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<p><font face="Verdana,Arial,Helvetica" size="3" color="#336666"><b>Closed Loop Analysis — Applied</b></font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Closed-Loop Analysis is a formal business intelligence process intended to set business goals, monitor progress, assess impact or effectiveness and then realign objectives. It becomes a real-time process when data is collected and evaluated continuously. </font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Closed-Loop Analysis (CLA) is not a new concept. It is the natural evolution of more traditional analytical strategic planning processes that operate on a much longer cycle. Planning cycles of a year or more were once the norm. In the case of a new marketing program, months might be dedicated to research, formulation of goals, and program design. The program is then implemented and allowed to run for 6 months to a year. While the program is running, data is gathered and compared to expected results, generally on a monthly basis. At the end of the period, the program managers evaluate the results and a decision is made: Does the program continue as is, is it tweaked, wholly revamped, or abandoned?</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">CLA progresses from investigation to execution to evaluation and then revision in cycles as short as a quarter or even a month. The change is more than a compression of the same activities into a shorter unit of time. While the sequence stays the same, the discrete activities are fundamentally different.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">One defining factor is that many components of the process are automated – from data collection to evaluation. At the same time, the process is more hands-on, more dynamic, and more iterative. It is more like piloting an airplane (multitudes of automated assistance with tactile immediate action) than being a passenger on an ocean liner (where you have no control and all actions have an agonizingly delayed response). With Closed-Loop Analysis, you are in control and the results of your actions have immediate consequences.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Let's explore an example: A fictitious company called Acme developed a web applet that tracks useful Internet activity like logging into registered sites and submitting forms. Most clickstream data is acquired for a single site or a collection of sites that share information. The data captured by Acme covers tens of thousands of popular sites. Acme's information is more focused and more valuable than the aimless wandering-in-the-wilderness nature of raw clickstream data.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">For instance, are web surfers who visit multiple book-selling sites more likely to buy online than single-site browsers? Do consumers who buy stocks online sign up with the financial account aggregators more than the average click-thru visitor? What are the top ten sites visited by frequent users of any given specific site?</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Acme's business model is to exploit this data to create precisely targeted market opportunities for their corporate partners. One service is to help their clients know definitively which sites provide a greater click-through potential. This provides a more accurate and convincing defensive for advertising rates. Acmes shares a percentage of the generated revenue.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">So how does Acme start with a massive, growing repository of event data and end up with a profitable marketing business? There are two possible starting points in the CLA process for Acme. On the one hand, they could forge an agreement with the owners of key web sites they already have in their tracking inventory and then ferret out correlated behaviors for them. On the other hand, they could first identify strong associations in the data and then sell to sites that have the most impressive correlated behavior. In either case, these opportunities form a premise that is the first step in a closed-loop process.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">The CLA phases are analogous to the steps in the PDCA process of Total Quality Management. PDCA means Plan, Do, Check, Act. Plan involves setting objectives and designing a program or process. Do means to execute the process; while Check is a milestone step using a predefined evaluation framework to monitor and test the success of your efforts. Act is a dynamic process of course-correction that loops back to the Plan step. You take what you learned and plow it back into the next PDCA cycle.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Closed-Loop Analysis starts with a premise – as in our example. The investigate phase entails the most sophisticated analysis. You search for correlations and associations that represent discernable patterns in the data. You may use clustering or segmentation techniques to find sets of common factors or behaviors. You may set out to validate or reject a hypothesis. In all cases, the result is a proposed course of action such as a marketing program or an additional web site feature or even a new product.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">The next step is to establish the evaluation framework and monitoring mechanism. We call this the initiation phase. A critical facet of CLA is that measurable success criteria are defined up front. The measurement mechanism may be as basic as periodic reporting, a more sophisticated method such as dynamic alerts, or some form of continuous tracking against a baseline target.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">With the evaluation framework in place, you are ready to begin the execution phase. Unlike the sequential nature of the PDCA process, monitoring takes place throughout the execution phase; doing and checking as you go. Instead of monthly checkpoints, we are more likely to monitor progress on a weekly, daily, or continuous basis.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">The evaluation framework should define thresholds. Thresholds can be volumes or rates or both. Examples include a sales volume target or an adoption rate or a percentage of click-thrus that generate sales. You generally define an "unqualified success" level, a "just getting by" or "marginal" target, and an "unacceptable attainment" or "oops" threshold. Thresholds can be a single number by a specified point in time but are more effective if the are a trended projection over time.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">With thresholds defined, the monitoring process has an ongoing evaluation component. Generally, you time-box the execution phase but can pull the plug at anytime. Time-boxing means you set an outside limit on how long the program runs before you force an evaluation milestone. At an evaluation milestone, you decide to expand, continue, revise, re-plan, or terminate.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">If you hit the "unqualified success" threshold, you might continue a successful program or expand the process (e.g. more clients or more products or more similar programs). If you only attain a "marginal" performance level, you may need to either revise or re-plan. To revise involves minor corrections to the execution strategy and may require additional investigation. A re-plan involves substantial new investigative analysis and potentially a modification to the original defining premise.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Tracking at or below the "oops" threshold may require you to terminate the program early. If the results are disastrous, you have to determine if the execution was fundamentally flawed. If it was, you must consider the value and feasibility of staging a new trial or redesigning your execution process. If it was not flawed, you may be forced to reject the whole premise and start from scratch.</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">In summary, the CLA steps are: (1) Propose a Premise, (2) Investigate the Data, (3) Design a Program, (4) Establish an Evaluation Framework, (5) Develop the Monitoring Mechanism, (6) Execute the Program, (7) Monitor Progress, (8) Evaluate Success, (9) Act on Results, (10) Do it Again (Recycle).</font></p>
<p><font face="Verdana,Arial,Helvetica" size="1">Let's walk through the steps using our Acme example:</font></p>
<ol>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Premise:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Sell marketing services to major Internet participants by developing profiles of associated activity by web browsers. In particular, optimize advertising revenue by selecting the best potential click-thru sites based on current web surfing behavior.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Investigation:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Identify dominant Internet companies by some criteria such as capitalization or volume of traffic or percentage of click-thrus. Sift the data to identify inbound and outbound sites. Rank the associated sites by volume of traffic between target site and associated site. Analyze repeat traffic. Mine the data to reveal any distinct segments of common behavior that might offer the opportunity for more focused targeting. This example investigation stage uses a myriad of different analytic methods, and thus different tools. A real-world process might potentially involve even more.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Design:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Acme plans to monitor 10 source sites to a single target site. They decide to concentrate on profiling outbound click-thrus from the source sites that offer the highest potential opportunity. They will sell banner ads on the source sites that are likely to bring more traffic to the target site.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Evaluation Framework:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Acme decides to establish an eight-week trial window. Their marginal goal is 20,000 click-thrus per week. Anything over 50,000 will be considered a rousing success. On the other hand, if they have less than 10,000 per week minimum or less than 50,000 after four weeks, the trial will be considered a failure.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Monitoring Mechanism:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Event data from consumer web interactions comes into Acme's back office system continuously from their web server farm. They place a filter directly on the message stream to select and store only click-thru events from the targeted sites. A Java applet is designed to display a daily counter and update a trend line for each of the top ten source sites. This will allow anyone on the marketing team to see the instantaneous and historical results on a corner of their portal home page at any time. Once a week, or on demand, they will be able to run a report that is a summary of all outbound site events, not just click-thrus to the target site. At any time, they can invoke an OLAP tool that has detailed results from the beginning of the trial through last night for in-depth analysis.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Execute:</b></font><font face="Verdana,Arial,Helvetica" size="1"> The banner control database for the target site is updated Tuesday night to be ready for the media tracking week that begins on Wednesday. The marketing teams want outside validation by Internet auditing companies to corroborate their results.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Monitor:</b></font><font face="Verdana,Arial,Helvetica" size="1"> The click counters move up more slowly than expected until the weekend when heavy surfing begins. By the second week, they are tracking over-the-top on one site with 240,000 click-thrus. Three others are above the marginal goal of 20,000 per week. Unfortunately, the other six are lagging far behind minimum expectations. They decide to continue the trial from all 10 sites but immediately begin investigation of behavior in and out of the six low-running sites. By the fourth week, the six sites are still below the "oops" threshold.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Evaluate:</b></font><font face="Verdana,Arial,Helvetica" size="1"> By now they have already looped back to investigation and must decide whether to pull the plug on the non-performing ads. Someone on the investigation team had a breakthrough insight: Might it be possible that a high proportion of the people that frequent the source sites are already frequent users of the target site? They would have no incentive to click on a banner ad to take them to the target site. Sure enough this turns out to be true for four of the six lower-incident sites.<br>
<br>
</font><font face="Verdana,Arial,Helvetica" size="1">What about the other two? By looking backward in the click behavior data for patterns, they find that many people come from a consistent set of sites, in the same order, through the source sites. The conclusion is that these folks followed a web-ring and did not come to these sites on their own. This was not an independent affinity decision. People following a web-ring rarely break out until they loop back around or get bored with the path.<br>
<br>
</font><font face="Verdana,Arial,Helvetica" size="1">At this point, one ad is making five times the money they expected. Three are in the green. The remaining six are now known to be consistently low performers.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Act:</b></font><font face="Verdana,Arial,Helvetica" size="1"> Acme decides to continue the blockbuster ad until the activity drops below the marginal threshold. This is an expansion of the original plan. The three other performers are continued to the end of the trial. The under-performers are terminated at the end of the first four weeks.<br>
<br>
</font>
<li><font face="Verdana,Arial,Helvetica" size="1" color="#003333"><b>Recycle:</b></font><font face="Verdana,Arial,Helvetica" size="1"> The main learning of the first cycle is that targeting must be refined in two ways. First, they must add a new factor into the selection process. They must deselect sites where the consumer is already a heavy user of both source and target sites. The trick is to define the frequency of returns to the associated site that is likely to make them less responsive to a new inducement. More investigation is warranted.<br>
<br>
Second, they must investigate the effect of web-rings on surfing behavior. How do you identify followers of a web-ring and, more critically, how do you seduce them into breaking out of the ring to follow your click path?<br>
<br>
One other high value opportunity area remains for new investigation. What is unique about the one super-performer and the people that come to its site? If you can distill the characteristics of this source site that generate a higher propensity to be interested in the target site, you might be able to find other high-performing feeder sites.<br>
</font>
</ol>
<p><font face="Verdana,Arial,Helvetica" size="1">A closing comment: This article highlights an Internet-style company because it demonstrates CLA in a fast-paced and information-rich mode. These techniques are being applied to supply chain management, product positioning, portfolio management, niche market development in several industries, and many more situations. There is likely an opportunity to exploit Closed-Loop Analysis where you work.</font></p>
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