Mining for insightUsing advanced analytics to make animpact on pricing and profitabilityIn recent years, new analytical me...
patterns, trends, and anomalies in data. These advances       Case 1: Building a better incentive programhave helped sever...
Micro-segmentation using self-organizing mapSource: Deloitte Consulting LLP Analysis 2011From the analysis, the company re...
lowest margin) products.                                   could be better managed by customer microsegment. This         ...
The goal is to prove to the organization that there is value                                        in using analytics and...
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Mining for insight: Using advanced analytics to make an impact on pricing and profitability

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Mining for insight: Using advanced analytics to make an impact on pricing and profitability

  1. 1. Mining for insightUsing advanced analytics to make animpact on pricing and profitabilityIn recent years, new analytical methods and technologies What’s changed?have emerged to help organizations across many industries In the past, traditional analytic methods and tools weremake better business decisions. Software applications have limited by data and computing power. Small data sets oftaken advantage of an exponential growth of computing just a few variables (about 10 to 20) and limited casespower as the amount of data has increased dramatically. (fewer than 100) were typical. As a result, unrealisticIn fact, the amount of digital information increases tenfold assumptions often were made about the data (linear,every five years.1 For instance, Wal-Mart handles more than normal, and independent). Static data sets were created1 million customer transactions per hour that are added to from pulling information from isolated computers, tapes,its estimated 2,500 terabyte database — that’s equal to and disks. Moreover, few software tools were available to167 times the number of books in the Library of Congress.2 store and analyze data.And computing power has roughly doubled every twoyears. For example, decoding the human genome took Today, new analytics software can handle massive dataa decade when it was completed the first time in 2003. sets, churning through potentially millions of variablesNow, it would take about a week.3 and billions of cases. These new applications can analyze unstructured data, such as text, emails, tweets, images,With the advancements in technology, it’s not surprising and audio and video files, and receive real-time updates tothat a lot of companies are pushing for advanced analytical the information. It can also perform exploratory analysistools to help take their pricing strategies to the next level. — without having an outcome in mind — to find relevantPricing analytics can help companies mine for insightsfrom a massive haystack of information. It can helporganizations better examine their customers, products,and marketplace dynamics to help improve their growthand profitability.The bottom line is that analytics can help organizationsmake informed decisions using real-time data, increasetheir bottom line, and provide a competitive edge. Butsome organizations may still have doubts about the valueof analytics. Or they may even be unsure about what’srequired to use this technology. Through case studiesand real-world examples, we’ll show how these analyticaltechniques can be a game changer and where they arebeing effectively applied.1 Source: The Economist, Feb. 25, 20102 http://www.deloitte.com/view/en_U.S./us/ Insights/Browse-by-Content-Type/deloitte-review/ a70f2da4f32b9210VgnVCM100000ba42f00aRCRD.htm3 Source: The Economist, Feb. 25, 2010
  2. 2. patterns, trends, and anomalies in data. These advances Case 1: Building a better incentive programhave helped several organizations effectively use analytics A large consumer products company with a long historyto make improvements, ranging from better customer of selling on volume had provided incentives to customersservice to fraud detection. through three distinct programs — discounts, rebates,• Nearly two-thirds of Netflix movies that are selected and micro-financing. However, the organization had not by customers come from the company’s analytics analyzed how each program influenced specific customer engine, which reviews movie ratings from millions of its segments, which were grouped according to size, subscribers. competitive intensity, and other traditional characteristics.• After identifying that 7 percent of its customers Plus, there were no guidelines regarding how and where accounted for 43 percent of sales, Best Buy reorganized these programs were being applied. For instance, some its stores to focus on the needs of those particular customers received multiple incentives, while others, who customers. shouldn’t have gotten any, were still getting them.• American Express discovered that people who rack up large credit card bills and register a new forwarding The organization decided it needed to analyze marginal address in Florida — which has one of the most liberal profitability and volume metrics for each customer and bankruptcy laws — are more likely to declare bankruptcy. incentive combination. First, it established a baseline• eBay analyzes millions of auction pages, bidding history, with a control group that was not offered any incentives. and feedback to detect fraud. The organization then built a matrix of several hundred• Google studies the timing and location of search-engine thousand customers, identifying which customers were queries to predict flu outbreaks and unemployment receiving what mix of incentives. Customers were assigned trends before official government statistics come out. to a distinct group each month based on the incentives that they received. Each group’s performance was thenAdvanced analytics can have that game-changing impact compared to the baseline group based on the impact toon pricing as well. With this technology, organizations volume and profit.can potentially make faster and more accurate pricingdecisions. It can help them to continually monitor and While some incentives would reduce profit percentagemeasure their pricing performance in the market and under this model, they would still generate more thansuss out new opportunities. Advanced analytics aimed enough sales to compensate. The incentives actuallyat customer and business outcomes are at the core of are not reduced, but only redistributed. Those with themodern pricing and profitability management, price highest return on investment are distributed to certainleveraging and profitability management, and trade spend groups; other groups get incentives that generate the mosteffectiveness. incremental volume. By weighting margin improvement by volume change, the total dollar return was increased.Three case studies for effective analytics Based on the results of this analysis, reassigning incentivesWhile many may agree that data has become an important to customers generated $10 million in margin lift to date.business asset, questions remain: How do you sort throughmammoth amounts of information to find those insights Case 2: Identifying profitable customersto make better decisions? How should a company use data A large financial services company wanted to identify thewithout succumbing to continual analysis-paralysis that characteristics of customers who consistently deliveredplagues many decision makers? The first step may seem profitable loans. The company conducted an advanceddaunting. But if companies can focus on the appropriate analysis of micro-segmentation using self-organizingquestions and problems, they can get off to a faster start maps. By grouping the customers into profit-basedon their analytics journey. segments, the company identified other variables that had a strong correlation to profit. For example, the company’sThe following case studies illustrate some practical and unprofitable loans seemed to have an origination amounteffective methods for using advanced analytics with in excess of $25,000, a 5 percent interest rate, no dealerexisting data to help companies make better pricing and markup, and interest rate overrides. These loans were alsoprofitability decisions. made to customers in the Fair Isaac Credit Organization range of 720–770. The maps below illustrate some of these findings. 2
  3. 3. Micro-segmentation using self-organizing mapSource: Deloitte Consulting LLP Analysis 2011From the analysis, the company realized that including When customers were grouped based on performanceperformance review with its segments could improve and other behavioral dimensions, model portfolios werecustomer performance. The company gained very specific built according to the most profitable customers in eachinformation from a very large data set and was able to segment. Sales implemented these portfolios with newidentify customers at risk of defaulting on loans. This customers, leading to a gradual adoption. The requiredinformation became the key to improved predictive power. product mix by subchannel and store characteristics helped poor performing stores improve by influencingBefore the analysis, the company grouped its existing what products were sold within those stores. The storescustomers on purely demographic variables and did not were grouped based on profitability — top stores rankedfactor in profitability and other performance measures. among the top 25 percent, while bottom stores were inPlus, customer segment management occurred ad hoc the lower 25 percent. Stores were further grouped withinwithin each selling unit. By applying advanced analytics, each channel based on various characteristics, such ascustomer segmentation was revisited using transactional average monthly revenue. For stores in the same channeldata to understand behavioral attributes with added and with the same revenue size, product portfolios werevariables, such as profitability and volume. Dashboards compared; specific products that sold more frequently inwere designed to allow for segment management at a the poor performing stores were found. The opportunityselling-unit level, including discounting guidelines, pricing, was quantified by holding volume constant, but adjustingand supply chain costs. When these new tools are fully the product mix of lower-tiered stores to emulate that ofdeployed, run-rate benefits for field customer management high-performing stores.could total $50 million in margin annually. With transactional analytics, the product mix could beCase 3: Increasing profit through product mix better managed by customer microsegment, indicatingA consumer packaged goods company gained $16 million that a product mix in consumer packaged goods is criticallyin annual profit improvements by using transactional important. Since margins vary widely across products,analytics to increase its product mix by customer identifying the appropriate product mix by specificmicrosegment. The company’s product mix varied within customer microsegment can have a huge impact. As aa given segment of the marketplace and no suggested result, the organization’s sales force proactively offersportfolios existed based on historical performance or insight to customers about what portfolio of products willprofitability. And with volume-driven metrics, the sales likely be more effective given the particular competitiveforce typically sold the lowest price (and often lowest landscape and customer set.margin) products. Following an analysis of performanceand profitability, this company found that their product mixcan be influenced using analytics to build model portfolios. 3
  4. 4. lowest margin) products. could be better managed by customer microsegment. This indicates that product mix in consumer packaged goods is When customers were segmented on performance, as well critically important. Margins vary widely across products, as other behavioral dimensions, model portfolios were thus identifying the appropriate product mix by specific built considering the most profitable customers in each customer microsegment can have a tremendous impact. segment. Sales implemented these portfolios with new As a result, the organization’s sales people now proactively customers, leading to a gradual adoption. The required offer insight to customers about what portfolio of products product mix by subchannel and store characteristics will likely work best given the particular competitive served to improve poor performing stores by influencing landscape and customer set. what products were sold within those stores. The stores were grouped based on profitability, with top storesSource: Deloitte Development LLCMake analytics work for you People matter. If your leadership isn’t on board withAdvanced analytics can help organizations get more out of analytics, it likely won’t get much traction across thethe data they’ve collected and stored if applied effectively. enterprise. Strong executive sponsorship can build 4They need to understand the capabilities required to consensus across all stakeholders. Include your smartestprovide analytic-driven improvements. In short, they’ll people, who are experienced not just in analyticneed to make changes to their data, processes, and techniques, but also in the business. Finally, you shouldorganization. understand that some results may be counterintuitive, challenging years of accumulated conventional wisdom.Mind the data. Data is the fuel that drives your analyticalengines. You never want to put the wrong fuel in an Leading a new wayengine. For that reason, don’t assume that your data is To make more effective use of advanced analytics,correct. Confirm that you’re pumping clean and accurate organizations need to anticipate challenges and continuallydata to make more effective and informed decisions. You make any necessary corrections. When you’re building yourshould pull and integrate data from multiple, disparate analytical capabilities, you’ll need to assess your currentsystems — otherwise you might not be getting the full performance. That way you can gauge any improvements.picture. Plus, you should design databases and data cubes Also, remember that your analytic insight is just oneto improve the speed and quality of your findings. Lastly, method to make good decisions. Don’t discount yourdon’t be afraid to include and analyze external data, both business insight. And keep in mind that databases canqualitative and quantitative. crash and people can make mistakes, so you’ll need to plan for these hiccups. You should always be certain ofBe flexible, be specific. Analytics should be used to view your analyses and only present them to executives afterthe same issue from multiple angles. This can help provide they have been scrutinized by the owners. With any newa clearer picture of the real issue. Also, refrain from talking initiative, results will likely be challenged so be prepared toor analyzing in averages because something might be respond.hidden from your view. If you deal at the most granularlevel, you’re more likely to see real benefits. And when youstart implementing analytics, think of where you can getmore bang out of your buck. By prioritizing value, you’relikely to also get buy-in from other people across yourorganization. 4
  5. 5. The goal is to prove to the organization that there is value in using analytics and that it’s well worth doing. Some organizations start with a small initiative and then build from there. You shouldn’t think of analytics as a single event, but a long-term investment. It should be developed over time and customized to your organization’s evolving needs. For more information, please contact: Mike Simonetto Principal Deloitte Consulting LLP msimonetto@deloitte.com Ranjit Singh Principal Deloitte Consulting LLP ransingh@deloitte.com Richard Eagles Senior Manager Deloitte Consulting LLP reagles@deloitte.com Shruti Kahlon Senior Manager Deloitte Consulting LLP skahlon@deloitte.com Join the analytics discussion at http://realanalyticsinsights.com/This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by meansof this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for suchprofessional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decisionor taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shallnot be responsible for any loss sustained by any person who relies on this publication.About DeloitteDeloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms,each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure ofDeloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure ofDeloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.Copyright © 2012 Deloitte Development LLC. All rights reserved.Member of Deloitte Touche Tohmatsu Limited

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