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The Holy Grail of TPO

TPM BLOG SERIES

written by Richard Burton, global director of customer success at Exceedra by telus

As a consultant in TPM, over the years you build up an understanding of how much language and terminology plays into being successful with a customer. Interpretation and assumption are dangerous paths we can go down, so absolutes and re-clarification of what we understand, and whether that aligns perfectly with what we’re being told is one step closer to success.

The reasons I bring this up is specifically because in our world of TPM, the use of the word ‘Optimization’ or Trade Spend Optimization (TPO) can often mean completely different things depending on who’s talking about it (vendor, customer), when they’re talking about it (pre/post sale) and what it can or should mean or achieve (whatever the ‘optimization’ is supposed to do).

 

One thing has been clear after reading and being involved in well over a hundred Request for Proposals (RFPs) for TPM – there are very few initiatives out there that don’t have an expectation to “reach for the stars” and be able to conduct ‘optimization’ on their trade promotions data when they are looking to market for a TPM solution.

Expectation vs Reality!

These blogs have probably uncovered some components of the nature of TPM that are not often talked about. If you’re in the middle of a selection process and all vendors are saying ‘yes’ to Request for Information (RFI) questions and capabilities, this may just raise a few alarms!

 

As in the classic ‘Python’ movie of the Holy Grail – for one to achieve the goal of TPO, one must go on a long quest challenged by all manner of calamities (including The Black Knight…!) in order to reach your virtuous destination.

 

The previous articles have given some background on how critical it is to have all the fundamental aspects of TPM working smoothly including data, integration, performance, business process alignment, business functions, financial rules, consolidation and simplification of terms, promotions, routes to market and more…

 

All the above absolutely MUST be in a stable state of maturity before the topic of ‘optimization’ should be brought up. Perhaps I’m alone in this belief, but the diagram of ‘Expectation vs Reality’ very much reflects most TPM deployments and the challenges faced, just to get the basic end-to-end commercial and volume processes working smoothly across all relevant departments.

 

OK – let’s say that we’ve got a big ‘tick’ on these parts and the business is stable and looking to lift their game with the solution to the next level – and optimization is lined up in the target sights.

The route to perfection

What does optimization mean?

 

“The meaning of optimization is an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible”

 

So, when vendors talk about TPO – what can this mean? Well, as indicated at the start of this blog, we absolutely must get past the word itself and specifically onto the details of how or what is going to be optimized, and if something is optimized – are the results fed automatically into the downstream processes or are they simply there as a guide for human interpretation to do something with?

 

As a generalization, my understanding of general vendor offerings to the market that involve the sales pitch of ‘TPO’ fall into a minimum of four categories, possibly more:

1. Scenario Planning

Without question, one of the more fundamental elements that most vendors I would say can support, in one form or another. The scenario may be as simple as comparing one promotion with another. It could be an entire program for an account including every single defined commercial element, covering the full fiscal or financial year. Scenarios will require multiple KPI’s to evaluate which scenario looks better, most will also require all volume and financial information, both historical and future, to be in place and accurate.

2. Smart Analytics

Effectively using a combination of historical data and reporting to present insights on “the good, the bad and the ugly” of the commercial strategies that have been used over a given period. We could look at the past performance of one promotion, how much we forecasted to sell vs. how much was sold, what we forecasted it would cost to run the promotion vs. how much it actually cost and so on.  All the KPI’s associated to promotion effectiveness can be pulled together, aggregated to account or brand level, and derive suggested opportunities to optimize what future promotional/term programs should look like but in general I believe these opportunities need to be interpreted from the reports and future programs defined (down to all the details of price, volume, profit, cost etc) by the users, manually.

3. Cross Category Analytics

This area may or not fall into conversations around TPO but in my mind if this function is being calculated either through Artificial Intelligence (AI) or Machine Learning (ML) or even traditional statistical algorithms (rather than users entering their own manual estimates), it can play a significant part in helping a sales team optimize their promotional strategy. There is no uncertainty within the CPG space that altering the price of a product in a specific category may have either a positive or negative effect, both on other products of the same manufacturer in that category and the competitors’ products in that same category, or effects on competing retailers selling the same product but not part of that particular price discount.

 

Vendors will talk about Promotion ROI a lot more than they will talk about TRUE promotional ROI.  True promotional ROI looks beyond the performance of the promotion being evaluated, but also at the effects this promotion has had on the baselines of other products in that brand (both internal and external). There are some categories that have so much influence on the consumer, that the consumer will deliberately go out of their way to purchase that product from a different retailer because it’s on discount at that retailer rather than their ‘usual’ weekly shop location (this is called store switching). Both cannibalization and switching effects of the ‘non-promoted’ products in terms of volume and commercials can be combined into the overall ROI to give a more honest and accurate picture of the True ROI of that specific promotion. This whole area, in my view – for specific manufacturers that own products/brands that compete with each other, and/or have high value products (dishwasher tablets, shaving blades, detergents and other cleaning products) – will all be influenced by this type of behavior and benefit from the statistical insights to then optimize future activities. Of course, it’s one step closer to the ‘Grail’ if the system is not only able to provide historical insight into True ROI but able to FORECAST the cross-category effects before a promotion has run, perhaps alerting the user to re-think that activity.

4. Goal Seeking Optimization

As a TPM ‘purist’, or let’s say an Orthodox TPM’er, goal seeking optimization of promotions, or promotion scenarios, programs, or plans, is the ‘true Grail’. For those Indiana Jones fans out there, you’ll remember the true Grail was not encrusted with gems and made from gold. It looked like this, a reflection on the humble truth we must seek in ourselves (and our data, business processes and organizational maturity 😉) for this quest.

 

The specific benefit this area of optimization should give, in theory, is to take the guess work out of knowing the best price points, deal discounts and dates to run a promotion.

 

The stars that need to be aligned to run goal seeking optimization effectively are complex to say the least, depending of course on the number of variables we are trying to tune automatically to achieve the outcome we want.

 

Outcomes can run along the lines of, ‘I want the promotion/scenario/program/plan to be optimized for’:

      1. Profit
      2. Revenue
      3. Cost

 

The optimization should not only take into consideration all the manufacturer’s revenue and cost variables, but also the costs and variables that the consumer & manufacturers customer will be considering (whether that’s the retailer, wholesaler, or distributor).

 

With all the right data, it’s then the job of whatever ML, AI, Regression Algorithm, or statistical process has been developed to ‘solve’ the problem, hopefully quickly enough that the user doesn’t get bored and walk away.

Slow and Steady

We discussed some of the vendor/buyer challenges in one of the earlier blogs. TPO is one of those areas that a company needs a reality check on. Is the level of maturity of the business, data, users, and the market being sold in right for this specific capability?

 

Be realistic, be humble, and, if nothing else, be curious. If you are in the market for a solution, it’s worth asking what their meaning of TPO capabilities are, who’s using it, and to what success. There is no question that this capability has enormous potential – used in the right manner, with the right supporting processes and data around it.

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