If you read our earlier article Is Your Distribution As Good As Your Product?, you may be looking for help to prosecute your situation.
Funny you should ask! We have built a scenario analysis model to help firms quantify the cost savings and revenue uplifts that come from changing their distribution approach to people & process, adviser data and tools. It is a relatively comprehensive input / output model that provides full transparency around logic assumptions and delivers control to the user. This tool can be shared with vendors upon request (complete with a “driving” lesson).
Chart 1 – Adviser Ratings Distribution Scenario Analysis Model
The best way to road test the model is with a case study to show the sensitivities of different scenarios on the overall investment thesis. The results may surprise, but the key driver in the majority of cases is improving the effectiveness of established, well paid distribution & marketing teams. These improvements generally swamp any extra investment and ongoing costs associated with licensing 3rd party adviser data services and technology applications, especially when considered over a reasonable term of three years.
CASE STUDY – Fund Manager “Heikin” (Japanese for average)
Table 1 – Business Settings
| Parameter | Setting |
| FUM / average annual net fund flow | $5 billion / $250m p.a |
| Distribution team size | 7 sales, 1 enablement, 2 marketing |
| Distribution team fully loaded OTE | $2.73m p.a (includes 30% overhead) |
| Distribution team assessed quality | Sales = Average, change required Sales Enablement / Marketing = Excellent |
| Historical staff attrition rate | 20% |
| Annual budgets – Sales T&E | Marketing | $250k | $400k (2% revenue) |
| CRM licensed | Salesforce @ $100k p.a |
| CRM usage & optimisation | Limited, more client mgt tool than prospecting |
| CRM management | Handled internally |
| Adviser data quality assessment | Limited |
| Adviser data management | Handled internally |
| Platform monthly fund flow reporting | Handled internally |
Table 2 – Scenarios
| Action | Scenarios | ||
| 1 | 2 | 3 | |
| Partially rebuild sales team (over 2 years) | Yes | Yes | |
| License 3rd party adviser data source | Yes | Yes | |
| Configure Salesforce using 3rd party service | Yes | ||
| Configure Salesforce using internal SME resources | Yes | ||
| License 3rd party platform fund flow reporting service | Yes | Yes | |
| Improve use of existing / new tools & data | Yes | Yes | Yes |
Table 3 - Financial Outcomes
| Results (on 3-year basis)1 | Scenarios ($k) | ||
| 1 | 2 | 3 | |
| Net New Investment2 | 483 | 95 | 611 |
| Net Cost Savings3 | 409 | 730 | 1,138 |
| Revenue Uplift4 | 175 | 375 | 425 |
| Net Benefit | 147 | 1,009 | 952 |
| ROI | 24% | 1064% | 156% |
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Amortised investment over three years as warranted
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New investment relates to CRM & API configuration work initially and any team rebuilding costs including recruiting fees. Most investment occurs in Year 1 but team rebuilding spread over two years for sales, and one year for the other teams
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Combination of savings in sales, sales enablement and marketing operations though greater efficiencies, together with net differences between existing versus future state costs of maintaining systems, adviser data and platform fund flow reporting (internally or externally through licenses)
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Revenue uplift relates to improved business flows (FUM, FUA or other forms of commercial success) from the various modelled permutations and combinations of better teams, data, systems and application.
To find out more and to get your FREE copy of the Distribution ROI calculator contact our team.
