypothesis considers membership of a customer in a client community and higher CLV. In order to prove or reject it, it is needed to calculate average CLV among two clusters in the dataset: Members and Non-members.
Table 3. Variable means on a membership basis
|
communities_member |
avg_transactions |
avg_order |
retention_1year |
retention_6months |
||
|
Non-member |
Mean |
1,85 |
2,65 |
,86 |
,68 |
|
|
N |
66 |
66 |
66 |
66 |
||
|
Std. Deviation |
1,350 |
1,342 |
,346 |
,469 |
||
|
Member |
Mean |
1,59 |
2,93 |
,93 |
,83 |
|
|
N |
41 |
41 |
41 |
41 |
||
|
Std. Deviation |
1,161 |
1,233 |
,264 |
,381 |
||
|
Total |
Mean |
1,75 |
2,76 |
,89 |
,74 |
|
|
N |
107 |
107 |
107 |
107 |
||
|
Std. Deviation |
1,282 |
1,302 |
,317 |
,442 |
With the figures taken from the table, it is possible to calculate CLV for every cluster independently:
Units for CLV values are intentionally left unspecified, because a) the formula for CLV calculation was modified and b) values for average monthly transactions and average purchase are represented not in their actual units (i.e., times and rubles), but in so-called ranks with higher rank corresponding with higher value interval.
From the calculations above, it can clearly be seen that, on average, CLV is higher among members of client communities as opposed to non-members. Therefore, the first hypothesis is not rejected, and it is possible to assume that companies with implemented client communities, on average, benefit from higher CLV than companies without client communities.
In order to check results of content analysis conducted earlier, answers to fifteen questions (i.e., three questions for each of the five categories) were subject to Mann-Whitney U Test for independent samples because two samples are obviously not normally distributed (otherwise, the null hypotheses about similar distributions of answers to the questions among two clusters would be subject to Student's T-Test).
Almost all null hypotheses except the last one were rejected. In other words, distributions of fourteen out of fifteen variables are actually different. The last question asked respondents about their agreement with the statement that they would leave feedback for the company only in the case of some bonus provided. However, one retained hypothesis does not significantly change the obtained statistics: more than 93% (14 out of 15) of the factors influenced by client communities indeed vary from that of organizations without client communities implemented.
3.3 Limitations and future research directions
There are three main areas of limitations for the current research:
1) Content analysis. Sample for the first stage of the research included only sixteen case studies which were gathered from a single website.
2) CLV. The formula used for CLV was simplified by not considering costs of acquiring and retention a customer and average customer lifespan (in months) as this information is not easily available to a person not directly related to the company.
3) Survey. Sample for the second stage of the research was also limited: only 107 people were asked to fill in the questionnaire. Consequently, there are no enough evidence to expand research results into the general population as the sample is not fully representative of the latter.
It might be of great scientific interest to further expand the current study by considering other case studies with the emphasis on company's size, industry, organizational structure, country of origin, etc. Besides, the sample for the questionnaire may also be expanded and varied by deployment of the research methodology among people of other age intervals, occupations, or nations.
The last considerable limitation of the research is unpopularity of client communities themselves among Russian businesses and, as a consequence, lower rate of Russian consumers' awareness of such communities (that can be proved by distribution of members and non-members in the sample). Therefore, future development of the subject under consideration may be moved in the direction of cause-and-effect analysis of such situation and further driving popularity of client communities' implementation among Russian corporations.
Conclusion
Relationships with customers have always been vital for existence of every enterprise regardless of its distinctive features, such as industry, size, or corporation strategy. Customer relationship management, therefore, plays an extremely important role in an overall organization's performance - and of the most widely used tools for measuring the results from interaction with clients is customer lifetime value.
From the customer's perspective, CRM process may be divided into four main stages (Awareness, Evaluation, First purchase, and Ongoing retention), each of them being able to produce CLV in its own way and, simultaneously, being affected by implementation of client communities - online platforms for interaction of customers, company's representatives, and product or service experts with each other.
The aim of the paper was to discover ways of CLV increase within client communities. The goal was achieved by deployment of both qualitative (content analysis) and quantitative (survey) research methods. Results of the content analysis of sixteen case studies included revealing of eight categories of factors influenced by client communities' implementation, and creating a basis of questionnaire for the second part of the research. Quantitative method was represented by a questionnaire consisting of 14 statements and questions with various scales. A survey carried out on 107 respondents has shown that answers of members and non-members of client communities are statistically different from each other in the vast majority of cases (nearly 93%).
As a result, it was proved that companies which have client communities implemented in their CRM routine, on average, benefit from higher CLV level as opposed to companies that have not established such community. It was also found out that client communities had a positive impact not only on CLV, but also on other areas of the firm's activity that are also considered to be significant for its overall performance: better connection and relationships with customers, higher level of customers' engagement, improved knowledge-sharing, increased number of sales, greater customer satisfaction, call deflection, reduced support costs, and higher public feedback and insight.
The research has its own limitations connected with samples of both research stages and CLV formula but from the results obtained it is already possible to claim that implementation of client communities influences a lot more areas of a company's working environment than it might seem from the first sight, and that, in general, client communities may be advantageous for a company looking for ways of increasing customer engagement.
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