Pillars for Successful Analytics Implementation. marketing insights spring 2013

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1 3 7 Pillars for Successful Analytics Implementation

2 + A leader s guide to incorporating Big Data across the organization Jesse Harriott [email protected] You may remember the days before the Web, before Big Data, before social media and before mobile, when an annual customer survey, a customer database with basic information, retail purchasing data from a third party or credit reporting information was about as rich or as detailed as a company s data would get. At that time, companies were flying by on less information than they needed and the flow of deep customer, competitor and industry information didn t exist as it does today. Gut feel, or instinct, was a prized business characteristic and it, rather than data, drove many corporate leadership decisions., 35

3 Now almost every aspect of life can be tracked in one way or another, whether it be data from Web behavior, mobile phone usage patterns, in-store shopping activity, public surveillance videos, GPS tracking data, automotive driving patterns, physical fitness data, social media data, satellite imagery, video streams, car telematic data, the list goes on and on. As a result, data is the business focus du jour. Companies now say that they are data-driven and only make quantitatively based business decisions. However, companies also are overwhelmed by the data at their disposal to analyze against critical business questions. The issue today is not the lack of data, but rather how to prioritize, access and use the deluge of data in real time so that it has its greatest impact on the business. While some businesses don t even know where to start, others are still struggling to move beyond basic reporting. In some instances, executives don t have a clear understanding of how analytics can impact the organization. This article outlines a non-technical framework to help business leaders extract value from multiple big and little data streams across the organization. The following insights are grounded in lessons learned from firsthand experience in analytics leadership positions, helping companies large and small make the most of their data assets. Based on my experience, and based on my interviews with other analytics leaders for my recent book, Win With Advanced Analytics, I ve noticed several common themes regarding companies that are successful with analytics initiatives versus those that are not. From this knowledge, the framework called the business analytics success pillars (BASP) evolved. The BASP captures the key activities and similarities that thriving and successful business analytics functions share. The BASP can be used by the analytics professional as a self-check on what is or isn t being done well. The BASP also can be used by a non-analytics business leader to assess what is or isn t working with his analytics strategy. The BASP framework contains seven pillars that are critical to successful analytics implementation. The pillars are not designed to be followed in any particular order. Regardless of the specific situation, the pillar framework can be thought of as similar to the foundation of a house: One needs all of the areas of support in order to make the house stand strong and not collapse. Therefore, the goal of the BASP framework is to focus the organization s attention on the areas that are key to analytics success and will lead to the greatest return on investment. The BASP is a framework that a CMO or CEO can get behind, yet the individual analyst or manager also can use it as a blueprint to take analytics to the next level at her organization. The pillars are as follows: Business Challenges: Align analytics initiatives to the most pressing business problems that your organization needs to address. Data Foundation: The data foundation that will support the business analytics process must be strong in terms of reliability, validity and governance. Analytics Implementation: It s crucial that you ensure that analytics solutions are developed and provided to the enterprise with the end goals in mind. Insight: Analytics must transform data from information into intelligence and insight for the organization. Execution and Measurement: Analytics must be put to work, and must lead to organizational action and provide guidance on how to track the results of the actions taken. Distributed Knowledge: Analytics must be communicated in an effective and efficient manner, and made available to as broad a group of stakeholders as is appropriate. Innovation: Analytics must be relentlessly innovative, both in analytical approach and in how they affect the organization, by developing solutions that will wow customers. 36

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5 Business analytics success Pillars Let s take each pillar, one by one, and dive a bit deeper. BUSINESS CHALLENGES DATA FOUNDATION BUSINESS ANALYTICS ANALYTICS IMPLEMENTATION INSIGHT EXECUTION AND MEASUREMENT SUCCESS PILLARS DISTRIBUTED KNOWLEDGE INNOVATION Business Challenges A critical step of any business analytics implementation requires a clear understanding of organizational objectives, or business challenges, to ensure that any solution is aligned with and addresses the company s biggest or most pressing needs. This concept may sound obvious, but it s a deceptively simple concept that is often difficult to follow consistently. Any business analytics initiative must be grounded in critical business challenges challenges or questions for which the answers will result in the company increasing its revenues or reducing its cost. It s very easy for the analytics effort within an organization to drift gradually into issues of intellectual curiosity or merely to be a support function that answers questions at the whim of senior business leaders. This is how an analytics function can gradually turn into a cost center rather than a function that adds economic value to the organization. The BASP is a framework that a CMO or CEO can get behind, yet the individual analyst or manager also can use it as a blueprint to take analytics to the next level at her organization. Business Challenges: Align analytics initiatives to the most pressing business problems that your organization needs to address. Data Foundation: The data foundation that will support the business analytics process must be strong in terms of reliability, validity and governance. Analytics Implementation: It s crucial that you ensure that analytics solutions are developed and provided to the enterprise with the end goals in mind. Insight: Analytics must transform data from information into intelligence and insight for the organization. Execution and Measurement: Analytics must be put to work, and must lead to organizational action and provide guidance on how to track the results of the actions taken. Distributed Knowledge: Analytics must be communicated in an effective and efficient manner, and made available to as broad a group of stakeholders as is appropriate. Innovation: Analytics must be relentlessly innovative, both in analytical approach and in how they affect the organization, by developing solutions that will wow customers. Data Foundation At more progressive companies, disparate data sources once relegated to single departments or to silos within the organization are being integrated into a unified data foundation. More and more, companies are leveraging data across departments and breaking down traditional data silos in order to address the business challenges of the organization. This usually is driven by the fact that the critical business challenges cut across departments and, as such, require data from multiple places to address them. However, the issue of a strong data foundation is a common source of frustration for senior executives. Without data sources, definitions and auditing that cut across departments and silos, companies with well-intended advanced analytics capabilities will struggle to speak the same language across the organization. This problem often is exacerbated by an inability to agree on a universal definition for each metric. 38

6 For example, what is considered a customer to marketing may differ from what is a customer to finance. In this case, championing the importance of analytics as a way to standardize the data across the organization can ensure that everyone is speaking the same analytics language. If this is not done, analytics efforts may be undermined, and wasted effort will be spent on reconciling different data sources and dealing with the dueling data problem. Analytics Implementation The third pillar in the BASP framework relates to how the information-based solutions are developed and provided to the enterprise. This pillar obviously is broad and complex. However, the most important point for success is to start with the end in mind. How will analytics solutions be used by your customers, and what actions do you hope they will take? Successful implementation requires a relentless focus on the internal and external customer. Start by imagining how internal customers, whoever they may be (e.g., marketing leaders, salespeople, service reps, etc.), might leverage the information or tools that the analytics organization will provide. Insight The key to business impact, and often the most difficult aspect, lies in the ability of your business analytics team to take raw data and turn it into a compelling narrative that addresses specific business challenges and results in business action. Like any architect, you ve got to know your customer whom you re building for before you start construction. In business analytics, fulfilling any commission starts with statistical storytelling, addressing what has happened, why it happened and, most importantly, what s going to happen. Execution and Measurement Analytics must be put to work and must lead to organizational action, and What is considered a customer to marketing may differ from what is a customer to finance. In this case, championing the importance of analytics as a way to standardize the data across the organization can ensure that everyone is speaking the same analytics language. also must provide guidance on how to track the results of the actions taken. The goal should be to elevate the discussion on how to monitor, measure and analyze the business performance of actions taken by the organization. Every business decision or action has implied questions for the future in order to understand whether there was a successful business outcome. The execution and measurement pillar is about effectively following up on the results of action-based decisions across the organization. Business performance tracking and measurement should be an integrated process that assesses the performance of analytics-driven changes. Distributed Knowledge There must be a concerted effort to disseminate insights across the organization. Traditionally, analytics has taken an old-fashioned typing pool approach, whereby a business leader submitted a request to the analytics team for some specific information or an answer to a question. However, the distributed knowledge pillar focuses analytics toward using the wisdom of the organization to create greater enterprise value from its data, as well as to get the organization moving more rapidly toward the truth and away from corporate myths and legends. For example, there are salespeople in the organization who likely know more about the competitive threats that the company is under than anyone in strategy and development will ever know. Also, specialists in the product organization can quickly make sense of Web analytics data that might take a pure analyst a lot longer to interpret. This can be challenging in a command-andcontrol-style management structure, but by distributing the knowledge and data across the organization in a thoughtful manner, analytics will have more positive impact on the organization and will benefit from the collective wisdom within the company. Innovation Innovation sounds like common sense and even a bit trite, but it s surprising how many business analytics functions fail in this area. It seems to be too easy for a business analytics function to fall into the trap of providing the same information to internal customers because it s what has been provided historically, or because someone asked for it monthly eight months ago yet the analyst can t explain, specifically, if and how the information is being used by the business today. The analytics function must be relentlessly innovative, both in analytical approach and in how it affects the organization. 39

7 Case Study: Restaurant.com As part of my research for my latest book, I had the chance to interview Christopher Krohn, president and CMO of Arlington Heights, Ill.-based Restaurant.com, regarding his company s best practices surrounding analytics. The restaurant industry has historically been bifurcated between large, national brands that leverage extensive analytic capabilities and smaller independent restaurants that have limited analytic capacity, he said. Increasingly, restaurant owners are realizing that factors such as diner repeat purchase rates, food cost management, labor scheduling efficiencies, marketing investments and other key aspects of their business can be improved by insights coming out of analytics. Restaurant.com is partnering with restaurant owners to use analytics to improve their profits, and also to help diners choose the right restaurant for their needs, regardless of the dining occasion. Restaurant.com s approach to analytics exemplifies the BASP framework in many ways. For example, the BASP framework asserts that you should begin with the critical business challenges and align analytics to address them. Restaurant.com is focused on ensuring that analytics are directed at the organization s critical business objectives. Specifically, the company uses analytics in three primary areas. First, it assesses its business s opportunities and performance in each local market around the country, analyzing restaurant penetration rates, sales force targeting and account management effectiveness. Second, it uses analytics to measure the impact of its B-to-C and B-to-B marketing efforts, identifying channel opportunities or customer segment opportunities to profitably grow the business. Third, Restaurant.com uses analytics to build predictive models for financial forecasting and budgeting, and to evaluate the ROI of business initiatives and investments. In terms of the organizational structure for analytics, Retaurant.com committed to a centralized model reporting directly to the president and CMO in order to ensure that it remains a datadriven organization. According to Krohn, the decision to organize analytics in this way was driven by several factors, including the need for a deep competency in a variety of analytics disciplines and to effectively allocate those analytics resources across the various areas of the business in the most impactful way. The decision to structure analytics centrally also was driven by the desire to ensure that analytical insight remains entirely objective. Restaurant.com s restaurant owners are using analytics to improve their profits and to help diners choose the right restaurant for their needs. A centralized analytics team reporting directly to the company s president also helps the organization examine data with an unbiased view, rather than running the risk of individual departments aligning analytics outputs to their own particular agendas. Restaurant.com has learned a lot from the journey to implement analytics effectively. First, Krohn advocates that any organization looking to implement analytics effectively should work to obtain a commitment from senior management that data-driven decision making is an organizational imperative. Without that commitment, he believes, analytics resources can sometimes risk being perceived as an expensive luxury. Second, Restaurant.com worked hard to build consensus on the tools, sources and data definitions that will be used so that there is consistency across the organization. When Restaurant.com s finance, marketing and sales teams talk about net revenue, for example, they all use the same definition and pull their reports from the same data sources. Also, it s important to create a prioritization process for analytics projects that forces tradeoffs in allocating resources to different business initiatives and departments. Many organizations say that they have too much data and not enough 40

8 understanding of that data. However, Restaurant.com has worked diligently to build a solid data foundation and leverage its information in a way that leads to understanding. It has identified its top customers and their behaviors. In addition, the company prioritizes data collection initiatives so that it can augment the data that it already has, and it focuses on data management initiatives to improve data integrity and data security. Restaurant.com is still working to get all of the systems in place to take coordinated marketing and management actions based on the knowledge generated from its analytics initiatives. According to Krohn, the process of turning data into business insights, and insights into informed management actions, is a journey, not a destination. We will always be working hard to better understand the data we already have. mi Jesse Harriott is chief analytics officer at Constant Contact Inc., an online marketing solutions provider based in Waltham, Mass. Prior to Constant Contact, Harriott was chief knowledge officer at Monster Worldwide Inc. He is the author of Win With Advanced Business Analytics and Finding Keepers. 41