Insight on database searches queries from hackathon

1. Asia Data & Analytics Hackathon
April 21, 2023
SQL Group

2. Sun Life is a leading international financial
services company that provides a variety of
insurance products, as well as wealth and
asset management solutions, for both
individuals and corporations.
Sun Life managed to maintain its status as a
company that could balance growth, client
commitments, and employee welfare. The
end of the decade marked the year when
the regional office was transferred from
Hong Kong to Manila.
Sun Life Philippines begins with more focus
on its Asian identity as Sun Life Far-East
Division became Sun Life Asia-Pacific
Division.
Background of Sun Life
Source: https://www.sunlife.com.ph/en/about-us/who-we-are/

3. Situation
• Sun Life Asia has
recently acquired an
insurance company “αβ
Insurance” in
Philippines.
Complication
• Challenge to increase
Advisor Productivity.
• Deepen relationship
with Client.
• Identifying
opportunities to grow
business.
Question
• How to increase
advisor productivity?
• How to deepen
relationship with
client?
• How to identify
opportunities to grow
the business?
SCQ Framework of Problem Solving for Sun Life Asia Philippines

4. Data Analysis and Findings
◦ Four data sets related to αβ Insurance such as client, advisor, policy, and beneficiary data have been combined and
analyzed to find some insight with the following findings:
1. RFM Framework & Customer Demographic
2. Sales distribution channel
3. Sales spikes due to large manpower increase
4. New recruits from 2018 – 2022 are most effective on their 1st year
5. New recruits are more efficient in targeting high value clients
◦

5. RFM stands for Recency, Frequency, and Monetary. We would like to understand our clients based on:
• R : Recency, how long a client has been with us (loyalty)
duration (in months) between client’s first policy issue date up until now*
• F : Frequency, how frequent a client has been buying our products
Measured by total coverage count for all policies owned by clients
• M : Monetary, how much money a client has spent on our products
Measured by total AFYP for all policies owned by clients
Defining client value based on RFM approach
Jan 1st, 2018
1st policy
coverage count = 3
AFYP = 1 mio
Nov 1st, 2018
2nd policy
coverage count = 1
AFYP = 1.5 mio
Mar 1st, 2020
3rd policy
coverage count = 1
AFYP = 1 mio
Example
Based on this diagram, RFM metrics for this client are
• R : 48 months (1st Jan 2018-31st Jan 2022)
• F : 5
• M : 3.5 mio
*) Now refers to currently used valuation date. In this report, we set valuation date to 31 Jan 2023
Jan 31st 2022
Jan 1st , 2018

6. Finding High Value Clients Based on RFM Approach
Avg AFYP
per Client
Avg Coverage
Count per Client
Contribution to
Total AFYP
Contribution to
Total # of Policies
Bronze 57,165 2.51 2.5% 19.49%
Silver 147,346 3.32 6.5% 20.37%
Gold 407,603 4.08 21.5% 27.21%
Diamond 1,302,679 5.37 69.5% 32.93%
Median 116,014 4.00 - -
Figure 1
Percentage of Clients per Cluster
Figure 2
Client Cluster general statistics
All figures are calculated as total of In-force policies per client from 2015 to 2022
Based on RFM approach, we can group 127,033 active clients into 4 different clusters*
Bronze, lowest value overall, no action needed.
Silver, focus on increasing engagements.
Gold, focus on uplift and bundling strategies.
Diamond, highest value, need to provide special treatments to ensure loyalty.
Policies issued from Diamond clients consisted 33% of all in-force policies and contribute 70% of total AFYP (Figure 2).
*) Details for Clustering derivation can be seen on the Appendix

7. Customer Geographic & Demographic
Demographic
• Gender
Our customers are mostly Male 68,68%
(21,591clients).
• Occupation
Most of customer occupation are professional
(33,43%) and entrepreneur/business (18,33%).
• Age
The largest customer segment by age is the
Millennial Generation with age range of 26-35
years old (28,1%).
Geographic
• Location:
Based on location, active clients are
concentrated in:
• Manila : 36.848 clients
• Metro manila : 15,259 clients
• Batangas : 8,936 clients
• Bulacan : 8,871 clients
• Pangasinan : 5,581 clients
2 big cities are Manila (28,72%) &
Metro Manila (12,01%),
Income Level per Client Cluster
Yearly Income Bronze Silver Gold Diamond
0 - 250,000 49% 24% 11% 0%
250,000 - 500,000 39% 39% 32% 10%
500,000 - 750,000 8% 19% 17% 18%
750,000 - 1,000,000 1% 6% 7% 10%
1,000,000 - 1,250,000 1% 7% 12% 16%
1,250,000 - 1,500,000 0% 1% 2% 3%
1,500,000 - 1,750,000 0% 1% 4% 6%
1,750,000 - 2,000,000 0% 0% 1% 2%
2,000,000 - 2,250,000 0% 1% 4% 8%
2,250,000 - 2,500,000 0% 0% 0% 1%
> 2,500,000 0% 1% 8% 27%
Bronze and Silver are mostly concentrated on
the lower-end of yearly income level while Gold
and Diamond are more varied.
Moreover, Diamond has the highest percentage
of client with income level > 2.5 million (27%)
compared to other clusters.

8. Sales distribution channel
Distribution
Channel
Agent
49.2%
Broker
14.6%
General &
Salary Agent
20.0%
Others
(Dummy,
DM, Null)
16.2%
Information: Top 2 highest distribution channel is
Agent (49,2%) and Broker (14,6%).

9. Sales spikes due to large manpower increase
Figure 1 Agent recruitment trend 2018 - 2022 Figure 2 Sales trend 2018 – 2022
Agent recruitment spikes correlates
with Sales spikes in 2021
As expected from the norm within insurance landscape, agency manpower recruitments (Figure 1)
is one of the main driver of sales (Figure 2).
The trend is most prominent in 2021, in which spikes in agency recruitments also resulting in
spikes in sales.

10. New recruits from 2018 – 2022 are most effective on their 1st year
Figure 1 Sales trend 2018 – 2022 split by Policy Writing Agent recruitment year Figure 2 AFYP in millions, split by Agent recruitment year (row)
Most sales are from
agent recruited within
the same year
2015 2016 2017 2018 2019 2020 2021 2022
2012 374 443 409 594 428 398 357 467
2013 545 430 616 580 628 522 484 779
2014 470 411 397 431 932 393 423 524
2015 349 408 554 376 371 367 472 345
2016 - 312 259 190 157 484 177 146
2017 - - 651 217 257 210 266 192
2018 - - - 1,425 198 263 185 187
2019 - - - - 3,201 699 952 631
2020 - - - - - 8,639 1,107 **3,968
2021 - - - - - - 15,560 2,019
2022 - - - - - - - 7,569
Policy Issue Year
Agnt. Recruit Year
*) As an example, this means that from all agents recruited in year 2020, they generate 8.6 bio in 2020,
1.1 bio in 2021, and 3.97 bio in 2022
**) Outlier in Feb 2022
*
Sales spikes in year 2020, 2021, and 2022 are mostly generated from agent recruited within the same year
(Figure 1).
Sales from new recruits in 2018 onwards were significantly higher on 1st year (Figure 2, green) compared to sales
generated prior to 2018 (Figure 2, pink).
However, the following year sales are not persistence. For instance, 2018 recruits recorded 1.42 billion sales on 1st
year, but only managed to achieve 0.2 billion sales on 2nd year (Figure 2, yellow).
Notes: Sales from Agent recruited 2015 or earlier are not shown in this chart
Significantly higher on 1st year sales, but sales on the following years are not as high

11. New recruits are more efficient in targeting high value clients
Figure 1 Average AFYP per Active Agent*, in millions, split by Agent recruitment year (row) Figure 2 Policy average case sizeº in millions, split by Agent recruitment year (row)
2015 2016 2017 2018 2019 2020 2021 2022
2012 0.64 0.75 0.69 1.01 0.72 0.67 0.60 0.80
2013 0.76 0.60 0.86 0.81 0.87 0.72 0.68 1.11
2014 0.60 0.53 0.51 0.55 1.19 0.50 0.53 0.67
2015 0.52 0.62 0.83 0.55 0.56 0.55 0.70 0.54
2016 - 0.37 0.47 0.33 0.29 0.86 0.34 0.28
2017 - - 0.55 0.34 0.39 0.31 0.40 0.31
2018 - - - 1.48 0.37 0.49 0.34 0.36
2019 - - - - 1.22 0.63 0.87 0.64
2020 - - - - - 1.92 0.58 ***2.22
2021 - - - - - - 3.63 0.76
2022 - - - - - - - 4.03
Policy Issue Year
Agnt. Recruit Year
**
*) Active Agent refers to agent who have sales on that year
**) As an example, this means that from all Active Agents recruited in year 2020, on average they generated 1.92 mio in 2020, 0.58 mio in 2021, and 2.22 mio in 2022
***) Outlier in Feb 2022
º) Average case size measure how much AFYP per policy has on average
New recruits from 2018 onwards are more efficient in targeting high value clients. On the 1st year, AFYP per
Active Agent generated from recruited agent in 2018 onwards (Figure 1, green) is higher than pre-2018
figures (Figure 1, pink).
In addition, we also see increase in average case size on the 1st year (Figure 2, green).
2015 2016 2017 2018 2019 2020 2021 2022
2012 0.21 0.24 0.23 0.34 0.24 0.22 0.20 0.29
2013 0.25 0.20 0.28 0.27 0.29 0.24 0.23 0.40
2014 0.32 0.28 0.26 0.29 0.62 0.26 0.28 0.38
2015 0.25 0.30 0.39 0.27 0.27 0.27 0.34 0.27
2016 0.21 0.36 0.25 0.21 0.65 0.26 0.22
2017 0.25 0.25 0.30 0.24 0.30 0.24
2018 0.49 0.26 0.36 0.25 0.27
2019 0.52 0.51 0.73 0.54
2020 0.62 0.46 ***1.85
2021 0.53 0.51
2022 0.50
Significantly higher AFYP per Active Agent, indicating improvements in efficiency Higher average case size on 1st year

12. New recruits are more efficient in targeting high value clients (Continued)
Notes: Sales from Agent recruited 2015 or earlier are not shown in this chart
Figure 2 Sales trend 2018 – 2022 split by Policy Writing Agent recruitment year
Figure 1 Sales trend 2018 – 2022 split by Cluster
As expected, sales spikes are contributed by policies from Gold & Diamond clients (Figure 1).
As a reminder, we can correlate Figure 1 with Figure 2, further supporting that sales generated from
Agents recruited 2018 onward are efficiently targeted on high value clients, that is Gold & Diamond
clients.

13. Recommendation to Increase Advisor Productivity
1. Conduct deeper review on sold products, as current line of products seems
unattractive in reaching larger market
2. Focus on high-net-worth clients
3. Incentivize agents who reached PHP 1.5 millions via agent reward contest
• Active Agent*,
As explained, manpower growth is one of the main driver of AFYP growth, especially during the peaks in 2021.
Mass agent recruitments in this year proved to be highly successful, new agents are more efficient in targeting
high value clients on their 1st year.
However, the problem is this sales performance is not persistent. We assumed those agents brought their old
clients on the 1st year, thus in part explaining the trend.
Hence, we suspect some factors contributing to the problem, that is: (1) unattractive products, (2) low focus on
high-net-worth clients,
(3) low incentive for agent to keep sales persistent.
Remarks

14. Recommendation to Deepen Relationship With Client
1. Maximize current client profile to better serve and tap into potential market
2. Perform bundling and uplift strategies for Gold clients based on their demographics
3. Create customer loyalty program in the form of member-get-member for Diamond clients Active
Agent*,
Understanding our client demographics enable us to increase our business qualities in many ways.
By displaying general customer profile in dashboard, we can utilize the information provided to arrange better
business strategies, such as :
- Increase brand awareness by focusing on marketing and promotion programs in high customer population area
(Manila, Metro Manila, Batangas, Bulacan, Pangasinan).
- Develop new product that suit based on the most potential occupation group (Businessman & Managers), with
age ranged between 22-35.
As we already explained, different clusters have their own characteristics hence requiring different business
strategies:
- Gold cluster: most of the clients have decent disposable income (although are not the highest) and highly
engaged with our product. This opens an opportunity for us to sell more of our products using bundling
schemes or uplift their existing Insurance Coverages.
- Diamond cluster: most clients have high income (> 2.5 million / year) along with Large Policy Coverages. To
get the best out of them, we provide customer loyalty program to maintain their loyalty.
Remarks

15. Thank You