The Case for Discounting in Price Management

Historically, discounting has been both a challenge for Wealth Managers as well as an attractive revenue pickup lever that has the added benefit of also impacting the bottom-line by a virtually equivalent absolute impact.

Year-on-year discounting typically creates a high number of workflows[1] and involves a large number of stakeholders[2] within most Wealth Managers.

Considering the workload, it is difficult for decision makers to conduct adequate due diligence on requests, making remarks like “[…] I have to trust my RMs[3] […]” or “[…] I do not have the basis to judge […]” commonplace.

As such, recurring observations from previous cases show that most requests are approved (>90%) with limited visible objectivity and rationale. Also contributing to the absence of visible discounting rationale is the relatively common practice of discounting in return for an expected future ‘payout’[4], which typically goes untracked and is often never realized.

Discounting is also ‘democratic’, extending across clients and solutions and including high value offers such as discretionary mandates, where previous cases show it is not uncommon to find a relatively large proportion of clients and assets discounted (70% and 90% respectively) by a significant list price reduction (>40%).

Though ‘off-the-market’ pricing grids at times help explain high discounting levels, it is the lack of discounting discipline that typically is the main culprit in preventing Wealth Managers from sustaining higher levels of realised prices.

The challenges affecting Wealth Managers’ discounting discipline are varied, ranging from RMs’ excessive price sensitivity (15-40% higher than clients), to limited market transparency (Front Office’s perception of price gaps vis-à-vis peers is 5x wider than clients’), to vaguely defined guidelines, to material discounting discretions assigned to the ‘Front’ and to sub-optimal discounting processes to name a few.

Though all of the challenges above should be addressed by Wealth Managers, we focus on the discounting process as a key lever to improve discounting discipline as it has often been set up to create discounting hurdles rather than to ensure a rigorous scrutiny of requests.

As such, we believe that discounting processes should evolve to adequately equip decision makers with the information needed for making ‘no-regret’ pricing decisions. We regard an evolved, ‘smart’ discounting process one that:

1. Asks and answers pertinent questions regarding individual discount requests, without the need for any additional information from RMs or pricing teams;
2. Suggests an adequate course of action in ‘clear-cut’ cases, providing a clear rationale and an alternative price range in case of refusals, along with commercial counter-offers if the requested price is to be accepted;
3. Enriches and escalates the request to the appropriate decision maker, enabling informed decisions with a clear discounting rationale.

The questions addressed by a ‘smart’ discounting process (see Figure 1) can be multiple and should ultimately allow the Wealth Manager to assess its willingness to discount. In our view those questions should cover topics such as:

1. Behavior underlying the request, to counteract adverse patterns consistently comprising high discounting frequency and/or intensity;
2. Fairness of the price point, to ensure pricing consistency amongst ‘similar’ clients and to avoid any potential price discrimination;
3. Materiality of the discount to the client vis-à-vis the Wealth Manager, to capitalize on the goodwill potentially generated by requests that are significant to client but not to the Wealth Manager;
4. Payoff of previous discounts to the client, to ensure that the Wealth Manager is collecting the benefits that should be derived from discounting.

Contingent on the questions being asked and the level of sophistication of the answers sought, the algorithms required to address those questions can be relatively simple to build as they typically require plain vanilla data and simple Machine Learning use cases to be implemented.

The main challenge is to ensure that the insights provided are well-understood by decision makers and that those insights also adequately justify the final decision to RMs and clients alike.

Finally, the upside of addressing improving pricing discipline is significant as it can generate a revenue pickup of 5 to 10% depending on the starting point.

Considering the potential investment needed to address pricing discipline, this type of initiative is likely to carry an ROI of 25 to 35%.

Footnotes:

[1] Circa 50 to 150k workflows p.a. depending on the organization’s size

[2] E.g., Relationship Managers (RMs), Desk Heads, Market Heads, Region Heads, Middle Office pricing functions

[3] Relationship Managers

[4] E.g., the client brings more assets or a new family member onto the relationship