Share with your friends


Analytics Magazine

Analytics in Action: Sales force strategy

July/August 2011


HPs perspective on the emerging significance of analytics to boost sales performance across the enterprise.

Sales force strategy

By Rohit Tandon, Arnab Chakraborty and Debasish Das (left to right)By Rohit Tandon, Arnab Chakraborty and Debasish Das (left to right)

With the global economy showing signs of promise and moving away from the clouds of recession, there is a renewed focus on pushing aggressive growth strategies in the marketplace. Competitive pressures are driving companies to target multiple customer segments through various sales channels. Numerous sales transactions happen through online channels, phone, retails and direct sales force deployment. In such a multi-channel sales scenario, it becomes critical to leverage data and information about customers and channel partners to enhance the sales strategy. HP’s Global analytics organization deploys solutions across the enterprise to help translate information into insights and enable better decisions, faster. The team has implemented cutting-edge analytics solutions to help:

  • develop an effective sales coverage strategy,
  • improve customer understanding, and
  • elevate sales performance to maximize sell-through to various customer segments.

This article provides insights into how analytics is being deployed across the various sales channels within HP businesses at a worldwide level.

Multiple Routes to Market

HP, given its magnitude of operations, leverages multiple channels/routes to sell its 1,000-plus categories of products across the globe serving individual consumers, small-and medium-sized businesses (SMBs) and enterprises. These channels include:

  • a direct-touch route-to-market to serve its corporate and enterprise customer segments, wherein HP-owned sales reps directly connect with enterprises to enable sales for volume products, such as PCs, printers, etc., as well as for value products and IT solutions and services;
  • a mix of direct touch points with integrated support from various channel partners including ISVs, distributors and resellers to serve the SMB customer segment;
  • a retail channel to enable high customer touch and provide a better experience to the end consumers; and
  • an e-commerce channel to sell products to both the consumer and micro-business segments. Through this channel, customers have the option of customizing select HP products before they are shipped. They can configure their order either on the Web store or through the call center.

The multiple sales channels across various product categories and spanning multiple geographies increases the complexity in sales planning, creating a need for synchronization in decision-making through informed data and analysis.

Analytics as a Key Driver

Sales leaders are expected to make effective decisions at various stages of the sale value chain in order to drive tangible results. However, information-related constraints often hinder strategic and tactical decision-making. Some of these constraints include:

  • poor visibility of the sales funnel/pipeline leading to lower predictability on achievement of sales quotas;
  • inability to leverage available information on the existing customer base and inability to get a 360-degree view of customers through customer intelligence. This acts as a barrier in driving the right product offer to the right set of customers at the right price;
  • multiple business lines hitting the same customer without having a holistic view of the customer, thus failing to identify the E2E opportunity to sell more in that environment;
  • measuring productivity of sales reps to help them focus on customer-facing activities; and
  • inadequate sales force coverage and alignment with market opportunities.

Deployment of analytics has enabled sales functions at HP to establish a standardized analytical framework. This, in turn, has helped overcome the above-mentioned constraints and has aided better decision-making.

While analytics can deliver great value at multiple levels in an organization, it has made a telling difference for HP in specific phases of the sales value chain as shown in Figure 1. These include:

  • opportunity assessment: sizing and segmenting the market to prioritize sales investments and gain market share;
  • sales planning: sales force allocation, fixing sales quotas and targets, building account business plans, customer targeting plan, etc.;
  • sales enablement: empower sales reps with the right customer knowledge, sales tools, etc to have effective selling conversations with the customers;
  • sales operations: help with providing quotes, making the right offer to the customer, managing the sales pipeline, order follow-ups, etc.; and
  • post sales: measuring the overall performance of the sales force, product sell-through analysis and overall customer feedback/satisfaction.

Figure 1: Analytics solutions across the sales value chain.
Figure 1: Analytics solutions across the sales value chain.

Let’s take a deeper look at some of the analytics solutions and the impact they drive for the sales function.

Exhibit 1: Market sizing and segmentation to help drive sales force allocation

As sales leaders are under ever-increasing pressure to promptly respond to changing customer preferences, achieve double-digit growth targets and improve profitability, it’s critical for them to understand:

  • the incremental business opportunity in each market/customer segment;
  • how to segment and prioritize identified opportunities; and
  • the means to ensure appropriate sales coverage for various customer accounts.

The HP Analytics team has developed a solution to assess the total addressable market (TAM) and understand HP’s share of wallet in a customer account. This helps prioritize sales efforts and investments.

The analytics approach for market sizing and segmentation included the following:

  • modeling TAM at an account level for corporate and enterprise customers;
  • segmenting accounts based on TAM opportunity and HP’s share of wallet with the customers, to classify as a retain, acquire or develop (RAD) segment as shown in Figure 2;
  • identifying accounts for hunting and farming prospects based on the overall market opportunity; and
  • allocating sales force based on the opportunity, quota setting by each rep for an account and deciding their compensation targets.

Figure 2: RAD segmentation framework.
Figure 2: RAD segmentation framework.

This solution is deployed at the worldwide, regional and country levels and has helped increase new accounts by 13 percent and increasing revenue by 6 percent in the commercial and enterprise segment.

Exhibit 2: Campaign analytics — building an effective offer engine to enable sales

The online sales channels of HP target millions of customers and prospects through e-mail campaigns every month. However, lack of customization and targeting results in dipping conversion rates (typically lower than 1 percent or 2 percent).

HP Analytics helped build an effective offer engine that targets each customer with the product(s) they are most likely to buy next based on their historical transactions. This enabled customization of its marketing campaigns for the appropriate target customers, thereby influencing buying behavior and improving customer satisfaction.

The analytics approach involved the “next most likely product” (NMLP) model as depicted in Figure 3 and was built around input data of active HP customers, who had bought at least one HP category in a two-year time frame.

Figure 3: Analytics approach for NMLP model.
Figure 3: Analytics approach for NMLP model.

The team created a predictive model that leveraged conditional probability concepts to predict the next most logical product based on product ownership and buying behavior of other customers. The conditional probability model was built with the following approach P(A/B)=P(A ? B)/P(B), where A and B represented the product categories. The conditional probability matrix was created to understand the probability that a customer will buy category (A) given the customer has bought another category (B) in the past.

The implementation of the NMLP model for HP’s online sales motions has enabled effective cross-sell campaigns across various product categories and has already resulted in 15 percent to 20 percent lift in campaign response rates over six months of test period leading additional revenue enhancement for the business.

Exhibit 3: Pricing analytics — optimal deal pricing through discount allocation

In a typical enterprise scenario, sales deal cycles are quite long and involve a combination of hardware, software and services bundled together in a deal. Sales leaders need to make quick decisions on the optimal price and discounts that can be offered to the customer to remain competitive and profitable.

The HP Analytics team built an optimization model based on the maximum discount a stock-keeping unit (SKU) could bear and the overall deal margin that needed to be achieved. This provided the HP pricing team with an optimal price band they could offer to the customer. As indicated in Figure 4, this model could allocate discounts across product categories and families by following a Newton Raphson algorithm to achieve the business objectives. (In numerical analysis, the Newton-Raphson method, named after Isaac Newton and Joseph Raphson, is a method for finding successively better approximations to the roots, or zeroes, of a real-valued function.)

Figure 4: Deal-pricing optimization approach.
Figure 4: Deal-pricing optimization approach.

The deployment of the pricing optimization model enabled margin improvement by 1 percent. It also enabled quicker data-driven response to customers through faster pricing decisions and influenced multi-billion dollar deal closures.

Exhibit 4: Pipeline analytics to measure sales performance and efficiency

In a highly competitive and volatile marketplace, the ability to effectively manage the sales pipeline can have a profound impact on a company’s financial health. Sales leaders are looking for a unified and consistent view of sales data, pipeline and sales performance in a way that can aid strategic decision-making.

HP Analytics has been instrumental in providing better visibility into the HP sales pipeline. Consequently, it has influenced methodical sales force allocation and compensation, as well as execution through more informed competitive attack programs, and it has provided clarity on the health of the business, thereby enabling HP to respond swiftly to dynamic customer needs.

The sales pipeline analytics solution as depicted in Figure 5 is deployed on SQL and SAS platforms where data from multiple source systems is collated and cleaned. Clean data is plugged back into the SQL database. Customized tables are created for each end-user, which are then exported as MS Access databases. This sophisticated solution is capable of generating highly critical performance indicators on demand, such as pipeline velocity, funnel health, key milestones for high value sales, conversion ratio, new and inactive sales opportunities, sales cycle time, etc.

Figure 5: Sales pipeline analytics solution.
Figure 5: Sales pipeline analytics solution.

The solution also enables better predictability by simulating factors, such as deal conversion probability based on historical trends, prevailing market factors, etc., which then drive better planning and optimize financial outlays towards customer acquisition and retention.

The deployment of a systematic and data driven pipeline analytics solution has helped in:

  • creation of a proactive early warning system to correct deviation in performance against sales plans;
  • increased win and close rates and improvements in quota attainment; and
  • improved sales productivity and sales force allocation across HP businesses. Impact of Analytics Deployment

The deployment of analytics solutions across sales functions has led to various important transformations in the sales channel:

  • building up an integrated sales data warehouse in partnership with IT organization to get 360-degree view of customers/ pipeline;
  • streamlined and standardized sales processes and analytics approach across regions to globalize best practices and enable efficiency gains;
  • top down buy-in/sponsorship for analytics in day-to-day decision-making and building a culture of managing by metrics/analysis to back up gut feel in decision-making;
  • institutionalize an integrated selling approach across various businesses to enable visibility across business lines; and
  • develop sales tools and solutions and embed in decision-making that improves agility and speed.

The institutionalization of analytics to enable real-time decision-making across sales functions has helped influence key business levers for HP in terms of:

  • increasing the sales coverage by over 10% by identifying new customer accounts in the enterprise and SMB segment;
  • improved sales rep productivity by increasing the customer facing time for direct sales force by 15 percent;
  • improved efficiency of sales processes by increasing sales pipeline visibility. Overall visibility for enterprise sales team has increased by 15 percent; and
  • enable profitable growth by increasing deal profitability by 1 percent for HP’s hardware products business.

As organizations strive to deploy analytics to elevate sales effectiveness and performance across the enterprise, it will become very important for the practitioner community to gain sponsorship and legitimacy from the executive management, strike the right level of partnerships with the IT organization, drive management of change and impact the key business KPIs in a proactive manner.

Rohit Tandon ( is a vice president at HP Global Analytics, Arnab Chakraborty ( is a director at HP Global Analytics and Debasish Das ( is a senior manager at HP Global Analytics. This article is based on a recent paper presented by Tandon and Chakraborty at the INFORMS Conference on Business Analytics and O.R. Practice in Chicago. Copyright Hewlett-Packard Development Company, L.P. All rights reserved.



Using machine learning and optimization to improve refugee integration

Andrew C. Trapp, a professor at the Foisie Business School at Worcester Polytechnic Institute (WPI), received a $320,000 National Science Foundation (NSF) grant to develop a computational tool to help humanitarian aid organizations significantly improve refugees’ chances of successfully resettling and integrating into a new country. Built upon ongoing work with an international team of computer scientists and economists, the tool integrates machine learning and optimization algorithms, along with complex computation of data, to match refugees to communities where they will find appropriate resources, including employment opportunities. Read more →

Gartner releases Healthcare Supply Chain Top 25 rankings

Gartner, Inc. has released its 10th annual Healthcare Supply Chain Top 25 ranking. The rankings recognize organizations across the healthcare value chain that demonstrate leadership in improving human life at sustainable costs. “Healthcare supply chains today face a multitude of challenges: increasing cost pressures and patient expectations, as well as the need to keep up with rapid technology advancement, to name just a few,” says Stephen Meyer, senior director at Gartner. Read more →

Meet CIMON, the first AI-powered astronaut assistant

CIMON, the world’s first artificial intelligence-enabled astronaut assistant, made its debut aboard the International Space Station. The ISS’s newest crew member, developed and built in Germany, was called into action on Nov. 15 with the command, “Wake up, CIMON!,” by German ESA astronaut Alexander Gerst, who has been living and working on the ISS since June 8. Read more →



INFORMS Computing Society Conference
Jan. 6-8, 2019; Knoxville, Tenn.

INFORMS Conference on Business Analytics & Operations Research
April 14-16, 2019; Austin, Texas

INFORMS International Conference
June 9-12, 2019; Cancun, Mexico

INFORMS Marketing Science Conference
June 20-22; Rome, Italy

INFORMS Applied Probability Conference
July 2-4, 2019; Brisbane, Australia

INFORMS Healthcare Conference
July 27-29, 2019; Boston, Mass.

2019 INFORMS Annual Meeting
Oct. 20-23, 2019; Seattle, Wash.

Winter Simulation Conference
Dec. 8-11, 2019: National Harbor, Md.


Advancing the Analytics-Driven Organization
Jan. 28–31, 2019, 1 p.m.– 5 p.m. (live online)


CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:

For more information, go to