Price Optimization and Sales Incentives Deliver Profitability

Two software applications I follow, price and revenue optimization (PRO) and sales compensation and incentives, can be highly complementary when used together. Unfortunately, since they typically are developed and sold by different kinds of software vendors, scant attention has been paid to the value of using them in tandem. I advise companies that have adopted a PRO strategy to use an incentive management application also to support and reinforce their optimization efforts. It is also part of our research agenda and education on sales  for 2011 and beyond.

Price and revenue optimization is an analytics-driven business discipline that uses market segmentation techniques and historical data to set a price that represents the most an individual buyer is willing to pay. It is one example of how businesses have successfully applied predictive analytics to achieve both strategic and tactical objectives. Companies use PRO to achieve strategic objectives such as increased profitability or higher market share and tactically to adapt quickly to competitors’ moves.

In the 1980s the airline and hospitality industries were the first to adopt PRO as a strategic tool. Their experience illustrates the value of the PRO approach. Both industries sell to two distinct segments. On the one hand, there are travelers (typically those on business trips) who have less flexible schedules and are willing to pay more for last-minute bookings and convenient times and dates. On the other, there are those (typically vacationers) who want to spend as little as possible and can plan further in advance or have more flexible schedules. Both airlines and hospitality share the same basic economics: The opportunity cost of unsold inventory (an unfilled airplane seat or a hotel bed) is very large compared to their incremental cost of sales. Airlines and hotels were able to be early adopters of PRO because they had computer-based reservation systems that enabled them to use historical data to develop and endlessly refine predictive models used to automate pricing decisions and then apply them to their transactions. Well-established airlines with higher cost structures were able to use PRO to compete successfully with lower-cost upstart airlines, providing proof that it was a practical and effective discipline.

In the decades since, the cost of computing power has dropped, making PRO a practical option for broad set of industries, many of which are in the B-to-B category. This includes manufacturing, distribution, general business services and financial services.

Airlines and hotels share an important characteristic in that parts of their prices are fixed at the time they are quoted and have no room for negotiation. In many other industries, though, PRO is applied to negotiated sales where the sales agent has latitude in setting the price. In these circumstances I believe it is essential to integrate sales incentive management with PRO. This addresses the common tendency of sales people to offer the lowest available price immediately; this is especially true of inexperienced salespeople and those from fixed-price cultures who are less comfortable dickering with customers.

Sales compensation and incentive software, used in conjunction with PRO, gives sales managers and representatives flexibility to apply their judgment to specific situations. For example, sales people might be allocated a set amount of pricing discounts during a period and allowed to apply this as they wish. The software also enables companies to modulate the degree of pricing flexibility according to product (to move inventory or meet market share goals), region (to open up a new sales territory), customer or time of month or quarter (to achieve sales volume objectives). The software can make it easy to a adjust incentives and track results while virtually eliminating the administrative chores of tracking the data and presenting it in the proper context.

Both of these applications are part of what we call sales performance management and recently our assessment of vendors and products called the Sales Performance Management Value Index found an opportunity for the vendors to better integrate these two areas together. Our value index lays out requirements for this type of software and assesses how well vendors in this category address these requirements. In any instance where a company is using PRO and sales people have flexibility in negotiating prices, I think the value of any price optimization strategy will be enhanced substantially with a complementary sales incentive management initiative.

Regards,

Robert Kugel – SVP Research

Predictive Analytics Support Human Judgment

Predictive analytics can be valuable tools for performance management. When the term is applied to planning or forecasting, many people take it to mean the ability to automate plans or forecasts. It’s true that using predictive analytics correctly is likely to enhance their accuracy, but these techniques do not eliminate the need for judgment; in practice, many organizations may realize more value from applying predictive analytics  to assess results than to forecast outcomes. Moreover, as regards performance management the usefulness of predictive analytics extends beyond planning and forecasting. They also can serve to set benchmarks that can be used to assess performance or generate alerts to accelerate necessary action. Although I advise companies to be more aggressive in adopting predictive analytics, I doubt that they will adopt them as fast as they should because of perceptions that the tools are too hard to use and the data too hard to get at.    

That’s too bad, because predictive analytics have many uses in various aspects of business. For instance, as a short-term forecasting tool they can enable a fast-food franchise to project how many hamburgers, fries and soft drinks it will sell in 15-minute increments during the day based on factors such as the day of the week, the time of year, the weather, the volumes sold in the previous weeks, special advertising and promotions and other factors. The owner of the franchise can use the forecast to try to match employee scheduling to demand. For longer-range projections, a builder of Class 8 trucks can use it to forecast quarterly demand based on leading indicators, projected GDP, freight-car loadings, interest rates and other variables found relevant through analysis of historical data. Beyond sales forecasting and budgeting, the company can use these insights to inform its purchases of long-lead items to optimize its parts inventory.   

But as I said, predictive analytics don’t eliminate the importance of judgment in creating plans and forecasts. The analytics rely on historical data and historical relationships, but they shouldn’t be viewed as a black box spewing out unquestioned results. Indeed, these projections depend on a host of assumptions made by the forecaster. Yet applying them properly is a great replacement for naïve extrapolation in that they enable people to do more nuanced forecasts more intelligently. Human beings themselves should use common sense to decide whether the important assumptions apply. If, for example, a demand factor is the average replacement interval, the human mind is the best tool to assess whether this average is likely to lengthen temporarily (say, because buyers are anticipating a new generation of product) or permanently (for instance, because useful lives are lengthening).    

While predictive analytics have value in preparing forecasts, they can be even more powerful as an assessment tool. Especially when businesses are dealing with well-established cyclical patterns (such as a mature, high-volume product like fast food), they can measure results against assumptions. Greater than anticipated burger sales through midmorning may be a reliable indicator that there will be more customers throughout the day and alert the manager of the need to call up additional staff for the dinner hours. In another case, predictive analytics can be used to continuously compare sales volumes from retail scanner data against expected results. A shortfall from predicted sales levels for a heavily promoted item in the first two days of a month-long campaign can serve as a real-time alert. Does parsing the data indicate a specific cause? Further analysis (and the application of human judgment) can determine if there are specific sources of the shortfall. Individual judgment may be necessary because the factor may not be something the internal systems track. For instance, is the shortfall the result of a competitor’s action to neutralize the special promotion? Or is the messaging wrong? Predictive analytics can help companies respond faster to actual outcomes because they can establish reliable performance benchmarks that make it possible to assess results sooner and with greater certainty and thus act sooner rather than later to address an opportunity or issue. This sort of analysis can be applied broadly across the company or to a specific customer. If, for example, sales of letter-size (for the rest of the world, that’s roughly A4) laser paper to Dunder Mifflin is falling below trend, a call to the buyer is certainly in order to figure out why. (Good luck in getting a straight answer there.)    

These examples show that predictive analytics are a powerful tool that companies can use to enhance the accuracy of their plans and forecasts, enable more insightful performance reviews and alert organizations of unexpected outperformance or shortfalls. Yet our benchmark research on analytics in finance shows that only a relative handful of companies are utilizing them. Only 13 percent of organizations overall use them, mainly in specialized functions such as marketing. Just 8 percent of finance departments use them. While my colleague points out that predictive analytics are on the rise, I suspect that the slow adoption of predictive analytics is the result of two main factors. One is that the tools have not been easy for generalists to use since they require training. The other is that it has not been easy to access the data sets. (My colleague David Menninger has written about the IT dimensions retarding adoption here.) I believe these conditions are changing as some vendors try to spur demand by making their software  easier for generalists to use. Still, the issue of data accessibility may continue to impede adoption. Although technical reasons may not prevent a corporation from making needed data more accessible, historically they have shown a general reluctance to make the necessary investments. Moreover, until innovative companies demonstrate clear benefits from using predictive analytics broadly in core business processes, awareness of their potential is likely to remain low, further slowing adoption. Our firm will be research deeper into the adoption and best practices in 2011 but in the meantime, organizations that take the initiative to establish fully functional predictive analytics may put themselves in a superior competitive position.   

Regards,  

Robert Kugel – SVP Research