Challenge: To know the impact of the training sessions on an agents performance and the most preferred sessions
– Metrics like agent production, sessions attended per agent, number of attendees for each training and agent channel were studied.
– Agent performance is studied by taking average 12-month production pre and post-period of each training session.
Attendee ⇑ 9.5%
Non-Attendee ⇓ 36%
Value Addition: - This analysis helps in taking decision to conduct more training sessions as they have positive effect on an agent’s performance. These insights have provided solution to drive efficient sales and provide reports to the company that will be useful in conducting cost effective training programs.
Challenge: - To know the impact of the advertising campaign on the sales performance.
Analytical framework: - Click thru Rate throughout the campaign period was calculated for all the Email blasts, Ad streams and alert blasts. Production levels of the distribution channels who had participated in the campaign are compared for one year, pre and during campaign period.
All the agents who have participated in the campaign were selected and their production levels were measured to understand the effect of the emails on an agent’s production.
Result: - Click thru rate has increased gradually during the second half of the campaign and we can see the impact of this on production. Among all the distribution channels participated two distribution channels production has increased by 32% and 16%. Overall production of all the distribution channels participated has increased by 1% during the campaign period. The production of the other five distribution channels has decreased but when compared to the first six months the percentage of decrease has also come down.
The production of the distribution channels who have not participated in the campaign has decreased by 14%.
Value Addition: - This analysis helped the firm to know positive effect of the campaign on the production of the organization and it is also observed that advertisement cannot produce immediate sales as its impact is gradually seen over a certain period of time as results in the second half of the campaign is positive when compared to the first half.
Analytical Framework: - Net Promoter Score (NPS) methodology was used to categorize the policyholders as either “Promoters”, “Detractors” or “Neutral/Passive”, using available data.
Statistical models were developed which identify the differentiators of NPS, split between the Life and Annuities businesses, for the purpose of scoring all customers and identifying the most likely category in which they belong.
Result: - We find strong, inverse relationship between economic situation and education with the likelihood of being a Promoter, for both the Annuities and Life businesses. Having more number of policies is positively correlated for being a Promoter.
For Annuities, Women are more likely to be Detractors. Legacy Protectors are more likely to be Promoters in the Annuity model.
For Life, age at issue and promoter status is positively correlated with NPS. Having a Wellness rider raises the probability of being a Promoter whereas greater the Current face value the higher the likelihood of being a Detractor.
Value Addition: - Net Promoter Score positively correlates with revenue growth as this can be used to motivate an organization to become more focused on improving services for customers. This gives a clear view of the company’s performance according to the customers’ perspective.
As Detractors' relationship with agents’ status is positively correlated, company can take help of agents to reduce the proportion of Detractors.
For Annuities, demographics and socio-economic factors are the largest contributors and company can focus on these parameters to initiate implementation of targeting passives, with the intention of changing them to promoters.
Challenge: - To gain insight into the profile of the policyholder by splitting customers based on the policy type.
Analytical Framework: - All the policy holders were categorized based on their policy type. The policy holders’ attributes and policy attributes influence on placing a policy is studied. Each of the groups is compared to the entire customer base by year.
Result: - Adoption of each of these products has increased overtime.
One set of products have older, educated and affluent customers. These products have higher annual policy premium and lower face value.
Customers having the other set of products are more likely to be younger, less affluent with small nuclear families. They have lower premium and face values.
Value addition: - This analysis can be used in tailoring products and premiums for individual customer groups based on their demographic and socioeconomic factors. It can also be used to identify top customer groups who provide highest revenues. This will help to characterize policy features to suit the needs of special customer groups. This information provides patterns about customers and can play vital role in developing marketing strategies.
Challenge: - Advertising campaign effectiveness study.
Analytical Framework: - Sales, Marketing and Website Activity Dashboards were prepared where metrics like trend for returning users, click thru rate and number of sessions by time of day and day of week were observed.
Result: - Click thru rate is 0.01%. Trend for incoming calls was high in a particular period. Percentage of returning users has increased from 16.20% to 50% over a period of three months. Website Activity Dashboard showed that traffic to the site is more during 8th hour to 19th hour of the day and on Friday. Among six creatives that participated, one of the creative has more number of sessions.
Value Addition: -Dashboards representation helps management to visually depict the performance by presenting information to executives in a way that is accessible, easy to understand and accurate. These dashboards monitor campaign performance and explore different scenarios which help in making marketing decisions more efficient .