Bringing science to sales

The use of analytics has delivered competitive advantage in areas such as supply chain management and marketing, but in sales functions these techniques have yet to gain traction. Jan Van der Linden explains how pharmaceutical companies can employ analytics to create a more robust end-to-end sales process.

As pharmaceutical and biotech companies continue to invest and spend heavily on their sales capabilities, powerful new options for improving sales performance are emerging that enable these firms to take a more scientific approach to selling. Specifically, this involves using new analytical tools to complement sales people's intuition, judgement and experience and enable more effective, fact-based decisions. Just as analytics have helped the supply chain and marketing functions improve their effectiveness and efficiency, they can now help boost sales performance.

analytics at work

When applied to core business functions, analytics can generate significant competitive advantage. In the supply chain area, for instance, scientific approaches to decision support have delivered impressive results. Decision support systems for production, inventory and transportation problems emerged during the 1960s and 1970s, and they now permeate all aspects of supply chain management: activity-based costing, statistical quality control, demand forecasting, network optimisation, simulation, linear programming and capacity planning, to name a few. These tools have delivered tremendous payback, minimising waste and cost from misallocated resources.

Figure 1: Analytics applied across the end-to-end selling process

Click on the image to zoom

Source: Jan Van der Linden

Marketing teams have also embraced analytics, starting with sample-based consumer research and expanding into other areas, such as segmentation analysis, brand attitude and brand awareness research. More recently, sophisticated techniques for advertising and promotion effectiveness analysis, price elasticity analysis and media mix optimisation have helped marketers to optimise their marketing investments. These techniques have also given marketers a much keener understanding of customer needs, behaviours and preferences, enabling them to target more precisely and allocate resources more effectively.

So what about sales? Pockets of analytical support for sales certainly exist in some companies. The most common uses of sales analytics include mining of prospect databases (although arguably a marketing function) and providing sales reps with analytically driven customer segmentation schemes. However, compared with other business functions, the use of analytics in sales is still in its infancy. Obstacles include the prevailing attitude that analytics and selling do not mix, the difficulty in making analytical insights actionable for a sales rep and the lack of widely accepted tools and techniques.

the turning tide

Despite the historic obstacles, the desire to inject more analytics into the sales process is building. This is a general phenomenon across most industries and is being triggered by several factors. One is the increased availability of sales-related data captured through customer relationship management (CRM) systems. Another is the ever increasing complexity of product and service offerings. Such complexity makes it increasingly difficult, if not impossible, for individual sales reps to make effective decisions about prospecting, customer targeting, cross-selling and other key sales tasks without having a strong analytics capability to support their decisions. Finally, the acceptance of analytics by management has increased significantly over the past 12-18 months: it has become a very hot topic and one in which companies are seeking to invest.

embracing analytics

Once a company recognises the potential of analytics to improve its sales performance, its first consideration should be where and how to apply these capabilities. Figure 1 shows how analytics can support virtually every step in the end-to-end sales process.

This list is not exhaustive and not all items are relevant or feasible for every company. However, nearly all companies can create significant value by adopting some or all of these capabilities and extending their scope and reach throughout the sales organisations – in other words, by bringing more science to selling.

The following considers in more detail a few applications of analytics to the selling process.

sales force optimisation

Given that pharmaceutical companies have some of the largest sales forces in the world, the question of how many reps to employ in total and how many to deploy against specific customer segments or product lines is a major decision – with major economic repercussions. Generally speaking, adding sales reps increases an organisation's total sales, provided they are not tripping over one another. However, the return from adding more sales reps eventually diminishes. At this saturation point, the so-called "response curve" (the number of reps relative to total sales generated) flattens out, and the cost of adding begins to outstrip the incremental margin dollars they deliver.

To determine the optimal sales force size, a company requires a clear understanding of the total costs associated with each sales rep – their salary, benefits, expenses and car. That is the easy part. It also requires an understanding of the contribution an incremental sales rep makes to total sales. This involves constructing sales response curves: this is the challenge.

In the author's experience, the right approach to the development and optimisation of response curves is based on a variation of the Delphi method.¹ It involves a highly facilitated set of workshops with the company executives who are best informed and have the best judgement about the impact of adding more resources on sales results.

A number of estimating rounds are conducted, interspersed with discussion about the rationale for the estimates. After several rounds, an organisational consensus emerges on the right shape of the response curve. The various response curves by product line are then compiled to determine the optimal total number of reps, as well as the optimal allocation of reps across product lines. Sales force optimisation is a powerful example of how sound judgement can be combined with analytics to arrive at a better business outcome.

in the loop

Closed-loop promotion analytics go to the heart of the sales process within the healthcare sector. As the sales rep gets ready to conduct a sales call with a physician, customer segmentation information is used to help the rep select the most appropriate presentation tailored to the needs of a particular group of healthcare professionals. The presentations can consist of visual aids, clinical reprints, opinion leader videos, case studies, physician surveys, medical education invitations and managed care formulary data.

Figure 2: Closed-loop marketing analytics

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Source: Jan Van der Linden

Using a tablet PC with a pen-based interface, these presentations are interactively shared with the healthcare professional. A software component on the tablet PC records real-time data about each sales interaction, such as the order in which materials were presented, the healthcare professional's response to the messaging and the length of time spent discussing specific materials.

This information, together with the actual results of the sales call, is transmitted back to headquarters for aggregation and integration with other datasets from other sales reps. Analysis is then conducted on the dataset to establish what type and sequence of messages and visuals leads to the highest success rate for a particular healthcare professional segment. The analytical results can lead to new marketing campaigns and updated marketing materials, which can then be prepared and delivered to the sales force in a more timely and cost-effective manner (see Figure 2).

the war for talent

Talent management is another area that benefits greatly from infusing science into a process often based largely on judgement and intuition. So how can analytics help? The answer lies in truly understanding an organisation's best sales performers – generally, those in the top 20%. The author's work has involved an instrument that helps organisations determine what drives these higher performers, by measuring performance along three dimensions: personality, competencies and behaviours. The tool includes a set of 46 sales personality attributes, 59 sales manager competencies and 53 sales rep competencies. It enables a company to determine statistically what combination of specific personality factors, competencies and behaviours correlate with high sales performance at their organisation.

Different organisations have different cultures, products, sales models and incentives, and the skills and behaviours typical of high performers should be identified and evaluated in context. However, experience suggests that few companies understand this. For example, few recognise that the personality traits, behaviours and competencies that define high-performance sales teams can vary significantly from organisation to organisation and from one role to another. Consequently, many rely entirely on external benchmarks to assess the performance of their own sales teams, resulting in a generic view of performance, rather than meaningful insight into the factors that drive sales performance at their organisation.

Once a company understands what drives its own high performers, it can then use the same tool to measure its core performers – the remaining 80% – on the same factors. By comparing the results of core performers with those of the high performers, the company can identify gaps and the specific actions they can take to close them, including:

• pre-hire screening of new sales people who embody the traits of high performers;
• customising development plans for every member of the sales force;
• providing training for each sales person to help close his or her specific skills and competency gaps;
• implementing a more informed process for promoting sales reps to sales managers.

analytical sales capability

Given the wide array of analytical areas that now enable organisations to apply science to selling, understanding what these areas have in common can be useful. Most rely on the same or similar datasets, as well as similar analytical approaches and technologies. Consequently, the same core group of analytical resources can support multiple areas. Furthermore, several analytical areas build on each other. For example, many use segmentation as a foundation for other analytical capabilities. Similar mechanisms can and should be used to integrate analytics into day-to-day sales processes. A similar approach to change management can be used to facilitate adoption by the sales force.

These similarities allow companies to consider two strategies. The first approach is to focus on one analytical area expected to provide high value and, once this area is established, expanding into other areas. This strategy is pragmatic, focused and low-risk. However, the implementation sequence may limit opportunities for synergies with other areas and may require more time to achieve true competitive advantage.

The second approach is to lay out a vision and roadmap for evolving the entire sales function toward a broader use of analytics. This option allows the organisation to apply analytics in a logical sequence, while building out the foundational elements needed to support a more scientific approach to sales. Of course, this option also requires a clearer strategic intent and greater commitment to the concept at the outset.

As stated above, a key success factor is integrating analytics capabilities into day-to-day sales processes. The importance of this cannot be overstated. In a sales context, it is much more important to get this right compared with, say, a marketing context. Sales reps have little time or inclination to delve into complex analyses and insights. This does not mean that a company should skimp on rigour. However, it does mean that insights should be packaged and delivered in the most streamlined, user-friendly manner possible.

apply the analytics

As pharma and biotech companies reach the limits of how much they can invest in their sales forces, the emphasis is shifting from size and efficiency to focus and effectiveness. Analytics are the key to accelerate this shift. Now is the time for enterprises to take a more scientific approach that complements and augments their sales force's experience, judgement and intuition. By applying analytics to key areas across the sales process, an organisation can provide the objective data that can help sales people make more informed, fact-based decisions, use their time more effectively and boost the overall contribution of the sales organisation.

The right approach to achieving these results is a holistic one which gives proper attention not just to the analytical capabilities themselves, but also to the process for applying the analytics, the organisational alignment within the sales function, and the performance metrics and incentives needed to ensure selling behaviours align with the analytical insights. Adhering to these principles when bringing a scientific approach to sales will help move an organisation towards high performance.

References

1. The Delphi method, developed by Project RAND for the US armed forces during 1950-1960, is a systematic, interactive forecasting method which relies on a panel of experts.

Jan Van der Linden is a partner in Accenture's Sales Transformation practice. Email: [email protected].