Use Analytical Results to Drive Specific Process Improvement

Originally published January 25, 2012

Letís presume that we have satisfied the following prerequisites for benefitting from a business intelligence or analytics activity:

  1. A determination of one or more business processes for which there is a desire to evaluate and potentially improve performance

  2. The ability to collect data from the applications supporting the selected business process and prepare it for reporting and analysis

  3. One or more methods for presenting analytical results to the appropriate business information consumers

  4. Clearly defined measures of performance and the ability to determine when the business process has been improved

Now what? Even with all the technology components in place, and even with a program champion guaranteeing business sponsorship, one of the biggest challenges of integrating business intelligence into the organization is motivating behavioral change that leads to performance improvement.

Here is a more mundane analogy. Recently, we arranged for an energy audit at our house. The process involved setting up certain probes in different locations in the house, introducing pressures into the home environment (such as blowing air in from outdoors), as well as point inspections of particular aspects of energy consumption and leakage. The result was a report identifying some key opportunities for improving energy efficiency, such as better sealants in locations where air leaked in from outside and upgrading ceiling insulation. This aspect of the audit is analogous to the introduction of a BI framework and the creation of specific reports reflecting current performance of existing processes.

With the report in hand, we could then review the best opportunities for improvement. To continue the analogy, we looked at the costs associated with recommended improvements and what the benefits would be of taking those steps. For example, upgrading the ceiling insulation would cost $1000, but would reduce energy costs by $50 per month, providing a 20-month breakeven point, and we decided to take that step. On the other hand, some of the more expensive suggestions might not have led to significantly better efficiency, so those we postponed until the options made better sense to reconsider, such as if their costs were reduced or if the energy costs rise.

These decisions are also analogous to the BI process: When presented with analytical results and recommendations, weighing the value factors leads to making changes in the environment when it makes sense, but suggests continuing to monitor the inputs for other recommendations to determine when it makes sense to make other changes.

The basic idea is this: Given the framework for analysis and the presented results, the key stakeholders in the organization must be able to evaluate those results, make a choice regarding some change to the environment, and effect that change. Since we began with the selection of a business process for evaluation and potential improvement, we are already part of the way there. And if we have properly prepared the organization to evolve a culture that enables change, the next step is determining the best way to implement those changes.

Luckily, we probably have already done some of that work. First, having defined the objective and established measures, we already know what is to be improved and how that improvement is monitored. Our assessment of the business process selected for improvement will yield a valuable artifact: a model or mapping of the business process itself. Reviewing that business process model with the results of the analytical modeling should point to locations within the process where changes can be made.

For example, letís say the business objective is to increase sales revenues by 15%, and the selected process is increasing same-customer product sales. The analysis might report customer sales over selected time periods (daily, weekly, monthly, yearly) by channel (brick and mortar, phone, online), and by customer demographic (gender, age, location), and show that certain types of email follow-ups to brick-and-mortar purchases lead to a follow-up online purchase for customers over the age of 35. Synthesizing these types of results might include evaluating whether those same results can be replicated within other demographics, determining if the follow-up product sale can be made through more efficient channels, or if the types of emails can be adjusted to increase online sales.

But once those determinations have been made, the specifically targeted processes must be changed. Once the changes are made, the same measures are already in place to detect the improvement. Yet without making any changes, why would you expect anything to improve?

To continue the example, if the analysis suggests recommending the secondary product purchase (cross-sell) at the point of purchase, the brick-and-mortar sales team members need to be trained to present that offer at the time of sale. If the analysis suggests expanding the breadth of an email offer, the marketing campaign must be changed to include the broader customer set. That is the fundamental point in using analytical results for process improvement Ė if you donít change the process, all that analysis wonít ever lead to business benefits.

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