Strategy & solution consulting to build a data-driven marketing program
Strategy, platform and solution consulting to build a data-driven marketing program that brings preferred investor prospects into funnel so they can be nurtured over time
Tech solition consulting

Client

One of the largest Asset Management Company in India which offers wide range of savings and investment product across asset classes to retail and institutional customers


Problem Statement / Opportunity

Due to increasing competition, emergence of discount brokers etc., the client has been losing market share for the past two years. The fund house recently launched number of equity funds to plug gaps in its product basket and to increase the market share, get new investors into its fold and shrug off the negative image caused by underperformance of its key schemes.

 

Client decided to setup a Marketing Automation and Customer Data Analytics Platform to take full advantage of the available internal and external ‘data’ in order to make operations more efficient and use advanced analytics techniques in phased manner and benefit from the same for up-sell, cross-sell, and retention

 

Client floated a RFP for Data Analytics and Marketing Automation / CDP Platform, to which they received responses from 11 vendors. Scope for Marketing Automation evolved during RFP process and later on Personalization (Web & Mobile) was also added as a scope


Oneture's Role

Client requested Oneture to:

 

  • Refine their roadmap and evaluate RFPs and select an appropriate partner to build base data lake/lake house, BI and initial predictive analytical models
  • Program manage implementation while working with their internal teams (Digital, Info Security, CTO, QA, Sales), Cloud infra partner, Implementation partners
  • Help define an appropriate strategy (staffing internal & external) to then manage and grow the solution further (post initial launch)

Major Features

We decided to approach the solution based on time to market and time to value so that we can start driving and demonstrating business benefits soon, our key recommendation was to exploring Analytical models based on 3’rd party data (distributor led sale) that was readily available to achieve immediate value and then slowly moving to providing incremental utilisations

 

Marketing Automation Platform Build vs Buy – Our Initial Recommendation

 

Building your own complex marketing automation software in-house could take lots resources, cost and time. AI-enabled, MarTech platforms is complex and ever-changing space. Building platforms is not client’s core business and would divert lot of focus from main business operation, hence we recommended to go for solution available in market, there are many products available in market which offers off the shelf platform with journey builder, campaign automation, personalization etc.

 

Data Platform : Data Lake & Analytics Build vs Buy – Our Initial Recommendation

 

As per our experience, there is no one size fits all solution / platform available in market, secondly Client has various data sources like CRM, Customer Portal, Investor Portal, Market share data etc. which need to be integrated in one single platform, custom data pipeline needed to be developed, giving data access to any outside entity is not recommended. We recommended to develop an inhouse AWS Cloud based custom platform in Client owned & controlled environment, coupled with use of 3rd party tool like Power BI / Tableau / QlikView for data visualization

 

CDP Build vs Buy – Our Initial Recommendation

 

Our recommendation was buy, There are many of the shelf CDP product available in market, most of them can meet >80% of the CDP capabilities client need. Rest can be build/customized as needed. This can help reducing time to market, we followed Gartner’s guidelines too, as per report, Brands/Companies with lean budgets (<9% of revenue spent on marketing) should strongly consider buying instead of building. Also, CDP and other platform like Marketing Automation requires constant pace of innovation through new features and solution, pushing technical boundaries with in-house team would be difficult compare to getting it done from readily available platform

 

Personalisation Build vs Buy – Our Initial Recommendation

 

In our opinion, if done in-house, it requires major investment and you would have to either re-align existing in-house talent or hire externally. In long term buying personalization often equates to less than building it in-house. Personalization strategy needs to produce instant impact, not tomorrow, not today. By partnering with a third-party personalization vendor instead, you can jumpstart your personalization journey much faster – our recommendation was buy


Proposed Solution & Architecture

Given current business needs we recommended following approach and high-level solution to be implemented

 

 

 

  • Currently Client team have limited bandwidth to take up additional projects. Executing 4 different projects (Marketing Automation, Data Platform, CDP, Personalization) all together would not be feasible
  • All projects implementing will be time consuming and will take 6 to 12 months depending solution option selected, Client need to move fast to address current business need first compare to longer implementation cycle
  • Key system like CRM, Investor & Distributor portal are recently developed / implemented and not have too much legacy data
  • Approx. 80% data is customer transaction data which is well normalized / structured. This is key inputs for building predictive models, rest data source like Google Analytics, Social Media etc. are not very significant
  • Building data platform is a long journey, before investing full time & money into it, we should look for a quick wins. Start small, measure the results, learn from it and then with full scope as needed
  • Best approach would be creating a small data mart from existing data available for direct customer
  • Develop and train cross-sell / retention model for direct customer
  • Continue with incumbent marketing automation platform as it meets most of the requirements, Plan & execute one campaign with existing campaign tool and measure the results
  • Based on learning from this activity, go with full scope of developing Data Lake & Data Marts for Direct and Indirect customers
  • We evaluated multiple CDP platforms and recommended to go with off the shelf CDP platform, preferably AWS based. To start with short CDP pilot as step 1 and then go for full implementation
  • Instead of developing end to end data platform, go with small implementation of data mart for predictive modelling only
  • Leverage one of the current data analytics partners for predictive modelling output to run marketing campaign
  • Integrate marketing automation tool, of the shelf CDP platform and internal customer database as first step towards automating data flow between these system

Value Delivered
  • Executed two marketing campaign (Cross sale & Up sale) in few weeks by identifying quick wins solutions rather than waiting for 5-6 months to implement the complete solution
  • Selected best fit solution for client’s immediate needs in Marketing Automation, CDP, Data Platform and Personalization by evaluating by 22 techno-commercials proposals.
  • Suggested long term technology solution strategy by analyzing best-of-breed solution vs single platform approach. Our recommendation is to go with industry leading platform which offers all the solution on one platform.