Archive for the ‘Analytics’ Category

Does Your Firm Suffer from Information Disadvantage?

In many of our client assignments, we’ve been challenged to solve significant information gaps. Managers understood that they were significantly behind their competitors in leveraging information, but they didn’t really know where to start in evaluating their deficiencies.

We came up with a scorecard methodology to evaluate 5 key areas that make up an information platform. We approached the evaluations party on how well the information platform was built, but also partly on whether what was being built and used was aligned strategically with the goals of the organization. The 5 key areas are:

Info Disadvantage - Chart

1) Information Capture: how information gets captured or procured. Is the firm capturing the right internal information from their systems or personnel, and are they procuring the right third party data?

2) Data structuring and integration: how well the data is organized, structured, and integrated with other information within the firm. Are the data from different sources brought together to complement each other and matched so that similar information is well mapped and accessed together (for example information about a certain client)?

3) Data availability: making the data made available for review and analysis. Are tools built or made available for analysts and data scientists to explore the data?

4) Information context: enriching data and creating metadata to give more meaningful cuts and views of information that is more relevant to managers. This is related to the data structuring  point above, but differs in that much of this context is business-specific and in many cases need to be defined by the business, specifically product and sales managers. Are there consistent and global definitions in order to make sense of the information, for example consistent definition of products and regions?

5) Information delivery: creating tools for managers and salespeople to access, receive, and interact with their information so they can use it to enhance their business. Are these tools easy to use, intuitive, fast, and up-to-date.


What’s interesting is to note how many of the client’s past projects have identified and attempted to expensively address deficiencies in one or two of the five key areas but have often rendered sub-optimal results because problems in other areas were not addressed. In a lot of instances, for example, large investments have been made in information delivery, creating front-end tools to dress up information, but lesser investment in cleaning up the structure of the underlying data or not procuring 3rd party data that could add significant value to existing data.

Firms need to strive to realistically evaluate their platforms in all five fundamental areas and make sure that they understand, and invest strategically, in each of those areas. The knee-jerk response by managers is to argue that it would be prohibitively expensive to address the build-out of the entire platform. Our response is that you don’t have to build out all the areas in full and and they don’t have to be done at the same time. If you target certain specific deliverables that involve development across all five areas, you approach this problem more cohesively and inter-dependencies become much clearer. Also this will most likely be a multi-year project, so it should be planned and invested as such. The project also should be designed to include an element of flexibility to allow for evolution of requirements as the business changes over time.

With this broader approach, an organization can ensure that projects they embark with will be more successful and valuable to the organization.

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Truths in Cost Management

Sunny cloudsOne of the most dramatic changes that has occurred in Institutional Banking since the financial crisis hit in 2008 is an increased focus on management of costs. This is isn’t anything incredibly new. Financial firms have been focusing on cost management for years. What has changed is an increased intensity and urgency due to margin compression driven by increased regulatory pressures and more aggressive competition for a quickly shrinking client wallet.

Many banks have created teams fully dedicated to the analysis of their costs. We’ve worked with a few of those organizations to help them define the scope of their work. It’s important to determine upfront what kinds of costs will be targeted for analysis and what will be on the table for aggressive management.[more…] The sizing of the initiative is critical in order to make sure that expectations are in line with what can realistically be achieved. Senior management have to be on board with most of the changes that the analysis identifies. It’s all too common for management to have high hopes only to back down when they perceive that they are cutting too much into meat and bone.

It’s important to get comfortable with a few truths:

  • Cost efficiencies in some cases will only come from additional investments in the platform and will be realized only some years later.
  • Cutting costs implies in many cases a cutting of some revenue streams
  • The perceived “fat” that people talk about in an organization is not a discrete element separate from the productive “meat and bone”. Most banks now are running pretty lean, so the exercise is one of objectively evaluating prioritization of businesses, functions, and initiatives.
  • Every business or project has their merits which should be fully understood before any rash decisions are made about them, especially…
  • Projects with longer horizons often have less potential impact that is less visible on the organization so they have higher risk of being cut. The long-term strategy of the organization should weigh heavily upon evaluation of these.
  • Cost and profitability analyses use a great deal of assumptions to come up with their results. All layers of management who are part of the cost management decision-making process must be well-versed in these assumptions to ensure that implications of decisions are well understood.

I’ll be writing more about cost management in the next few posts as its a topic that seams to be top-of-mind for many managers as they plan for 2014.

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Measuring Client Profitability in Institutional Equities

BPCThe Institutional Equities business is a unique animal relative to its Capital Markets peers, with the big distinction that no one individual is responsible for revenue generation for any given account. Clients pay en effective lump sum, through trading, that goes to compensate the broker for a myriad of services. Between research sales, sales trading, program traders, electronic sales, research analysts, strategists, economists, and corporate CEOs that the brokers facilitate meetings with, the contact points can be large and hard to keep track of, making it a particularly challenging business to manage profitably.

Targeting Profitability

Success in the Equities world isn’t just defined by the amount of revenue coming in the door. Strategically, brokers need to ensure that the business coming through is profitable. There are a number of implications to this statement: [more…]

– Control of a broker’s profitability is driven by a delicate balance between targeting revenue opportunity and optimally managing expensive resources. Managing how resources are distributed to clients is the most effective way to increase profitability. Costs can be managed down through expense management initiatives to some degree, but these can only go so far given that the majority of expenses are compensation related which are market-driven. This is assuming, of course, that the firm is on a growth strategy.

– Management needs to develop capabilities to measure and track profitability at the client level so that they can have an ongoing tally of the impact their resources are having on overall profitability.

– As revealing as an understanding of an account’s profitability is, it’s critical to understand the future potential revenue for each account. In addition, managers need to develop a view on what resources are required to capture that potential revenue for any given account, and develop plans to migrate resources for lowest revenue potential to highest revenue potential accounts.

– Firms need to be willing to concentrate their resources towards the highest return accounts, meaning that they need to entertain culling accounts. The idea of cutting off revenue generating accounts is not intuitive for many managers, so this has to be handled in a methodical and intelligent way. More on this is a future post.

Profitability Analysis

Developing a detailed profitability analysis of accounts is a critical step in the process to maximize profitability. First, managers need a clear understanding of revenues across all Equities business lines, specifically an agreement what contra-revenue items (CSA, commission splits, third-party broker fees, trading loses) to subtract from gross revenues to reach a net revenue number that reflects true retention. These vary from sub-product to sub-product, where the dynamics of block cash equities differ from programs which differ from equity derivatives. Differing methodologies reach different conclusions, so it’s important for managers from all business lines agree on approach.

The second step, allocation of resource costs, can range from simple approaches that allocate using simple rules to highly complex allocation methodologies that attempt to be scientific about measurement of time spent and derivation of detailed unit costs. The degree of complexity  should be dictated by the complexity of the organization and the amount of information that being captured, or are willing to invest to capture. Regardless, each resource type should be thought through individually to make sure it makes sense. Salespeople and research analysts could be done through periodic time-spent survey, through a time billing system similar to law firms, or activity capture through an integrated CRM system. Sales traders and program traders can be done through number of trade orders and time-spent survey. Middle Office can be done using number of trade order, or even better, number of trade orders weighted by client processing automation to allocate more costs to more manual accounts.

The results of the analysis should be a full income statement detailing a net income for each account. Because of the sometimes complex nature of this kind of analysis, we often find managers who want to circumvent the process, asking for instance, if we can create profitability analysis for just the top 25 or 30 accounts. While it is possible to estimate costs for a subset of accounts in order to create an income statement, we strongly advise against this because the goal of the exercise isn’t to create an income statement; the goal is to have an analysis that will allow managers to effectively manage resources by moving them from low profit/low potential accounts to high profit/ high potential accounts.

Analysis on the Analysis

The income statement is not the end of the analysis. In fact, we feel that this is just the beginning. The value of really comes from the ability to make sense of the new profitability data and create actions that change how the business is engaging with clients. A second layer of analysis using the profitability results should give deep insights into the structure and dynamics of the account base. Managers can categorize accounts based on type of revenue and intensity of resource consumption. For instance, grouping accounts that predominantly trade on block and consume a ton of research versus accounts that have send a ton of smaller orders and only consume research opportunistically. What’s important is to better understand the drivers of profitability (or lack thereof) and then create strategies around those drivers to maximize profits by targeting resources to the right accounts.

Future Potential

It’s tricky, and in some cases risky, to assume that you can easily increase profitability by taking resources away from unprofitable accounts and move them to profitable ones. Pulling resources from large, yet unprofitable accounts may risk large chunks of stable revenues and may in the end decrease profitability. Similarly, an increase in intensity on profitable accounts may not result in an increase in revenue and will effectively make these accounts less profitable. An important element in the overall analysis is estimating the future potential revenue of the account, and trying to understand what effort and what resources will be needed to capture that revenue potential. In many cases this is not a straight-forward analysis. Size of wallet, market share, right resource matching, depth of relationship – these are all considerations in identifying and quantifying opportunities. In some cases you want to make the account less profitable by dedicating resources in order to gain a foothold with a new product, which is something that many Equity houses are doing to capture chunky prime services business.

Working Smarter

We’ve seen a general reluctance by many firms to tackle the problem of understanding account profitability because they feel it’s going to be a significant investment in time and money, with results that are not immediately tangible (or at least not as tangible as hiring a salesperson who can easily bring in two million dollars or more if incremental commission just from their existing relationships). Our message to them is that there is most likely a lot of low-hanging fruit within their account base and this is the best approach to realize those returns. In addition, once the strategy and analysis are in place, which no doubt will requires some investment, the maintenance going forward will be inconsequential to even the most conservative gains.

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Client-centric Financial Metrics (Capability #3)

The CFO’s office, being responsible for all financial reporting in any firm, are the seemingly logical choice as owners of client-level reporting given their access and understanding of the data, technical expertise in financial reporting, and broad understanding of the overall flows of the firm. Regardless, Client Management should play a key role in the definition and structuring of client-level analysis and reporting, for one key reason: a traditional financial management function often has competing (and sometimes conflicting) mandates regarding how they report financial results. We’ve seen instances where legal entity or divisional reporting take precedence over client-oriented reporting that often creates distortions on how an account is reported so it’s hard to see its true performance and who within the organization is responsible for variances.

Client ReportingTake the example of a global client that does business in multiple geographies and across multiple products. Each regional manager and each product manager, working with their respective finance heads, will vie to earmark as much revenue for their division as possible, creating double-counting of revenues and confusing results for a given client. The Client Management team should be tasked with unraveling these knots to provide a clear, holistic picture of an account.[more…] They should also identify what information needs to be delivered within Sales and Sales Management to ensure the right strategic decisions and focus.

In some cases we’ve seen Client Management take full ownership of client-level financial reporting, while in others we’ve seen a close collaboration between CFO and Client Management. Regardless, the Client Management team should have a strong global mandate and management support to oversee or co-manage this process.

Like any financial analysis and reporting function, client reporting involves establishing target budgets, reporting account revenue periodically, looking at trends, and flagging outliers that should be raised and discussed. Additionally, client-specific KPIs (key performance indicators) that help better understand client behavior and assess the quality of business flowing in should also be identified and tracked. Examples include volume and order size, movements versus peers, pricing changes, and changes in wallet size and market share. Client profitability analysis, which involves applying costs against client revenues, is probably one of the most useful, if not most complex, analysis to implement.

Identifying meaningful client segments and creating statistics around those also helps in identifying trends and uncovering opportunities. A strong Client Management team is usually adept at understanding the marketplace to best categorize the account base in useful groups. In many cases, the obvious segments may not necessarily be the best groups to track. The distinction between hedge funds and traditional asset managers, for example, is becoming blurred so tracking these is becoming less meaningful. Segments based on investment style, investment markets, or even firm “personality” may prove more useful for aligning resources.

By aligning client financial analysis and reporting with a strong Client Management function, a firm will set itself up to better manage their account base from a client-centric perspective and help realize the value of a clear client strategy.

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Top 5 Technology Priorities for Capital Markets Sales in 2013

Fave_modHere are our top 5 technology priorities we see Capital Markets organizations focusing on this year:

1)  Increased leverage of existing data

Broker-dealers capture an incredible amount of data, partly for compliance purposes, but also from systems that have come on line in the past 5 years aimed at capturing both client and sales interactions. Firms are sitting on immense repositories of trading and client information but have not had the systems or resources to mine that data to create a competitive advantage. New technologies have dramatically driven the cost down of storing, retrieving, aggregating, and analyzing the data. We’ll see firms combining phone, messaging, and email data with transaction and market data to develop new insights about customer and market trends.[more…]


2)  Mobile integration

As more people become dependent on their mobile devices to manage their personal lives, they are going to look to manage their work life in the same way. This means a higher degree of mobile integration of their work applications. There are many difficult and unresolved technical and compliance questions, but we feel that firms that have invested in mobile and BYOD (Bring Your Own Device) infrastructure over the past two years will see significant realization of productivity this year.


3)  Migration to the Cloud

Security has long been an issue with financial firms (especially in securities firms) when talking about hosting sensitive internal data on the Cloud. We believe that new security advancements in Cloud computing will allay most of those fears and will compel many firms to revisit this option since it provides advantages in cost, stability, and accessibility.  As mentioned in the last point, mobile will become a big priority for productivity, but would be severely constrained if data has to sit in slow and inaccessible local servers.


4)  Incorporation of social media

Social data, meaning data from social internet sites such as Twitter and LinkedIn, will be made more readily available, if not to all sales and trading staff, then at least to a critical beta group. LinkedIn data is being made available for integration with in-house CRM systems so that internal contact data can also feature LinkedIn profile and company information. Most CRM systems worth their salt have recently built LinkedIn data integration capabilities. Many firms still haven’t seen the value of Twitter, with most firms blocking the application entirely. But a few upstart Twitter analytics firms, with access to Twitter’s data fire-hose and some serious data analytics engines, are providing extremely useful insights on market and client trends. The most notable is Dataminr, which focuses primarily on financial markets.


5)  Mining of unstructured data

We now have technology that can systematically ferret out and analyze information from unstructured data sources, such as text in emails, client call reports, research reports, and even phone conversations. We admit that this will be more of a 2014 focus for most firms, but many will start to look into it this year since it can provide a significant new stream of data to analyze.

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Big Data: What’s the Hadoopla all about?

Big DataThe latest buzzword dominating business headlines today is “Big Data”. What was once known in past incarnations as Data Processing, shifted to become Data Mining, then transforming into Business Intelligence, and now has finally adopted the name “Big Data”. It’s still unclear to many business managers what Big Data is. A lot of technology managers have a hard time explaining it. All this makes it hard to make the value it promises tangible enough to invest in. The core of the problem is that “Big Data” isn’t just one thing. It’s an umbrella category for a series of technologies and methodologies that have made dramatic strides in innovation and business application, with a core goal of leveraging data to create dramatic and sustainable competitive advantages.[more…]

I’ll set a baseline definition for each of the four main categories implied by the term “Big Data”: data sourcing; data storage and staging; analytics and insight creation; new business models. This should give a non-technology manager a basic understanding that could get them through a cocktail party.

Data Sourcing

This is simply identifying, or in some cases creating, sources of information that can provide, alone or integrated with other data, new insights for a business. New technologies have emerged that make it much easier to source information that wasn’t easy to capture before, like customer interactions. Other technologies have created new fountains of data that businesses can tap into to gain additional insights, such as Twitter and GPS location information. The gap between what the optimal or “dream” data universe has to look like to create a sustainable competitive advantage versus what is currently available to a business is quickly closing. Managers should think big about what information would be game-changers and challenge their technology groups to find it and capture it. In previous posts I’ve recommend a book by Douglas Hubbard called How to Measure Anything which provides a fantastic framework.

Data Storage & Staging

A radical drop in the cost of storage has made the warehousing of large amounts of data a manageable economic proposition for most firms. We now talk about capturing and storing terabytes and petabytes of information. More importantly, new technologies, like the much hyped Hadoop framework, can pull data from clusters of data servers in an extremely efficient manner which solves two major headaches: 1) stored data can be quickly extracted for analysis, in some cases near real-time, and 2) fragmented data from multiple silos can be combined and analyzed together.  Firms like Cloudera and MapR provide cloud software and services that can help in developing the right structure. 

This new kind of infrastructure allows data to reside in large distributed networks that provide speed, perpetual access, and data redundancy, and has been labeled Cloud computing. Cloud services are offered by a growing group of firms that provide simple and scalable services. Two of the best known are Amazon and Rackspace.

Analytics & Insight Creation

What analytics you want to create largely depends on what data you have and what form it takes. An important feature of “Big Data” analytics is its flexibility with both structured and unstructured data. Traditional structured data is organized within set fields and in relational databases, while unstructured and semi-structured data require additional enrichment and organization to allow them to be useful for analysis and insight generation. An experienced analyst, for example, can create algorithms that ferret out useful items found in written text documents, then match them to other relevant data.

There are a variety of analytical tools in the market, most of them very rich with features. The level of sophistication of the tools, and subsequent modeling, used in any analysis depends on the complexity and volume of the data, as well as the kind of insights that are being sought. A common mistake is to procure overly complicated tools and build overly complicated models that don’t improve business outcomes. Managers need to define very clearly up front what kinds of results they want to get.

SAS and R are the standard-bearers for statistical analysis, and the go-to tools when you are looking to find correlations and testing causation in your data. The biggest bang, though, comes from an ability to create visualizations of your analysis, making it easier to digest, remember, communicate, and inspire action. A variety of vendors, such as Spotfire, Tableau, and QlikView, provide visually-driven Business Intelligence software that can be used to create intuitive analysis and dashboards used by managers or salespeople for decision making.

New Business Models

Putting a “Big Data” infrastructure in place at a firm won’t automatically ensure a return on investment. Businesses need to change in order to incorporate Big Data insights into their DNA. In some instances, completely new businesses models have emerged, like in the cases of Amazon and Netflix. In existing businesses though, a new data discipline needs to be put in place. It starts with management articulating clear goals and measures of success, followed by planning and execution of a Big Data plan that is in-line with company culture and talent, and lastly developing incentives and training that promote data reliance.


Despite the hype, Big Data really isn’t anything new. There’s been a renewed interest in the four principal areas that comprise it due to new innovations and impressive results of businesses that have fully adopted it. There is a tremendous opportunity to realize value using these tools, so it’s important to have a strong working knowledge of what they are and have it in the back of your mind as you think about your business strategy.

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Disparity in Technology Investment between Sales and Trading

HighTechnology budgets at Institutional Broker-Dealers have in general been disproportionately focused on the trading side of the business, leaving the sales side with limited technology innovation. This is not to say that much of the technology built doesn’t benefit salespeople. Much of it facilitate the sales workflow, especially when dealing with order tracking, capital, and market data. What has been missing is a meaningful investment in tools that do three things: 1) provide more (and more relevant) client information to salespeople in a consolidated and timely manner; 2) analytic solutions that serve up algorithmically driven insights that can help salespeople better engage clients and provide differentiated service; 3) new ways for salespeople to interact with information and systems, like alerts and mobile solutions.

Consolidated Client Information

Client information at most banks is comprised usually of core client data (addresses, key contacts), internal control data, trade and production data, plus any client interaction data (meetings, calls) that the firm happens to captures. For the later, some firms are disciplined at capturing every interaction, but most only capture parts. There is, however, a great amount of information that can be very useful for salespeople in their dealings with clients. Many salespeople actually spend a significant amount of their time digging through varied internal systems, Bloomberg, Reuters, and the web to find pieces of relevant data that they can use, much of which could be served up in a more consolidated and automated fashion, commingled with basic client information. When looking at a client in their client system or CRM, they could also have client fund holdings and performance data, trends in names or sectors clients are interested in, research readership, and social media data (such as Twitter feeds). For organizations offering multiple asset classes, providing a salesperson relevant activity data from other asset classes can be invaluable, such as providing Equity salespeople data on a client’s corporate bond trading activity.

Analytic Solutions

Sales & Trading organizations have been investing hundreds of millions of dollars in algorithmic analytic and trading technologies. Some of that discipline (and investment capital) should be leveraged to provide more timely and automated insights to salespeople. Specific types of movements in securities or sectors that might interest specific clients, movements in other areas that might be correlated to client interests, and general behavior of different client segments of which a particular salesperson’s clients might fall into. This would give salespeople lot of processed information that provides differentiated insights to offer clients or help better manage a book of clients.

New Ways to Interact with Client Information

A good institutional salesperson in any asset class is skilled at collecting and synthesizing information quickly, either to provide relevant information to their clients or to help them strategically manage their client coverage. Creating efficient ways of getting pertinent information quickly is the key to success. Development of intelligent alerts is one way to serve up relevant activity, analysis and trends. They can simply be an alert that someone else in another product group called on that same client. The more robust the analytical solutions (from point 2) that are in place, the smarter and more relevant the alerts could be, such as a spike in trading for given client segments after a particular event or research published. With current mobile technology, these types of alerts and analysis can be delivered and actioned upon very quickly.

There is a big opportunity for Sales & Trading organizations to greatly differentiate themselves if the right level of investment is made on the Sales side of the organization. This is going to become more critical as innovation in trading technology becomes less and less differentiated across competitors and new regulations constrains the way trading revenue is achieved.

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Measuring Profitability: Initial Questions

Over the past 10 years, the trend of measuring profitability of clients and products within financial companies has gained momentum, with a steep up-tick since the 2008 financial crisis. Managers have become interested, in some cases obsessively so, in understanding what nuclear parts (clients, products, initiatives) of their businesses are contributing to the bottom line and which are taking away from it. It’s become apparent that managing by looking only at business unit or desk level does not provide the right strategic insight necessary to compete effectively. Profitability analysis, when executed correctly, can significantly impact product pricing, resource optimization, process streamlining, and client relationship management.  We’ll talk further about uses and specific methodologies in later posts, but I want to address three main questions that I get asked when clients start looking at implementing a profitability discipline within their firm.[more…]

How complicate and detailed should the analysis be?

It goes without saying that the complexity of the analysis is largely driven by the complexity of the firm or business unit being analyzed. The key thing is to make sure that the analysis drives toward some kind of action, so it’s important to define the key questions you are trying to answer with the results. For example, the level of detail to determine whether a client is getting over-serviced is different from the detail necessary to perform a process reengineering project.

A lot of people, when in the throes of analysis, get bogged down on precision and complexity, which in many cases adds no additional directional insights.  In many cases many interesting insights can be gained from very simple analysis.  I recommend taking a look at a book by Douglas Hubbard called ‘How to Measure Anything’ which gives some useful cases studies.

Should I hire an expert or consulting firm?

I’ve often said that profitability/costing analysis is a specific skillset that is different from other finance or accounting functions. Absolutely it requires a command of the financial accounts, but it also requires a clear understanding of the underlying drivers that impact how the expenses are “consumed” by clients, products, or projects. This requires an understanding (and an ability to model) business process flows. Probably most importantly, it requires an ability to create useful insights from the model results once it has been complete. A consultant or expert in this area are usually very good at ferreting out and mapping underlying business processes and have experience turning the large mound of results data into actionable strategies. They don’t have to completely own the entire profitability initiative, but they can provide invaluable perspective on methodology at the beginning of the project and have an arsenal of profit-enhancing solutions once the profitability results have been created.

Should I bring in an ABC/ABM tool, or can I build it on Excel?

This is a tough question because most managers are extremely hesitant to invest too much at the beginning of a profitability initiative, which is completely understandable.  They want a cheap proof-of-concept first with one or two people, and Excel, which is a fine place to start. I would argue, though, that you should incorporate an ABM tool (such as Acorn ABM or SAS ABM) as soon as humanly possible, for three main reasons:

1)       As the model becomes more and more complicated, with incorporation of more clients, client segment, products, expense groups, and driver data, the harder it is to maintain and manipulate.  In the end, it’s usually one person who understands the model and if they happen to leave, it’s a massive setback.  ABM tools standardize the logic and methodology which make them much easier to endure a transfer of ownership.

2)      Allocation methodologies often lead to multiple allocations across the model, so transparency is lost almost immediately. In the end, you may be able to determine what is profitable or unprofitable, but not why. ABM tools maintain the integrity of multiple allocations, so you can always easily trace the assigned cost (to a client for instance), back to the financial account detail. Reconciliation then becomes a cinch rather than a painful two week exercise.

3)      Version control becomes a big issue as the model gets updated over time with new financial or driver data, or allocation methodologies change. ABM tools allow you to not only better organize and track version of the model, they also allow you to make changes that retroactively impact past models. This is absolutely required if you want to do period-on-period or trend analysis of results.

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