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Posts Tagged ‘Sean Williams’

Keeping Strategy Execution on Track (Five Measurement Mistakes and How to Avoid Them)

By Donncha Carroll and Sean Williams

While 80% of leaders feel their company is good at crafting strategy, only 44% believe this is true for implementation.  According to Harvard University, a recent survey of more than 400 global CEOs found that executional excellence was the number one challenge facing corporate leaders in Asia, Europe, and the United States, heading a list of some 80 issues. 

Business intelligence (BI) solutions—in particular, dashboards developed using those tools—are often designed to support the execution of strategy. These dashboards provide a means of monitoring internal and external factors that influence success, providing insights on how execution is progressing.  This information is invaluable because it enables the redeployment of resources to maintain focus on the activities and invest in the capabilities that make the biggest difference to results.  These benefits are exactly why according to Gartner, global spending on BI and analytics will approach $18 billion in 2017.  And yet despite all that investment in Business Intelligence, executional excellence is still the top challenge for CEOs.

During the execution phase of any strategy, it’s crucial to provide leadership with visibility to the factors that are driving or influencing success.  Good measurement and reporting can highlight progress on the initiatives that influence results and provide the management team with critical insights that support more agile execution.  Agility is important because it allows the team to more quickly change focus as new information comes to light.  Of course, becoming more agile is not easy because it demands a higher level of comfort with change, and leaders and managers need focused, actionable and fast information to support efforts to make those changes. 

Unfortunately, many companies are getting lost in the complexity of getting their arms around the big data revolution by taking a bottoms-up approach to data management and analysis.  This approach results in data-related initiatives that are too large in scope, take too long, have a higher risk of failure, and deliver a lower ROI.  The leaders that embark on this journey will find that prioritization pays higher dividends and delivers real value on an accelerated timeline.

In our experience, companies with good measurement and execution tie their efforts directly back to the strategy, and focus on the critical few areas where they can identify, understand and react to changes quickly.  Some years ago, Jeff Bezos communicated the Amazon retail strategy to his team by sharing three very simple customer preferences: lower prices, bigger selection, and faster delivery.  Each customer preference can be addressed by pulling on certain organization levers that can be readily measured—and that’s exactly where organizations like Amazon set their priorities for measurement.

 

This diagram, called a “value tree,” can be further developed to the right depending on how deep you want to go (e.g., local inventory on-hand requires warehousing facilities that are close to population centers, which in turn requires the efficient build-out of each facility).  Unfortunately, organizations often start with the data they have or the infrastructure they think they need and not the business problem they’re trying to solve.  In our experience, the five most common mistakes organizations make in building out their measurement infrastructure are:

1. Focus on too much too soon.  Organizations often start by seeking to measure everything that can be measured in order to satisfy all needs.  This introduces unnecessary complexity not only to the project, requiring higher levels of resourcing, but also to the dashboard itself, making it much less useful.

2. Develop the wrong measures. The infrastructure is created from the bottom up instead of starting with the essential needs of the business.  Starting from the bottom almost guarantees that the process will be exploratory rather than strategic, and much less efficient.

3. Allow data quality to overly influence the approach.  Data is never perfect, and few organizations have everything they need to measure with perfect accuracy.  Some analytics leaders use data quality challenges to explain why dashboards don’t effectively support decision-making, or to stop measurement development altogether, rather than seeing an opportunity to identify and address what’s broken in the measurement process.

4. Emphasize technology at the expense of value and impact. Real-time data, embedded analytics and self-service enablement are incredible business intelligence technologies.  But displaying data faster or better is not what dashboards are built to do.  If they are not closely tied to what a user needs to make good decisions, these technologies can be expensive distractions.

5. Choose cool displays over useful information.  How the measure is defined, the types of charts used to display information, data filters applied and the timeframe for presentation can all influence the utility of the dashboard.  Many organizations spend a great deal of time iterating on data visualizations that provide interesting views of the data, but do not tie those visualizations to the decisions they need to make.

Strategy development and execution is all about making decisions on the future direction of the organization; business intelligence is about providing insight to inform those decisions.  If these things are not tightly connected, then dashboards at best become an unnecessary complication and, at worst, a misleading distraction.  But there is a relatively simple, five-step process that is both efficient and delivers high-impact results.

1. Start with the business strategy.  Whatever the organization’s strategy, start by identifying the specific things the organization will need to achieve in order to execute against it.  Both dashboard developers and business users must be prepared to frame their interactions in terms of how the organization will get there.

2. Identify the critical drivers of value.  Build a value tree by determining which capabilities you need to have, or the activities you need to perform well, in order to execute effectively.  Then determine which of those are most important.  This exercise of unbundling the organization’s strategy establishes a direct link to the strategy and prioritizes what leadership needs to monitor.

3. List related questions and decisions.  Focusing on the critical drivers, list the most important questions that need to be asked and answered and the key decisions that will need to be made.  Taking the time to understand how leaders and business users will actually use the dashboard is critical to getting the focus and design right. 

4. Define the measures that matter.  Identify the measures that will provide the information needed for each question and decision, and then determine the primary data source and format required.  This is the opportunity to identify and address gaps in data availability and quality.  Again, everything you do here ties back to the strategy so investments made at this point will be highly targeted and impactful.

5.  Develop designs that present information in ways that draw out key insights and address a number of different business scenarios.  Review and iterate designs with leaders and users and constantly test for utility. Develop a design that supports decision making. 

Applying these five simple steps will give your team the information they need to more effectively monitor and adjust the execution of your strategy, leading to higher growth, profitability and realization of organizational goals.

Contact Donncha Carroll and Sean Williams.

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Using Customer Insights to Drive Ethical, Profitable Growth

By Sean Williams

The recent Wells Fargo settlement—to the tune of $185 million—has given business leaders pause to examine the contributing dynamics. A combination of aggressive cross-selling strategy, insufficient employee monitoring, and incentives misaligned with the best interests of customers set the stage for a massive employee fraud in the creation of unauthorized accounts driven by the desire to earn bonus compensation.

Hardly unique, Wells Fargo is one of many companies placing aggressive goals on customer-facing employees, most in a direct position to inadvertently do harm, and they must carefully evaluate the strategic, legal, and reputational risks.
 
Clearly, substantial changes are often necessary to remain competitive as employees adjust to new ways of interacting with customers to deliver profitable growth. While post-Wells Fargo activities may include new value statements and requisite hours of training, actual employee behaviors are much more difficult to monitor, and frequently fraught with blind spots.

But how is a leader to know if employees are upholding and reinforcing company policies and standards rather than potentially destroying value by pursuing individual goals at the expense of customers? 

One source of safeguarding against these risks is to employ something many companies already have: a voice of customer program. 

How could a voice of customer program help a bank address its blind spots in employee behavior?  Obviously, customers will not know—at least not immediately—if a bank employee opened an account for them without their permission. But a voice of customer program employing outreach to recently-opened account holders asking for customer feedback on the sales process—a common activity—can illuminate such a disconnect.
 
But such an approach is not foolproof in that this type of feedback is solicited only from the small number of customers who have opened accounts, and many will routinely decline the interview for reasons that have little to do with unethical bank behaviors (e.g., customers commonly believe there was a mistake). Beyond customer declinations however, it may be likely that either the email address or phone number designated to the new account is bogus.

However, if the question was asked of any customer conducting any transaction on an active account, it would provide a broad data sample that could easily be used to monitor and evaluate employee behavior, notably illuminating any discrepancies between the customer’s assertion that they have not opened an account and bank records indicating otherwise. More importantly, these disconnects will reveal potential problems in branch locations where new goals have been rolled out, strategies implemented, etc. 

Such dynamics transcend the financial services industry, certainly, and an effective voice of customer program can be useful for managing risk from employee behaviors in any company. For example, many automobile manufacturers reimburse dealer service departments for the parts sold for repairs, but not for actual car repair. This creates a subtle mismatch between the ideal activities of the service personnel (repairing cars) and the service for which the manufacturer is paying (installation). Such a disconnect creates easy opportunities to “game the system,” which in turn creates clear risk for the manufacturer.  

When a customer returns for the same service on the same vehicle, the manufacturer can safely assume that the repair was not correctly performed on the first attempt. But the manufacturer may be unaware if service personnel created duplicate entries for the same repair, if repairs sold were not required, or if the customer had repairs performed elsewhere.
 
These blind spots can be easily addressed by asking the right questions of customers. For example, inquiring with a customer as to when the last time a specific repair was performed may indicate if service personnel unnecessarily replaced parts not yet past their lifetime expectancy.

In the insurance sector, companies that are increasing pressure on claims can install new processes and due diligence, but the clear potential to harm claimants means that insurance companies should be auditing customers to ensure such processes are being followed. Manufacturers changing warranties and retailers modifying return policies could also better manage risks by more frequently surveying the customer experience.
 
Of course, collected customer data may never be perfect—and will be effective only insomuch as the customer remembers interactions. For example, in the earlier banking illustration, the customer could incorrectly remember opening an account, when such a transaction in fact occurred, or inaccurately recall the bank, if they do business with more than one. 

But this random noise will not change significantly over time or vary much by location, which are exactly the indicators a bank should be seeking. And the voice of customer data can provide vital information exactly where there are few other options for examining employee behavior. To realize monitoring value of voice of client programs, the programs should be integrated into the strategy implementation and risk management processes.  Consider the following five steps:
  
1) Examine the new strategy with the appropriate cross-functional team of leaders from strategy, sales, marketing or human resources. Identify where the new strategy unintentionally incentivizes employee misbehavior toward customers. Eliminate as many of these incentives as possible.

2) Explicitly list any remaining potential avenues for employee misbehavior. The more specific about the different ways this misbehavior could manifest, the better. 

3) Work with the data and analytics function to map out which of these manifestations can be identified today, and which could be monitored through existing infrastructure with some additional work. Think through the analytics and reporting that would be necessary to ensure that what can be seen, will be seen. The remaining manifestations of employee misbehavior are the blind spots.

4) Work with the voice of customer program to shed light on the blind spots, by asking the right questions of the right customers at the right time. Make sure that the voice of customer program and the data and analytics functions work together to produce integrated reporting for the leadership on the manifestations of misbehavior, including rates, trends, and locations.  Establish baseline values of customer responses before rolling out the new strategy, goals or incentives.

5) Make sure there are no remaining blind spots. If blind spots remain, or new blind spots are identified later, consider adding a customer survey or other data collection mechanism as necessary to address the new blind spots.

This approach helps ensure that problems are identified before they become widespread, which will help put both executives and regulators at ease while minimizing situations where employees may feel encouraged to act against the best interests of the customer, mapping and monitoring such behaviors to course correct when required.

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Data Rich, Decision Poor

Awash in data, firms struggle to develop and leverage insights—but getting them right can have tremendous upside

By Mark Masson and Sean Williams

Data is everywhere. Insights? Not so much.

Looking for deep intel on customer or client needs? To target new markets? Understand performance? Any firm or company today pursuing profitable growth may ignore, at its own peril, the role of data in this strategy. If business transformation is the key to your growth model for tomorrow, leveraging your existing data for insights into key drivers should be a mandate.

The availability of data is set to increase at lightning pace. The volume of data created annually is expected to reach 44 trillion gigabytes by 2020, an increase of nearly ten times just since 2013. And yet only a fraction of that data is actually helpful—37% of that 44 trillion gigabytes is expected to be useful, and only after it is analyzed (1).  Inundated by seemingly useless data, you may be wondering whether investing in your organization’s analytical capabilities was worth it.

And you wouldn’t be alone. The majority of big data and analytics-building efforts fail or are never completed. Just because the capabilities are built doesn’t mean they truly add value. One recent study (2) found that three-quarters of businesses extract little to no advantage from their data, including many that have invested heavily in building analytics capabilities.

The problem for most organizations and leadership teams is that simply having more data—or software or even data analysts—doesn’t directly lead to answers. The good news is that our experience shows that nearly any level of investment in analytics can generate surprisingly large value. Realizing that value often requires understanding what barriers leaders are encountering, and then addressing the (sometimes surprising) root causes.

Assessing Your Unique Challenges

Leaders understandably focus on dealing with the most obvious causes—bad data, bad analytics, or even bad analysts—but while these are the most obvious, they are not necessarily the most likely to be the real problem. You should also make sure the business processes that touch analytics are not getting in the way, that you manage the cultural challenges to acting on analytics, and that you align on what is needed, from the C-suite down to the analytics teams.

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To fully realize the value from analytics, leaders must understand and activate all of the elements that support a data-driven firm.

  • Alignment : Aligning leadership (Senior Team and key leaders) on the most critical decisions to be made, and analysts on the knowledge and data required to make them; sizing the gaps between the current level of insights and information provided and the strategic needs 
  • Means : Evaluating the adequacy of means (processes, technology and data-related interactions) that lead to decisions
  • People : Assessing the current type and level of analytic and decision-making capability within the firm 
  • Processes : Measuring whether current business processes enable decision-making from insight or get in the way 
  • Culture : Highlighting and removing cultural weaknesses around or barriers to data-enabled decision-making

Finding Value in Your Data

Unsurprisingly, we find that each organization has strengths and weaknesses among these elements, but few are capable of objectively identifying them or knowing what levers to pull to change the situation. Successful ones, however, do not become data-driven by throwing more data at the problem. Rather, they work to first understand their gaps related to these elements and find solutions to close them, leveraging the natural strengths of the organization.

Complacency around your data will ultimately impede business transformation. But eliminating company barriers, aligning critical elements and making investments in analytics is an assured route to future growth. Every organization today, regardless of size, has potentially game-changing insights sitting right in its very own data, and the value is significant. Understanding and delivering on client or customer needs you’re missing or that other organizations can’t “see” makes getting this equation right worth full percentage points to your firm’s bottom line, competitive advantage and continued livelihood.

(1) EMC Digital Universe Study, 2014

(2) Seizing the Information Advantage, 2015

Mark Masson advises senior leaders of major law, accounting, and consulting firms. He specializes in firm and client growth strategies and effective execution by aligning and engaging leadership teams, boards, and partnerships on strategic growth priorities. His work blends the nuance and experience of professional service firm leadership with cutting-edge business analytics.

Sean Williams helps clients craft insight-based and data-driven solutions to their business problems, with experience in analytics and predictive modelling in a variety of industries, including financial services, hospitality, and manufacturing. He works with clients to build their analytical capabilities and to ensure they closely support strategy, and to help clients overcome their cultural and organizational barriers to realizing the value of their analytics.

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