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People Solutions > Tech Problems

Jonathan McWilliams, Director, Revenue Operations - Digital Properties, Viacom
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As I wrote this article, it became evident that focusing solely on the tech-side of revenue management would be ignoring the crucial ingredient that underpins every digitized system that has ever existed; people. The he’s and she’s that implicitly decide if a company’s revenue management systems will either drive business decisions going forward or be ananalysis chokepoint. As you look to upgrade or completely overhaul revenue operations and revenue management, consider the four principles below:


1. Clearly define how a revenue management system will advance your company’s business strategy


The definition of a Revenue Management System (RMS) differs by who you ask and what their job-function is.In general, the technology investments your company makes – particularly RMS – should depend largely on which industry your company deals in and the strategic goals your company has made. Understanding the nuances of your industry will help define what your company’s revenue lifecycle looks like. Naturally, the nuances of your company’s revenue lifecycle should translate directly into your company’s selling strategy. Finally, the selling strategy should result in subsequent technology decisions – RMS being one of the most crucial of these technology decisions.


Takeaway:Honestly assess whatstrategic impacts an effective Revenue Management Systemwould have for your company before looking at potential products and system enhancements.


Thoughts to ponder


• What is the current landscape of your company’s industry?


o Is your business high-volume?


o Is the pricing function commoditized?


o How long is your sales cycle?


• Would anupgradeto RMStruly advance your business strategy oris it just another checkbox on the technology to-do list?


2. Build the foundation of your reporting infrastructure before adding new pieces


A few years ago, I was walking through Brooklyn and saw a sinkhole large enough to swallow a car. Apparently, this part of the street lacked a solid foundation, but that deficiency had been covered up when the first layer of pavement was applied. Over the years, additional layers of pavement were poured until the root issue was buried underfive feet of tarmac. Eventually the facade crumbled, and Brooklyn was left with a Volvo-size hole in its street.


Is your company’s approach to revenue management systems similar to the tarmac example? Has your team piled on vast amounts of reporting tools in hopes that they will “fix” the lack of data infrastructure? If the basic components of revenue reporting –like consensus on revenue type definitions, data flow, and data QA– are not solid, then an RMS will add very little value.


To visualize your firm’s data reporting foundation, try this exercise;


1. Write out your company’s revenue formula by breaking out each individual component (the exampleshown below is for digital media advertising):


Revenue = [total viewers] x [proportion of viewers that are served an ad] x [price charged for the ad]


2. Drill down on each component you have listed and decide which dimensions are needed;time and sales product are obvious inclusions. What about sales region, customer identity, sales method (online, in-store, phone), or even the salesperson? Furthermore, when are these dimensions captured in the sales cycle - at the time of sale or throughout the entire sales cycle?


3. Visually draw aprocess map showing how each of these dimensionsfeed into a data warehouse. This step is crucial and can potentially show weak points in the “data plumbing”.


Takeaway: Stacking random systems on top of each other to fix data problems will exacerbate the root issue. First, focus on mapping out the current reporting architecture - data flow, data sources, and data quality checks – and make sure a specific team is accountable for each of these areas.


Thoughts to ponder


• If a new or upgraded RMS is introduced:


o Will new components, KPIs, or dimensions be introduced?


o Will the team that is currently in charge of dataflow retain that responsibility?


o Will the dataflow change?


• Has the data collection and aggregation process been thoroughly mapped out? Is it reviewed and refreshed periodically by the team who owns it?


3. Spend more time diagnosing the sickness than prescribing a medication


The last decade has seen tremendous growth in digitized solutions geared toward revenueanalysis: reporting, forecasting, even visualization can happen with a few mouse clicks. So with all this technical progress, why is it that that analytical solutions are still difficult to uncover? Why do simple questions from the C-suite turn into a prolonged and painful analyses? When finding answers feels like an uphill battle, it is easy to succumb to the belief that newersystems will be the magic potion. However, this approach can be ineffective, can be infinitely costly, and could expose your organization to additional problems while addressing none of the root issues.


The disconnect between diagnosis and solution has been experienced by yours truly. In one of my previous positions, there was a specific situation where Management informed us that we were gifted with a brand-newrevenue analytics system. Imagine our disappointment when we found out that the new platform didnot allow drill downs– so there was no way to substantiate the data. It did not even allow the user to export the data – all analysis had to be done inside the dashboard! How could technology so crude even pass the first round of reviews?Basically, the users who would be using the system were never asked what they truly needed–management just assumed a new system would suffice. The outcome:out of a 30+ person analytics team, there was only1 weekly active user.A total flop.


Takeaway:Define internal needs and build a solid use-case before launching into the blackhole of product reviews and vendor presentations; time spent on building a tangible argument ofwhat needs the new system is addressingwill improve ROI exponentially once your company makes the jump.


Thoughts to ponder


• Is the interest in upgrading or investing in a new RMS rooted in the idea that something is operationally broken?If so, can you clearly name that thing that is broken?Is your opinion shared by others or is there a lack of consensus?


• Has time been spent addressing the root problems that have made RMS a focal point?


o Is it bad quality data?


o Are business answers cumbersome to find or do they take too long to complete?


o Are there numerous internal revenue systems that are siloed?


• Have the voices of day-to-day operational users been heard before deciding on a solution? Do these operational users have representation in the system review process?


4. Good employees have greater impact on value thanbad systems - thereverse is also true


A rudimentary RMS will be effective if it has the right people tailoring it to changing business needs, testing it for QA, and proselytizing its use. On the flipside, a sophisticated RMS will be deadin the water without the right supporting cast. Upgraded technology in and of itself will rarely be as valuable as a driven, intellectually curious, and knowledgeable team. The true value-differentiator is the people behind the system. I have seen high-performance teams turn unsophisticated technology, like shared Google docs, into analytical gold!Before blaming your RMS for analytical dysfunctionality, take an honest assessment of all the individuals who influence it.


Takeaway: Even the best RMS will rarely hide flaws from unrealistic expectations, ineffective employees, or bad quality data. If you are dealing with issues in these areas it would be wise to address them before looking for a tech-based solution.


Thoughts to ponder


• Are the managerial and supporting teams that influence RMS performance aligned with the operational users?


o Are all teams informed on business goals?


o Have all teams been fully enabled to perform or are they hamstrung?


o Is someone held accountable for the current RMS system?


• Is data-quality preached at the lowest level?


o Are employees who partake in data input continually trained on data hygiene?


o Are training materials accessible to these employees?


• Have high-achieving employees been given an opportunity to teach their workarounds solutions?


In summary, while each business will differ in their unique approach to the RMS problem – the solutions discussed above translate effectively across many business types. With the recent advances in all aspects of technology, it is easy to lose sight of the true engine behind business performance – people. Before deciding on a piece of technology: hit the pause button, ask the right questions, find the hard answers, and put the correct operational people in the driver seat. Your ROI will thank you.


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