Summary
Presenting our innovative, multi-channel campaign for Cross-sell/Up-sell - Product Recommendation Analytical Model campaign | An industry-first, personalized, automated, advanced analytics led product propensity campaign that delivered personalized value propositions & campaigns for incremental Cross-sell/Up-sell impact Life insurance, a life stage based product, faces low policy density (number of policies per customer) and inadequacy of coverage (under insurance), even among existing customers. This campaign is a step towards solving for this challenge through Data Driven Marketing.
Challenge
Life insurance is a life stage based product, which sees an uptake with consumers as they grow from initially starting as a ‘first jobber' to later ‘starting a family' and planning for life's goals like family's financial security, child's future, retirement etc. Secondly, life insurance faces a challenge of low policy density ie number of policies per customer are not more than 1 or 2 policies. Further, inadequacy of life insurance coverage accentuates the need for Cross-selling/Up-selling. Also, from a profitability and business growth standpoint, it's important to Cross-sell/Up-sell to existing policyholders as they are affined to the brand and its offerings.
Objective
Life insurance is a life stage based product, which sees an uptake with consumers as they grow from initially starting as a ‘first jobber' to later ‘starting a family' and planning for life's goals like family's financial security, child's future, retirement etc. Secondly, life insurance faces a challenge of low policy density ie number of policies per customer are not more than 1 or 2 policies. Further, inadequacy of life insurance coverage accentuates the need for Cross-selling/Up-selling. Also, from a profitability and business growth standpoint, it's important to Cross-sell/Up-sell to existing policyholders as they are affined to the brand and its offerings. Thus, the campaign objective set out was as follows: - To drive incremental Cross-sell/Up-sell among existing policyholders through centralized Cross-sell Marketing campaigns (via Email/SMS/OBD/Whatsapp etc) by leveraging advanced analytics, resulting in 1.2X intents and 1.5X premiums incremental business impact
Strategy
We looked at a three-pronged strategic approach for incremental Cross-sell/Up-sell impact: 1. Product Recommendation Analytical Model – With the help of our in-house Data & Analytics team, we build a product recommendation analytical model, which helped us in below key ways: a. Accurate next best fit product & category recommendations basis customer profiles b. Propensity to purchase (eg high, medium, low) for segmented pitches 2. Higher levels of segmentation – Understanding the profiling of the policyholder base and defining core segments with business potential lies at the centre of how we approach Cross-sell Marketing campaign strategy. In line with the Product Recommendation Analytical Model, we redefined segments as below: a. 9 key segments basis life stage, related demographic attributes (eg First Jobber Unmarried Males vs First Jobber Unmarried Females, Married with/without and younger/older kids etc) 3. Personalized pitches and campaigns – We were able to serve personalized Cross-sell propositions to policyholders on the back of following input streams at our helm: a. Understanding of 9 key segments and linked product & category recommendations with propensity to purchase b. Best practices on communication preferences like Preferred Channel/Day/Time for sharper targeting c. Historical response behaviour analytics across different types of campaigns and communications Also, we aligned this strategic initiative with other key internal stakeholders like Sales Channels, who worked on the lead fulfilment of intents received from centralized CRM campaigns. We ensured the following integrations: 1. Live lead transfer through API integrations to share the intents from CRM campaigns with the Sales Teams and their call centers on a real time basis, ensuring that the interested customers are handled on priority basis assigned lead ranking. 2. Common view of Product Recommendation Analytical Model with Sales Teams and their call centers. 3. Business monitoring & reconciliation through sales reporting and intelligence
Data
As the Cross-sell Marketing team, the fulcrum of running centralized CRM campaigns through direct marketing digital channels like Email/SMS/OBD/Whatsapp etc is policyholder database. A broad outline of how we approach the database for running Product Recommendation Analytical Model campaigns is as below: Step 1 – Identifying contactable database Step 2 – Data clean up and suppressions, if any Step 3 – Defining segments Step 4 – A/B test and scale basis results Besides, there are multiple other data streams which are layered with Product Recommendation Model to unleash actionable insights for campaigns, with key ones highlighted below: 1. Historical response behaviour data - With the experience of CRM campaigns over the years, we have developed an understanding of following areas by analyzing the past trends: a. Customer segment level behaviour – Analytics on which customer segments responds better to which type of offerings/campaigns eg Parents respond better to ‘child's secure financial future' related offerings/campaigns. b. Campaign communication & content level insights – We have built a repository of actionable insights from our learning on campaigns over the years eg i. In Emailer subject lines, mention of a ‘Special Day' helps in better open rates eg This Children's Day ii. SMS with personalized data fields tend to get better response etc c. Channel mix for preferred customer experience & response – Data is analyzed to understand the channels being preferred by various customer segments for communicating with them. i. For instance, 45+ yrs audience tend to better respond to Emails than younger audiences ii. SMS/Whatsapp/Voice tend to work better in lower tier geographies 2. Consumer insights from Researches – A deeper understanding of customer segments, their needs, preferences, financial behaviour, triggers & barriers etc is looked at to enrich customer propositions.
Solution
The family of personalized campaign creatives for Product Recommendation Analytical Model are thematically bound so that they have a consistent look & feel across 9 key segments. Besides, we leverage campaign automations to deliver these personalized experiences to policyholders. Here's the broad approach we adopt for devising and roll out of the campaign creatives: 1. Common theme for creatives – We apply a common creative theme that cuts across value propositions for all 9 key segments and bounds the campaign together from a brand look & feel consistency. Some examples and pointers below with key highlights: a. For instance, ‘Make it possible' with our as a common creative theme and induces customer action by harping on problem-solution approach (Worried about rising education costs? Make it possible with our Savings & Investment Plans) b. Creative theme is visually depicted through a unique mnemonic identity across campaigns. c. Besides, we follow a 4 monthly creative refresh cycle to ensure that there is no fatigue and related impact on responses. 2. Personalization and automation – a. The campaigns & communications designed are personalized basis the profile of the policyholders. Personalization fields like customer name, policy name/number etc help in making the communications relevant and relatable. b. These campaigns are delivered to policyholders through an automated journey according to their communication preferences and basis past response behaviour. 3. Call-to-action with live lead transfer – All the campaign creatives carry a strong call-to-action (buy now, click here to get a call back etc), which once clicked directly shares the data on real time basis with Sales Teams and their call centers to align for follow-up and closures. 4. Insights and learning for future campaign optimization/strategy - Understanding what works and what not across campaigns & creatives helps us in navigating the course of correction.
Results
As a marketing unit, a successful campaign for us is the one which solves for customer's needs as well as aids in organizational objectives. And, the Product Recommendation Analytical Model campaign delivered on both. Below is a snapshot of the significant impact delivered by our campaign on set KPIs: KPIs: - To drive incremental Cross-sell/Up-sell among existing policyholders through centralized Cross-sell Marketing campaigns (via Email/SMS/OBD/Whatsapp etc) by leveraging advanced analytics, resulting in 1.2X intents and 1.5X premiums incremental business impact Impact delivered: 1. Business Metrics – 1.5X intents and 2X premiums 2. Communication Metrics – 2X response (Open/Click rates) across all communication channels 3. Others a. Activated 22% of previously inactive customers b. Generated cross sell leads from areas which were physically non-serviceable by sales, resulting in incremental business.
