A lot of innovation has happened in the last fifteen years in the realm of CRM, sales and marketing automation, ecommerce, data analytics and visualization, artificial intelligence, machine learning, and predictive analytics.
Despite the broad range of sophisticated SaaS technology available to support the revenue functions, executives at mid-cap companies think their organizations are bad at pricing. But why?
A couple years back, a mid-cap B2B products company was being actively courted by several big names in pricing software — Pros, Zilliant, Vendavo — and the company hired my team to validate the size of the revenue and margin opportunity these tech firms were promising through AI-enabled pricing optimization.
That pricing analytics project was challenging, to say the least. Like other Big Data projects I’ve worked on, the analysis itself is neither difficult nor time-consuming. Where do the hours of effort go? Into all the preparatory work to gather, organize, cleanse and prepare the data for analysis.
This preparatory work is like a batch set-up in manufacturing; it takes the same amount of time regardless of whether it can be used to produce just one or many analyses. The batch set-up for a big data analytics project is measured in weeks, not days. As a rule of thumb, the older and more undocumented the systems, the longer the timeline needs to be in the preparatory phase.
Why? There’s a lot of work involved in gathering data in a usable form. It’s typically an iterative process to work with the data, uncover previously unasked questions, and clarify what various fields mean, which of several similar fields should be used, and confirm the multi-level relationships for customer, brand, product, and other drilldowns.
Midway through our project, the client’s CEO asked my team: Why are we so bad at pricing? What are the barriers to pricing well?
These are important questions, but the answers are unfortunately never simple. I responded to the CEO with five categories and more than 20 specific barriers that his organization faced. Surprisingly, despite all of the innovation in business software since then, some of the barriers this client faced were ones I’d experienced fifteen years prior in another organization of similar size.
The barriers to effective pricing I’ve seen at various organizations fit into five categories: data architecture, data quality, systems, hardware & tools, and process & policy.
The data architecture at many companies often makes sense for the business it was 15 or 20 years ago, not the business it is today. Both the company’s own chart of accounts and its customer lists may be out of alignment after multiple acquisitions or restructuring activities. The data architecture, nearly always, was not built to flexibly accommodate areas of business growth or future needs.
Data quality challenges, such as discrepancies between different systems or missing entries in transactional data, drive manual effort to analyze and manage pricing data.
Systems are often minimally documented. Poor documentation creates a long and steep learning curve for the pricing team’s new hires. It also creates a dependence on the handful of individuals who have been at an organization since the systems were built.
Pricing teams are using yesterday’s tools to do today’s tasks. The sheer volume of data in an analytics project has grown, from maybe 10GB ten years ago to 40–50GB today. Excel is underpowered relative to the size of data sets and the number of fields involved, which limits the scope of analysis. Pricing analysts need a lot more processing power, modern tools, and multiple monitors to analyze these huge data sets.
Modern tools are not a complete solution to pricing challenges. Business processes and policies should be reviewed and simplified to enforce pricing and margin discipline. Excessive exceptions and workarounds complicate training, analysis, piloting, implementation of new pricing programs, and the evaluation of business outcomes. If your pricing is too complex to communicate clearly, it will be too complex to manage. Next time you do a SKU rationalization, consider doing a discount rationalization as well. For many companies, simpler pricing is ultimately the better strategy.