Breaking down the B2B pricing strategies of the new economy

Executives are bombarded with headlines about accelerating inflation, a phenomenon many of today’s generation of business leaders have never experienced.

Rather than trying to predict macroeconomic trends, B2B business executives are best served by taking advantage of this flow environment to implement long overdue tactical price optimization initiatives with reduced risk of customer push-back.

Inflation or not?

PPI inflation has undeniably been significant at over 7% per year, although it is measured from July 2029 to July 2021. Although most agree that we are not yet in a price inflation spiral. -70s style wages, there is a valid concern. One camp of economists sees current inflation as largely transient, essentially an expected and desirable outcome of restarting the complex global supply chains that have been disrupted by COVID-19. When COVID hit, producer prices fell about 10% in the first quarter of 2020 and have been trending steadily since.

Merchandise Photo: Federal Reserve Economic Data

The other panel is concerned about a combination of expansionary monetary policies (the potentially unprecedented fiscal stimulus in the form of various infrastructure bills and unspent COVID relief funds. The Fed’s balance sheet has doubled from an already high $ 4 trillion before the pandemic to $ 8 trillion in just 12 months. While the Fed plans tighter policies by the end of 2023 with two rate hikes d interest, a lot can happen in the meantime.

Hans Oped Image 2 Total Federal Reserve Assets Photo: Federal Reserve Board

While both views have merit, corporate finance executives need to be prepared for either outcome and immediately rethink their pricing strategy and tactics. Clearly, customers will be much more receptive to price changes in a global environment of adjustments across the global supply chain. This state of flux is the impetus for smart executives to act while the window of opportunity exists.

Price optimization imperative

Pricing is one of the most important drivers of net profitability and investing in better tactical pricing capabilities has a very high ROI proposition. Too often pricing is seen as static, macro market driven, and ignores readily available data that contains information that is highly relevant to tactical pricing decisions. Even if executives do not change overall price levels, the current environment has created an opportunity to implement tactical price optimization initiatives with less likelihood of negative customer reactions than ever before.

I’m going to focus on transactional B2B, i.e. tactical pricing here, which is where we see the most leverage in the current environment. At the most basic level, pricing is determined by the cost of production, the degree of competition in the market and the shape of the demand curve, i.e. price elasticity. Most companies have good cost control, at least at the aggregate level, have a good understanding of competitive dynamics in key markets, but tend to ignore significant differences in customer sensitivity to price which are often very much related to a transaction specific.

The goal of B2B analytical pricing is to maximize the expected value of the customer by using all available data and applying information maximization models to optimize the terms of each business transaction.

Hans Oped Image 3 Price erosion Photo: Statistics

The impact of reversing the loss of margin due to price erosion can be huge. In complex pricing environments (i.e. many products, geographies, distribution channels, large sales force, etc.) we have seen up to 25% increase in gross margin due to to price optimization.

Simple verification of opportunity assessment

There are quick and easy ways to analyze the CRM data available to assess the size of the opportunity and possibly identify the fastest-growing opportunities. For example, finance managers should ask the following basic questions:

  • Is there a constant correlation between discounts and trade size?
  • Are sales reps going to their maximum remitting authority quickly?
  • Is there a large variation in discounts for similar sized offers?
  • Are the discount levels arbitrary, i.e. in increments of 5%, 10%, 15%?

Often times, the answer to all of these questions is yes, as the graph below illustrates, indicating a significant opportunity for price optimization.

Hans Oped Image 4 Sales vs discounts Photo: Statistics

Running some basic diagnostics on CRM data will also likely reveal other obvious issues that can be fixed quickly. There may be list prices that are totally overpriced (e.g. below cost), consistent discounts abusing salespeople, loss-making customers, etc.

The remainder of this article describes the typical steps required to create sophisticated tactical pricing capability for lasting competitive advantage.

1. Assemble data with high informative content.

It is essential to create a dataset for subsequent machine learning that is consistent and likely to contain signals relevant to the pricing decision. The signal is likely to be multivariate and not obvious at this state, but at least the source of the data should be logically related to the pricing challenge. Examples include social media data, blogging, emails indicating social closeness between customers and company employees, sales pipeline data from the CRM system, channel promotions, data from customer purchasing, etc. It is less of a problem to include weak signal data as models. just ignore it, but great care should be taken to make the data consistent and to understand any systematic biases that may have affected the way the data was generated. For example, a CRM system might contain data from multiple pre-merger entities that had very different sales force compensation policies. It is extremely important to understand these problems and correct them.

Examples of Variables Used in Machine Learning Models

Hans Oped Image 5 Information source Photo: Statistics

Maximizing customer lifetime value is at the heart of optimization. The goal should be to maximize the expected lifetime customer value, i.e. be indifferent between a high / low probability and a low / high probability customer. Depending on the industry, customer LTV models may be more or less developed, but generally they exist or can be created without much difficulty. The creation of own result variables is often more difficult. Clean result coding is rare in CRM systems, but essential for machine learning. A simple “Lost Sale” code in the CRM system could cover anything from “I’ll never buy anything from your business” to “We love your product but we’re out of budget for this quarter” and clearly product a very poor classification model. CRM data needs to be cleansed and made consistent across different organizational units, products, markets, channels, and sales representatives. With very poor data but high transactions, it is often better to establish proper coding of results and processing and run the model with only three months of history rather than years of bad data.

2. Create a robust customer outcome model based on LTV

Hans Oped Picture 6 (2) Data Photo: Statistics

Hans Oped Image 7 Data Photo: Statistics

3. Perform analyzes

Once the data is assembled and cleaned up and clear objectives established, the analysis will extract the information content and identify the high signal variables. Proven analytical machine learning approaches focus on predicting pipeline opportunities that will drive sales success under various pricing scenarios. The end result is an opportunity score and a price recommendation for each specific customer transaction.

If analytical capabilities do not exist in the organization, it must be hired from outside. The machine learning part is a necessary but insufficient success factor. The old mantra “trash in the trash can” applies here. Unfortunately, we’ve seen many organizations place too much trust in sophisticated out-of-the-box software models that rely on poor data, leading to very poor results.

Hans Oped Image 8 Data Photo: Statistics

4. Interpret the results of machine learning

This is a critical stage where art meets science to some extent. First, data interpretation can lead to the discovery of unexpected data integrity issues that can be resolved at this point. Machine learning by definition is hampered by a ‘black box’ mentality, but an intuitive interpretation of the results is invaluable for the organization’s buy-in to the implementation. Ideally, the interpretation of the machine learning result fits human intuition and confirms the anecdotal evidence of the sales force. Taking the time to do this step goes a long way in helping to implement any recommendation across the organization about a black box.

Hans Oped Image 9 (1) Variables Photo: IBT

5. Implement the recommendations.

This final typically requires a combination of system and organizational changes to ensure effective implementation. The existing CRM system can typically be used to implement model recommendations by simply populating model pricing recommendations and scoring leads. The exercise often reveals differential performance of salespeople who are also likely to be unable to implement the new processes. In our experience, situations where half of the sales force could be reliably predicted to close less than 10% of total sales are common.

Hans Oped Picture 10 Data Photo: Statistics

Conclusion

We believe that an analytical approach to pricing is one of the most important profit drivers available to financial executives today. Cutting costs can be difficult in an environment of limited resources and inflation, while simply raising prices at all levels is risky and leaves money on the table compared to the analytical approach described here. The creative use of data and advanced analytics techniques enable intelligent executives to identify the most desirable customer, price optimally, and effectively focus their sales resources.

Hans Dau is CEO of the Mitchell Madison Group

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